On the distributions of annual and seasonal daily rainfall extremes in central Arizona and their spatial variability

NASA Astrophysics Data System (ADS)

Mascaro, Giuseppe

2018-04-01

This study uses daily rainfall records of a dense network of 240 gauges in central Arizona to gain insights on (i) the variability of the seasonal distributions of rainfall extremes; (ii) how the seasonal distributions affect the shape of the annual distribution; and (iii) the presence of spatial patterns and orographic control for these distributions. For this aim, recent methodological advancements in peak-over-threshold analysis and application of the Generalized Pareto Distribution (GPD) were used to assess the suitability of the GPD hypothesis and improve the estimation of its parameters, while limiting the effect of short sample sizes. The distribution of daily rainfall extremes was found to be heavy-tailed (i.e., GPD shape parameter ξ > 0) during the summer season, dominated by convective monsoonal thunderstorms. The exponential distribution (a special case of GPD with ξ = 0) was instead showed to be appropriate for modeling wintertime daily rainfall extremes, mainly caused by cold fronts transported by westerly flow. The annual distribution exhibited a mixed behavior, with lighter upper tails than those found in summer. A hybrid model mixing the two seasonal distributions was demonstrated capable of reproducing the annual distribution. Organized spatial patterns, mainly controlled by elevation, were observed for the GPD scale parameter, while ξ did not show any clear control of location or orography. The quantiles returned by the GPD were found to be very similar to those provided by the National Oceanic and Atmospheric Administration (NOAA) Atlas 14, which used the Generalized Extreme Value (GEV) distribution. Results of this work are useful to improve statistical modeling of daily rainfall extremes at high spatial resolution and provide diagnostic tools for assessing the ability of climate models to simulate extreme events.

Timing of floods in southeastern China: Seasonal properties and potential causes

NASA Astrophysics Data System (ADS)

Zhang, Qiang; Gu, Xihui; Singh, Vijay P.; Shi, Peijun; Luo, Ming

2017-09-01

Flood hazards and flood risks in southeastern China have been causing increasing concerns due to dense population and highly-developed economy. This study attempted to address changes of seasonality, timing of peak floods and variability of occurrence date of peak floods using circular statistical methods and the modified Mann-Kendall trend detection method. The causes of peak flood changes were also investigated. Results indicated that: (1) floods were subject to more seasonality and temporal clustering when compared to precipitation extremes. However, seasonality of floods and extreme precipitation was subject to spatial heterogeneity in northern Guangdong. Similar changing patterns of peak floods and extreme precipitation were found in coastal regions; (2) significant increasing/decreasing seasonality, but no confirmed spatial patterns, were observed for peak floods and extreme precipitation. Peak floods in northern Guangdong province had decreasing variability, but had larger variability in coastal regions; (3) tropical cyclones had remarkable impacts on extreme precipitation changes in coastal regions of southeastern China, and peak floods as well. The landfalling of tropical cyclones was decreasing and concentrated during June-September; this is the major reason for earlier but enhanced seasonality of peak floods in coastal regions. This study sheds new light on flood behavior in coastal regions in a changing environment.

Observed and simulated hydrologic response for a first-order catchment during extreme rainfall 3 years after wildfire disturbance

USGS Publications Warehouse

Ebel, Brian A.; Rengers, Francis K.; Tucker, Gregory E.

2016-01-01

Hydrologic response to extreme rainfall in disturbed landscapes is poorly understood because of the paucity of measurements. A unique opportunity presented itself when extreme rainfall in September 2013 fell on a headwater catchment (i.e., <1 ha) in Colorado, USA that had previously been burned by a wildfire in 2010. We compared measurements of soil-hydraulic properties, soil saturation from subsurface sensors, and estimated peak runoff during the extreme rainfall with numerical simulations of runoff generation and subsurface hydrologic response during this event. The simulations were used to explore differences in runoff generation between the wildfire-affected headwater catchment, a simulated unburned case, and for uniform versus spatially variable parameterizations of soil-hydraulic properties that affect infiltration and runoff generation in burned landscapes. Despite 3 years of elapsed time since the 2010 wildfire, observations and simulations pointed to substantial surface runoff generation in the wildfire-affected headwater catchment by the infiltration-excess mechanism while no surface runoff was generated in the unburned case. The surface runoff generation was the result of incomplete recovery of soil-hydraulic properties in the burned area, suggesting recovery takes longer than 3 years. Moreover, spatially variable soil-hydraulic property parameterizations produced longer duration but lower peak-flow infiltration-excess runoff, compared to uniform parameterization, which may have important hillslope sediment export and geomorphologic implications during long duration, extreme rainfall. The majority of the simulated surface runoff in the spatially variable cases came from connected near-channel contributing areas, which was a substantially smaller contributing area than the uniform simulations.

Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach.

PubMed

Wong, Man Sing; Ho, Hung Chak; Yang, Lin; Shi, Wenzhong; Yang, Jinxin; Chan, Ta-Chien

2017-07-24

Dust events have long been recognized to be associated with a higher mortality risk. However, no study has investigated how prolonged dust events affect the spatial variability of mortality across districts in a downwind city. In this study, we applied a spatial regression approach to estimate the district-level mortality during two extreme dust events in Hong Kong. We compared spatial and non-spatial models to evaluate the ability of each regression to estimate mortality. We also compared prolonged dust events with non-dust events to determine the influences of community factors on mortality across the city. The density of a built environment (estimated by the sky view factor) had positive association with excess mortality in each district, while socioeconomic deprivation contributed by lower income and lower education induced higher mortality impact in each territory planning unit during a prolonged dust event. Based on the model comparison, spatial error modelling with the 1st order of queen contiguity consistently outperformed other models. The high-risk areas with higher increase in mortality were located in an urban high-density environment with higher socioeconomic deprivation. Our model design shows the ability to predict spatial variability of mortality risk during an extreme weather event that is not able to be estimated based on traditional time-series analysis or ecological studies. Our spatial protocol can be used for public health surveillance, sustainable planning and disaster preparation when relevant data are available.

Rainfall variability over southern Africa: an overview of current research using satellite and climate model data

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2009-04-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, satellite-derived rainfall data are used as a basis for undertaking model experiments using a state-of-the-art climate model, run at both high and low spatial resolution. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, a brief overview is given of the authors' research to date, pertaining to southern African rainfall. This covers (i) a description of present-day rainfall variability over southern Africa; (ii) a comparison of model simulated daily rainfall with the satellite-derived dataset; (iii) results from sensitivity testing of the model's domain size; and (iv) results from the idealised SST experiments.

AgMIP Regional Activities in a Global Framework: The Brazil Experience

NASA Technical Reports Server (NTRS)

Assad, Eduardo D.; Marin, Fabio R.; Valdivia, Roberto O.; Rosenzweig, Cynthia E.

2012-01-01

Climate variability and change are projected to increate the frequency of extreme high-temperature events, floods, and droughts, which can lead to subsequent changes in soil moister in many locations (Alexandrov and Hoogenboom, 2000). In Brazil, observations reveal a tendency for increasing frequency of extreme rainfall events particularly in south Brazil (Alexander et al., 2006; Carvalho et al., 2014; Groissman et al., 2005), as well as projections for increasing extremes in both maximum and minimum temperatures and high spatial variability for rainfall under the IPCC SRES A2 and B2 scenarios (Marengo et al., 2009).

Tradeoffs between vegetation management goals and livestock production under Adapative Grazing Management

USDA-ARS?s Scientific Manuscript database

Rangeland ecosystems are characterized by substantial temporal variability in weather overlaid on spatial variability associated with topography and soils (Fuhlendorf et al. 2012). Semiarid rangelands in particular are characterized by more extreme intra- and inter-annual variation in precipitation ...

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

DOE PAGES

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

2016-01-01

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

Mapping extreme rainfall in the Northwest Portugal region: statistical analysis and spatial modelling

NASA Astrophysics Data System (ADS)

Santos, Monica; Fragoso, Marcelo

2010-05-01

Extreme precipitation events are one of the causes of natural hazards, such as floods and landslides, making its investigation so important, and this research aims to contribute to the study of the extreme rainfall patterns in a Portuguese mountainous area. The study area is centred on the Arcos de Valdevez county, located in the northwest region of Portugal, the rainiest of the country, with more than 3000 mm of annual rainfall at the Peneda-Gerês mountain system. This work focus on two main subjects related with the precipitation variability on the study area. First, a statistical analysis of several precipitation parameters is carried out, using daily data from 17 rain-gauges with a complete record for the 1960-1995 period. This approach aims to evaluate the main spatial contrasts regarding different aspects of the rainfall regime, described by ten parameters and indices of precipitation extremes (e.g. mean annual precipitation, the annual frequency of precipitation days, wet spells durations, maximum daily precipitation, maximum of precipitation in 30 days, number of days with rainfall exceeding 100 mm and estimated maximum daily rainfall for a return period of 100 years). The results show that the highest precipitation amounts (from annual to daily scales) and the higher frequency of very abundant rainfall events occur in the Serra da Peneda and Gerês mountains, opposing to the valleys of the Lima, Minho and Vez rivers, with lower precipitation amounts and less frequent heavy storms. The second purpose of this work is to find a method of mapping extreme rainfall in this mountainous region, investigating the complex influence of the relief (e.g. elevation, topography) on the precipitation patterns, as well others geographical variables (e.g. distance from coast, latitude), applying tested geo-statistical techniques (Goovaerts, 2000; Diodato, 2005). Models of linear regression were applied to evaluate the influence of different geographical variables (altitude, latitude, distance from sea and distance to the highest orographic barrier) on the rainfall behaviours described by the studied variables. The techniques of spatial interpolation evaluated include univariate and multivariate methods: cokriging, kriging, IDW (inverse distance weighted) and multiple linear regression. Validation procedures were used, assessing the estimated errors in the analysis of descriptive statistics of the models. Multiple linear regression models produced satisfactory results in relation to 70% of the rainfall parameters, suggested by lower average percentage of error. However, the results also demonstrates that there is no an unique and ideal model, depending on the rainfall parameter in consideration. Probably, the unsatisfactory results obtained in relation to some rainfall parameters was motivated by constraints as the spatial complexity of the precipitation patterns, as well as to the deficient spatial coverage of the territory by the rain-gauges network. References Diodato, N. (2005). The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. Internacional Journal of Climatology, 25(3), 351-363. Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113 - 129.

Some Spatial Aspects of Southeastern United States Climatology.

ERIC Educational Resources Information Center

Soule, Peter T.

1998-01-01

Focuses on the climatology of an eight-state region in the southern and southeastern United States. Discusses general controls of climate and spatial patterns of various climatic averages. Examines mapped extremes as a means of fostering increased awareness of the variability that exists for climatic conditions in the region. (CMK)

Quantifying spatiotemporal variability of fine particles in an urban environment using combined fixed and mobile measurements

NASA Astrophysics Data System (ADS)

Sullivan, R. C.; Pryor, S. C.

2014-06-01

Spatiotemporal variability of fine particle concentrations in Indianapolis, Indiana is quantified using a combination of high temporal resolution measurements at four fixed sites and mobile measurements with instruments attached to bicycles during transects of the city. Average urban PM2.5 concentrations are an average of ˜3.9-5.1 μg m-3 above the regional background. The influence of atmospheric conditions on ambient PM2.5 concentrations is evident with the greatest temporal variability occurring at periods of one day and 5-10 days corresponding to diurnal and synoptic meteorological processes, and lower mean wind speeds are associated with episodes of high PM2.5 concentrations. An anthropogenic signal is also evident. Higher PM2.5 concentrations coincide with morning rush hour, the frequencies of PM2.5 variability co-occur with those for carbon monoxide, and higher extreme concentrations were observed mid-week compared to weekends. On shorter time scales (

Analysis and trends of precipitation lapse rate and extreme indices over north Sikkim eastern Himalayas under CMIP5ESM-2M RCPs experiments

NASA Astrophysics Data System (ADS)

Singh, Vishal; Goyal, Manish Kumar

2016-01-01

This paper draws attention to highlight the spatial and temporal variability in precipitation lapse rate (PLR) and precipitation extreme indices (PEIs) through the mesoscale characterization of Teesta river catchment, which corresponds to north Sikkim eastern Himalayas. A PLR rate is an important variable for the snowmelt runoff models. In a mountainous region, the PLR could be varied from lower elevation parts to high elevation parts. In this study, a PLR was computed by accounting elevation differences, which varies from around 1500 m to 7000 m. A precipitation variability and extremity were analysed using multiple mathematical functions viz. quantile regression, spatial mean, spatial standard deviation, Mann-Kendall test and Sen's estimation. For this reason, a daily precipitation, in the historical (years 1980-2005) as measured/observed gridded points and projected experiments for the 21st century (years 2006-2100) simulated by CMIP5 ESM-2 M model (Coupled Model Intercomparison Project Phase 5 Earth System Model 2) employing three different radiative forcing scenarios (Representative Concentration Pathways), utilized for the research work. The outcomes of this study suggest that a PLR is significantly varied from lower elevation to high elevation parts. The PEI based analysis showed that the extreme high intensity events have been increased significantly, especially after 2040s. The PEI based observations also showed that the numbers of wet days are increased for all the RCPs. The quantile regression plots showed significant increments in the upper and lower quantiles of the various extreme indices. The Mann-Kendall test and Sen's estimation tests clearly indicated significant changing patterns in the frequency and intensity of the precipitation indices across all the sub-basins and RCP scenario in an intra-decadal time series domain. The RCP8.5 showed extremity of the projected outcomes.

The Heat Exposure Integrated Deprivation Index (HEIDI): A data-driven approach to quantifying neighborhood risk during extreme hot weather.

PubMed

Krstic, Nikolas; Yuchi, Weiran; Ho, Hung Chak; Walker, Blake B; Knudby, Anders J; Henderson, Sarah B

2017-12-01

Mortality attributable to extreme hot weather is a growing concern in many urban environments, and spatial heat vulnerability indexes are often used to identify areas at relatively higher and lower risk. Three indexes were developed for greater Vancouver, Canada using a pool of 20 potentially predictive variables categorized to reflect social vulnerability, population density, temperature exposure, and urban form. One variable was chosen from each category: an existing deprivation index, senior population density, apparent temperature, and road density, respectively. The three indexes were constructed from these variables using (1) unweighted, (2) weighted, and (3) data-driven Heat Exposure Integrated Deprivation Index (HEIDI) approaches. The performance of each index was assessed using mortality data from 1998-2014, and the maps were compared with respect to spatial patterns identified. The population-weighted spatial correlation between the three indexes ranged from 0.68-0.89. The HEIDI approach produced a graduated map of vulnerability, whereas the other approaches primarily identified areas of highest risk. All indexes performed best under extreme temperatures, but HEIDI was more useful at lower thresholds. Each of the indexes in isolation provides valuable information for public health protection, but combining the HEIDI approach with unweighted and weighted methods provides richer information about areas most vulnerable to heat. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Identifying Population Vulnerable to Extreme Heat Events in San Jose, California.

NASA Astrophysics Data System (ADS)

Rivera, A. L.

2016-12-01

The extreme heat days not only make cities less comfortable for living but also they are associated with increased morbidity and mortality. Mapping studies have demonstrated spatial variability in heat vulnerability. A study conducted between 2000 and 2011 in New York City shows that deaths during heat waves was more likely to occur in black individuals, at home in census tracts which received greater public assistance. This map project intends to portray areas in San Jose California that are vulnerable to extreme heat events. The variables considered to build a vulnerability index are: land surface temperature, vegetated areas (NDVI), and people exposed to these area (population density).

Spatial extreme value analysis to project extremes of large-scale indicators for severe weather

PubMed Central

Gilleland, Eric; Brown, Barbara G; Ammann, Caspar M

2013-01-01

Concurrently high values of the maximum potential wind speed of updrafts (Wmax) and 0–6 km wind shear (Shear) have been found to represent conducive environments for severe weather, which subsequently provides a way to study severe weather in future climates. Here, we employ a model for the product of these variables (WmSh) from the National Center for Atmospheric Research/United States National Center for Environmental Prediction reanalysis over North America conditioned on their having extreme energy in the spatial field in order to project the predominant spatial patterns of WmSh. The approach is based on the Heffernan and Tawn conditional extreme value model. Results suggest that this technique estimates the spatial behavior of WmSh well, which allows for exploring possible changes in the patterns over time. While the model enables a method for inferring the uncertainty in the patterns, such analysis is difficult with the currently available inference approach. A variation of the method is also explored to investigate how this type of model might be used to qualitatively understand how the spatial patterns of WmSh correspond to extreme river flow events. A case study for river flows from three rivers in northwestern Tennessee is studied, and it is found that advection of WmSh from the Gulf of Mexico prevails while elsewhere, WmSh is generally very low during such extreme events. © 2013 The Authors. Environmetrics published by JohnWiley & Sons, Ltd. PMID:24223482

Extreme Rainfall Events Over Southern Africa: Assessment of a Climate Model to Reproduce Daily Extremes

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2007-12-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable extreme events, due to a number of factors including extensive poverty, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of a state-of-the-art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. Once the model's ability to reproduce extremes has been assessed, idealised regions of SST anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, results from sensitivity testing of the UK Meteorological Office Hadley Centre's climate model's domain size are firstly presented. Then simulations of current climate from the model, operating in both regional and global mode, are compared to the MIRA dataset at daily timescales. Thirdly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset. Finally, the results from the idealised SST experiments are briefly presented, suggesting associations between rainfall extremes and both local and remote SST anomalies.

Spatiotemporal variation in heat-related out-of-hospital cardiac arrest during the summer in Japan.

PubMed

Onozuka, Daisuke; Hagihara, Akihito

2017-04-01

Although several studies have reported the impacts of extremely high temperature on cardiovascular diseases, few studies have investigated the spatiotemporal variation in the incidence of out-of-hospital cardiac arrest (OHCA) due to extremely high temperature in Japan. Daily OHCA data from 2005 to 2014 were acquired from all 47 prefectures of Japan. We used time-series Poisson regression analysis combined with a distributed lag non-linear model to assess the temporal variability in the effects of extremely high temperature on OHCA incidence in each prefecture, adjusted for time trends. Spatial variability in the relationships between extremely high temperature and OHCA between prefectures was estimated using a multivariate random-effects meta-analysis. We analyzed 166,496 OHCA cases of presumed cardiac origin occurring during the summer (June to September) that met the inclusion criteria. The minimum morbidity percentile (MMP) was the 51st percentile of temperature during the summer in Japan. The overall cumulative relative risk at the 99th percentile vs. the MMP over lags 0-10days was 1.21 (95% CI: 1.12-1.31). There was also a strong low temperature effect during the summer periods. No substantial difference in spatial or temporal variability was observed over the study period. Our study demonstrated spatiotemporal homogeneity in the risk of OHCA during periods of extremely high temperature between 2005 and 2014 in Japan. Our findings suggest that public health strategies for OHCA due to extremely high temperatures should be finely adjusted and should particularly account for the unchanging risk during the summer. Copyright © 2017 Elsevier B.V. All rights reserved.

Effect of trotting speed on kinematic variables measured by use of extremity-mounted inertial measurement units in nonlame horses performing controlled treadmill exercise.

PubMed

Cruz, Antonio M; Vidondo, Beatriz; Ramseyer, Alessandra A; Maninchedda, Ugo E

2018-02-01

OBJECTIVE To assess effects of speed on kinematic variables measured by use of extremity-mounted inertial measurement units (IMUs) in nonlame horses performing controlled exercise on a treadmill. ANIMALS 10 nonlame horses. PROCEDURES 6 IMUs were attached at predetermined locations on 10 nonlame Franches Montagnes horses. Data were collected in triplicate during trotting at 3.33 and 3.88 m/s on a high-speed treadmill. Thirty-three selected kinematic variables were analyzed. Repeated-measures ANOVA was used to assess the effect of speed. RESULTS Significant differences between the 2 speeds were detected for most temporal (11/14) and spatial (12/19) variables. The observed spatial and temporal changes would translate into a gait for the higher speed characterized by increased stride length, protraction and retraction, flexion and extension, mediolateral movement of the tibia, and symmetry, but with similar temporal variables and a reduction in stride duration. However, even though the tibia coronal range of motion was significantly different between speeds, the high degree of variability raised concerns about whether these changes were clinically relevant. For some variables, the lower trotting speed apparently was associated with more variability than was the higher trotting speed. CONCLUSIONS AND CLINICAL RELEVANCE At a higher trotting speed, horses moved in the same manner (eg, the temporal events investigated occurred at the same relative time within the stride). However, from a spatial perspective, horses moved with greater action of the segments evaluated. The detected changes in kinematic variables indicated that trotting speed should be controlled or kept constant during gait evaluation.

Evaluating wind extremes in CMIP5 climate models

NASA Astrophysics Data System (ADS)

Kumar, Devashish; Mishra, Vimal; Ganguly, Auroop R.

2015-07-01

Wind extremes have consequences for renewable energy sectors, critical infrastructures, coastal ecosystems, and insurance industry. Considerable debates remain regarding the impacts of climate change on wind extremes. While climate models have occasionally shown increases in regional wind extremes, a decline in the magnitude of mean and extreme near-surface wind speeds has been recently reported over most regions of the Northern Hemisphere using observed data. Previous studies of wind extremes under climate change have focused on selected regions and employed outputs from the regional climate models (RCMs). However, RCMs ultimately rely on the outputs of global circulation models (GCMs), and the value-addition from the former over the latter has been questioned. Regional model runs rarely employ the full suite of GCM ensembles, and hence may not be able to encapsulate the most likely projections or their variability. Here we evaluate the performance of the latest generation of GCMs, the Coupled Model Intercomparison Project phase 5 (CMIP5), in simulating extreme winds. We find that the multimodel ensemble (MME) mean captures the spatial variability of annual maximum wind speeds over most regions except over the mountainous terrains. However, the historical temporal trends in annual maximum wind speeds for the reanalysis data, ERA-Interim, are not well represented in the GCMs. The historical trends in extreme winds from GCMs are statistically not significant over most regions. The MME model simulates the spatial patterns of extreme winds for 25-100 year return periods. The projected extreme winds from GCMs exhibit statistically less significant trends compared to the historical reference period.

Nonlinear responses of southern African rainfall to forcing from Atlantic SST in a high-resolution regional climate model

NASA Astrophysics Data System (ADS)

Williams, C.; Kniveton, D.; Layberry, R.

2009-04-01

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. In this research, high resolution satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA) are used as a basis for undertaking model experiments using a state-of-the-art regional climate model. The MIRA dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. Once the model's ability to reproduce extremes has been assessed, idealised regions of sea surface temperature (SST) anomalies are used to force the model, with the overall aim of investigating the ways in which SST anomalies influence rainfall extremes over southern Africa. In this paper, results from sensitivity testing of the regional climate model's domain size are briefly presented, before a comparison of simulated daily rainfall from the model with the satellite-derived dataset. Secondly, simulations of current climate and rainfall extremes from the model are compared to the MIRA dataset at daily timescales. Finally, the results from the idealised SST experiments are presented, suggesting highly nonlinear associations between rainfall extremes remote SST anomalies.

Extremely Low Frequency (ELF) Communications System Ecological Monitoring Program: Summary of 1986 Progress.

DTIC Science & Technology

1987-12-01

assessment of data collection techniques *quantification of temporal and spatial patterns of variables *assessment of end point variability...nutrient variables are also being examined as covarlates. Development of a model to test for differences in growth patterns is continuing. At each of...condition. These variables are recorded at the end of each growing season. For evaluation of height growth patterns , a subsample of 100 seedlings per

Observed variability of summer precipitation pattern and extreme events in East China associated with variations of the East Asian summer monsoon: VARIABILITY OF SUMMER PRECIPITATION AND EXTREME EVENT IN EAST CHINA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Lei; Qian, Yun; Zhang, Yaocun

This paper presents a comprehensive analysis of interannual and interdecadal variations of summer precipitation and precipitation-related extreme events in China associated with variations of the East Asian summer monsoon (EASM) from 1979-2012. A high-quality daily precipitation dataset covering 2287 weather stations in China is analyzed. Based on the precipitation pattern analysis using empirical orthogonal functions, three sub-periods of 1979-1992 (period I), 1993-1999 (period II) and 2000-2012 (period III) are identified to be representative of the precipitation variability. Similar significant variability of the extreme precipitation indices is found across four sub-regions in eastern China. The spatial patterns of summer mean precipitation,more » the number of days with daily rainfall exceeding 95th percentile precipitation (R95p) and the maximum number of consecutive wet days (CWD) anomalies are consistent, but opposite to that of maximum consecutive dry days (CDD) anomalies during the three sub-periods. However, the spatial patterns of hydroclimatic intensity (HY-INT) are notably different from that of the other three extreme indices, but highly correlated to the dry events. The changes of precipitation anomaly patterns are accompanied by the change of the EASM regime and the abrupt shift of the position of the west Pacific subtropical high around 1992/1993 and 1999/2000, respectively, which influence the moisture transport that contributes most to the precipitation anomalies. Lastly, the EASM intensity is linked to sea surface temperature anomaly over the tropical Indian and Pacific Ocean that influences deep convection over the oceans.« less

Spatial and temporal variation in emergency transport during periods of extreme heat in Japan: A nationwide study.

PubMed

Onozuka, Daisuke; Hagihara, Akihito

2016-02-15

Several studies have reported the burden of climate change on extreme heat-related mortality or morbidity. However, few studies have investigated the spatial and temporal variation in emergency transport during periods of extreme heat on a national scale. Daily emergency ambulance dispatch data from 2007 to 2010 were acquired from all 47 prefectures of Japan. The temporal variability in the relationship between heat and morbidity in each prefecture was estimated using Poisson regression combined with a distributed lag non-linear model and adjusted for time trends. The spatial variability in the heat-morbidity relationships between prefectures was estimated using a multivariate meta-analysis. A total of 5,289,660 emergency transports were reported during the summer months (June through September) within the study period. The overall cumulative relative risk (RR) at the 99th percentile vs. the minimum morbidity percentile was 1.292 (95% CI: 1.251-1.333) for all causes, 1.039 (95% CI: 0.989-1.091) for cardiovascular diseases, and 1.287 (95% CI: 1.210-1.368) for respiratory diseases. Temporal variation in the estimated effects indicated a non-linear relationship, and there were differences in the temporal variations between heat and all-cause and cause-specific morbidity. Spatial variation between prefectures was observed for all causes (Cochran Q test, p<0.001; I(2)=45.8%); however, there was no significant spatial heterogeneity for cardiovascular (Cochran Q test, p=0.054; I(2)=15.1%) and respiratory (Cochran Q test, p=0.681; I(2)=1.0%) diseases. Our nationwide study demonstrated differences in the spatial and temporal variations in the relative risk for all-cause and cause-specific emergency transport during periods of extreme heat in Japan between 2007 and 2010. Our results suggest that public health strategies aimed at controlling heat-related morbidity should be tailored according to region-specific weather conditions. Copyright © 2015 Elsevier B.V. All rights reserved.

Geographic dimensions of heat-related mortality in seven U.S. cities.

PubMed

Hondula, David M; Davis, Robert E; Saha, Michael V; Wegner, Carleigh R; Veazey, Lindsay M

2015-04-01

Spatially targeted interventions may help protect the public when extreme heat occurs. Health outcome data are increasingly being used to map intra-urban variability in heat-health risks, but there has been little effort to compare patterns and risk factors between cities. We sought to identify places within large metropolitan areas where the mortality rate is highest on hot summer days and determine if characteristics of high-risk areas are consistent from one city to another. A Poisson regression model was adapted to quantify temperature-mortality relationships at the postal code scale based on 2.1 million records of daily all-cause mortality counts from seven U.S. cities. Multivariate spatial regression models were then used to determine the demographic and environmental variables most closely associated with intra-city variability in risk. Significant mortality increases on extreme heat days were confined to 12-44% of postal codes comprising each city. Places with greater risk had more developed land, young, elderly, and minority residents, and lower income and educational attainment, but the key explanatory variables varied from one city to another. Regression models accounted for 14-34% of the spatial variability in heat-related mortality. The results emphasize the need for public health plans for heat to be locally tailored and not assume that pre-identified vulnerability indicators are universally applicable. As known risk factors accounted for no more than one third of the spatial variability in heat-health outcomes, consideration of health outcome data is important in efforts to identify and protect residents of the places where the heat-related health risks are the highest. Copyright © 2015 Elsevier Inc. All rights reserved.

Leveraging organismal biology to forecast the effects of climate change.

PubMed

Buckley, Lauren B; Cannistra, Anthony F; John, Aji

2018-04-26

Despite the pressing need for accurate forecasts of ecological and evolutionary responses to environmental change, commonly used modelling approaches exhibit mixed performance because they omit many important aspects of how organisms respond to spatially and temporally variable environments. Integrating models based on organismal phenotypes at the physiological, performance and fitness levels can improve model performance. We summarize current limitations of environmental data and models and discuss potential remedies. The paper reviews emerging techniques for sensing environments at fine spatial and temporal scales, accounting for environmental extremes, and capturing how organisms experience the environment. Intertidal mussel data illustrate biologically important aspects of environmental variability. We then discuss key challenges in translating environmental conditions into organismal performance including accounting for the varied timescales of physiological processes, for responses to environmental fluctuations including the onset of stress and other thresholds, and for how environmental sensitivities vary across lifecycles. We call for the creation of phenotypic databases to parameterize forecasting models and advocate for improved sharing of model code and data for model testing. We conclude with challenges in organismal biology that must be solved to improve forecasts over the next decade.acclimation, biophysical models, ecological forecasting, extremes, microclimate, spatial and temporal variability.

Cool-Season Moisture Delivery and Multi-Basin Streamflow Anomalies in the Western United States

NASA Astrophysics Data System (ADS)

Malevich, Steven B.

Widespread droughts can have a significant impact on western United States streamflow, but the causes of these events are not fully understood. This dissertation examines streamflow from multiple western US basins and establishes the robust, leading modes of variability in interannual streamflow throughout the past century. I show that approximately 50% of this variability is associated with spatially widespread streamflow anomalies that are statistically independent from streamflow's response to the El Nino-Southern Oscillation (ENSO). The ENSO-teleconnection accounts for approximately 25% of the interannual variability in streamflow, across this network. These atmospheric circulation anomalies associated with the most spatially widespread variability are associated with the Aleutian low and the persistent coastal atmospheric ridge in the Pacific Northwest. I use a watershed segmentation algorithm to explicitly track the position and intensity of these features and compare their variability to the multi-basin streamflow variability. Results show that latitudinal shifts in the coastal atmospheric ridge are more strongly associated with streamflow's north-south dipole response to ENSO variability while more spatially widespread anomalies in streamflow most strongly relate to seasonal changes in the coastal ridge intensity. This likely reflects persistent coastal ridge blocking of cool-season precipitation into western US river basins. I utilize the 35 model runs of the Community Earth System Model Large Ensemble (CESMLE) to determine whether the model ensemble simulates the anomalously strong coastal ridges and extreme widespread wintertime precipitation anomalies found in the observation record. Though there is considerable bias in the CESMLE, the CESMLE runs simulate extremely widespread dry precipitation anomalies with a frequency of approximately one extreme event per century during the historical simulations (1920 - 2005). These extremely widespread dry events correspond significantly with anomalously intense coastal atmospheric ridges. The results from these three papers connect widespread interannual streamflow anomalies in the western US--and especially extremely widespread streamflow droughts--with semi-permanent atmospheric ridge anomalies near the coastal Pacific Northwest. This is important to western US water managers because these widespread events appear to have been a robust feature of the past century. The semi-permanent atmospheric features associated with these widespread dry streamflow anomalies are projected to change position significantly in the next century as a response to global climate change. This may change widespread streamflow anomaly characteristic in the western US, though my results do not show evidence of these changes within the instrument record of last century.

Impact of Urbanization on Spatial Variability of Rainfall-A case study of Mumbai city with WRF Model

NASA Astrophysics Data System (ADS)

Mathew, M.; Paul, S.; Devanand, A.; Ghosh, S.

2015-12-01

Urban precipitation enhancement has been identified over many cities in India by previous studies conducted. Anthropogenic effects such as change in land cover from hilly forest areas to flat topography with solid concrete infrastructures has certain effect on the local weather, the same way the greenhouse gas has on climate change. Urbanization could alter the large scale forcings to such an extent that it may bring about temporal and spatial changes in the urban weather. The present study investigate the physical processes involved in urban forcings, such as the effect of sudden increase in wind velocity travelling through the channel space in between the dense array of buildings, which give rise to turbulence and air mass instability in urban boundary layer and in return alters the rainfall distribution as well as rainfall initiation. A numerical model study is conducted over Mumbai metropolitan city which lies on the west coast of India, to assess the effect of urban morphology on the increase in number of extreme rainfall events in specific locations. An attempt has been made to simulate twenty extreme rainfall events that occurred over the summer monsoon period of the year 2014 using high resolution WRF-ARW (Weather Research and Forecasting-Advanced Research WRF) model to assess the urban land cover mechanisms that influences precipitation variability over this spatially varying urbanized region. The result is tested against simulations with altered land use. The correlation of precipitation with spatial variability of land use is found using a detailed urban land use classification. The initial and boundary conditions for running the model were obtained from the global model ECMWF(European Centre for Medium Range Weather Forecast) reanalysis data having a horizontal resolution of 0.75 °x 0.75°. The high resolution simulations show significant spatial variability in the accumulated rainfall, within a few kilometers itself. Understanding the spatial variability of precipitation will help in the planning and management of the built environment more efficiently.

Sensitivity of Hydrologic Extremes to Spatial Resolution of Meteorological Forcings: A Case Study of the Conterminous United States

NASA Astrophysics Data System (ADS)

Kao, S. C.; Naz, B. S.; Gangrade, S.; Ashfaq, M.; Rastogi, D.

2016-12-01

The magnitude and frequency of hydroclimate extremes are projected to increase in the conterminous United States (CONUS) with significant implications for future water resource planning and flood risk management. Nevertheless, apart from the change of natural environment, the choice of model spatial resolution could also artificially influence the features of simulated extremes. To better understand how the spatial resolution of meteorological forcings may affect hydroclimate projections, we test the runoff sensitivity using the Variable Infiltration Capacity (VIC) model that was calibrated for each CONUS 8-digit hydrologic unit (HUC8) at 1/24° ( 4km) grid resolution. The 1980-2012 gridded Daymet and PRISM meteorological observations are used to conduct the 1/24° resolution control simulation. Comparative simulations are achieved by smoothing the 1/24° forcing into 1/12° and 1/8° resolutions which are then used to drive the VIC model for the CONUS. In addition, we also test how the simulated high and low runoff conditions would react to change in precipitation (±10%) and temperature (+1°C). The results are further analyzed for various types of hydroclimate extremes across different watersheds in the CONUS. This work helps us understand the sensitivity of simulated runoff to different spatial resolutions of climate forcings and also its sensitivity to different watershed sizes and characteristics of extreme events in the future climate conditions.

Spatial and temporal variation in daily temperature indices in summer and winter seasons over India (1969-2012)

NASA Astrophysics Data System (ADS)

Kumar, Naresh; Jaswal, A. K.; Mohapatra, M.; Kore, P. A.

2017-08-01

Spatial and temporal variations in summer and winter extreme temperature indices are studied by using daily maximum and minimum temperatures data from 227 surface meteorological stations well distributed over India for the period 1969-2012. For this purpose, time series for six extreme temperature indices namely, hot days (HD), very hot days (VHD), extremely hot days (EHD), cold nights (CN), very cold nights (VCN), and extremely cold nights (ECN) are calculated for all the stations. In addition, time series for mean extreme temperature indices of summer and winter seasons are also analyzed. Study reveals high variability in spatial distribution of threshold temperatures of extreme temperature indices over the country. In general, increasing trends are observed in summer hot days indices and decreasing trends in winter cold night indices over most parts of the country. The results obtained in this study indicate warming in summer maximum and winter minimum temperatures over India. Averaged over India, trends in summer hot days indices HD, VHD, and EHD are significantly increasing (+1.0, +0.64, and +0.32 days/decade, respectively) and winter cold night indices CN, VCN, and ECN are significantly decreasing (-0.93, -0.47, and -0.15 days/decade, respectively). Also, it is observed that the impact of extreme temperature is higher along the west coast for summer and east coast for winter.

Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

NASA Astrophysics Data System (ADS)

Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Devers, Alexandre; Graff, Benjamin

2017-06-01

The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989-1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

Detection of the relationship between peak temperature and extreme precipitation

NASA Astrophysics Data System (ADS)

Yu, Y.; Liu, J.; Zhiyong, Y.

2017-12-01

Under the background of climate change and human activities, the characteristics and pattern of precipitation have changed significantly in many regions. As the political and cultural center of China, the structure and character of precipitation in Jingjinji District has varied dramatically in recent years. In this paper, the daily precipitation data throughout the period 1960-2013 are selected for analyzing the spatial-temporal variability of precipitation. The results indicate that the frequency and intensity of precipitation presents an increasing trend. Based on the precipitation data, the maximum, minimum and mean precipitation in different temporal and spatial scales is calculated respectively. The temporal and spatial variation of temperature is obtained by using statistical methods. The relationship between temperature and precipitation in different range is analyzed. The curve relates daily precipitation extremes with local temperatures has a peak structure, increasing at the low-medium range of temperature variations but decreasing at high temperatures. The relationship between extreme precipitation is stronger in downtown than that in suburbs.

Soil variability along a nitrogen mineralization and nitrification gradient in a nitrogen-saturated hardwood forest

Treesearch

Frank S. Gilliam; Nikki L. Lyttle; Ashley Thomas; Mary Beth Adams

2005-01-01

Some N-saturated watersheds of the Fernow Experimental Forest (FEF), West Virginia, exhibit a high degree of spatial heterogeneity in soil N processing. We used soils from four sites at FEF representing a gradient in net N mineralization and nitrification to consider the causes and consequences of such spatial heterogeneity. We collected soils with extremely high vs....

Increased Spatial Variability and Intensification of Extreme Monsoon Rainfall due to Urbanization.

PubMed

Paul, Supantha; Ghosh, Subimal; Mathew, Micky; Devanand, Anjana; Karmakar, Subhankar; Niyogi, Dev

2018-03-02

While satellite data provides a strong robust signature of urban feedback on extreme precipitation; urbanization signal is often not so prominent with station level data. To investigate this, we select the case study of Mumbai, India and perform a high resolution (1 km) numerical study with Weather Research and Forecasting (WRF) model for eight extreme rainfall days during 2014-2015. The WRF model is coupled with two different urban schemes, the Single Layer Urban Canopy Model (WRF-SUCM), Multi-Layer Urban Canopy Model (WRF-MUCM). The differences between the WRF-MUCM and WRF-SUCM indicate the importance of the structure and characteristics of urban canopy on modifications in precipitation. The WRF-MUCM simulations resemble the observed distributed rainfall. WRF-MUCM also produces intensified rainfall as compared to the WRF-SUCM and WRF-NoUCM (without UCM). The intensification in rainfall is however prominent at few pockets of urban regions, that is seen in increased spatial variability. We find that the correlation of precipitation across stations within the city falls below statistical significance at a distance greater than 10 km. Urban signature on extreme precipitation will be reflected on station rainfall only when the stations are located inside the urban pockets having intensified precipitation, which needs to be considered in future analysis.

GeoProMT: A Collaborative Platform Supporting Natural Hazards Project Management From Assessment to Resilience

NASA Astrophysics Data System (ADS)

Renschler, C.; Sheridan, M. F.; Patra, A. K.

2008-05-01

The impact and consequences of extreme geophysical events (hurricanes, floods, wildfires, volcanic flows, mudflows, etc.) on properties and processes should be continuously assessed by a well-coordinated interdisciplinary research and outreach approach addressing risk assessment and resilience. Communication between various involved disciplines and stakeholders is the key to a successful implementation of an integrated risk management plan. These issues become apparent at the level of decision support tools for extreme events/disaster management in natural and managed environments. The Geospatial Project Management Tool (GeoProMT) is a collaborative platform for research and training to document and communicate the fundamental steps in transforming information for extreme events at various scales for analysis and management. GeoProMT is an internet-based interface for the management of shared geo-spatial and multi-temporal information such as measurements, remotely sensed images, and other GIS data. This tool enhances collaborative research activities and the ability to assimilate data from diverse sources by integrating information management. This facilitates a better understanding of natural processes and enhances the integrated assessment of resilience against both the slow and fast onset of hazard risks. Fundamental to understanding and communicating complex natural processes are: (a) representation of spatiotemporal variability, extremes, and uncertainty of environmental properties and processes in the digital domain, (b) transformation of their spatiotemporal representation across scales (e.g. interpolation, aggregation, disaggregation.) during data processing and modeling in the digital domain, and designing and developing tools for (c) geo-spatial data management, and (d) geo-spatial process modeling and effective implementation, and (e) supporting decision- and policy-making in natural resources and hazard management at various spatial and temporal scales of interest. GeoProMT is useful for researchers, practitioners, and decision-makers, because it provides an integrated environmental system assessment and data management approach that considers the spatial and temporal scales and variability in natural processes. Particularly in the occurrence or onset of extreme events it can utilize the latest data sources that are available at variable scales, combine them with existing information, and update assessment products such as risk and vulnerability assessment maps. Because integrated geo-spatial assessment requires careful consideration of all the steps in utilizing data, modeling and decision-making formats, each step in the sequence must be assessed in terms of how information is being scaled. At the process scale various geophysical models (e.g. TITAN, LAHARZ, or many other examples) are appropriate for incorporation in the tool. Some examples that illustrate our approach include: 1) coastal parishes impacted by Hurricane Rita (Southwestern Louisiana), 2) a watershed affected by extreme rainfall induced debris-flows (Madison County, Virginia; Panabaj, Guatemala; Casita, Nicaragua), and 3) the potential for pyroclastic flows to threaten a city (Tungurahua, Ecuador). This research was supported by the National Science Foundation.

Assessing Regional Scale Variability in Extreme Value Statistics Under Altered Climate Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brunsell, Nathaniel; Mechem, David; Ma, Chunsheng

Recent studies have suggested that low-frequency modes of climate variability can significantly influence regional climate. The climatology associated with extreme events has been shown to be particularly sensitive. This has profound implications for droughts, heat waves, and food production. We propose to examine regional climate simulations conducted over the continental United States by applying a recently developed technique which combines wavelet multi–resolution analysis with information theory metrics. This research is motivated by two fundamental questions concerning the spatial and temporal structure of extreme events. These questions are 1) what temporal scales of the extreme value distributions are most sensitive tomore » alteration by low-frequency climate forcings and 2) what is the nature of the spatial structure of variation in these timescales? The primary objective is to assess to what extent information theory metrics can be useful in characterizing the nature of extreme weather phenomena. Specifically, we hypothesize that (1) changes in the nature of extreme events will impact the temporal probability density functions and that information theory metrics will be sensitive these changes and (2) via a wavelet multi–resolution analysis, we will be able to characterize the relative contribution of different timescales on the stochastic nature of extreme events. In order to address these hypotheses, we propose a unique combination of an established regional climate modeling approach and advanced statistical techniques to assess the effects of low-frequency modes on climate extremes over North America. The behavior of climate extremes in RCM simulations for the 20th century will be compared with statistics calculated from the United States Historical Climatology Network (USHCN) and simulations from the North American Regional Climate Change Assessment Program (NARCCAP). This effort will serve to establish the baseline behavior of climate extremes, the validity of an innovative multi–resolution information theory approach, and the ability of the RCM modeling framework to represent the low-frequency modulation of extreme climate events. Once the skill of the modeling and analysis methodology has been established, we will apply the same approach for the AR5 (IPCC Fifth Assessment Report) climate change scenarios in order to assess how climate extremes and the the influence of lowfrequency variability on climate extremes might vary under changing climate. The research specifically addresses the DOE focus area 2. Simulation of climate extremes under a changing climate. Specific results will include (1) a better understanding of the spatial and temporal structure of extreme events, (2) a thorough quantification of how extreme values are impacted by low-frequency climate teleconnections, (3) increased knowledge of current regional climate models ability to ascertain these influences, and (4) a detailed examination of the how the distribution of extreme events are likely to change under different climate change scenarios. In addition, this research will assess the ability of the innovative wavelet information theory approach to characterize extreme events. Any and all of these results will greatly enhance society’s ability to understand and mitigate the regional ramifications of future global climate change.« less

A Long-Term Dissipation of the EUV He ii (30.4 nm) Segmentation in Full-Disk Solar Images

NASA Astrophysics Data System (ADS)

Didkovsky, Leonid

2018-06-01

Some quiet-Sun days observed by the Atmospheric Imaging Assembly (AIA) on-board the Solar Dynamics Observatory (SDO) during the time interval in 2010 - 2017 were used to continue our previous analyses reported by Didkovsky and Gurman ( Solar Phys. 289, 153, 2014a) and Didkovsky, Wieman, and Korogodina ( Solar Phys. 292, 32, 2017). The analysis consists of determining and comparing spatial spectral ratios (spectral densities over some time interval) from spatial (segmentation-cell length) power spectra. The ratios were compared using modeled compatible spatial frequencies for spectra from the Extreme ultraviolet Imaging Telescope (EIT) on-board the Solar and Heliospheric Observatory (SOHO) and from AIA images. With the new AIA data added to the EIT data we analyzed previously, the whole time interval from 1996 to 2017 reported here is approximately the length of two "standard" solar cycles (SC). The spectral ratios of segmentation-cell dimension structures show a significant and steady increase with no detected indication of SC-related returns to the values that characterize the SC minima. This increase in spatial power at high spatial frequencies is interpreted as a dissipation of medium-size EUV network structures to smaller-size structures in the transition region. Each of the latest ratio changes for 2010 through 2017 spectra calculated for a number of consecutive short-term intervals has been converted into monthly mean ratio (MMR) changes. The MMR values demonstrate variable sign and magnitudes, thus confirming the solar nature of the changes. These changes do not follow a "typical" trend of instrumental degradation or a long-term activity profile from the He ii (30.4 nm) irradiance measured by the Extreme ultraviolet Spectrophotometer (ESP) either. The ESP is a channel of the Extreme ultraviolet Variability Experiment (EVE) on-board SDO.

Inferring Spatio-temporal Variations in the Risk of Extreme Precipitation in the Western United States from Tree-ring Chronologies

NASA Astrophysics Data System (ADS)

Steinschneider, S.; Ho, M.; Cook, E. R.; Lall, U.

2017-12-01

This work explores how extreme cold-season precipitation dynamics along the west coast of the United States have varied in the past under natural climate variability through an analysis of the moisture anomalies recorded by tree-ring chronologies across the coast and interior of the western U.S. Winters with high total precipitation amounts in the coastal regions are marked by a small number of extreme storms that exhibit distinct spatial patterns of precipitation across the coast and further inland. Building from this observation, this work develops a novel application of dendroclimatic evidence to explore the following questions: a) how is extreme precipitation variability expressed in a network of tree-ring chronologies; b) can this information provide insight on the space-time variability of storm tracks that cause these extreme events; and c) how can the joint variability of extreme precipitation and storm tracks be modeled to develop consistent, multi-centennial reconstructions of both? We use gridded, tree-ring based reconstructions of the summer Palmer Drought Severity Index (PDSI) extending back 500 years within the western U.S. to build and test a novel statistical framework for reconstructing the space-time variability of coastal extreme precipitation and the associated wintertime storm tracks. Within this framework, we (1) identify joint modes of variability of extreme precipitation fields and tree-ring based PDSI reconstructions; (2) relate these modes to previously identified, unique storm track patterns associated with atmospheric rivers (ARs), which are the dominant type of storm that is responsible for extreme precipitation in the region; and (3) determine latitudinal variations of landfalling ARs across the west coast and their relationship to the these joint modes. To our knowledge, this work is the first attempt to leverage information on storm track patterns stored in a network of paleoclimate proxies to improve reconstruction fidelity.

Inferring Spatio-temporal Variations in the Risk of Extreme Precipitation in the Western United States from Tree-ring Chronologies

NASA Astrophysics Data System (ADS)

Steinschneider, S.; Ho, M.; Cook, E. R.; Lall, U.

2016-12-01

This work explores how extreme cold-season precipitation dynamics along the west coast of the United States have varied in the past under natural climate variability through an analysis of the moisture anomalies recorded by tree-ring chronologies across the coast and interior of the western U.S. Winters with high total precipitation amounts in the coastal regions are marked by a small number of extreme storms that exhibit distinct spatial patterns of precipitation across the coast and further inland. Building from this observation, this work develops a novel application of dendroclimatic evidence to explore the following questions: a) how is extreme precipitation variability expressed in a network of tree-ring chronologies; b) can this information provide insight on the space-time variability of storm tracks that cause these extreme events; and c) how can the joint variability of extreme precipitation and storm tracks be modeled to develop consistent, multi-centennial reconstructions of both? We use gridded, tree-ring based reconstructions of the summer Palmer Drought Severity Index (PDSI) extending back 500 years within the western U.S. to build and test a novel statistical framework for reconstructing the space-time variability of coastal extreme precipitation and the associated wintertime storm tracks. Within this framework, we (1) identify joint modes of variability of extreme precipitation fields and tree-ring based PDSI reconstructions; (2) relate these modes to previously identified, unique storm track patterns associated with atmospheric rivers (ARs), which are the dominant type of storm that is responsible for extreme precipitation in the region; and (3) determine latitudinal variations of landfalling ARs across the west coast and their relationship to the these joint modes. To our knowledge, this work is the first attempt to leverage information on storm track patterns stored in a network of paleoclimate proxies to improve reconstruction fidelity.

Disturbance Impacts on Thermal Hot Spots and Hot Moments at the Peatland-Atmosphere Interface

NASA Astrophysics Data System (ADS)

Leonard, R. M.; Kettridge, N.; Devito, K. J.; Petrone, R. M.; Mendoza, C. A.; Waddington, J. M.; Krause, S.

2018-01-01

Soil-surface temperature acts as a master variable driving nonlinear terrestrial ecohydrological, biogeochemical, and micrometeorological processes, inducing short-lived or spatially isolated extremes across heterogeneous landscape surfaces. However, subcanopy soil-surface temperatures have been, to date, characterized through isolated, spatially discrete measurements. Using spatially complex forested northern peatlands as an exemplar ecosystem, we explore the high-resolution spatiotemporal thermal behavior of this critical interface and its response to disturbances by using Fiber-Optic Distributed Temperature Sensing. Soil-surface thermal patterning was identified from 1.9 million temperature measurements under undisturbed, trees removed and vascular subcanopy removed conditions. Removing layers of the structurally diverse vegetation canopy not only increased mean temperatures but it shifted the spatial and temporal distribution, range, and longevity of thermal hot spots and hot moments. We argue that linking hot spots and/or hot moments with spatially variable ecosystem processes and feedbacks is key for predicting ecosystem function and resilience.

Spatial correlation of probabilistic earthquake ground motion and loss

USGS Publications Warehouse

Wesson, R.L.; Perkins, D.M.

2001-01-01

Spatial correlation of annual earthquake ground motions and losses can be used to estimate the variance of annual losses to a portfolio of properties exposed to earthquakes A direct method is described for the calculations of the spatial correlation of earthquake ground motions and losses. Calculations for the direct method can be carried out using either numerical quadrature or a discrete, matrix-based approach. Numerical results for this method are compared with those calculated from a simple Monte Carlo simulation. Spatial correlation of ground motion and loss is induced by the systematic attenuation of ground motion with distance from the source, by common site conditions, and by the finite length of fault ruptures. Spatial correlation is also strongly dependent on the partitioning of the variability, given an event, into interevent and intraevent components. Intraevent variability reduces the spatial correlation of losses. Interevent variability increases spatial correlation of losses. The higher the spatial correlation, the larger the variance in losses to a port-folio, and the more likely extreme values become. This result underscores the importance of accurately determining the relative magnitudes of intraevent and interevent variability in ground-motion studies, because of the strong impact in estimating earthquake losses to a portfolio. The direct method offers an alternative to simulation for calculating the variance of losses to a portfolio, which may reduce the amount of calculation required.

A comparison of extreme rainfall characteristics in the Brazilian Amazon derived from two gridded data sets and a national rain gauge network

NASA Astrophysics Data System (ADS)

Clarke, Robin T.; Bulhoes Mendes, Carlos Andre; Costa Buarque, Diogo

2010-07-01

Two issues of particular importance for the Amazon watershed are: whether annual maxima obtained from reanalysis and raingauge records agree well enough for the former to be useful in extending records of the latter; and whether reported trends in Amazon annual rainfall are reflected in the behavior of annual extremes in precipitation estimated from reanalyses and raingauge records. To explore these issues, three sets of daily precipitation data (1979-2001) from the Brazilian Amazon were analyzed (NCEP/NCAR and ERA-40 reanalyses, and records from the raingauge network of the Brazilian water resources agency - ANA), using the following variables: (1) mean annual maximum precipitation totals, accumulated over one, two, three and five days; (2) linear trends in these variables; (3) mean length of longest within-year "dry" spell; (4) linear trends in these variables. Comparisons between variables obtained from all three data sources showed that reanalyses underestimated time-trends and mean annual maximum precipitation (over durations of one to five days), and the correlations between reanalysis and spatially-interpolated raingauge estimates were small for these two variables. Both reanalyses over-estimated mean lengths of dry period relative to the mean length recorded by the raingauge network. Correlations between the trends calculated from all three data sources were small. Time-trends averaged over the reanalysis grid-squares, and spatially-interpolated time trends from raingauge data, were all clustered around zero. In conclusion, although the NCEP/NCAR and ERA-40 gridded data-sets may be valuable for studies of inter-annual variability in precipitation totals, they were found to be inappropriate for analysis of precipitation extremes.

Changes in the extreme wave heights over the Baltic Sea

NASA Astrophysics Data System (ADS)

Kudryavtseva, Nadia; Soomere, Tarmo

2017-04-01

Storms over the Baltic Sea and northwestern Europe have a large impact on the population, offshore industry, and shipping. The understanding of extreme events in sea wave heights and their change due to the climate change and variability is critical for assessment of flooding risks and coastal protection. The BACCII Assessment of Climate Change for the Baltic Sea Basin showed that the extreme events analysis of wind waves is currently not very well addressed, as well as satellite observations of the wave heights. Here we discuss the analysis of all existing satellite altimetry data over the Baltic Sea Basin regarding extremes in the wave heights. In this talk for the first time, we present an analysis of 100-yr return periods, fitted generalized Pareto and Weibull distributions, number, and frequency of extreme events in wave heights in the Baltic Sea measured by the multi-mission satellite altimetry. The data span more than 23 years and provide an excellent spatial coverage over the Baltic Sea, allowing to study in details spatial variations and changes in extreme wave heights. The analysis is based on an application of the Initial Distribution Method, Annual Maxima method and Peak-Over-Threshold approach to satellite altimetry data, all validated in comparison with in-situ wave height measurements. Here we show that the 100-yr return periods of wave heights show significant spatial changes over the Baltic Sea indicating a decrease in the southern part of the Baltic Sea and an increase in adjacent areas, which can significantly affect coast vulnerability. Here we compare the observed shift with storm track database data and discuss a spatial correlation and possible connection between the changes in the storm tracks over the Baltic Sea and the change in the extreme wave heights.

NLDAS Views of North American 2011 Extreme Events

NASA Technical Reports Server (NTRS)

Rui, Hualan; Teng, William L.; Vollmer, Bruce; Mocko, David; Lei, Guang-Dih

2014-01-01

2011 was marked as one of the most extreme years in recent history. Over the course of the year, weather-related extreme events, such as floods, heat waves, blizzards, tornadoes, and wildfires, caused tremendous loss of human life and property. The North American Land Data Assimilation System (NLDAS, http:ldas.gsfc.nasa.govnldas) data set, with high spatial and temporal resolutions (0.125 x 0.125, hourly) and various water- and energy-related variables, is an excellent data source for case studies of extreme events. This presentation illustrates some extreme events from 2011 in North America, including the Groundhog Day Blizzard, the July heat wave, Hurricane Irene, and Tropical Storm Lee, all utilizing NLDAS Phase 2 (NLDAS-2) data.

Statistical analysis of corn yields responding to climate variability at various spatio-temporal resolutions

NASA Astrophysics Data System (ADS)

Jiang, H.; Lin, T.

2017-12-01

Rain-fed corn production systems are subject to sub-seasonal variations of precipitation and temperature during the growing season. As each growth phase has varied inherent physiological process, plants necessitate different optimal environmental conditions during each phase. However, this temporal heterogeneity towards climate variability alongside the lifecycle of crops is often simplified and fixed as constant responses in large scale statistical modeling analysis. To capture the time-variant growing requirements in large scale statistical analysis, we develop and compare statistical models at various spatial and temporal resolutions to quantify the relationship between corn yield and weather factors for 12 corn belt states from 1981 to 2016. The study compares three spatial resolutions (county, agricultural district, and state scale) and three temporal resolutions (crop growth phase, monthly, and growing season) to characterize the effects of spatial and temporal variability. Our results show that the agricultural district model together with growth phase resolution can explain 52% variations of corn yield caused by temperature and precipitation variability. It provides a practical model structure balancing the overfitting problem in county specific model and weak explanation power in state specific model. In US corn belt, precipitation has positive impact on corn yield in growing season except for vegetative stage while extreme heat attains highest sensitivity from silking to dough phase. The results show the northern counties in corn belt area are less interfered by extreme heat but are more vulnerable to water deficiency.

Extreme Events in Urban Streams Leading to Extreme Temperatures in Birmingham, UK

NASA Astrophysics Data System (ADS)

Rangecroft, S.; Croghan, D.; Van Loon, A.; Sadler, J. P.; Hannah, D. M.

2016-12-01

Extreme flows and high water temperature events act as critical stressors on the ecological health of rivers. Urban headwater streams are considered particularly vulnerable to the effects of these extreme events. Despite this, such catchments remain poorly characterised and the effect of differences in land use is rarely quantified, especially in relation to water temperature. Thus a key research gap has emerged in understanding the patterns of water temperature during extreme events within contrasting urban, headwater catchments. We studied the headwaters of two bordering urban catchments of contrasting land use within Birmingham, UK. To characterise response to extreme events, precipitation and flow were analysed for the period of 1970-2016. To analyse the effects of extreme events on water temperature, 10 temperature loggers recording at 15 minute intervals were placed within each catchment covering a range of land use for the period May 2016 - present. During peak over threshold flood events higher average peaks were observed in the less urbanised catchment; however highest maximum flow peaks took place in the more densely urbanised catchment. Very similar average drought durations were observed between the two catchments with average flow drought durations of 27 days in the most urbanised catchment, and 29 in the less urbanised catchment. Flashier water temperature regimes were observed within the more urbanised catchment and increases of up to 5 degrees were apparent within 30 minutes during certain storms at the most upstream sites. Only in the most extreme events did the more densely urban stream appear more susceptible to both extreme high flows and extreme water temperature events, possibly resultant from overland flow emerging as the dominant flow pathway during intense precipitation events. Water temperature surges tended to be highly spatially variable indicating the importance of local land use. During smaller events, water temperature was less changeable and spatially variable, suggesting that overland flow may not the dominant flow pathway in such events. During drought events, the effect of catchment land use on water temperature was less apparent.

Assessment of floodplain vulnerability during extreme Mississippi River flood 2011

USGS Publications Warehouse

Goodwell, Allison E.; Zhu, Zhenduo; Dutta, Debsunder; Greenberg, Jonathan A.; Kumar, Praveen; Garcia, Marcelo H.; Rhoads, Bruce L.; Holmes, Robert R.; Parker, Gary; Berretta, David P.; Jacobson, Robert B.

2014-01-01

Regional change in the variability and magnitude of flooding could be a major consequence of future global climate change. Extreme floods have the capacity to rapidly transform landscapes and expose landscape vulnerabilities through highly variable spatial patterns of inundation, erosion, and deposition. We use the historic activation of the Birds Point-New Madrid Floodway during the Mississippi and Ohio River Flooding of 2011 as a scientifically unique stress experiment to analyze indicators of floodplain vulnerability. We use pre- and postflood airborne Light Detection and Ranging data sets to locate erosional and depositional hotspots over the 540 km2 agricultural Floodway. While riparian vegetation between the river and the main levee breach likely prevented widespread deposition, localized scour and deposition occurred near the levee breaches. Eroded gullies nearly 1 km in length were observed at a low ridge of a relict meander scar of the Mississippi River. Our flow modeling and spatial mapping analysis attributes this vulnerability to a combination of erodible soils, flow acceleration associated with legacy fluvial landforms, and a lack of woody vegetation to anchor soil and enhance flow resistance. Results from this study could guide future mitigation and adaptation measures in cases of extreme flooding.

Assessment of floodplain vulnerability during extreme Mississippi River flood 2011.

PubMed

Goodwell, Allison E; Zhu, Zhenduo; Dutta, Debsunder; Greenberg, Jonathan A; Kumar, Praveen; Garcia, Marcelo H; Rhoads, Bruce L; Holmes, Robert R; Parker, Gary; Berretta, David P; Jacobson, Robert B

2014-01-01

Regional change in the variability and magnitude of flooding could be a major consequence of future global climate change. Extreme floods have the capacity to rapidly transform landscapes and expose landscape vulnerabilities through highly variable spatial patterns of inundation, erosion, and deposition. We use the historic activation of the Birds Point-New Madrid Floodway during the Mississippi and Ohio River Flooding of 2011 as a scientifically unique stress experiment to analyze indicators of floodplain vulnerability. We use pre- and postflood airborne Light Detection and Ranging data sets to locate erosional and depositional hotspots over the 540 km(2) agricultural Floodway. While riparian vegetation between the river and the main levee breach likely prevented widespread deposition, localized scour and deposition occurred near the levee breaches. Eroded gullies nearly 1 km in length were observed at a low ridge of a relict meander scar of the Mississippi River. Our flow modeling and spatial mapping analysis attributes this vulnerability to a combination of erodible soils, flow acceleration associated with legacy fluvial landforms, and a lack of woody vegetation to anchor soil and enhance flow resistance. Results from this study could guide future mitigation and adaptation measures in cases of extreme flooding.

NLDAS Views of North American 2011 Extreme Events

NASA Technical Reports Server (NTRS)

Rui, Hualan; Teng, William; Vollmer, Bruce; Mocko, David; Lei, Guang-Dih

2012-01-01

2011 was marked as one of the most extreme years in recent history. Over the course of the year, weather-related extreme events, such as floods, heat waves, blizzards, tornadoes, and wildfires, caused tremendous loss of human life and property. The North American Land Data Assimilation System (NLDAS, http://ldas.gsfc.nasa.gov/nldas/) data set, with high spatial and temporal resolutions (0.125? x 0.125?, hourly) and various water- and energy-related variables, is an excellent data source for case studies of extreme events. This presentation illustrates some extreme events from 2011 in North America, including the Groundhog Day Blizzard, the July heat wave, Hurricane Irene, and Tropical Storm Lee, all utilizing NLDAS Phase 2 (NLDAS-2) data.

Long-term trend of satellite-observed significant wave height and impact on ecosystem in the East/Japan Sea

NASA Astrophysics Data System (ADS)

Woo, Hye-Jin; Park, Kyung-Ae

2017-09-01

Significant wave height (SWH) data of nine satellite altimeters were validated with in-situ SWH measurements from buoy stations in the East/Japan Sea (EJS) and the Northwest Pacific Ocean. The spatial and temporal variability of extreme SWHs was investigated by defining the 90th, 95th, and 99th percentiles based on percentile analysis. The annual mean of extreme SWHs was dramatically increased by 3.45 m in the EJS, which is significantly higher than the normal mean of about 1.44 m. The spatial distributions of SWHs showed significantly higher values in the eastern region of the EJS than those in the western part. Characteristic seasonality was found from the time-series SWHs with high SWHs (>2.5 m) in winter but low values (<1 m) in summer. The trends of the normal and extreme (99th percentile) SWHs in the EJS had a positive value of 0.0056 m year-1 and 0.0125 m year-1, respectively. The long-term trend demonstrated that higher SWH values were more extreme with time during the past decades. The predominant spatial distinctions between the coastal regions in the marginal seas of the Northwest Pacific Ocean and open ocean regions were presented. In spring, both normal and extreme SWHs showed substantially increasing trends in the EJS. Finally, we first presented the impact of the long-term trend of extreme SWHs on the marine ecosystem through vertical mixing enhancement in the upper ocean of the EJS.

Relationships between interdecadal variability and extreme precipitation events in South America during the monsoon season

NASA Astrophysics Data System (ADS)

Grimm, Alice; Laureanti, Nicole; Rodakoviski, Rodrigo

2016-04-01

This study aims to clarify the impact of interdecadal climate oscillations (periods of 8 years and longer) on the frequency of extreme precipitation events over South America in the monsoon season (austral spring and summer), and determine the influence of these oscillations on the daily precipitation frequency distribution. Interdecadal variability modes of precipitation during the monsoon season are provided by a continental-scale rotated empirical orthogonal function analysis for the 60 years period 1950-2009. The main disclosed modes are robust, since they are reproduced for different periods. They can produce differences around 50% in monthly precipitation between opposite phases. Oceanic and atmospheric anomalous fields associated with these modes indicate that they have physical basis. The first modes in spring and summer display highest correlation with the Interdecadal Pacific Oscillation (IPO) SST mode, while the second modes have strongest correlation with the Atlantic Multidecadal Oscillation (AMO) SST mode. However, there are also other influences on these modes. As the most dramatic consequences of climate variability stem from its influence on the frequency of extreme precipitation events, it is important to also assess this influence, since variations in monthly or seasonal precipitation do not necessarily imply significant alterations in their extreme events. This study seeks to answer the questions: i) Do opposite phases of the main interdecadal modes of seasonal precipitation produce significant anomalies in the frequency of extreme events? ii) Does the interdecadal variability of the frequency of extreme events show similar spatial and temporal structure as the interdecadal variability of the seasonal precipitation? iii) Does the interdecadal variability change the daily precipitation probability distribution between opposite phases? iv) In this case, which ranges of daily precipitation are most affected? The significant anomalies of the extreme events frequency in opposite phases of the interdecadal oscillations display spatial patterns very similar to those of the corresponding modes. In addition, the modes of extreme events frequency bear similarity to the modes of seasonal precipitation, although a complete assessment of this similarity is not possible with the daily data available. The Kolmogorov-Smirnov test is applied to the daily precipitation series for positive and negative phases of the interdecadal modes, in regions with high factor loadings. It shows, with significance level better than 0.01, that daily precipitation from opposite phases pertains to different frequency distributions. Further analyses disclose clearly that there is much greater relative impact of the interdecadal oscillations on the extreme ranges of daily rainfall than in the ranges of moderate and light rainfall. This impact is more linear is spring than in summer. Acknowledgments: This work was supported by: Inter-American Institute for Global Change Research (IAI) CRN3035 which is supported by the US National Science Foundation (Grant GEO-1128040), European Community's Seventh Framework Programme under Grant Agreement n° 212492 (CLARIS LPB), and CNPq-Brazil (National Council for Scientific and Technologic Development).

Spatiotemporal variability of rainfall extremes in monsoonal climates - examples from the South American Monsoon and the Indian Monsoon Systems (Invited)

NASA Astrophysics Data System (ADS)

Bookhagen, B.; Boers, N.; Marwan, N.; Malik, N.; Kurths, J.

2013-12-01

Monsoonal rainfall is the crucial component for more than half of the world's population. Runoff associated with monsoon systems provide water resources for agriculture, hydropower, drinking-water generation, recreation, and social well-being and are thus a fundamental part of human society. However, monsoon systems are highly stochastic and show large variability on various timescales. Here, we use various rainfall datasets to characterize spatiotemporal rainfall patterns using traditional as well as new approaches emphasizing nonlinear spatial correlations from a complex networks perspective. Our analyses focus on the South American (SAMS) and Indian (ISM) Monsoon Systems on the basis of Tropical Rainfall Measurement Mission (TRMM) using precipitation radar and passive-microwave products with horizontal spatial resolutions of ~5x5 km^2 (products 2A25, 2B31) and 25x25 km^2 (3B42) and interpolated rainfall-gauge data for the ISM (APHRODITE, 25x25 km^2). The eastern slopes of the Andes of South America and the southern front of the Himalaya are characterized by significant orographic barriers that intersect with the moisture-bearing, monsoonal wind systems. We demonstrate that topography exerts a first-order control on peak rainfall amounts on annual timescales in both mountain belts. Flooding in the downstream regions is dominantly caused by heavy rainfall storms that propagate deep into the mountain range and reach regions that are arid and without vegetation cover promoting rapid runoff. These storms exert a significantly different spatial distribution than average-rainfall conditions and assessing their recurrence intervals and prediction is key in understanding flooding for these regions. An analysis of extreme-value distributions of our high-spatial resolution data reveal that semi-arid areas are characterized by low-frequency/high-magnitude events (i.e., are characterized by a ';heavy tail' distribution), whereas regions with high mean annual rainfall have a less skewed distribution. In a second step, an analysis of the spatial characteristics of extreme rainfall synchronicity by means of complex networks reveals patterns of the propagation of extreme rainfall events. These patterns differ substantially from those obtained from the mean annual rainfall distribution. In addition, we have developed a scheme to predict rainfall extreme events in the eastern Central Andes based on event synchronization and spatial patterns of complex networks. The presented methods and result will allow to critically evaluate data and models in space and time.

Assessment of Observational Uncertainty in Extreme Precipitation Events over the Continental United States

NASA Astrophysics Data System (ADS)

Slinskey, E. A.; Loikith, P. C.; Waliser, D. E.; Goodman, A.

2017-12-01

Extreme precipitation events are associated with numerous societal and environmental impacts. Furthermore, anthropogenic climate change is projected to alter precipitation intensity across portions of the Continental United States (CONUS). Therefore, a spatial understanding and intuitive means of monitoring extreme precipitation over time is critical. Towards this end, we apply an event-based indicator, developed as a part of NASA's support of the ongoing efforts of the US National Climate Assessment, which assigns categories to extreme precipitation events based on 3-day storm totals as a basis for dataset intercomparison. To assess observational uncertainty across a wide range of historical precipitation measurement approaches, we intercompare in situ station data from the Global Historical Climatology Network (GHCN), satellite-derived precipitation data from NASA's Tropical Rainfall Measuring Mission (TRMM), gridded in situ station data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), global reanalysis from NASA's Modern Era Retrospective-Analysis version 2 (MERRA 2), and regional reanalysis with gauge data assimilation from NCEP's North American Regional Reanalysis (NARR). Results suggest considerable variability across the five-dataset suite in the frequency, spatial extent, and magnitude of extreme precipitation events. Consistent with expectations, higher resolution datasets were found to resemble station data best and capture a greater frequency of high-end extreme events relative to lower spatial resolution datasets. The degree of dataset agreement varies regionally, however all datasets successfully capture the seasonal cycle of precipitation extremes across the CONUS. These intercomparison results provide additional insight about observational uncertainty and the ability of a range of precipitation measurement and analysis products to capture extreme precipitation event climatology. While the event category threshold is fixed in this analysis, preliminary results from the development of a flexible categorization scheme, that scales with grid resolution, are presented.

Sea-level rise impacts on the temporal and spatial variability of extreme water levels: A case study for St. Peter-Ording, Germany

NASA Astrophysics Data System (ADS)

Santamaria-Aguilar, S.; Arns, A.; Vafeidis, A. T.

2017-04-01

Both the temporal and spatial variability of storm surge water level (WL) curves are usually not taken into account in flood risk assessments as observational data are often scarce. In addition, sea-level rise (SLR) can further affect the variability of WLs. We analyze the temporal and spatial variability of the WL curve of 75 historical storm surge events that have been numerically simulated for St. Peter-Ording at the German North Sea coast, considering the effects induced by three SLR scenarios (RCP 4.5, RCP 8.5, and a RCP 8.5 high end scenario). We assess potential impacts of these scenarios on two parameters related to flooding: overflow volumes and fullness. Our results indicate that due to both the temporal and spatial variability of those events the resulting overflow volume can be two or even three times greater. We observe a steepening of the WL curve with an increase of the tidal range under the three SLR scenarios, although SLR induced effects are relatively higher for the RCP 4.5. The steepening of the WL curve with SLR produces a reduction of the fullness, but the changes in overflow volumes also depend on the magnitude of the storm surge event.

Scale dependency of regional climate modeling of current and future climate extremes in Germany

NASA Astrophysics Data System (ADS)

Tölle, Merja H.; Schefczyk, Lukas; Gutjahr, Oliver

2017-11-01

A warmer climate is projected for mid-Europe, with less precipitation in summer, but with intensified extremes of precipitation and near-surface temperature. However, the extent and magnitude of such changes are associated with creditable uncertainty because of the limitations of model resolution and parameterizations. Here, we present the results of convection-permitting regional climate model simulations for Germany integrated with the COSMO-CLM using a horizontal grid spacing of 1.3 km, and additional 4.5- and 7-km simulations with convection parameterized. Of particular interest is how the temperature and precipitation fields and their extremes depend on the horizontal resolution for current and future climate conditions. The spatial variability of precipitation increases with resolution because of more realistic orography and physical parameterizations, but values are overestimated in summer and over mountain ridges in all simulations compared to observations. The spatial variability of temperature is improved at a resolution of 1.3 km, but the results are cold-biased, especially in summer. The increase in resolution from 7/4.5 km to 1.3 km is accompanied by less future warming in summer by 1 ∘C. Modeled future precipitation extremes will be more severe, and temperature extremes will not exclusively increase with higher resolution. Although the differences between the resolutions considered (7/4.5 km and 1.3 km) are small, we find that the differences in the changes in extremes are large. High-resolution simulations require further studies, with effective parameterizations and tunings for different topographic regions. Impact models and assessment studies may benefit from such high-resolution model results, but should account for the impact of model resolution on model processes and climate change.

Response and Recovery of Streams From an Extreme Flood

NASA Astrophysics Data System (ADS)

Kantack, K. M.; Renshaw, C. E.; Magilligan, F. J.; Dethier, E.

2015-12-01

In temperate regions, channels are expected to recover from intense floods in a matter of months to years, but quantitative empirical support for this idea remains limited. Moreover, existing literature fails to address the spatial variability of the recovery process. Using an emerging technology, we investigate the immediate response to and progressive recovery of channels in the Northeastern United States from an extreme flood. We seek to determine what factors, including the nature and extent of the immediate response of the channel to the flood and post-flood availability of sediment, contribute to the spatial variability of the rate of recovery. Taking advantage of the 2011 flooding from Tropical Storm Irene, for which pre- and post-flood aerial lidar exist, along with a third set of terrestrial lidar collected in 2015, we assess channel response and recovery with multi-temporal lidar comparison. This method, with kilometers of continuous data, allows for analysis beyond traditional cross-section and reach-scale studies. Results indicate that landscape-scale factors, such as valley morphology and gradients in unit stream power, are controls on channel response to the flood, producing spatially variable impacts. Along a 16.4-km section (drainage area = 82 km2) of the Deerfield River in Vermont, over 148,000 m3 or erosion occurred during the flood. The spatial variation of impacts was correlated (R2= 0.476) with the ratio of channel width to valley width. We expect the recovery process will similarly exhibit spatial variation in rate and magnitude, possibly being governed by gradients in unit stream power and sediment availability. We test the idea that channel widening during the flood reduces post-flood unit stream power, creating a pathway for deposition and recovery to pre-flood width. Flood-widened reaches downstream of point-sources of sediment, such as landslides, will recover more quickly than those without consistent sediment supply. Results of this study will improve our ability to predict the nature and location of flood impacts and determine what factors contribute to the spatial variability of channel recovery.

Impacts of ozone air pollution and temperature extremes on crop yields: Spatial variability, adaptation and implications for future food security

NASA Astrophysics Data System (ADS)

Tai, Amos P. K.; Val Martin, Maria

2017-11-01

Ozone air pollution and climate change pose major threats to global crop production, with ramifications for future food security. Previous studies of ozone and warming impacts on crops typically do not account for the strong ozone-temperature correlation when interpreting crop-ozone or crop-temperature relationships, or the spatial variability of crop-to-ozone sensitivity arising from varietal and environmental differences, leading to potential biases in their estimated crop losses. Here we develop an empirical model, called the partial derivative-linear regression (PDLR) model, to estimate the spatial variations in the sensitivities of wheat, maize and soybean yields to ozone exposures and temperature extremes in the US and Europe using a composite of multidecadal datasets, fully correcting for ozone-temperature covariation. We find generally larger and more spatially varying sensitivities of all three crops to ozone exposures than are implied by experimentally derived concentration-response functions used in most previous studies. Stronger ozone tolerance is found in regions with high ozone levels and high consumptive crop water use, reflecting the existence of spatial adaptation and effect of water constraints. The spatially varying sensitivities to temperature extremes also indicate stronger heat tolerance in crops grown in warmer regions. The spatial adaptation of crops to ozone and temperature we find can serve as a surrogate for future adaptation. Using the PDLR-derived sensitivities and 2000-2050 ozone and temperature projections by the Community Earth System Model, we estimate that future warming and unmitigated ozone pollution can combine to cause an average decline in US wheat, maize and soybean production by 13%, 43% and 28%, respectively, and a smaller decline for European crops. Aggressive ozone regulation is shown to offset such decline to various extents, especially for wheat. Our findings demonstrate the importance of considering ozone regulation as well as ozone and climate change adaptation (e.g., selecting heat- and ozone-tolerant cultivars, irrigation) as possible strategies to enhance future food security in response to imminent environmental threats.

Autocorrelation structure of convective rainfall in semiarid-arid climate derived from high-resolution X-Band radar estimates

NASA Astrophysics Data System (ADS)

Marra, Francesco; Morin, Efrat

2018-02-01

Small scale rainfall variability is a key factor driving runoff response in fast responding systems, such as mountainous, urban and arid catchments. In this paper, the spatial-temporal autocorrelation structure of convective rainfall is derived with extremely high resolutions (60 m, 1 min) using estimates from an X-Band weather radar recently installed in a semiarid-arid area. The 2-dimensional spatial autocorrelation of convective rainfall fields and the temporal autocorrelation of point-wise and distributed rainfall fields are examined. The autocorrelation structures are characterized by spatial anisotropy, correlation distances 1.5-2.8 km and rarely exceeding 5 km, and time-correlation distances 1.8-6.4 min and rarely exceeding 10 min. The observed spatial variability is expected to negatively affect estimates from rain gauges and microwave links rather than satellite and C-/S-Band radars; conversely, the temporal variability is expected to negatively affect remote sensing estimates rather than rain gauges. The presented results provide quantitative information for stochastic weather generators, cloud-resolving models, dryland hydrologic and agricultural models, and multi-sensor merging techniques.

High-Resolution Dynamical Downscaling Ensemble Projections of Future Extreme Temperature Distributions for the United States

NASA Astrophysics Data System (ADS)

Zobel, Zachary; Wang, Jiali; Wuebbles, Donald J.; Kotamarthi, V. Rao

2017-12-01

The aim of this study is to examine projections of extreme temperatures over the continental United States (CONUS) for the 21st century using an ensemble of high spatial resolution dynamically downscaled model simulations with different boundary conditions. The downscaling uses the Weather Research and Forecast model at a spatial resolution of 12 km along with outputs from three different Coupled Model Intercomparison Project Phase 5 global climate models that provide boundary conditions under two different future greenhouse gas (GHG) concentration trajectories. The results from two decadal-length time slices (2045-2054 and 2085-2094) are compared with a historical decade (1995-2004). Probability density functions of daily maximum/minimum temperatures are analyzed over seven climatologically cohesive regions of the CONUS. The impacts of different boundary conditions as well as future GHG concentrations on extreme events such as heat waves and days with temperature higher than 95°F are also investigated. The results show that the intensity of extreme warm temperature in future summer is significantly increased, while the frequency of extreme cold temperature in future winter decreases. The distribution of summer daily maximum temperature experiences a significant warm-side shift and increased variability, while the distribution of winter daily minimum temperature is projected to have a less significant warm-side shift with decreased variability. Using "business-as-usual" scenario, 5-day heat waves are projected to occur at least 5-10 times per year in most CONUS and ≥95°F days will increase by 1-2 months by the end of the century.

Spatial variability of harmful algal blooms in Milford Lake, Kansas, July and August 2015

USGS Publications Warehouse

Foster, Guy M.; Graham, Jennifer L.; Stiles, Tom C.; Boyer, Marvin G.; King, Lindsey R.; Loftin, Keith A.

2017-01-09

Cyanobacterial harmful algal blooms (CyanoHABs) tend to be spatially variable vertically in the water column and horizontally across the lake surface because of in-lake and weather-driven processes and can vary by orders of magnitude in concentration across relatively short distances (meters or less). Extreme spatial variability in cyanobacteria and associated compounds poses unique challenges to collecting representative samples for scientific study and public-health protection. The objective of this study was to assess the spatial variability of cyanobacteria and microcystin in Milford Lake, Kansas, using data collected on July 27 and August 31, 2015. Spatially dense near-surface data were collected by the U.S. Geological Survey, nearshore data were collected by the Kansas Department of Health and Environment, and open-water data were collected by U.S. Army Corps of Engineers. CyanoHABs are known to be spatially variable, but that variability is rarely quantified. A better understanding of the spatial variability of cyanobacteria and microcystin will inform sampling and management strategies for Milford Lake and for other lakes with CyanoHAB issues throughout the Nation.The CyanoHABs in Milford Lake during July and August 2015 displayed the extreme spatial variability characteristic of cyanobacterial blooms. The phytoplankton community was almost exclusively cyanobacteria (greater than 90 percent) during July and August. Cyanobacteria (measured directly by cell counts and indirectly by regression-estimated chlorophyll) and microcystin (measured directly by enzyme-linked immunosorbent assay [ELISA] and indirectly by regression estimates) concentrations varied by orders of magnitude throughout the lake. During July and August 2015, cyanobacteria and microcystin concentrations decreased in the downlake (towards the outlet) direction.Nearshore and open-water surface grabs were collected and analyzed for microcystin as part of this study. Samples were collected in the uplake (Zone C), midlake (Zone B), and downlake (Zone A) parts of the lake. Overall, no consistent pattern was indicated as to which sample location (nearshore or open water) had the highest microcystin concentrations. In July, the maximum microcystin concentration observed in each zone was detected at a nearshore site, and in August, maximum microcystin concentrations in each zone were detected at an open-water site.The Kansas Department of Health and Environment uses two guidance levels (a watch and a warning level) to issue recreational public-health advisories for CyanoHABs in Kansas lakes. The levels are based on concentrations of microcystin and numbers of cyanobacteria. In July and August, discrete water-quality samples were predominantly indicative of warning status in Zone C, watch status in Zone B, and no advisories in Zone A. Regression-estimated microcystin concentrations, which provided more thorough coverage of Milford Lake (n=683–720) than discrete samples (n=21–24), generally indicated the same overall pattern. Regardless of the individual agencies sampling approach, the overall public-health advisory status of each zone in Milford Lake was similar according to the Kansas Department of Health and Environment guidance levels.

Bayesian Non-Stationary Index Gauge Modeling of Gridded Precipitation Extremes

NASA Astrophysics Data System (ADS)

Verdin, A.; Bracken, C.; Caldwell, J.; Balaji, R.; Funk, C. C.

2017-12-01

We propose a Bayesian non-stationary model to generate watershed scale gridded estimates of extreme precipitation return levels. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset is used to obtain gridded seasonal precipitation extremes over the Taylor Park watershed in Colorado for the period 1981-2016. For each year, grid cells within the Taylor Park watershed are aggregated to a representative "index gauge," which is input to the model. Precipitation-frequency curves for the index gauge are estimated for each year, using climate variables with significant teleconnections as proxies. Such proxies enable short-term forecasting of extremes for the upcoming season. Disaggregation ratios of the index gauge to the grid cells within the watershed are computed for each year and preserved to translate the index gauge precipitation-frequency curve to gridded precipitation-frequency maps for select return periods. Gridded precipitation-frequency maps are of the same spatial resolution as CHIRPS (0.05° x 0.05°). We verify that the disaggregation method preserves spatial coherency of extremes in the Taylor Park watershed. Validation of the index gauge extreme precipitation-frequency method consists of ensuring extreme value statistics are preserved on a grid cell basis. To this end, a non-stationary extreme precipitation-frequency analysis is performed on each grid cell individually, and the resulting frequency curves are compared to those produced by the index gauge disaggregation method.

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

NASA Astrophysics Data System (ADS)

Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

2017-10-01

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

Large scale variability, long-term trends and extreme events in total ozone over the northern mid-latitudes based on satellite time series

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Ribatet, M.; Davison, A. C.

2009-04-01

Various generations of satellites (e.g. TOMS, GOME, OMI) made spatial datasets of column ozone available to the scientific community. This study has a special focus on column ozone over the northern mid-latitudes. Tools from geostatistics and extreme value theory are applied to analyze variability, long-term trends and frequency distributions of extreme events in total ozone. In a recent case study (Rieder et al., 2009) new tools from extreme value theory (Coles, 2001; Ribatet, 2007) have been applied to the world's longest total ozone record from Arosa, Switzerland (e.g. Staehelin 1998a,b), in order to describe extreme events in low and high total ozone. Within the current study this analysis is extended to satellite datasets for the northern mid-latitudes. Further special emphasis is given on patterns and spatial correlations and the influence of changes in atmospheric dynamics (e.g. tropospheric and lower stratospheric pressure systems) on column ozone. References: Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, ISBN:1852334592, Springer, Berlin, 2001. Ribatet, M.: POT: Modelling peaks over a threshold, R News, 7, 34-36, 2007. Rieder, H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and Davison, A.C.: From ozone mini holes and mini highs towards extreme value theory: New insights from extreme events and non stationarity, submitted to J. Geophys. Res., 2009. Staehelin, J., Kegel, R., and Harris, N. R.: Trend analysis of the homogenized total ozone series of Arosa (Switzerland), 1929-1996, J. Geophys. Res., 103(D7), 8389-8400, doi:10.1029/97JD03650, 1998a. Staehelin, J., Renaud, A., Bader, J., McPeters, R., Viatte, P., Hoegger, B., Bugnion, V., Giroud, M., and Schill, H.: Total ozone series at Arosa (Switzerland): Homogenization and data comparison, J. Geophys. Res., 103(D5), 5827-5842, doi:10.1029/97JD02402, 1998b.

Understanding spatial variability in extreme estuarine water levels to inform better coastal management practise.

NASA Astrophysics Data System (ADS)

Lyddon, Charlotte; Plater, Andy, ,, Prof.; Brown, Jenny, ,, Dr.; Leonardi, Nicoletta, ,, Dr.

2017-04-01

Coastal zones worldwide are subject to short term, local variations in sea-level, particularly communities and industries developed on estuaries. Astronomical high tides, meteorological storm surges and increased river flow present a combined flood hazard. This can elevate water level at the coast above predicted levels, generating extreme water levels. These contributions can also interact to alter the phase and amplitude of tides and surges, and thus cause significant mismatches between the predicted and observed water level. The combined effect of tide, surge, river flow and their interactions are the key to understanding and assessing flood risk in estuarine environments for design purposes. Delft3D-FLOW, a hydrodynamic model which solves the unsteady shallow-water equation, is used to access spatial variability in extreme water levels for a range of historical events of different severity within the Severn Estuary, southwest England. Long-term tide gauge records from Ilfracombe and Mumbles and river level data from Sandhurst are analysed to generate a series of extreme water level events, representing the 90th, 95th and 99th percentile conditions, to force the model boundaries. To separate out the time-varying contributions of tidal, fluvial, meteorological processes and their interactions the model is run with different physical forcing. A low pass filter is applied to "de-tide" the residual water elevation, to separate out the time-varying meteorological residual and the tide-surge interactions within the surge. The filtered surge is recombined with the predicted tide so the peak occurs at different times relative to high water. The resulting time series are used to force the model boundary to identify how the interactive processes influence the timing of extreme water level across the estuarine domain. This methodology is first validated using the most extreme event on record to ensure that modelled extreme water levels can be predicted with confidence. Changes in maximum water level are observed in areas where nuclear assets are located (Hinkley, Oldbury & Berkeley) and further upstream, e.g., close to the tidal limit of the Severn Estuary at Epney. Change in crest shape (area and duration above the MSHW) are analysed to understand changes to flood hazard around the peak of the tide. The work concludes that changes in maximum water level can be attributed to the change in time of the peak of the surge relative to high water, the surge shape (classified by skew and kurtosis) and severity of the event. The results can be used to understand the spatial variability in extreme water levels relative to a tide gauge location, which can then be applied to other management needs in hypertidal estuaries worldwide.

Subwavelength grating enabled on-chip ultra-compact optical true time delay line

PubMed Central

Wang, Junjia; Ashrafi, Reza; Adams, Rhys; Glesk, Ivan; Gasulla, Ivana; Capmany, José; Chen, Lawrence R.

2016-01-01

An optical true time delay line (OTTDL) is a basic photonic building block that enables many microwave photonic and optical processing operations. The conventional design for an integrated OTTDL that is based on spatial diversity uses a length-variable waveguide array to create the optical time delays, which can introduce complexities in the integrated circuit design. Here we report the first ever demonstration of an integrated index-variable OTTDL that exploits spatial diversity in an equal length waveguide array. The approach uses subwavelength grating waveguides in silicon-on-insulator (SOI), which enables the realization of OTTDLs having a simple geometry and that occupy a compact chip area. Moreover, compared to conventional wavelength-variable delay lines with a few THz operation bandwidth, our index-variable OTTDL has an extremely broad operation bandwidth practically exceeding several tens of THz, which supports operation for various input optical signals with broad ranges of central wavelength and bandwidth. PMID:27457024

The Signature of Southern Hemisphere Atmospheric Circulation Patterns in Antarctic Precipitation

PubMed Central

Thompson, David W. J.; van den Broeke, Michiel R.

2017-01-01

Abstract We provide the first comprehensive analysis of the relationships between large‐scale patterns of Southern Hemisphere climate variability and the detailed structure of Antarctic precipitation. We examine linkages between the high spatial resolution precipitation from a regional atmospheric model and four patterns of large‐scale Southern Hemisphere climate variability: the southern baroclinic annular mode, the southern annular mode, and the two Pacific‐South American teleconnection patterns. Variations in all four patterns influence the spatial configuration of precipitation over Antarctica, consistent with their signatures in high‐latitude meridional moisture fluxes. They impact not only the mean but also the incidence of extreme precipitation events. Current coupled‐climate models are able to reproduce all four patterns of atmospheric variability but struggle to correctly replicate their regional impacts on Antarctic climate. Thus, linking these patterns directly to Antarctic precipitation variability may allow a better estimate of future changes in precipitation than using model output alone. PMID:29398735

Subwavelength grating enabled on-chip ultra-compact optical true time delay line.

PubMed

Wang, Junjia; Ashrafi, Reza; Adams, Rhys; Glesk, Ivan; Gasulla, Ivana; Capmany, José; Chen, Lawrence R

2016-07-26

An optical true time delay line (OTTDL) is a basic photonic building block that enables many microwave photonic and optical processing operations. The conventional design for an integrated OTTDL that is based on spatial diversity uses a length-variable waveguide array to create the optical time delays, which can introduce complexities in the integrated circuit design. Here we report the first ever demonstration of an integrated index-variable OTTDL that exploits spatial diversity in an equal length waveguide array. The approach uses subwavelength grating waveguides in silicon-on-insulator (SOI), which enables the realization of OTTDLs having a simple geometry and that occupy a compact chip area. Moreover, compared to conventional wavelength-variable delay lines with a few THz operation bandwidth, our index-variable OTTDL has an extremely broad operation bandwidth practically exceeding several tens of THz, which supports operation for various input optical signals with broad ranges of central wavelength and bandwidth.

Anomalies detected in Central European Temperature series reconstructed from documentary evidence and instrumental records for the last 500 years

NASA Astrophysics Data System (ADS)

Dobrovolný, P.; Brázdil, R.; Moberg, A.; Wilson, R.

2009-09-01

Various types of documentary evidence from Germany, Switzerland and the Czech Republic have been used to create temperature indices for the period 1500-1854. Homogenized temperature series from 11 Central European stations covering the period 1760-2007 served as target values to reconstruct monthly, seasonal and annual temperatures in Central Europe since AD 1500. Spatial coherency of the compiled Central European Temperature (CEuT) series is presented. The CEuT series is further used to define extremely cold/warm months and seasons and the spatial and temporal distribution of such extremes are presented in context of existing knowledge of climate variability within Europe. The CEuT extremes are compared to corresponding documentary based chronologies from other European countries or regions as well as reconstructions from other proxies (e.g. tree rings). The most pronounced cold/warm seasons are analyzed with respect to potential causes and also with respect to recent warming trends. We discuss the potential of documentary evidence to study weather and climate extremes and show that such data provide valuable information for studying past human response to climatic extremes.

Towards a More Biologically-meaningful Climate Characterization: Variability in Space and Time at Multiple Scales

NASA Astrophysics Data System (ADS)

Christianson, D. S.; Kaufman, C. G.; Kueppers, L. M.; Harte, J.

2013-12-01

Sampling limitations and current modeling capacity justify the common use of mean temperature values in summaries of historical climate and future projections. However, a monthly mean temperature representing a 1-km2 area on the landscape is often unable to capture the climate complexity driving organismal and ecological processes. Estimates of variability in addition to mean values are more biologically meaningful and have been shown to improve projections of range shifts for certain species. Historical analyses of variance and extreme events at coarse spatial scales, as well as coarse-scale projections, show increasing temporal variability in temperature with warmer means. Few studies have considered how spatial variance changes with warming, and analysis for both temporal and spatial variability across scales is lacking. It is unclear how the spatial variability of fine-scale conditions relevant to plant and animal individuals may change given warmer coarse-scale mean values. A change in spatial variability will affect the availability of suitable habitat on the landscape and thus, will influence future species ranges. By characterizing variability across both temporal and spatial scales, we can account for potential bias in species range projections that use coarse climate data and enable improvements to current models. In this study, we use temperature data at multiple spatial and temporal scales to characterize spatial and temporal variability under a warmer climate, i.e., increased mean temperatures. Observational data from the Sierra Nevada (California, USA), experimental climate manipulation data from the eastern and western slopes of the Rocky Mountains (Colorado, USA), projected CMIP5 data for California (USA) and observed PRISM data (USA) allow us to compare characteristics of a mean-variance relationship across spatial scales ranging from sub-meter2 to 10,000 km2 and across temporal scales ranging from hours to decades. Preliminary spatial analysis at fine-spatial scales (sub-meter to 10-meter) shows greater temperature variability with warmer mean temperatures. This is inconsistent with the inherent assumption made in current species distribution models that fine-scale variability is static, implying that current projections of future species ranges may be biased -- the direction and magnitude requiring further study. While we focus our findings on the cross-scaling characteristics of temporal and spatial variability, we also compare the mean-variance relationship between 1) experimental climate manipulations and observed conditions and 2) temporal versus spatial variance, i.e., variability in a time-series at one location vs. variability across a landscape at a single time. The former informs the rich debate concerning the ability to experimentally mimic a warmer future. The latter informs space-for-time study design and analyses, as well as species persistence via a combined spatiotemporal probability of suitable future habitat.

Spatial structure of monthly rainfall measurements average over 25 years and trends of the hourly variability of a current rainy day in Rwanda.

NASA Astrophysics Data System (ADS)

Nduwayezu, Emmanuel; Kanevski, Mikhail; Jaboyedoff, Michel

2013-04-01

Climate plays a vital role in a wide range of socio-economic activities of most nations particularly of developing countries. Climate (rainfall) plays a central role in agriculture which is the main stay of the Rwandan economy and community livelihood and activities. The majority of the Rwandan population (81,1% in 2010) relies on rain fed agriculture for their livelihoods, and the impacts of variability in climate patterns are already being felt. Climate-related events like heavy rainfall or too little rainfall are becoming more frequent and are impacting on human wellbeing.The torrential rainfall that occurs every year in Rwanda could disturb the circulation for many days, damages houses, infrastructures and causes heavy economic losses and deaths. Four rainfall seasons have been identified, corresponding to the four thermal Earth ones in the south hemisphere: the normal season (summer), the rainy season (autumn), the dry season (winter) and the normo-rainy season (spring). Globally, the spatial rainfall decreasing from West to East, especially in October (spring) and February (summer) suggests an «Atlantic monsoon influence» while the homogeneous spatial rainfall distribution suggests an «Inter-tropical front» mechanism. What is the hourly variability in this mountainous area? Is there any correlation with the identified zones of the monthly average series (from 1965 to 1990 established by the Rwandan meteorological services)? Where could we have hazards with several consecutive rainy days (using forecasted datas from the Norwegian Meteorological Institute)? Spatio-temporal analysis allows for identifying and explaining large-scale anomalies which are useful for understanding hydrological characteristics and subsequently predicting these hydrological events. The objective of our current research (Rainfall variability) is to proceed to an evaluation of the potential rainfall risk by applying advanced geospatial modelling tools in Rwanda: geostatistical predictions and simulations, machine learning algorithm (different types of neural networks) and GIS. Hybrid models - mixing geostatistics and machine learning, will be applied to study spatial non-stationarity of rainfall fields. The research will include rainfalls variability mapping and probabilistic analyses of extreme events. Key words: rainfall variability, Rwanda, extreme event, model, mapping, geostatistics.

Impacts of climate variability and change on crop yield in sub-Sahara Africa

NASA Astrophysics Data System (ADS)

Pan, S.; Zhang, J.; Yang, J.; Chen, G.; Xu, R.; Zhang, B.; Lou, Y.

2017-12-01

Much concern has been raised about the impacts of climate change and climate extremes on Africa's food security. The impact of climate change on Africa's agriculture is likely to be severe compared to other continents due to high rain-fed agricultural dependence, and limited ability to mitigate and adapt to climate change. In recent decades, warming in Africa is more pronounced and faster than the global average and this trend is likely to continue in the future. However, quantitative assessment on impacts of climate extremes and climate change on crop yield has not been well investigated yet. By using an improved agricultural module of the Dynamic Land Ecosystem Model (DLEM-AG2) driven by spatially-explicit information on land use, climate and other environmental changes, we have assessed impacts of historical climate variability and future climate change on food crop yield across the sub-Sahara Africa during1980-2016 and the rest of the 21st century (2017-2099). Our simulated results indicate that African crop yield in the past three decades shows an increasing trend primarily due to cropland expansion. However, crop yield shows substantially spatial and temporal variation due to inter-annual and inter-decadal climate variability and spatial heterogeneity of environmental drivers. Droughts have largely reduced crop yield in the most vulnerable regions of Sub-Sahara Africa. Future projections with DLEM-AG2 show that food crop production in Sub-Sahara Africa would be favored with limiting end-of-century warming to below 1.50 C.

Intrapersonal, interpersonal, and physical space in anorexia nervosa: a virtual reality and repertory grid investigation.

PubMed

Cipolletta, Sabrina; Malighetti, Clelia; Serino, Silvia; Riva, Giuseppe; Winter, David

2017-06-01

Anorexia nervosa (AN) is an eating disorder characterized by severe body image disturbances. Recent studies from spatial cognition showed a connection between the experience of body and of space. The objectives of this study were to explore the meanings that characterize AN experience and to deepen the examination of spatiality in relational terms, through the study of how the patient construes herself and her interpersonal world. More specifically this study aimed (1) to verify whether spatial variables and aspects of construing differentiate patients with AN and healthy controls (HCs) and are related to severity of anorexic symptomatology; (2) to explore correlations between impairments in spatial abilities and interpersonal construing. A sample of 12 AN patients and 12 HCs participated in the study. The Eating Disorder Inventory, a virtual reality-based procedure, traditional measures of spatial abilities, and repertory grids were administered. The AN group compared to HCs showed significant impairments in spatial abilities, more unidimensional construing, and more extreme construing of the present self and of the self as seen by others. All these dimensions correlated with the severity of symptomatology. Extreme ways of construing characterized individuals with AN and might represent the interpersonal aspect of impairment in spatial reference frames. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Validation of China-wide interpolated daily climate variables from 1960 to 2011

NASA Astrophysics Data System (ADS)

Yuan, Wenping; Xu, Bing; Chen, Zhuoqi; Xia, Jiangzhou; Xu, Wenfang; Chen, Yang; Wu, Xiaoxu; Fu, Yang

2015-02-01

Temporally and spatially continuous meteorological variables are increasingly in demand to support many different types of applications related to climate studies. Using measurements from 600 climate stations, a thin-plate spline method was applied to generate daily gridded climate datasets for mean air temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, atmospheric pressure, and precipitation over China for the period 1961-2011. A comprehensive evaluation of interpolated climate was conducted at 150 independent validation sites. The results showed superior performance for most of the estimated variables. Except for wind speed, determination coefficients ( R 2) varied from 0.65 to 0.90, and interpolations showed high consistency with observations. Most of the estimated climate variables showed relatively consistent accuracy among all seasons according to the root mean square error, R 2, and relative predictive error. The interpolated data correctly predicted the occurrence of daily precipitation at validation sites with an accuracy of 83 %. Moreover, the interpolation data successfully explained the interannual variability trend for the eight meteorological variables at most validation sites. Consistent interannual variability trends were observed at 66-95 % of the sites for the eight meteorological variables. Accuracy in distinguishing extreme weather events differed substantially among the meteorological variables. The interpolated data identified extreme events for the three temperature variables, relative humidity, and sunshine duration with an accuracy ranging from 63 to 77 %. However, for wind speed, air pressure, and precipitation, the interpolation model correctly identified only 41, 48, and 58 % of extreme events, respectively. The validation indicates that the interpolations can be applied with high confidence for the three temperatures variables, as well as relative humidity and sunshine duration based on the performance of these variables in estimating daily variations, interannual variability, and extreme events. Although longitude, latitude, and elevation data are included in the model, additional information, such as topography and cloud cover, should be integrated into the interpolation algorithm to improve performance in estimating wind speed, atmospheric pressure, and precipitation.

Investigating the Influence of Anthropogenic Forcing on Observed Mean and Extreme Sea Level Pressure Trends over the Mediterranean Region

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barkhordarian, Armineh

We investigate whether the observed mean sea level pressure (SLP) trends over the Mediterranean region in the period from 1975 to 2004 are significantly consistent with what 17 models projected as response of SLP to anthropogenic forcing (greenhouse gases and sulphate aerosols, GS). Obtained results indicate that the observed trends in mean SLP cannot be explained by natural (internal) variability. Externally forced changes are detectable in all seasons, except spring. The large-scale component (spatial mean) of the GS signal is detectable in all the 17 models in winter and in 12 of the 17 models in summer. However, the small-scalemore » component (spatial anomalies about the spatial mean) of GS signal is only detectable in winter within 11 of the 17 models. We also show that GS signal has a detectable influence on observed decreasing (increasing) tendency in the frequencies of extremely low (high) SLP days in winter and that these changes cannot be explained by internal climate variability. While the detection of GS forcing is robust in winter and summer, there are striking inconsistencies in autumn, where analysis points to the presence of an external forcing, which is not GS forcing.« less

Investigating the Influence of Anthropogenic Forcing on Observed Mean and Extreme Sea Level Pressure Trends over the Mediterranean Region

DOE PAGES

Barkhordarian, Armineh

2012-01-01

We investigate whether the observed mean sea level pressure (SLP) trends over the Mediterranean region in the period from 1975 to 2004 are significantly consistent with what 17 models projected as response of SLP to anthropogenic forcing (greenhouse gases and sulphate aerosols, GS). Obtained results indicate that the observed trends in mean SLP cannot be explained by natural (internal) variability. Externally forced changes are detectable in all seasons, except spring. The large-scale component (spatial mean) of the GS signal is detectable in all the 17 models in winter and in 12 of the 17 models in summer. However, the small-scalemore » component (spatial anomalies about the spatial mean) of GS signal is only detectable in winter within 11 of the 17 models. We also show that GS signal has a detectable influence on observed decreasing (increasing) tendency in the frequencies of extremely low (high) SLP days in winter and that these changes cannot be explained by internal climate variability. While the detection of GS forcing is robust in winter and summer, there are striking inconsistencies in autumn, where analysis points to the presence of an external forcing, which is not GS forcing.« less

Multivariate hydrological frequency analysis for extreme events using Archimedean copula. Case study: Lower Tunjuelo River basin (Colombia)

NASA Astrophysics Data System (ADS)

Gómez, Wilmar

2017-04-01

By analyzing the spatial and temporal variability of extreme precipitation events we can prevent or reduce the threat and risk. Many water resources projects require joint probability distributions of random variables such as precipitation intensity and duration, which can not be independent with each other. The problem of defining a probability model for observations of several dependent variables is greatly simplified by the joint distribution in terms of their marginal by taking copulas. This document presents a general framework set frequency analysis bivariate and multivariate using Archimedean copulas for extreme events of hydroclimatological nature such as severe storms. This analysis was conducted in the lower Tunjuelo River basin in Colombia for precipitation events. The results obtained show that for a joint study of the intensity-duration-frequency, IDF curves can be obtained through copulas and thus establish more accurate and reliable information from design storms and associated risks. It shows how the use of copulas greatly simplifies the study of multivariate distributions that introduce the concept of joint return period used to represent the needs of hydrological designs properly in frequency analysis.

Review of complex networks application in hydroclimatic extremes with an implementation to characterize spatio-temporal drought propagation in continental USA

NASA Astrophysics Data System (ADS)

Konapala, Goutam; Mishra, Ashok

2017-12-01

The quantification of spatio-temporal hydroclimatic extreme events is a key variable in water resources planning, disaster mitigation, and preparing climate resilient society. However, quantification of these extreme events has always been a great challenge, which is further compounded by climate variability and change. Recently complex network theory was applied in earth science community to investigate spatial connections among hydrologic fluxes (e.g., rainfall and streamflow) in water cycle. However, there are limited applications of complex network theory for investigating hydroclimatic extreme events. This article attempts to provide an overview of complex networks and extreme events, event synchronization method, construction of networks, their statistical significance and the associated network evaluation metrics. For illustration purpose, we apply the complex network approach to study the spatio-temporal evolution of droughts in Continental USA (CONUS). A different drought threshold leads to a new drought event as well as different socio-economic implications. Therefore, it would be interesting to explore the role of thresholds on spatio-temporal evolution of drought through network analysis. In this study, long term (1900-2016) Palmer drought severity index (PDSI) was selected for spatio-temporal drought analysis using three network-based metrics (i.e., strength, direction and distance). The results indicate that the drought events propagate differently at different thresholds associated with initiation of drought events. The direction metrics indicated that onset of mild drought events usually propagate in a more spatially clustered and uniform approach compared to onsets of moderate droughts. The distance metric shows that the drought events propagate for longer distance in western part compared to eastern part of CONUS. We believe that the network-aided metrics utilized in this study can be an important tool in advancing our knowledge on drought propagation as well as other hydroclimatic extreme events. Although the propagation of droughts is investigated using the network approach, however process (physics) based approaches is essential to further understand the dynamics of hydroclimatic extreme events.

A Spatial Framework to Map Heat Health Risks at Multiple Scales.

PubMed

Ho, Hung Chak; Knudby, Anders; Huang, Wei

2015-12-18

In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord G(i) index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord G(i) index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.

Spatial drought reconstructions for central High Asia based on tree rings

NASA Astrophysics Data System (ADS)

Fang, Keyan; Davi, Nicole; Gou, Xiaohua; Chen, Fahu; Cook, Edward; Li, Jinbao; D'Arrigo, Rosanne

2010-11-01

Spatial reconstructions of drought for central High Asia based on a tree-ring network are presented. Drought patterns for central High Asia are classified into western and eastern modes of variability. Tree-ring based reconstructions of the Palmer drought severity index (PDSI) are presented for both the western central High Asia drought mode (1587-2005), and for the eastern central High Asia mode (1660-2005). Both reconstructions, generated using a principal component regression method, show an increased variability in recent decades. The wettest epoch for both reconstructions occurred from the 1940s to the 1950s. The most extreme reconstructed drought for western central High Asia was from the 1640s to the 1650s, coinciding with the collapse of the Chinese Ming Dynasty. The eastern central High Asia reconstruction has shown a distinct tendency towards drier conditions since the 1980s. Our spatial reconstructions agree well with previous reconstructions that fall within each mode, while there is no significant correlation between the two spatial reconstructions.

High-Resolution Dynamical Downscaling Ensemble Projections of Future Extreme Temperature Distributions for the United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zobel, Zachary; Wang, Jiali; Wuebbles, Donald J.

The aim of this study is to examine projections of extreme temperatures over the continental United States (CONUS) for the 21st century using an ensemble of high spatial resolution dynamically downscaled model simulations with different boundary conditions. The downscaling uses the Weather Research and Forecast model at a spatial resolution of 12 km along with outputs from three different Coupled Model Intercomparison Project Phase 5 global climate models that provide boundary con- ditions under two different future greenhouse gas (GHG) concentration trajectories. The results from two decadal-length time slices (2045–2054 and 2085–2094) are compared with a historical decade (1995–2004). Probabilitymore » density functions of daily maximum/minimum temperatures are analyzed over seven climatologically cohesive regions of the CONUS. The impacts of different boundary conditions as well as future GHG concentrations on extreme events such as heat waves and days with temperature higher than 95°F are also investigated. The results show that the intensity of extreme warm temperature in future summer is significantly increased, while the frequency of extreme cold temperature in future winter decreases. The distribution of summer daily maximum temperature experiences a significant warm-side shift and increased variability, while the distribution of winter daily minimum temperature is projected to have a less significant warm-side shift with decreased variability. Finally, using "business-as-usual" scenario, 5-day heat waves are projected to occur at least 5–10 times per year in most CONUS and ≥ 95°F days will increase by 1–2 months by the end of the century.« less

High-Resolution Dynamical Downscaling Ensemble Projections of Future Extreme Temperature Distributions for the United States

DOE PAGES

Zobel, Zachary; Wang, Jiali; Wuebbles, Donald J.; ...

2017-11-20

The aim of this study is to examine projections of extreme temperatures over the continental United States (CONUS) for the 21st century using an ensemble of high spatial resolution dynamically downscaled model simulations with different boundary conditions. The downscaling uses the Weather Research and Forecast model at a spatial resolution of 12 km along with outputs from three different Coupled Model Intercomparison Project Phase 5 global climate models that provide boundary con- ditions under two different future greenhouse gas (GHG) concentration trajectories. The results from two decadal-length time slices (2045–2054 and 2085–2094) are compared with a historical decade (1995–2004). Probabilitymore » density functions of daily maximum/minimum temperatures are analyzed over seven climatologically cohesive regions of the CONUS. The impacts of different boundary conditions as well as future GHG concentrations on extreme events such as heat waves and days with temperature higher than 95°F are also investigated. The results show that the intensity of extreme warm temperature in future summer is significantly increased, while the frequency of extreme cold temperature in future winter decreases. The distribution of summer daily maximum temperature experiences a significant warm-side shift and increased variability, while the distribution of winter daily minimum temperature is projected to have a less significant warm-side shift with decreased variability. Finally, using "business-as-usual" scenario, 5-day heat waves are projected to occur at least 5–10 times per year in most CONUS and ≥ 95°F days will increase by 1–2 months by the end of the century.« less

Trading Space for Time in Design Storm Estimation Using Radar Data

NASA Astrophysics Data System (ADS)

Haberlandt, U.; Berndt, C.

2017-12-01

Intensity-duration-frequency (IDF) curves are frequently used for the derivation of design storms. These curves are usually estimated from rain gauges and are valid for extreme rainfall at local observed points. Two common problems are involved. Regionalization of rainfall statistics for unobserved locations and the use of areal reduction factors (ARF) for the adjustment to larger catchments are required. Weather radar data are available with large spatial coverage and high resolution in space and could be used for a direct derivation of areal design storms for any location and catchment size. However, one problem with radar data is the relatively short observation period for the estimation of extreme events. This study deals with the estimation of area-intensity-duration-frequency (AIDF) curves and areal-reduction-factors (ARF) directly from weather radar data. The main objective is to answer the question if it is possible to trade space for time in the estimation of both characteristics to compensate for the short radar observation periods. In addition, a stratification of the temporal sample according to annual temperature indices is tried to distinguish "colder" and "warmer" climate years. This might eventually show a way for predicting future changes in AIDF curves and ARFs. First, radar data are adjusted with rainfall observations from the daily station network. Thereafter, AIDF curves and ARFs are calculated for different spatial and temporal sample sizes. The AIDF and ARFs are compared regarding their temporal and spatial variability considering also the temperature conditions. In order to reduce spatial variability a grouping of locations according to their climatological and physiographical characteristics is carried out. The data used for this study cover about 20 years of observations from the radar device located near Hanover in Northern Germany and 500 non-recording rain gauges as well as a set of 8 recording rain gauges for validation. AIDF curves and ARFS are analyzed for rainfall durations from 5 minutes to 24 hours and return periods from 1 year to 30 years. It is hypothesized, that the spatial variability of AIDF and ARF characteristics decreases with increasing sample size, grouping and normalization and is finally comparable to temporal variability.

Modeling short duration extreme precipitation patterns using copula and generalized maximum pseudo-likelihood estimation with censoring

NASA Astrophysics Data System (ADS)

Bargaoui, Zoubeida Kebaili; Bardossy, Andràs

2015-10-01

The paper aims to develop researches on the spatial variability of heavy rainfall events estimation using spatial copula analysis. To demonstrate the methodology, short time resolution rainfall time series from Stuttgart region are analyzed. They are constituted by rainfall observations on continuous 30 min time scale recorded over a network composed by 17 raingages for the period July 1989-July 2004. The analysis is performed aggregating the observations from 30 min up to 24 h. Two parametric bivariate extreme copula models, the Husler-Reiss model and the Gumbel model are investigated. Both involve a single parameter to be estimated. Thus, model fitting is operated for every pair of stations for a giving time resolution. A rainfall threshold value representing a fixed rainfall quantile is adopted for model inference. Generalized maximum pseudo-likelihood estimation is adopted with censoring by analogy with methods of univariate estimation combining historical and paleoflood information with systematic data. Only pairs of observations greater than the threshold are assumed as systematic data. Using the estimated copula parameter, a synthetic copula field is randomly generated and helps evaluating model adequacy which is achieved using Kolmogorov Smirnov distance test. In order to assess dependence or independence in the upper tail, the extremal coefficient which characterises the tail of the joint bivariate distribution is adopted. Hence, the extremal coefficient is reported as a function of the interdistance between stations. If it is less than 1.7, stations are interpreted as dependent in the extremes. The analysis of the fitted extremal coefficients with respect to stations inter distance highlights two regimes with different dependence structures: a short spatial extent regime linked to short duration intervals (from 30 min to 6 h) with an extent of about 8 km and a large spatial extent regime related to longer rainfall intervals (from 12 h to 24 h) with an extent of 34 to 38 km.

Current and future pluvial flood hazard analysis for the city of Antwerp

NASA Astrophysics Data System (ADS)

Willems, Patrick; Tabari, Hossein; De Niel, Jan; Van Uytven, Els; Lambrechts, Griet; Wellens, Geert

2016-04-01

For the city of Antwerp in Belgium, higher rainfall extremes were observed in comparison with surrounding areas. The differences were found statistically significant for some areas and may be the result of the heat island effect in combination with the higher concentrations of aerosols. A network of 19 rain gauges but with varying records length (the longest since the 1960s) and continuous radar data for 10 years were combined to map the spatial variability of rainfall extremes over the city at various durations from 15 minutes to 1 day together with the uncertainty. The improved spatial rainfall information was used as input in the sewer system model of the city to analyze the frequency of urban pluvial floods. Comparison with historical flood observations from various sources (fire brigade and media) confirmed that the improved spatial rainfall information also improved sewer impact results on both the magnitude and frequency of the sewer floods. Next to these improved urban flood impact results for recent and current climatological conditions, the new insights on the local rainfall microclimate were also helpful to enhance future projections on rainfall extremes and pluvial floods in the city. This was done by improved statistical downscaling of all available CMIP5 global climate model runs (160 runs) for the 4 RCP scenarios, as well as the available EURO-CORDEX regional climate model runs. Two types of statistical downscaling methods were applied for that purpose (a weather typing based method, and a quantile perturbation approach), making use of the microclimate results and its dependency on specific weather types. Changes in extreme rainfall intensities were analyzed and mapped as a function of the RCP scenario, together with the uncertainty, decomposed in the uncertainties related to the climate models, the climate model initialization or limited length of the 30-year time series (natural climate variability) and the statistical downscaling (albeit limited to two types of methods). These were finally transferred into future pluvial flash flood hazard maps for the city together with the uncertainties, and are considered as basis for spatial planning and adaptation.

Forecasting seasonal hydrologic response in major river basins

NASA Astrophysics Data System (ADS)

Bhuiyan, A. M.

2014-05-01

Seasonal precipitation variation due to natural climate variation influences stream flow and the apparent frequency and severity of extreme hydrological conditions such as flood and drought. To study hydrologic response and understand the occurrence of extreme hydrological events, the relevant forcing variables must be identified. This study attempts to assess and quantify the historical occurrence and context of extreme hydrologic flow events and quantify the relation between relevant climate variables. Once identified, the flow data and climate variables are evaluated to identify the primary relationship indicators of hydrologic extreme event occurrence. Existing studies focus on developing basin-scale forecasting techniques based on climate anomalies in El Nino/La Nina episodes linked to global climate. Building on earlier work, the goal of this research is to quantify variations in historical river flows at seasonal temporal-scale, and regional to continental spatial-scale. The work identifies and quantifies runoff variability of major river basins and correlates flow with environmental forcing variables such as El Nino, La Nina, sunspot cycle. These variables are expected to be the primary external natural indicators of inter-annual and inter-seasonal patterns of regional precipitation and river flow. Relations between continental-scale hydrologic flows and external climate variables are evaluated through direct correlations in a seasonal context with environmental phenomenon such as sun spot numbers (SSN), Southern Oscillation Index (SOI), and Pacific Decadal Oscillation (PDO). Methods including stochastic time series analysis and artificial neural networks are developed to represent the seasonal variability evident in the historical records of river flows. River flows are categorized into low, average and high flow levels to evaluate and simulate flow variations under associated climate variable variations. Results demonstrated not any particular method is suited to represent scenarios leading to extreme flow conditions. For selected flow scenarios, the persistence model performance may be comparable to more complex multivariate approaches, and complex methods did not always improve flow estimation. Overall model performance indicates inclusion of river flows and forcing variables on average improve model extreme event forecasting skills. As a means to further refine the flow estimation, an ensemble forecast method is implemented to provide a likelihood-based indication of expected river flow magnitude and variability. Results indicate seasonal flow variations are well-captured in the ensemble range, therefore the ensemble approach can often prove efficient in estimating extreme river flow conditions. The discriminant prediction approach, a probabilistic measure to forecast streamflow, is also adopted to derive model performance. Results show the efficiency of the method in terms of representing uncertainties in the forecasts.

Changing Amazon biomass and the role of atmospheric CO2 concentration, climate, and land use

NASA Astrophysics Data System (ADS)

Almeida Castanho, Andrea D.; Galbraith, David; Zhang, Ke; Coe, Michael T.; Costa, Marcos H.; Moorcroft, Paul

2016-01-01

The Amazon tropical evergreen forest is an important component of the global carbon budget. Its forest floristic composition, structure, and function are sensitive to changes in climate, atmospheric composition, and land use. In this study biomass and productivity simulated by three dynamic global vegetation models (Integrated Biosphere Simulator, Ecosystem Demography Biosphere Model, and Joint UK Land Environment Simulator) for the period 1970-2008 are compared with observations from forest plots (Rede Amazónica de Inventarios Forestales). The spatial variability in biomass and productivity simulated by the DGVMs is low in comparison to the field observations in part because of poor representation of the heterogeneity of vegetation traits within the models. We find that over the last four decades the CO2 fertilization effect dominates a long-term increase in simulated biomass in undisturbed Amazonian forests, while land use change in the south and southeastern Amazonia dominates a reduction in Amazon aboveground biomass, of similar magnitude to the CO2 biomass gain. Climate extremes exert a strong effect on the observed biomass on short time scales, but the models are incapable of reproducing the observed impacts of extreme drought on forest biomass. We find that future improvements in the accuracy of DGVM predictions will require improved representation of four key elements: (1) spatially variable plant traits, (2) soil and nutrients mediated processes, (3) extreme event mortality, and (4) sensitivity to climatic variability. Finally, continued long-term observations and ecosystem-scale experiments (e.g. Free-Air CO2 Enrichment experiments) are essential for a better understanding of the changing dynamics of tropical forests.

Lifestyle and nutrition related to male longevity in Sardinia: an ecological study.

PubMed

Pes, G M; Tolu, F; Poulain, M; Errigo, A; Masala, S; Pietrobelli, A; Battistini, N C; Maioli, M

2013-03-01

A demographic analysis in the Mediterranean island of Sardinia revealed marked differences in extreme longevity across the 377 municipalities and particularly identified a mountain inner area where the proportion of oldest subjects among male population has one of the highest validated value worldwide. The cause(s) of this unequal distribution of male longevity may be attributed to a concurrence of environmental, lifestyle and genetic factors. In this study we focussed on some lifestyle and nutrition variables recorded in the island's population in early decades of 20th century, when agricultural and pastoral economy was still prevalent, and try to verify through ecological spatial models if they may account for the variability in male longevity. By computing the Extreme Longevity Index (the proportion of newborns in a given municipality who reach age 100) the island's territory was divided in two areas with relatively higher and lower level of population longevity. Most nutritional variables do not show any significant difference between these two areas whereas a significant difference was found with respect to pastoralism (P = 0.0001), physical activity estimated by the average slope of the territory in each municipality (P = 0.0001), and average daily distance required by the active population to reach the usual workplace (P = 0.0001). Overall, these findings suggest that factors affecting the average energy expenditure of male population such as occupational activity and geographic characteristics of the area where the population mainly resides, are important in explaining the spatial variation of Sardinian extreme longevity. Copyright © 2011 Elsevier B.V. All rights reserved.

Measuring high-density built environment for public health research: Uncertainty with respect to data, indicator design and spatial scale.

PubMed

Sun, Guibo; Webster, Chris; Ni, Michael Y; Zhang, Xiaohu

2018-05-07

Uncertainty with respect to built environment (BE) data collection, measure conceptualization and spatial scales is evident in urban health research, but most findings are from relatively lowdensity contexts. We selected Hong Kong, an iconic high-density city, as the study area as limited research has been conducted on uncertainty in such areas. We used geocoded home addresses (n=5732) from a large population-based cohort in Hong Kong to extract BE measures for the participants' place of residence based on an internationally recognized BE framework. Variability of the measures was mapped and Spearman's rank correlation calculated to assess how well the relationships among indicators are preserved across variables and spatial scales. We found extreme variations and uncertainties for the 180 measures collected using comprehensive data and advanced geographic information systems modelling techniques. We highlight the implications of methodological selection and spatial scales of the measures. The results suggest that more robust information regarding urban health research in high-density city would emerge if greater consideration were given to BE data, design methods and spatial scales of the BE measures.

Sampling design for spatially distributed hydrogeologic and environmental processes

USGS Publications Warehouse

Christakos, G.; Olea, R.A.

1992-01-01

A methodology for the design of sampling networks over space is proposed. The methodology is based on spatial random field representations of nonhomogeneous natural processes, and on optimal spatial estimation techniques. One of the most important results of random field theory for physical sciences is its rationalization of correlations in spatial variability of natural processes. This correlation is extremely important both for interpreting spatially distributed observations and for predictive performance. The extent of site sampling and the types of data to be collected will depend on the relationship of subsurface variability to predictive uncertainty. While hypothesis formulation and initial identification of spatial variability characteristics are based on scientific understanding (such as knowledge of the physics of the underlying phenomena, geological interpretations, intuition and experience), the support offered by field data is statistically modelled. This model is not limited by the geometric nature of sampling and covers a wide range in subsurface uncertainties. A factorization scheme of the sampling error variance is derived, which possesses certain atttactive properties allowing significant savings in computations. By means of this scheme, a practical sampling design procedure providing suitable indices of the sampling error variance is established. These indices can be used by way of multiobjective decision criteria to obtain the best sampling strategy. Neither the actual implementation of the in-situ sampling nor the solution of the large spatial estimation systems of equations are necessary. The required values of the accuracy parameters involved in the network design are derived using reference charts (readily available for various combinations of data configurations and spatial variability parameters) and certain simple yet accurate analytical formulas. Insight is gained by applying the proposed sampling procedure to realistic examples related to sampling problems in two dimensions. ?? 1992.

About climate variabilitiy leading the hydric condition of the soil in the rainfed region of Argentina

NASA Astrophysics Data System (ADS)

Pántano, V. C.; Penalba, O. C.

2013-05-01

Extreme events of temperature and rainfall have a socio-economic impact in the rainfed agriculture production region in Argentina. The magnitude of the impact can be analyzed through the water balance which integrates the characteristics of the soil and climate conditions. Changes observed in climate variables during the last decades affected the components of the water balance. As a result, a displacement of the agriculture border towards the west was produced, improving the agricultural production of the region. The objective of this work is to analyze how the variability of rainfall and temperature leads the hydric condition of the soil, with special focus on extreme events. The hydric conditions of the soil (HC= Excess- Deficit) were estimated from the monthly water balance (Thornthwaite and Mather method, 1957), using monthly potential evapotranspiration (PET) and monthly accumulated rainfall (R) for 33 stations (period 1970-2006). Information of temperature and rainfall was provided by National Weather Service and the effective capacity of soil water was considered from Forte Lay and Spescha (2001). An agricultural extreme condition occurs when soil moisture and rainfall are inadequate or excessive for the development of the crops. In this study, we define an extreme event when the variable is less (greater) than its 20% and 10% (80% and 90%) percentile. In order to evaluate how sensitive is the HC to water and heat stress in the region, different conditional probabilities were evaluated. There is a weaker response of HC to extreme low PET while extreme low R leads high values of HC. However, this behavior is not always observed, especially in the western region where extreme high and low PET show a stronger influence over the HC. Finally, to analyze the temporal variability of extreme PET and R, leading hydric condition of the soil, the number of stations presenting extreme conditions was computed for each month. As an example, interesting results were observed for April. During this month, the water recharge of the soil is crucial to let the winter crops manage with the scarce rainfalls occurring in the following months. In 1970, 1974, 1977, 1978 and 1997 more than 50% of the stations were under extreme high PET; while 1970, 1974, 1978 and 1988 presented more than 40% under extreme low R. Thus, the 70s was the more threatened decade of the period. Since the 80s (except for 1997), extreme dry events due to one variable or the other are mostly presented separately, over smaller areas. The response of the spatial distribution of HC is stronger when both variables present extreme conditions. In particular, during 1997 the region presents extreme low values of HC as a consequence of extreme low R and high PET. Communities dependent on agriculture are highly sensitive to climate variability and its extremes. In the studied region, it was shown that scarce water and heat stress contribute to the resulting hydric condition, producing strong impact over different productive activities. Extreme temperature seems to have a stronger influence over extreme unfavorable hydric conditions.

Spatial clustering and meteorological drivers of summer ozone in Europe

NASA Astrophysics Data System (ADS)

Carro-Calvo, Leopoldo; Ordóñez, Carlos; García-Herrera, Ricardo; Schnell, Jordan L.

2017-10-01

We have applied the k-means clustering technique on a maximum daily 8-h running average near-surface ozone (MDA8 O3) gridded dataset over Europe at 1° × 1° resolution for summer 1998-2012. This has resulted in a spatial division of nine regions where ozone presents coherent spatiotemporal patterns. The role of meteorology in the variability of ozone at different time scales has been investigated by using daily meteorological fields from the NCEP-NCAR meteorological reanalysis. In the five regions of central-southern Europe ozone extremes (exceedances of the summer 95th percentile) occur mostly under anticyclonic circulation or weak sea level pressure gradients which trigger elevated temperatures and the recirculation of air masses. In the four northern regions extremes are associated with high-latitude anticyclones that divert the typical westerly flow at those latitudes and cause the advection of aged air masses from the south. The impact of meteorology on the day-to-day variability of ozone has been assessed by means of two different types of multiple linear models. These include as predictors meteorological fields averaged within the regions (;region-based; approach) or synoptic indices indicating the degree of resemblance between the daily meteorological fields over a large domain (25°-70° N, 35° W - 35° E) and their corresponding composites for extreme ozone days (;index-based; approach). With the first approach, a reduced set of variables, always including daily maximum temperature within the region, explains 47-66% of the variability (adjusted R2) in central-southern Europe, while more complex models are needed to explain 27-49% of the variability in the northern regions. The index-based approach yields better results for the regions of northern Europe, with adjusted R2 = 40-57%. Finally, both methodologies have also been applied to reproduce the interannual variability of ozone, with the best models explaining 66-88% of the variance in central-southern Europe and 45-66% in the north. Thus, the regionalisation carried out in this work has allowed establishing clear distinctions between the meteorological drivers of ozone in northern Europe and in the rest of the continent. These drivers are consistent across the different time scales examined (extremes, day-to-day and interannual), which gives confidence in the robustness of the results.

Patterns of precipitation and soil moisture extremes in Texas, US: A complex network analysis

NASA Astrophysics Data System (ADS)

Sun, Alexander Y.; Xia, Youlong; Caldwell, Todd G.; Hao, Zengchao

2018-02-01

Understanding of the spatial and temporal dynamics of extreme precipitation not only improves prediction skills, but also helps to prioritize hazard mitigation efforts. This study seeks to enhance the understanding of spatiotemporal covariation patterns embedded in precipitation (P) and soil moisture (SM) by using an event-based, complex-network-theoretic approach. Events concurrences are quantified using a nonparametric event synchronization measure, and spatial patterns of hydroclimate variables are analyzed by using several network measures and a community detection algorithm. SM-P coupling is examined using a directional event coincidence analysis measure that takes the order of event occurrences into account. The complex network approach is demonstrated for Texas, US, a region possessing a rich set of hydroclimate features and is frequented by catastrophic flooding. Gridded daily observed P data and simulated SM data are used to create complex networks of P and SM extremes. The uncovered high degree centrality regions and community structures are qualitatively in agreement with the overall existing knowledge of hydroclimate extremes in the study region. Our analyses provide new visual insights on the propagation, connectivity, and synchronicity of P extremes, as well as the SM-P coupling, in this flood-prone region, and can be readily used as a basis for event-driven predictive analytics for other regions.

STAMMEX high resolution gridded daily precipitation dataset over Germany: a new potential for regional precipitation climate research

NASA Astrophysics Data System (ADS)

Zolina, Olga; Simmer, Clemens; Kapala, Alice; Mächel, Hermann; Gulev, Sergey; Groisman, Pavel

2014-05-01

We present new high resolution precipitation daily grids developed at Meteorological Institute, University of Bonn and German Weather Service (DWD) under the STAMMEX project (Spatial and Temporal Scales and Mechanisms of Extreme Precipitation Events over Central Europe). Daily precipitation grids have been developed from the daily-observing precipitation network of DWD, which runs one of the World's densest rain gauge networks comprising more than 7500 stations. Several quality-controlled daily gridded products with homogenized sampling were developed covering the periods 1931-onwards (with 0.5 degree resolution), 1951-onwards (0.25 degree and 0.5 degree), and 1971-2000 (0.1 degree). Different methods were tested to select the best gridding methodology that minimizes errors of integral grid estimates over hilly terrain. Besides daily precipitation values with uncertainty estimates (which include standard estimates of the kriging uncertainty as well as error estimates derived by a bootstrapping algorithm), the STAMMEX data sets include a variety of statistics that characterize temporal and spatial dynamics of the precipitation distribution (quantiles, extremes, wet/dry spells, etc.). Comparisons with existing continental-scale daily precipitation grids (e.g., CRU, ECA E-OBS, GCOS) which include considerably less observations compared to those used in STAMMEX, demonstrate the added value of high-resolution grids for extreme rainfall analyses. These data exhibit spatial variability pattern and trends in precipitation extremes, which are missed or incorrectly reproduced over Central Europe from coarser resolution grids based on sparser networks. The STAMMEX dataset can be used for high-quality climate diagnostics of precipitation variability, as a reference for reanalyses and remotely-sensed precipitation products (including the upcoming Global Precipitation Mission products), and for input into regional climate and operational weather forecast models. We will present numerous application of the STAMMEX grids spanning from case studies of the major Central European floods to long-term changes in different precipitation statistics, including those accounting for the alternation of dry and wet periods and precipitation intensities associated with prolonged rainy episodes.

Synoptic Conditions and Moisture Sources Actuating Extreme Precipitation in Nepal

NASA Astrophysics Data System (ADS)

Bohlinger, Patrik; Sorteberg, Asgeir; Sodemann, Harald

2017-12-01

Despite the vast literature on heavy-precipitation events in South Asia, synoptic conditions and moisture sources related to extreme precipitation in Nepal have not been addressed systematically. We investigate two types of synoptic conditions—low-pressure systems and midlevel troughs—and moisture sources related to extreme precipitation events. To account for the high spatial variability in rainfall, we cluster station-based daily precipitation measurements resulting in three well-separated geographic regions: west, central, and east Nepal. For each region, composite analysis of extreme events shows that atmospheric circulation is directed against the Himalayas during an extreme event. The direction of the flow is regulated by midtropospheric troughs and low-pressure systems traveling toward the respective region. Extreme precipitation events feature anomalous high abundance of total column moisture. Quantitative Lagrangian moisture source diagnostic reveals that the largest direct contribution stems from land (approximately 75%), where, in particular, over the Indo-Gangetic Plain moisture uptake was increased. Precipitation events occurring in this region before the extreme event likely provided additional moisture.

Evaluating a process-based model for use in streambank stabilization and stream restoration: insights on the bank stability and toe erosion model (BSTEM)

USDA-ARS?s Scientific Manuscript database

Streambank retreat is a complex cyclical process involving subaerial processes, fluvial erosion, seepage erosion, and geotechnical failures and is driven by several soil properties that themselves are temporally and spatially variable. Therefore, it can be extremely challenging to predict and model ...

Phenological response of an Arizona dryland forest to short-term climatic extremes

USGS Publications Warehouse

Walker, Jessica; de Beurs, Kirsten; Wynne, Randolph

2015-01-01

Baseline information about dryland forest phenology is necessary to accurately anticipate future ecosystem shifts. The overarching goal of our study was to investigate the variability of vegetation phenology across a dryland forest landscape in response to climate alterations. We analyzed the influence of site characteristics and climatic conditions on the phenological patterns of an Arizona, USA, ponderosa pine (Pinus ponderosa) forest during a five-year period (2005 to 2009) that encompassed extreme wet and dry precipitation regimes. We assembled 80 synthetic Landsat images by applying the spatial and temporal adaptive reflectance fusion method (STARFM) to 500 m MODIS and 30 m Landsat-5 Thematic Mapper (TM) data. We tested relationships between site characteristics and the timing of peak Normalized Difference Vegetation Index (NDVI) to assess the effect of climatic stress on the green-up of individual pixels during or after the summer monsoon. Our results show that drought-induced stress led to a fragmented phenological response that was highly dependent on microsite parameters, as both the spatial autocorrelation of peak timing and the number of significant site variables increased during the drought year. Pixels at lower elevations and with higher proportions of herbaceous vegetation were more likely to exhibit dynamic responses to changes in precipitation conditions. Our study demonstrates the complexity of responses within dryland forest ecosystems and highlights the need for standardized monitoring of phenology trends in these areas. The spatial and temporal variability of phenological signals may provide a quantitative solution to the problem of how to evaluate dryland land surface trends across time.

Study of Environmental Data Complexity using Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

Spatial vs. individual variability with inheritance in a stochastic Lotka-Volterra system

NASA Astrophysics Data System (ADS)

Dobramysl, Ulrich; Tauber, Uwe C.

2012-02-01

We investigate a stochastic spatial Lotka-Volterra predator-prey model with randomized interaction rates that are either affixed to the lattice sites and quenched, and / or specific to individuals in either population. In the latter situation, we include rate inheritance with mutations from the particles' progenitors. Thus we arrive at a simple model for competitive evolution with environmental variability and selection pressure. We employ Monte Carlo simulations in zero and two dimensions to study the time evolution of both species' densities and their interaction rate distributions. The predator and prey concentrations in the ensuing steady states depend crucially on the environmental variability, whereas the temporal evolution of the individualized rate distributions leads to largely neutral optimization. Contrary to, e.g., linear gene expression models, this system does not experience fixation at extreme values. An approximate description of the resulting data is achieved by means of an effective master equation approach for the interaction rate distribution.

Exact extreme-value statistics at mixed-order transitions.

PubMed

Bar, Amir; Majumdar, Satya N; Schehr, Grégory; Mukamel, David

2016-05-01

We study extreme-value statistics for spatially extended models exhibiting mixed-order phase transitions (MOT). These are phase transitions that exhibit features common to both first-order (discontinuity of the order parameter) and second-order (diverging correlation length) transitions. We consider here the truncated inverse distance squared Ising model, which is a prototypical model exhibiting MOT, and study analytically the extreme-value statistics of the domain lengths The lengths of the domains are identically distributed random variables except for the global constraint that their sum equals the total system size L. In addition, the number of such domains is also a fluctuating variable, and not fixed. In the paramagnetic phase, we show that the distribution of the largest domain length l_{max} converges, in the large L limit, to a Gumbel distribution. However, at the critical point (for a certain range of parameters) and in the ferromagnetic phase, we show that the fluctuations of l_{max} are governed by novel distributions, which we compute exactly. Our main analytical results are verified by numerical simulations.

A new framework for estimating return levels using regional frequency analysis

NASA Astrophysics Data System (ADS)

Winter, Hugo; Bernardara, Pietro; Clegg, Georgina

2017-04-01

We propose a new framework for incorporating more spatial and temporal information into the estimation of extreme return levels. Currently, most studies use extreme value models applied to data from a single site; an approach which is inefficient statistically and leads to return level estimates that are less physically realistic. We aim to highlight the benefits that could be obtained by using methodology based upon regional frequency analysis as opposed to classic single site extreme value analysis. This motivates a shift in thinking, which permits the evaluation of local and regional effects and makes use of the wide variety of data that are now available on high temporal and spatial resolutions. The recent winter storms over the UK during the winters of 2013-14 and 2015-16, which have caused wide-ranging disruption and damaged important infrastructure, provide the main motivation for the current work. One of the most impactful natural hazards is flooding, which is often initiated by extreme precipitation. In this presentation, we focus on extreme rainfall, but shall discuss other meteorological variables alongside potentially damaging hazard combinations. To understand the risks posed by extreme precipitation, we need reliable statistical models which can be used to estimate quantities such as the T-year return level, i.e. the level which is expected to be exceeded once every T-years. Extreme value theory provides the main collection of statistical models that can be used to estimate the risks posed by extreme precipitation events. Broadly, at a single site, a statistical model is fitted to exceedances of a high threshold and the model is used to extrapolate to levels beyond the range of the observed data. However, when we have data at many sites over a spatial domain, fitting a separate model for each separate site makes little sense and it would be better if we could incorporate all this information to improve the reliability of return level estimates. Here, we use the regional frequency analysis approach to define homogeneous regions which are affected by the same storms. Extreme value models are then fitted to the data pooled from across a region. We find that this approach leads to more spatially consistent return level estimates with reduced uncertainty bounds.

Heat exposure in cities: combining the dynamics of temperature and population

NASA Astrophysics Data System (ADS)

Hu, L.; Wilhelmi, O.; Uejio, C. K.

2017-12-01

Assessment of human exposure to extreme heat requires the distributions of temperature and population. However, both variables are dynamic, thus presenting many challenges in capturing temperature and population patterns spatially and over time in an urban context. This study aims to improve the understanding of spatiotemporal patterns of urban population exposure to heat, taking Chicago, USA as an example. We estimate the hourly, geographically variable, population distribution considering commute of workers and students in a regular weekday and analyze the diurnal air temperature patterns during different meteorological conditions from satellite observations. The results show a relatively larger temperature increase in less urbanized areas during extreme heat events (EHEs), resulting in a spatially homogeneous temperature distribution over Chicago Metropolitan area. A lake cooling effect is weaker during EHEs. Population dynamics due to daily commute determine higher population density in more urbanized areas during daytime. The city-wide analysis reveals that the exposure is more sensitive to the nighttime temperature increases, and EHEs enhance this sensitivity. The high exposure hotspots are identified at the northwest Chicago, Cicero and Oak Park areas, where the influence from Lake Michigan is weakened, while the spatial extent of high outdoor exposure areas varies diurnally. This study's findings have potential to better inform general heat mitigation strategies during hot summer months and facilitate emergency response during EHEs. Availability of remotely-sensed temperature observations as well as the workers and students commute-adjusted population data allows for the adoption of this study's methodology in other major metropolitan areas. A better understanding of space-time patterns of urban population's exposure to heat will further enable local decision makers to mitigate extreme heat health risks and develop more targeted heat preparedness and response strategies.

Regionally dependent summer heat wave response to increased surface temperature in the US

NASA Astrophysics Data System (ADS)

Lopez, H.; Dong, S.; Kirtman, B. P.; Goni, G. J.; Lee, S. K.; Atlas, R. M.; West, R.

2017-12-01

Climate projections for the 21st Century suggest an increase in the occurrence of heat waves. However, the time it takes for the externally forced signal of climate change to emerge against the background of natural variability (i.e., Time of Emergence, ToE) particularly on the regional scale makes reliable future projection of heat waves challenging. Here, we combine observations and model simulations under present and future climate forcing to assess internal variability versus external forcing in modulating US heat waves. We characterized the most common heat wave patterns over the US by the use of clustering of extreme events by their spatial distribution. For each heat wave cluster, we assess changes in the probability density function (PDF) of summer temperature extremes by modeling the PDF as a stochastically generated skewed (SGS) distribution. The probability of necessary causation for each heat wave cluster was also quantified, allowing to make assessments of heat extreme attribution to anthropogenic climate change. The results suggest that internal variability will dominate heat wave occurrence over the Great Plains with ToE occurring in the 2050s (2070s) and of occurrence of ratio of warm-to-cold extremes of 1.7 (1.7) for the Northern (Southern) Plains. In contrast, external forcing will dominate over the Western (Great Lakes) region with ToE occurring as early as in the 2020s (2030s) and warm-to-cold extremes ratio of 6.4 (10.2), suggesting caution in attributing heat extremes to external forcing due to their regional dependence.

When time affects space: Dispersal ability and extreme weather events determine metacommunity organization in marine sediments.

PubMed

Corte, Guilherme N; Gonçalves-Souza, Thiago; Checon, Helio H; Siegle, Eduardo; Coleman, Ross A; Amaral, A Cecília Z

2018-05-01

Community ecology has traditionally assumed that the distribution of species is mainly influenced by environmental processes. There is, however, growing evidence that environmental (habitat characteristics and biotic interactions) and spatial processes (factors that affect a local assemblage regardless of environmental conditions - typically related to dispersal and movement of species) interactively shape biological assemblages. A metacommunity, which is a set of local assemblages connected by dispersal of individuals, is spatial in nature and can be used as a straightforward approach for investigating the interactive and independent effects of both environmental and spatial processes. Here, we examined (i) how environmental and spatial processes affect the metacommunity organization of marine macroinvertebrates inhabiting the intertidal sediments of a biodiverse coastal ecosystem; (ii) whether the influence of these processes is constant through time or is affected by extreme weather events (storms); and (iii) whether the relative importance of these processes depends on the dispersal abilities of organisms. We found that macrobenthic assemblages are influenced by each of environmental and spatial variables; however, spatial processes exerted a stronger role. We also found that this influence changes through time and is modified by storms. Moreover, we observed that the influence of environmental and spatial processes varies according to the dispersal capabilities of organisms. More effective dispersers (i.e., species with planktonic larvae) are more affected by spatial processes whereas environmental variables had a stronger effect on weaker dispersers (i.e. species with low motility in larval and adult stages). These findings highlight that accounting for spatial processes and differences in species life histories is essential to improve our understanding of species distribution and coexistence patterns in intertidal soft-sediments. Furthermore, it shows that storms modify the structure of coastal assemblages. Given that the influence of spatial and environmental processes is not consistent through time, it is of utmost importance that future studies replicate sampling over different periods so the influence of temporal and stochastic factors on macrobenthic metacommunities can be better understood. Copyright © 2018 Elsevier Ltd. All rights reserved.

Development of heat and drought related extreme weather events and their effect on winter wheat yields in Germany

NASA Astrophysics Data System (ADS)

Lüttger, Andrea B.; Feike, Til

2018-04-01

Climate change constitutes a major challenge for high productivity in wheat, the most widely grown crop in Germany. Extreme weather events including dry spells and heat waves, which negatively affect wheat yields, are expected to aggravate in the future. It is crucial to improve the understanding of the spatiotemporal development of such extreme weather events and the respective crop-climate relationships in Germany. Thus, the present study is a first attempt to evaluate the historic development of relevant drought and heat-related extreme weather events from 1901 to 2010 on county level (NUTS-3) in Germany. Three simple drought indices and two simple heat stress indices were used in the analysis. A continuous increase in dry spells over time was observed over the investigated periods from 1901-1930, 1931-1960, 1961-1990 to 2001-2010. Short and medium dry spells, i.e., precipitation-free periods longer than 5 and 8 days, respectively, increased more strongly compared to longer dry spells (longer than 11 days). The heat-related stress indices with maximum temperatures above 25 and 28 °C during critical wheat growth phases showed no significant increase over the first three periods but an especially sharp increase in the final 1991-2010 period with the increases being particularly pronounced in parts of Southwestern Germany. Trend analysis over the entire 110-year period using Mann-Kendall test revealed a significant positive trend for all investigated indices except for heat stress above 25 °C during flowering period. The analysis of county-level yield data from 1981 to 2010 revealed declining spatial yield variability and rather constant temporal yield variability over the three investigated (1981-1990, 1991-2000, and 2001-2010) decades. A clear spatial gradient manifested over time with variability in the West being much smaller than in the east of Germany. Correlating yield variability with the previously analyzed extreme weather indices revealed strong spatiotemporal fluctuations in explanatory power of the different indices over all German counties and the three time periods. Over the 30 years, yield deviations were increasingly well correlated with heat and drought-related indices, with the number of days with maximum temperature above 25 °C during anthesis showing a sharp increase in explanatory power over entire Germany in the final 2001-2010 period.

Interannual hydroclimatic variability and the 2009-2011 extreme ENSO phases in Colombia: from Andean glaciers to Caribbean lowlands

NASA Astrophysics Data System (ADS)

Bedoya-Soto, Juan Mauricio; Poveda, Germán; Trenberth, Kevin E.; Vélez-Upegui, Jorge Julián

2018-03-01

During 2009-2011, Colombia experienced extreme hydroclimatic events associated with the extreme phases of El Niño-Southern Oscillation (ENSO). Here, we study the dynamics of diverse land-atmosphere phenomena involved in such anomalous events at continental, regional, and local scales. Standardized anomalies of precipitation, 2-m temperature, total column water (TCW), volumetric soil water (VSW), temperature at 925 hPa, surface sensible heat (SSH), latent heat (SLH), evaporation (EVP), and liquid water equivalent thickness (LWET) are analyzed to assess atmosphere-land controls and relationships over tropical South America (TropSA) during 1986-2013 (long term) and 2009-2011 (ENSO extreme phases). An assessment of the interannual covariability between precipitation and 2-m temperature is performed using singular value decomposition (SVD) to identify the dominant spatiotemporal modes of hydroclimatic variability over the region's largest river basins (Amazon, Orinoco, Tocantins, Magdalena-Cauca, and Essequibo). ENSO, its evolution in time, and strong and consistent spatial structures emerge as the dominant mode of variability. In situ anomalies during both extreme phases of ENSO 2009-2011 over the Magdalena-Cauca River basins are linked at the continental scale. The ENSO-driven hydroclimatic effects extend from the diurnal cycle to interannual timescales, as reflected in temperature data from tropical glaciers and the rain-snow boundary in the highest peaks of the Central Andes of Colombia to river levels along the Caribbean lowlands of the Magdalena-Cauca River basin.

Increased tree-ring network density reveals more precise estimations of sub-regional hydroclimate variability and climate dynamics in the Midwest, USA

NASA Astrophysics Data System (ADS)

Maxwell, Justin T.; Harley, Grant L.

2017-08-01

Understanding the historic variability in the hydroclimate provides important information on possible extreme dry or wet periods that in turn inform water management plans. Tree rings have long provided historical context of hydroclimate variability of the U.S. However, the tree-ring network used to create these countrywide gridded reconstructions is sparse in certain locations, such as the Midwest. Here, we increase ( n = 20) the spatial resolution of the tree-ring network in southern Indiana and compare a summer (June-August) Palmer Drought Severity Index (PDSI) reconstruction to existing gridded reconstructions of PDSI for this region. We find both droughts and pluvials that were previously unknown that rival the most intense PDSI values during the instrumental period. Additionally, historical drought occurred in Indiana that eclipsed instrumental conditions with regard to severity and duration. During the period 1962-2004 CE, we find that teleconnections of drought conditions through the Atlantic Meridional Overturning Circulation have a strong influence ( r = -0.60, p < 0.01) on secondary tree growth in this region for the late spring-early summer season. These findings highlight the importance of continuing to increase the spatial resolution of the tree-ring network used to infer past climate dynamics to capture the sub-regional spatial variability. Increasing the spatial resolution of the tree-ring network for a given region can better identify sub-regional variability, improve the accuracy of regional tree-ring PDSI reconstructions, and provide better information for climatic teleconnections.

Inter-model variability in hydrological extremes projections for Amazonian sub-basins

NASA Astrophysics Data System (ADS)

Andres Rodriguez, Daniel; Garofolo, Lucas; Lázaro de Siqueira Júnior, José; Samprogna Mohor, Guilherme; Tomasella, Javier

2014-05-01

Irreducible uncertainties due to knowledge's limitations, chaotic nature of climate system and human decision-making process drive uncertainties in Climate Change projections. Such uncertainties affect the impact studies, mainly when associated to extreme events, and difficult the decision-making process aimed at mitigation and adaptation. However, these uncertainties allow the possibility to develop exploratory analyses on system's vulnerability to different sceneries. The use of different climate model's projections allows to aboard uncertainties issues allowing the use of multiple runs to explore a wide range of potential impacts and its implications for potential vulnerabilities. Statistical approaches for analyses of extreme values are usually based on stationarity assumptions. However, nonstationarity is relevant at the time scales considered for extreme value analyses and could have great implications in dynamic complex systems, mainly under climate change transformations. Because this, it is required to consider the nonstationarity in the statistical distribution parameters. We carried out a study of the dispersion in hydrological extremes projections using climate change projections from several climate models to feed the Distributed Hydrological Model of the National Institute for Spatial Research, MHD-INPE, applied in Amazonian sub-basins. This model is a large-scale hydrological model that uses a TopModel approach to solve runoff generation processes at the grid-cell scale. MHD-INPE model was calibrated for 1970-1990 using observed meteorological data and comparing observed and simulated discharges by using several performance coeficients. Hydrological Model integrations were performed for present historical time (1970-1990) and for future period (2010-2100). Because climate models simulate the variability of the climate system in statistical terms rather than reproduce the historical behavior of climate variables, the performances of the model's runs during the historical period, when feed with climate model data, were tested using descriptors of the Flow Duration Curves. The analyses of projected extreme values were carried out considering the nonstationarity of the GEV distribution parameters and compared with extremes events in present time. Results show inter-model variability in a broad dispersion on projected extreme's values. Such dispersion implies different degrees of socio-economic impacts associated to extreme hydrological events. Despite the no existence of one optimum result, this variability allows the analyses of adaptation strategies and its potential vulnerabilities.

Back to the Future -Precipitation Extremes, Climate Variability, Environmental Planning and Adaptation

NASA Astrophysics Data System (ADS)

Barros, A. P.

2008-12-01

--"The last major climatic oscillation peak was about 1856, or 74 years ago. Practically all of our important railroad and public highway work has been done since that time. Most of our parks systems driveways, and roads of all type for auto travel, in the various States, have been completed within the past 30 years, namely, beginning at the very lowest point of our climatic swing (1900-1910). There is every reason to believe, therefore, as the next 20 years comes on apace, we will witness considerable damage to work done during the past regime of weather."-- Schuman, 1931 At the beginning of the 21st century, as at the beginning of the 20th century, the fundamental question is whether the nation is more prepared for natural disasters today than it was eight decades ago. Indeed, the question is whether the best science, engineering and policy tools are in place to prepare for and respond to extreme events. Changes in the risk and magnitude of extreme precipitation events rank among the most studied impacts, and indicators (symptoms) of climatic variations. Extreme precipitation translates generally into extreme flooding, landslides, collapse of lifeline infrastructure, and the breakdown of public health services among others. In approaching the problem of quantifying the risk and magnitude of extreme precipitation events, there are two major challenges: 1) it is difficult to characterize "observed" (20th century) conditions due to the lack of long-term observations - i.e., short and incomplete historical records; and 2) it is difficult to characterize "predicted" (21st century) conditions due to the lack of skill of precipitation forecasts at spatial and temporal scales meaningful for impact studies, and the short-duration of climate model simulations themselves. The first challenge translates in estimating the probability of occurrence (rare) and magnitude (very large) of events that may have not happened yet. The second challenge is that of quantifying uncertainty and separating climatic variability and change from model error. Nonstationarity and persistence at multiple scales confound the problem. From an economics perspective, the unprecedented success of environmental control and "conservation" in the 20th century, present another yet challenge in terms of social expectations and human development, including the right to sustainable (high) quality of life. In this presentation, we illustrate these challenges by considering first the estimation of Probable Maximum Precipitation, an engineering design criterion typically used in dam design, and examine how it varies spatially across the continental US according to physiographic region and as a function of climate regime. Second, we explore the spatial and temporal scales that link climate variability to macroscale environmental planning, and the notion of place-based adaptive riskgrade analysis.

Long-Term Trends, Variability and Extremes of In Situ Sea Surface Temperature Measured Along the Eastern Adriatic Coast and its Relationship to Hemispheric Processes

NASA Astrophysics Data System (ADS)

Grbec, Branka; Matić, Frano; Beg Paklar, Gordana; Morović, Mira; Popović, Ružica; Vilibić, Ivica

2018-02-01

This paper examines long-term series of in situ sea surface temperature (SST) data measured at nine coastal and one open sea stations along the eastern Adriatic Sea for the period 1959-2015. Monthly and yearly averages were used to document SST trends and variability, while clustering and connections to hemispheric indices were achieved by applying the Principal Component Analysis (PCA) and Self-Organizing Maps (SOM) method. Both PCA and SOM revealed the dominance of temporal changes with respect to the effects of spatial differences in SST anomalies, indicating the prevalence of hemispheric processes over local dynamics, such as bora wind spatial inhomogeneity. SST extremes were connected with blocking atmospheric patterns. A substantial warming between 1979 and 2015, in total exceeding 1 °C, was preceded by a period with a negative SST trend, implying strong multidecadal variability in the Adriatic. The strongest connection was found between yearly SST and the East Atlantic (EA) pattern, while North Atlantic Oscillation (NAO) and East Atlantic/West Russia (EAWR) patterns were found to also affect February SST values. Quantification of the Adriatic SST and their connection to hemispheric indices allow for more precise projections of future SST, considered to be rather important for Adriatic thermohaline circulation, biogeochemistry and fisheries, and sensitive to ongoing climate change.

Temperature extremes in Alaska: temporal variability and circulation background

NASA Astrophysics Data System (ADS)

Sulikowska, Agnieszka; Walawender, Jakub P.; Walawender, Ewelina

2018-06-01

The aims of this study are to characterize the spatial and temporal variability of extremely warm days (WDs) and warm spells (WSs) in summer as well as extremely cold days (CDs) and cold spells (CSs) in winter in Alaska in the years 1951-2015 and to determine the role of atmospheric circulation in their occurrence. The analysis is performed using daily temperature maxima (T MAX) and minima (T MIN) measured at 10 weather stations in Alaska as well as mean daily values of sea level pressure and wind direction at the 850 hPa isobaric level. WD (CD) is defined as a day with T MAX above the 95th (T MIN below the 5th) percentile of a probability density function calculated from observations, and WS (CS) equals at least three consecutive WDs (CDs). Frequency of the occurrence and severity of warm and cold extremes as well as duration of WSs and CSs is analyzed. In order to characterize synoptic conditions during temperature extremes, the objective classification scheme of advection types considering jointly the direction of the air influx and type of pressure system is employed. The results show that the general trend is towards the warmer temperatures, and the warming is greater in the winter than summer and for T MAX as opposed to T MIN. This is reflected in changes in the frequency of occurrence and intensity of temperature extremes which are much more pronounced in the case of winter cold extremes (decreasing tendencies) than summer warm extremes (increasing tendencies). The occurrence of temperature extremes is generally favored by anticyclonic weather with advection direction indicating air mass flows from the interior of the North American continent as well as the south (warm extremes in summer) and north (cold extremes in winter).

Moisture sources and pathways associated with the spatial variability of seasonal extreme precipitation over Canada

NASA Astrophysics Data System (ADS)

Tan, Xuezhi; Gan, Thian Yew; Chen, Yongqin David

2018-01-01

Nine regions with spatially coherent seasonal 3-day total precipitation extremes across Canada were identified using a clustering method that is compliant to the extreme value theory. Using storm back-trajectory analyses, we then identified possible moisture sources and pathways that are conducive to occurrences of seasonal extreme precipitation events in four seasons for the nine regions identified. Moisture pathways for all extreme precipitation events were clustered to nine dominant moisture pathway patterns using the self-organizing map method. Results show that horizontal moisture pathway patterns and their occurrences were not evidently different between seasons. However, warm (summer and fall) and cold (winter and spring) seasons show considerable differences in the spreading of moisture sources in all nine regions, even though many sources do not frequently contribute to extreme precipitation events. In all four seasons, terrestrial evapotranspiration had provided major moisture sources to many extreme precipitation events occurred in inland regions. Central Canada had received more widespread moisture sources over surrounding oceans of North America than western and eastern Canada, because of more diverse moisture pathway patterns for central Canada that transport moisture from all surrounding oceans to central Canada. Extreme precipitation in southwestern Canada mainly resulted from atmospheric rivers over the North Pacific Ocean. For northwestern Canada, moisture pathway patterns were from the northern Pacific, Arctic and northern Atlantic oceans, even though more than 78% of trajectories for northwestern Canada were from the North Pacific. Westerlies from the North Pacific Ocean and northern polar jet streams controlled dominant pathways to central and eastern Canada. More extreme precipitation events over Canada were fed by the Arctic Ocean in warm than in cold seasons.

Moisture sources and pathways associated with the spatial variability of seasonal extreme precipitation over Canada

NASA Astrophysics Data System (ADS)

Gan, T. Y. Y.; Tan, X.; Chen, Y. D.

2017-12-01

Nine regions with spatially coherent seasonal 3-day total precipitation extremes across Canada were identified using a clustering method that is compliant to the extreme value theory. Using storm back-trajectory analyses, we then identified possible moisture sources and pathways that are conducive to occurrences of seasonal extreme precipitation events in four seasons for the nine regions identified.Moisture pathways for all extreme precipitation events were clustered to nine dominant moisture pathway patterns using the self-organizing map method. Results show that horizontal moisture pathway patterns and their occurrences were not evidently different between seasons. However, warm (summer and fall) and cold (winter and spring) seasons show considerable differences in the spreading ofmoisture sources in all nine regions, even though many sources do not frequently contribute to extreme precipitation events. In all four seasons, terrestrial evapotranspiration had provided major moisture sources to many extreme precipitation events occurred in inland regions. Central Canada had received more widespread moisture sources over surrounding oceans of North America than western and eastern Canada, because of more diverse moisture pathway patterns for central Canada that transport moisture from all surrounding oceans to central Canada. Extreme precipitation in southwestern Canada mainly resulted from atmospheric rivers over the North Pacific Ocean. For northwestern Canada, moisture pathway patterns were from the northern Pacific, Arctic and northern Atlantic oceans, even though more than 78% of trajectories for northwestern Canada were from the North Pacific. Westerlies from the North Pacific Ocean and northern polar jet streams controlled dominant pathways to central and eastern Canada. More extreme precipitation events over Canada were fed by the Arctic Ocean in warm than in cold seasons.

Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Kusangaya, Samuel; Warburton Toucher, Michele L.; van Garderen, Emma Archer

2018-02-01

Downscaled General Circulation Models (GCMs) output are used to forecast climate change and provide information used as input for hydrological modelling. Given that our understanding of climate change points towards an increasing frequency, timing and intensity of extreme hydrological events, there is therefore the need to assess the ability of downscaled GCMs to capture these extreme hydrological events. Extreme hydrological events play a significant role in regulating the structure and function of rivers and associated ecosystems. In this study, the Indicators of Hydrologic Alteration (IHA) method was adapted to assess the ability of simulated streamflow (using downscaled GCMs (dGCMs)) in capturing extreme river dynamics (high and low flows), as compared to streamflow simulated using historical climate data from 1960 to 2000. The ACRU hydrological model was used for simulating streamflow for the 13 water management units of the uMngeni Catchment, South Africa. Statistically downscaled climate models obtained from the Climate System Analysis Group at the University of Cape Town were used as input for the ACRU Model. Results indicated that, high flows and extreme high flows (one in ten year high flows/large flood events) were poorly represented both in terms of timing, frequency and magnitude. Simulated streamflow using dGCMs data also captures more low flows and extreme low flows (one in ten year lowest flows) than that captured in streamflow simulated using historical climate data. The overall conclusion was that although dGCMs output can reasonably be used to simulate overall streamflow, it performs poorly when simulating extreme high and low flows. Streamflow simulation from dGCMs must thus be used with caution in hydrological applications, particularly for design hydrology, as extreme high and low flows are still poorly represented. This, arguably calls for the further improvement of downscaling techniques in order to generate climate data more relevant and useful for hydrological applications such as in design hydrology. Nevertheless, the availability of downscaled climatic output provide the potential of exploring climate model uncertainties in different hydro climatic regions at local scales where forcing data is often less accessible but more accurate at finer spatial scales and with adequate spatial detail.

A reconstruction of global hydroclimate and dynamical variables over the Common Era.

PubMed

Steiger, Nathan J; Smerdon, Jason E; Cook, Edward R; Cook, Benjamin I

2018-05-22

Hydroclimate extremes critically affect human and natural systems, but there remain many unanswered questions about their causes and how to interpret their dynamics in the past and in climate change projections. These uncertainties are due, in part, to the lack of long-term, spatially resolved hydroclimate reconstructions and information on the underlying physical drivers for many regions. Here we present the first global reconstructions of hydroclimate and associated climate dynamical variables over the past two thousand years. We use a data assimilation approach tailored to reconstruct hydroclimate that optimally combines 2,978 paleoclimate proxy-data time series with the physical constraints of an atmosphere-ocean climate model. The global reconstructions are annually or seasonally resolved and include two spatiotemporal drought indices, near-surface air temperature, an index of North Atlantic variability, the location of the intertropical convergence zone, and monthly Niño indices. This database, called the Paleo Hydrodynamics Data Assimilation product (PHYDA), will provide a critical new platform for investigating the causes of past climate variability and extremes, while informing interpretations of future hydroclimate projections.

Spatio-temporal variability of dry and wet periods in mainland Portugal and its relationships with teleconnection patterns

NASA Astrophysics Data System (ADS)

Espírito Santo, Fátima; de Lima, Isabel P.; Silva, Álvaro; Pires, Vanda; de Lima, João L. M. P.

2014-05-01

Large-scale atmospheric circulation patterns and their persistence are known to drive inter-annual variability of precipitation in Europe, although depending on geographical location; this includes precipitation extremes and their trends. The vast range of time and space scales involved leads sometimes to precipitation deficits and surpluses which might affect in a different way the society, the environment and the economy at the local and regional scales, depending on specific conditions. In addition, changes in the climate are expected to affect the occurrence of extreme weather and climate events that might influence significantly the distribution, availability and sustainability of regional water resources. The location of mainland Portugal on the Northeast Atlantic region, in South-western Europe, together with other geographical features, makes this territory vulnerable to extreme dry/wet hydro-meteorological events, driven by the strong variability in precipitation. In our study we discuss, for this territory, the relation between the spatio-temporal variability in those events, including their persistence at different scales, and the variability in several modes of low frequency variability; special attention is dedicated to the North Atlantic Oscillation (NAO) and Scandinavian pattern (SCAND). Some of these dry/wet episodes affect different aspects of the hydrologic cycle and are likely to lead to drought and soil wetness/saturation conditions that can enhance flood events. Such episodes were categorized here using the Standardized Precipitation Index (SPI), which was calculated at short (3 and 6-month) and long (12 and 24-month) time scales from monthly precipitation data recorded in the 1941-2012 period (72 years) at 50 precipitation stations scattered across the study area. Moreover, because SPI is a normalized index, it is also suitable to provide spatial representations of these conditions, allowing the comparison between areas within the same region. Thus, indices were interpolated for the whole territory using deterministic and geostatistical methods, and the zonal statistics results were mapped; the spatial interpolation, analysis and mapping were implemented in ArcGIS. Results confirm that the precipitation in this region is strongly influenced by the NAO and SCAND, in particular in the wettest months. Moreover, the annual SPI shows a significant increase in the extent of dry extremes and a non-significant decrease in the extent of wet extremes. For shorter time scales, the behaviour depends on the season. We discuss the observed SPI trends and the uncertainties for the precipitation regime in the southern and western parts of the Iberian Peninsula, which includes mainland Portugal. Results underline potential applications of SPI for water resources management, which is discussed in the context of the regional hydrological conditions and increasing demand for water for different uses.

Multi-band implications of external-IC flares

NASA Astrophysics Data System (ADS)

Richter, Stephan; Spanier, Felix

2015-02-01

Very fast variability on scales of minutes is regularly observed in Blazars. The assumption that these flares are emerging from the dominant emission zone of the very high energy (VHE) radiation within the jet challenges current acceleration and radiation models. In this work we use a spatially resolved and time dependent synchrotron-self-Compton (SSC) model that includes the full time dependence of Fermi-I acceleration. We use the (apparent) orphan γ -ray flare of Mrk501 during MJD 54952 and test various flare scenarios against the observed data. We find that a rapidly variable external radiation field can reproduce the high energy lightcurve best. However, the effect of the strong inverse Compton (IC) cooling on other bands and the X-ray observations are constraining the parameters to rather extreme ranges. Then again other scenarios would require parameters even more extreme or stronger physical constraints on the rise and decay of the source of the variability which might be in contradiction with constraints derived from the size of the black hole's ergosphere.

High-resolution stochastic generation of extreme rainfall intensity for urban drainage modelling applications

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2016-04-01

Urban drainage response is highly dependent on the spatial and temporal structure of rainfall. Therefore, measuring and simulating rainfall at a high spatial and temporal resolution is a fundamental step to fully assess urban drainage system reliability and related uncertainties. This is even more relevant when considering extreme rainfall events. However, the current space-time rainfall models have limitations in capturing extreme rainfall intensity statistics for short durations. Here, we use the STREAP (Space-Time Realizations of Areal Precipitation) model, which is a novel stochastic rainfall generator for simulating high-resolution rainfall fields that preserve the spatio-temporal structure of rainfall and its statistical characteristics. The model enables a generation of rain fields at 102 m and minute scales in a fast and computer-efficient way matching the requirements for hydrological analysis of urban drainage systems. The STREAP model was applied successfully in the past to generate high-resolution extreme rainfall intensities over a small domain. A sub-catchment in the city of Luzern (Switzerland) was chosen as a case study to: (i) evaluate the ability of STREAP to disaggregate extreme rainfall intensities for urban drainage applications; (ii) assessing the role of stochastic climate variability of rainfall in flow response and (iii) evaluate the degree of non-linearity between extreme rainfall intensity and system response (i.e. flow) for a small urban catchment. The channel flow at the catchment outlet is simulated by means of a calibrated hydrodynamic sewer model.

Flood probability quantification for road infrastructure: Data-driven spatial-statistical approach and case study applications.

PubMed

Kalantari, Zahra; Cavalli, Marco; Cantone, Carolina; Crema, Stefano; Destouni, Georgia

2017-03-01

Climate-driven increase in the frequency of extreme hydrological events is expected to impose greater strain on the built environment and major transport infrastructure, such as roads and railways. This study develops a data-driven spatial-statistical approach to quantifying and mapping the probability of flooding at critical road-stream intersection locations, where water flow and sediment transport may accumulate and cause serious road damage. The approach is based on novel integration of key watershed and road characteristics, including also measures of sediment connectivity. The approach is concretely applied to and quantified for two specific study case examples in southwest Sweden, with documented road flooding effects of recorded extreme rainfall. The novel contributions of this study in combining a sediment connectivity account with that of soil type, land use, spatial precipitation-runoff variability and road drainage in catchments, and in extending the connectivity measure use for different types of catchments, improve the accuracy of model results for road flood probability. Copyright © 2016 Elsevier B.V. All rights reserved.

Atmospheric Moisture Budget and Spatial Resolution Dependence of Precipitation Extremes in Aquaplanet Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Qing; Leung, Lai-Yung R.; Rauscher, Sara

This study investigates the resolution dependency of precipitation extremes in an aqua-planet framework. Strong resolution dependency of precipitation extremes is seen over both tropics and extra-tropics, and the magnitude of this dependency also varies with dynamical cores. Moisture budget analyses based on aqua-planet simulations with the Community Atmosphere Model (CAM) using the Model for Prediction Across Scales (MPAS) and High Order Method Modeling Environment (HOMME) dynamical cores but the same physics parameterizations suggest that during precipitation extremes moisture supply for surface precipitation is mainly derived from advective moisture convergence. The resolution dependency of precipitation extremes mainly originates from advective moisturemore » transport in the vertical direction. At most vertical levels over the tropics and in the lower atmosphere over the subtropics, the vertical eddy transport of mean moisture field dominates the contribution to precipitation extremes and its resolution dependency. Over the subtropics, the source of moisture, its associated energy, and the resolution dependency during extremes are dominated by eddy transport of eddies moisture at the mid- and upper-troposphere. With both MPAS and HOMME dynamical cores, the resolution dependency of the vertical advective moisture convergence is mainly explained by dynamical changes (related to vertical velocity or omega), although the vertical gradients of moisture act like averaging kernels to determine the sensitivity of the overall resolution dependency to the changes in omega at different vertical levels. The natural reduction of variability with coarser resolution, represented by areal data averaging (aggregation) effect, largely explains the resolution dependency in omega. The thermodynamic changes, which likely result from non-linear feedback in response to the large dynamical changes, are small compared to the overall changes in dynamics (omega). However, after excluding the data aggregation effect in omega, thermodynamic changes become relatively significant in offsetting the effect of dynamics leading to reduce differences between the simulated and aggregated results. Compared to MPAS, the simulated stronger vertical motion with HOMME also results in larger resolution dependency. Compared to the simulation at fine resolution, the vertical motion during extremes is insufficiently resolved/parameterized at the coarser resolution even after accounting for the natural reduction in variability with coarser resolution, and this is more distinct in the simulation with HOMME. To reduce uncertainties in simulated precipitation extremes, future development in cloud parameterizations must address their sensitivity to spatial resolution as well as dynamical cores.« less

Climate Change and Hydrological Extreme Events - Risks and Perspectives for Water Management in Bavaria and Québec

NASA Astrophysics Data System (ADS)

Ludwig, R.

2017-12-01

There is as yet no confirmed knowledge whether and how climate change contributes to the magnitude and frequency of hydrological extreme events and how regional water management could adapt to the corresponding risks. The ClimEx project (2015-2019) investigates the effects of climate change on the meteorological and hydrological extreme events and their implications for water management in Bavaria and Québec. High Performance Computing is employed to enable the complex simulations in a hydro-climatological model processing chain, resulting in a unique high-resolution and transient (1950-2100) dataset of climatological and meteorological forcing and hydrological response: (1) The climate module has developed a large ensemble of high resolution data (12km) of the CRCM5 RCM for Central Europe and North-Eastern North America, downscaled from 50 members of the CanESM2 GCM. The dataset is complemented by all available data from the Euro-CORDEX project to account for the assessment of both natural climate variability and climate change. The large ensemble with several thousand model years provides the potential to catch rare extreme events and thus improves the process understanding of extreme events with return periods of 1000+ years. (2) The hydrology module comprises process-based and spatially explicit model setups (e.g. WaSiM) for all major catchments in Bavaria and Southern Québec in high temporal (3h) and spatial (500m) resolution. The simulations form the basis for in depth analysis of hydrological extreme events based on the inputs from the large climate model dataset. The specific data situation enables to establish a new method for `virtual perfect prediction', which assesses climate change impacts on flood risk and water resources management by identifying patterns in the data which reveal preferential triggers of hydrological extreme events. The presentation will highlight first results from the analysis of the large scale ClimEx model ensemble, showing the current and future ratio of natural variability and climate change impacts on meteorological extreme events. Selected data from the ensemble is used to drive a hydrological model experiment to illustrate the capacity to better determine the recurrence periods of hydrological extreme events under conditions of climate change.

Generalized radiative transfer theory for scattering by particles in an absorbing gas: Addressing both spatial and spectral integration in multi-angle remote sensing of optically thin aerosol layers

NASA Astrophysics Data System (ADS)

Davis, Anthony B.; Xu, Feng; Diner, David J.

2018-01-01

We demonstrate the computational advantage gained by introducing non-exponential transmission laws into radiative transfer theory for two specific situations. One is the problem of spatial integration over a large domain where the scattering particles cluster randomly in a medium uniformly filled with an absorbing gas, and only a probabilistic description of the variability is available. The increasingly important application here is passive atmospheric profiling using oxygen absorption in the visible/near-IR spectrum. The other scenario is spectral integration over a region where the absorption cross-section of a spatially uniform gas varies rapidly and widely and, moreover, there are scattering particles embedded in the gas that are distributed uniformly, or not. This comes up in many applications, O2 A-band profiling being just one instance. We bring a common framework to solve these problems both efficiently and accurately that is grounded in the recently developed theory of Generalized Radiative Transfer (GRT). In GRT, the classic exponential law of transmission is replaced by one with a slower power-law decay that accounts for the unresolved spectral or spatial variability. Analytical results are derived in the single-scattering limit that applies to optically thin aerosol layers. In spectral integration, a modest gain in accuracy is obtained. As for spatial integration of near-monochromatic radiance, we find that, although both continuum and in-band radiances are affected by moderate levels of sub-pixel variability, only extreme variability will affect in-band/continuum ratios.

Studying Weather and Climate Extremes in a Non-stationary Framework

NASA Astrophysics Data System (ADS)

Wu, Z.

2010-12-01

The study of weather and climate extremes often uses the theory of extreme values. Such a detection method has a major problem: to obtain the probability distribution of extremes, one has to implicitly assume the Earth’s climate is stationary over a long period within which the climatology is defined. While such detection makes some sense in a purely statistical view of stationary processes, it can lead to misleading statistical properties of weather and climate extremes caused by long term climate variability and change, and may also cause enormous difficulty in attributing and predicting these extremes. To alleviate this problem, here we report a novel non-stationary framework for studying weather and climate extremes in a non-stationary framework. In this new framework, the weather and climate extremes will be defined as timescale-dependent quantities derived from the anomalies with respect to non-stationary climatologies of different timescales. With this non-stationary framework, the non-stationary and nonlinear nature of climate system will be taken into account; and the attribution and the prediction of weather and climate extremes can then be separated into 1) the change of the statistical properties of the weather and climate extremes themselves and 2) the background climate variability and change. The new non-stationary framework will use the ensemble empirical mode decomposition (EEMD) method, which is a recent major improvement of the Hilbert-Huang Transform for time-frequency analysis. Using this tool, we will adaptively decompose various weather and climate data from observation and climate models in terms of the components of the various natural timescales contained in the data. With such decompositions, the non-stationary statistical properties (both spatial and temporal) of weather and climate anomalies and of their corresponding climatologies will be analyzed and documented.

Consequences of environmental and biological variances for range margins: a spatially explicit theoretical model

NASA Astrophysics Data System (ADS)

Malanson, G. P.; DeRose, R. J.; Bekker, M. F.

2016-12-01

The consequences of increasing climatic variance while including variability among individuals and populations are explored for range margins of species with a spatially explicit simulation. The model has a single environmental gradient and a single species then extended to two species. Species response to the environment is a Gaussian function with a peak of 1.0 at their peak fitness on the gradient. The variance in the environment is taken from the total variance in the tree ring series of 399 individuals of Pinus edulis in FIA plots in the western USA. The variability is increased by a multiplier of the standard deviation for various doubling times. The variance of individuals in the simulation is drawn from these same series. Inheritance of individual variability is based on the geographic locations of the individuals. The variance for P. edulis is recomputed as time-dependent conditional standard deviations using the GARCH procedure. Establishment and mortality are simulated in a Monte Carlo process with individual variance. Variance for P. edulis does not show a consistent pattern of heteroscedasticity. An obvious result is that increasing variance has deleterious effects on species persistence because extreme events that result in extinctions cannot be balanced by positive anomalies, but even less extreme negative events cannot be balanced by positive anomalies because of biological and spatial constraints. In the two species model the superior competitor is more affected by increasing climatic variance because its response function is steeper at the point of intersection with the other species and so the uncompensated effects of negative anomalies are greater for it. These theoretical results can guide the anticipated need to mitigate the effects of increasing climatic variability on P. edulis range margins. The trailing edge, here subject to increasing drought stress with increasing temperatures, will be more affected by negative anomalies.

Local finite-amplitude wave activity as an objective diagnostic of midlatitude extreme weather

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Gang; Lu, Jian; Burrows, Alex D.

Midlatitude extreme weather events are responsible for a large part of climate related damage, yet our understanding of these extreme events is limited, partly due to the lack of a theoretical basis for midlatitude extreme weather. In this letter, the local finite-amplitude wave activity (LWA) of Huang and Nakamura [2015] is introduced as a diagnostic of the 500-hPa geopotential height (Z500) to characterizing midlatitude weather events. It is found that the LWA climatology and its variability associated with the Arctic Oscillation (AO) agree broadly with the previously reported blocking frequency in literature. There is a strong seasonal and spatial dependencemore » in the trend13 s of LWA in recent decades. While there is no observational evidence for a hemispheric-scale increase in wave amplitude, robust trends in wave activity can be identified at the regional scales, with important implications for regional climate change.« less

Evaluating historical climate and hydrologic trends in the Central Appalachian region of the United States

NASA Astrophysics Data System (ADS)

Gaertner, B. A.; Zegre, N.

2015-12-01

Climate change is surfacing as one of the most important environmental and social issues of the 21st century. Over the last 100 years, observations show increasing trends in global temperatures and intensity and frequency of precipitation events such as flooding, drought, and extreme storms. Global circulation models (GCM) show similar trends for historic and future climate indicators, albeit with geographic and topographic variability at regional and local scale. In order to assess the utility of GCM projections for hydrologic modeling, it is important to quantify how robust GCM outputs are compared to robust historical observations at finer spatial scales. Previous research in the United States has primarily focused on the Western and Northeastern regions due to dominance of snow melt for runoff and aquifer recharge but the impact of climate warming in the mountainous central Appalachian Region is poorly understood. In this research, we assess the performance of GCM-generated historical climate compared to historical observations primarily in the context of forcing data for macro-scale hydrologic modeling. Our results show significant spatial heterogeneity of modeled climate indices when compared to observational trends at the watershed scale. Observational data is showing considerable variability within maximum temperature and precipitation trends, with consistent increases in minimum temperature. The geographic, temperature, and complex topographic gradient throughout the central Appalachian region is likely the contributing factor in temperature and precipitation variability. Variable climate changes are leading to more severe and frequent climate events such as temperature extremes and storm events, which can have significant impacts on our drinking water supply, infrastructure, and health of all downstream communities.

Beach response dynamics of a littoral cell using a 17-year single-point time series of sand thickness

USGS Publications Warehouse

Barnard, P.L.; Hubbard, D.M.; Dugan, J.E.

2012-01-01

A 17-year time series of near-daily sand thickness measurements at a single intertidal location was compared with 5. years of semi-annual 3-dimensional beach surveys at the same beach, and at two other beaches within the same littoral cell. The daily single point measurements correlated extremely well with the mean beach elevation and shoreline position of ten high-spatial resolution beach surveys. Correlations were statistically significant at all spatial scales, even for beach surveys 10s of kilometers downcoast, and therefore variability at the single point monitoring site was representative of regional coastal behavior, allowing us to examine nearly two decades of continuous coastal evolution. The annual cycle of beach oscillations dominated the signal, typical of this region, with additional, less intense spectral peaks associated with seasonal wave energy fluctuations (~. 45 to 90. days), as well as full lunar (~. 29. days) and semi-lunar (~. 13. days; spring-neap cycle) tidal cycles. Sand thickness variability was statistically linked to wave energy with a 2. month peak lag, as well as the average of the previous 7-8. months of wave energy. Longer term anomalies in sand thickness were also apparent on time scales up to 15. months. Our analyses suggest that spatially-limited morphological data sets can be extremely valuable (with robust validation) for understanding the details of beach response to wave energy over timescales that are not resolved by typical survey intervals, as well as the regional behavior of coastal systems. ?? 2011.

A Normal Incidence X-ray Telescope (NIXT) Sounding Rocket Payload

NASA Technical Reports Server (NTRS)

Golub, Leon

1998-01-01

The solar corona, and the coronae of solar-type stars, consist of a low-density magnetized plasma at temperatures exceeding 10(exp 6) K. The primary coronal emission is therefore in the UV and soft X-ray range. The observed close connection between solar magnetic fields and the physical parameters of the corona implies a fundamental role for the magnetic field in coronal structuring and dynamics. Variability of the corona occurs on all temporal and spatial scales - at one extreme, as the result of plasma instabilities, and at the other extreme driven by the global magnetic flux emergence patterns of the solar cycle.

Future summer mega-heatwave and record-breaking temperatures in a warmer France climate

NASA Astrophysics Data System (ADS)

Bador, Margot; Terray, Laurent; Boé, Julien; Somot, Samuel; Alias, Antoinette; Gibelin, Anne-Laure; Dubuisson, Brigitte

2017-07-01

This study focuses on future very hot summers associated with severe heatwaves and record-breaking temperatures in France. Daily temperature observations and a pair of historical and scenario (greenhouse gas radiative concentration pathway 8.5) simulations with the high-resolution (∼12.5 km) ALADIN regional climate model provide a robust framework to examine the spatial distribution of these extreme events and their 21st century evolution. Five regions are identified with an extreme event spatial clustering algorithm applied to observed temperatures. They are used to diagnose the 21st century heatwave spatial patterns. In the 2070s, we find a simulated mega-heatwave as severe as the 2003 observed heatwave relative to its contemporaneous climate. A 20-member initial condition ensemble is used to assess the sensitivity of this future heatwave to the internal variability in the regional climate model and to pre-existing land surface conditions. Even in a much warmer and drier climate in France, late spring dry land conditions may lead to a significant amplification of summer extreme temperatures and heatwave intensity through limitations in evapotranspiration. By 2100, the increase in summer temperature maxima exhibits a range from 6 °C to almost 13 °C in the five regions in France, relative to historical maxima. These projections are comparable with the estimates given by a large number of global climate models.

Changes in field workability and drought risk from projected climate change drive spatially variable risks in Illinois cropping systems.

PubMed

Tomasek, Bradley J; Williams, Martin M; Davis, Adam S

2017-01-01

As weather patterns become more volatile and extreme, risks introduced by weather variability will become more critical to agricultural production. The availability of days suitable for field work is driven by soil temperature and moisture, both of which may be altered by climate change. We projected changes in Illinois season length, spring field workability, and summer drought risk under three different emissions scenarios (B1, A1B, and A2) down to the crop district scale. Across all scenarios, thermal time units increased in parallel with a longer frost-free season. An increase in late March and Early April field workability was consistent across scenarios, but a decline in overall April through May workable days was observed for many cases. In addition, summer drought metrics were projected to increase for most scenarios. These results highlight how the spatial and temporal variability in climate change may present unique challenges to mitigation and adaptation efforts.

Assessing the impact of future climate extremes on the US corn and soybean production

NASA Astrophysics Data System (ADS)

Jin, Z.

2015-12-01

Future climate changes will place big challenges to the US agricultural system, among which increasing heat stress and precipitation variability were the two major concerns. Reliable prediction of crop productions in response to the increasingly frequent and severe extreme climate is a prerequisite for developing adaptive strategies on agricultural risk management. However, the progress has been slow on quantifying the uncertainty of computational predictions at high spatial resolutions. Here we assessed the risks of future climate extremes on the US corn and soybean production using the Agricultural Production System sIMulator (APSIM) model under different climate scenarios. To quantify the uncertainty due to conceptual representations of heat, drought and flooding stress in crop models, we proposed a new strategy of algorithm ensemble in which different methods for simulating crop responses to those extreme climatic events were incorporated into the APSIM. This strategy allowed us to isolate irrelevant structure differences among existing crop models but only focus on the process of interest. Future climate inputs were derived from high-spatial-resolution (12km × 12km) Weather Research and Forecasting (WRF) simulations under Representative Concentration Pathways 4.5 (RCP 4.5) and 8.5 (RCP 8.5). Based on crop model simulations, we analyzed the magnitude and frequency of heat, drought and flooding stress for the 21st century. We also evaluated the water use efficiency and water deficit on regional scales if farmers were to boost their yield by applying more fertilizers. Finally we proposed spatially explicit adaptation strategies of irrigation and fertilizing for different management zones.

Attempting to physically explain space-time correlation of extremes

NASA Astrophysics Data System (ADS)

Bernardara, Pietro; Gailhard, Joel

2010-05-01

Spatial and temporal clustering of hydro-meteorological extreme events is scientific evidence. Moreover, the statistical parameters characterizing their local frequencies of occurrence show clear spatial patterns. Thus, in order to robustly assess the hydro-meteorological hazard, statistical models need to be able to take into account spatial and temporal dependencies. Statistical models considering long term correlation for quantifying and qualifying temporal and spatial dependencies are available, such as multifractal approach. Furthermore, the development of regional frequency analysis techniques allows estimating the frequency of occurrence of extreme events taking into account spatial patterns on the extreme quantiles behaviour. However, in order to understand the origin of spatio-temporal clustering, an attempt to find physical explanation should be done. Here, some statistical evidences of spatio-temporal correlation and spatial patterns of extreme behaviour are given on a large database of more than 400 rainfall and discharge series in France. In particular, the spatial distribution of multifractal and Generalized Pareto distribution parameters shows evident correlation patterns in the behaviour of frequency of occurrence of extremes. It is then shown that the identification of atmospheric circulation pattern (weather types) can physically explain the temporal clustering of extreme rainfall events (seasonality) and the spatial pattern of the frequency of occurrence. Moreover, coupling this information with the hydrological modelization of a watershed (as in the Schadex approach) an explanation of spatio-temporal distribution of extreme discharge can also be provided. We finally show that a hydro-meteorological approach (as the Schadex approach) can explain and take into account space and time dependencies of hydro-meteorological extreme events.

Post-fire reconstructions of fire intensity from fire severity data: quantifying the role of spatial variability of fire intensity on forest dynamics

NASA Astrophysics Data System (ADS)

Baker, Patrick; Oborne, Lisa

2015-04-01

Large, high-intensity fires have direct and long-lasting effects on forest ecosystems and present a serious threat to human life and property. However, even within the most catastrophic fires there is important variability in local-scale intensity that has important ramifications for forest mortality and regeneration. Quantifying this variability is difficult due to the rarity of catastrophic fire events, the extreme conditions at the time of the fires, and their large spatial extent. Instead fire severity is typically measured or estimated from observed patterns of vegetation mortality; however, differences in species- and size-specific responses to fires often makes fire severity a poor proxy for fire intensity. We developed a statistical method using simple, plot-based measurements of individual tree mortality to simultaneously estimate plot-level fire intensity and species-specific mortality patterns as a function of tree size. We applied our approach to an area of forest burned in the catastrophic Black Saturday fires that occurred near Melbourne, Australia, in February 2009. Despite being the most devastating fire in the past 70 years and our plots being located in the area that experienced some of the most intense fires in the 350,000 ha fire complex, we found that the estimated fire intensity was highly variable at multiple spatial scales. All eight tree species in our study differed in their susceptibility to fire-induced mortality, particularly among the largest size classes. We also found that seedling height and species richness of the post-fire seedling communities were both positively correlated with fire intensity. Spatial variability in disturbance intensity has important, but poorly understood, consequences for the short- and long-term dynamics of forests in the wake of catastrophic wildfires. Our study provides a tool to estimate fire intensity after a fire has passed, allowing new opportunities for linking spatial variability in fire intensity to forest ecosystem dynamics.

Spatially Resolving Ocean Color and Sediment Dispersion in River Plumes, Coastal Systems, and Continental Shelf Waters

NASA Technical Reports Server (NTRS)

Aurin, Dirk Alexander; Mannino, Antonio; Franz, Bryan

2013-01-01

Satellite remote sensing of ocean color in dynamic coastal, inland, and nearshorewaters is impeded by high variability in optical constituents, demands specialized atmospheric correction, and is limited by instrument sensitivity. To accurately detect dispersion of bio-optical properties, remote sensors require ample signal-to-noise ratio (SNR) to sense small variations in ocean color without saturating over bright pixels, an atmospheric correction that can accommodate significantwater-leaving radiance in the near infrared (NIR), and spatial and temporal resolution that coincides with the scales of variability in the environment. Several current and historic space-borne sensors have met these requirements with success in the open ocean, but are not optimized for highly red-reflective and heterogeneous waters such as those found near river outflows or in the presence of sediment resuspension. Here we apply analytical approaches for determining optimal spatial resolution, dominant spatial scales of variability ("patches"), and proportions of patch variability that can be resolved from four river plumes around the world between 2008 and 2011. An offshore region in the Sargasso Sea is analyzed for comparison. A method is presented for processing Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra imagery including cloud detection, stray lightmasking, faulty detector avoidance, and dynamic aerosol correction using short-wave- and near-infrared wavebands in extremely turbid regions which pose distinct optical and technical challenges. Results showthat a pixel size of approx. 520 mor smaller is generally required to resolve spatial heterogeneity in ocean color and total suspended materials in river plumes. Optimal pixel size increases with distance from shore to approx. 630 m in nearshore regions, approx 750 m on the continental shelf, and approx. 1350 m in the open ocean. Greater than 90% of the optical variability within plume regions is resolvable with 500 m resolution, and small, but significant, differences were found between peak and nadir river flow periods in terms of optimal resolution and resolvable proportion of variability.

Intra-seasonal variability of extreme boreal stratospheric polar vortex events and their precursors

NASA Astrophysics Data System (ADS)

Díaz-Durán, Adelaida; Serrano, Encarna; Ayarzagüena, Blanca; Abalos, Marta; de la Cámara, Alvaro

2017-11-01

The dynamical variability of the boreal stratospheric polar vortex has been usually analysed considering the extended winter as a whole or only focusing on December, January and February. Yet recent studies have found intra-seasonal differences in the boreal stratospheric dynamics. In this study, the intra-seasonal variability of anomalous wave activity preceding polar vortex extremes in the Northern Hemisphere is examined using ERA-Interim reanalysis data. Weak (WPV) and strong (SPV) polar vortex events are grouped into early, mid- or late winter sub-periods depending on the onset date. Overall, the strongest (weakest) wave-activity anomalies preceding polar vortex extremes are found in mid- (early) winter. Most of WPV (SPV) events in early winter occur under the influence of east (west) phase of the Quasi-Biennial Oscillation (QBO) and an enhancement (inhibition) of wavenumber-1 wave activity (WN1). Mid- and late winter WPV events are preceded by a strong vortex and an enhancement of WN1 and WN2, but the spatial structure of the anomalous wave activity and the phase of the QBO are different. Prior to mid-winter WPVs the enhancement of WN2 is related to the predominance of La Niña and linked to blockings over Siberia. Mid-winter SPV events show a negative phase of the Pacific-North America pattern that inhibits WN1 injected into the stratosphere. This study suggests that dynamical features preceding extreme polar vortex events in mid-winter should not be generalized to other winter sub-periods.

Geo(spatial) Health Investigation of Rotavirus in an Endemic Region: Hydroclimatic Influences and Epidemiology of Rotavirus in Bangladesh

NASA Astrophysics Data System (ADS)

Hasan, M. A.; Akanda, A. S.; Jutla, A.; Colwell, R. R.

2016-12-01

Rotavirus is the leading cause of severe dehydrating diarrhea among children under 5. Over 80% of the approximate half a million child deaths every year occur in South Asia and sub-Saharan Africa alone. Although less explored than cholera as a climate driven and influenced global health problem, recent studies have showed that the disease shown strong seasonality and spatio-temporal variability depending on regional hydroclimatic and local environmental conditions. Understanding the epidemiology of this disease, especially the spatio-temporal incidence patterns with respect to environmental factors is vitally important to allow for identification of "hotspots", preventative preparations, and vaccination strategies to improve wellbeing of the vulnerable populations. With climate change, spatio-temporal signatures and footprints of the disease are changing along with increasing burden. However, a robust understanding of the relationships between rotavirus epidemiology and hydroclimatic drivers is yet to be developed. In this study, we evaluate the seasonality and epidemiologic characteristics of rotavirous infection and its spatio-temporal incidence patterns with respect to regional hydroclimatic variables and their extremes in an endemic region in South Asia. Hospital-based surveillance data from different geographic locations allowed us to explore the detailed spatial and temporal characteristics of rotavirus propagation under the influence of climate variables in both coastal and inland areas. The rotavirus transmission patterns show two peaks in a year in the capital city of Dhaka, where winter season (highest in January) shows a high peak and the July-August monsoon season shows a smaller peak. Correlation with climate variables revealed that minimum temperature has strong influence on the winter season outbreak, while rainfall extremes show a strong positive association with the secondary monsoon peak. Spatial analysis also revealed that humidity and soil wetness may influence the timing as drier areas experience earlier outbreaks than wetter areas. Accurate understanding of rotavirus propagation with respect to hydroclimatic and environmental variability can be utilized to establish global surveillance and forecast imminent risk of diarrheal outbreaks in vulnerable regions.

The Development of a New Model of Solar EUV Irradiance Variability

NASA Technical Reports Server (NTRS)

Warren, Harry; Wagner, William J. (Technical Monitor)

2002-01-01

The goal of this research project is the development of a new model of solar EUV (Extreme Ultraviolet) irradiance variability. The model is based on combining differential emission measure distributions derived from spatially and spectrally resolved observations of active regions, coronal holes, and the quiet Sun with full-disk solar images. An initial version of this model was developed with earlier funding from NASA. The new version of the model developed with this research grant will incorporate observations from SoHO as well as updated compilations of atomic data. These improvements will make the model calculations much more accurate.

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes.

PubMed

de Mendoza, Guillermo; Ventura, Marc; Catalan, Jordi

2015-07-01

Aiming to elucidate whether large-scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran's eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in-lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in-lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances.

Environmental factors prevail over dispersal constraints in determining the distribution and assembly of Trichoptera species in mountain lakes

PubMed Central

de Mendoza, Guillermo; Ventura, Marc; Catalan, Jordi

2015-01-01

Aiming to elucidate whether large-scale dispersal factors or environmental species sorting prevail in determining patterns of Trichoptera species composition in mountain lakes, we analyzed the distribution and assembly of the most common Trichoptera (Plectrocnemia laetabilis, Polycentropus flavomaculatus, Drusus rectus, Annitella pyrenaea, and Mystacides azurea) in the mountain lakes of the Pyrenees (Spain, France, Andorra) based on a survey of 82 lakes covering the geographical and environmental extremes of the lake district. Spatial autocorrelation in species composition was determined using Moran’s eigenvector maps (MEM). Redundancy analysis (RDA) was applied to explore the influence of MEM variables and in-lake, and catchment environmental variables on Trichoptera assemblages. Variance partitioning analysis (partial RDA) revealed the fraction of species composition variation that could be attributed uniquely to either environmental variability or MEM variables. Finally, the distribution of individual species was analyzed in relation to specific environmental factors using binomial generalized linear models (GLM). Trichoptera assemblages showed spatial structure. However, the most relevant environmental variables in the RDA (i.e., temperature and woody vegetation in-lake catchments) were also related with spatial variables (i.e., altitude and longitude). Partial RDA revealed that the fraction of variation in species composition that was uniquely explained by environmental variability was larger than that uniquely explained by MEM variables. GLM results showed that the distribution of species with longitudinal bias is related to specific environmental factors with geographical trend. The environmental dependence found agrees with the particular traits of each species. We conclude that Trichoptera species distribution and composition in the lakes of the Pyrenees are governed predominantly by local environmental factors, rather than by dispersal constraints. For boreal lakes, with similar environmental conditions, a strong role of dispersal capacity has been suggested. Further investigation should address the role of spatial scaling, namely absolute geographical distances constraining dispersal and steepness of environmental gradients at short distances. PMID:26257867

Modeling the Spatial and Temporal Variation of Monthly and Seasonal Precipitation on the Nevada Test Site and Vicinity, 1960-2006

USGS Publications Warehouse

Blainey, Joan B.; Webb, Robert H.; Magirl, Christopher S.

2007-01-01

The Nevada Test Site (NTS), located in the climatic transition zone between the Mojave and Great Basin Deserts, has a network of precipitation gages that is unusually dense for this region. This network measures monthly and seasonal variation in a landscape with diverse topography. Precipitation data from 125 climate stations on or near the NTS were used to spatially interpolate precipitation for each month during the period of 1960 through 2006 at high spatial resolution (30 m). The data were collected at climate stations using manual and/or automated techniques. The spatial interpolation method, applied to monthly accumulations of precipitation, is based on a distance-weighted multivariate regression between the amount of precipitation and the station location and elevation. This report summarizes the temporal and spatial characteristics of the available precipitation records for the period 1960 to 2006, examines the temporal and spatial variability of precipitation during the period of record, and discusses some extremes in seasonal precipitation on the NTS.

Numerical modeling of macrodispersion in heterogeneous media: a comparison of multi-Gaussian and non-multi-Gaussian models

NASA Astrophysics Data System (ADS)

Wen, Xian-Huan; Gómez-Hernández, J. Jaime

1998-03-01

The macrodispersion of an inert solute in a 2-D heterogeneous porous media is estimated numerically in a series of fields of varying heterogeneity. Four different random function (RF) models are used to model log-transmissivity (ln T) spatial variability, and for each of these models, ln T variance is varied from 0.1 to 2.0. The four RF models share the same univariate Gaussian histogram and the same isotropic covariance, but differ from one another in terms of the spatial connectivity patterns at extreme transmissivity values. More specifically, model A is a multivariate Gaussian model for which, by definition, extreme values (both high and low) are spatially uncorrelated. The other three models are non-multi-Gaussian: model B with high connectivity of high extreme values, model C with high connectivity of low extreme values, and model D with high connectivities of both high and low extreme values. Residence time distributions (RTDs) and macrodispersivities (longitudinal and transverse) are computed on ln T fields corresponding to the different RF models, for two different flow directions and at several scales. They are compared with each other, as well as with predicted values based on first-order analytical results. Numerically derived RTDs and macrodispersivities for the multi-Gaussian model are in good agreement with analytically derived values using first-order theories for log-transmissivity variance up to 2.0. The results from the non-multi-Gaussian models differ from each other and deviate largely from the multi-Gaussian results even when ln T variance is small. RTDs in non-multi-Gaussian realizations with high connectivity at high extreme values display earlier breakthrough than in multi-Gaussian realizations, whereas later breakthrough and longer tails are observed for RTDs from non-multi-Gaussian realizations with high connectivity at low extreme values. Longitudinal macrodispersivities in the non-multi-Gaussian realizations are, in general, larger than in the multi-Gaussian ones, while transverse macrodispersivities in the non-multi-Gaussian realizations can be larger or smaller than in the multi-Gaussian ones depending on the type of connectivity at extreme values. Comparing the numerical results for different flow directions, it is confirmed that macrodispersivities in multi-Gaussian realizations with isotropic spatial correlation are not flow direction-dependent. Macrodispersivities in the non-multi-Gaussian realizations, however, are flow direction-dependent although the covariance of ln T is isotropic (the same for all four models). It is important to account for high connectivities at extreme transmissivity values, a likely situation in some geological formations. Some of the discrepancies between first-order-based analytical results and field-scale tracer test data may be due to the existence of highly connected paths of extreme conductivity values.

Analysis of the dependence of extreme rainfalls

NASA Astrophysics Data System (ADS)

Padoan, Simone; Ancey, Christophe; Parlange, Marc

2010-05-01

The aim of spatial analysis is to quantitatively describe the behavior of environmental phenomena such as precipitation levels, wind speed or daily temperatures. A number of generic approaches to spatial modeling have been developed[1], but these are not necessarily ideal for handling extremal aspects given their focus on mean process levels. The areal modelling of the extremes of a natural process observed at points in space is important in environmental statistics; for example, understanding extremal spatial rainfall is crucial in flood protection. In light of recent concerns over climate change, the use of robust mathematical and statistical methods for such analyses has grown in importance. Multivariate extreme value models and the class of maxstable processes [2] have a similar asymptotic motivation to the univariate Generalized Extreme Value (GEV) distribution , but providing a general approach to modeling extreme processes incorporating temporal or spatial dependence. Statistical methods for max-stable processes and data analyses of practical problems are discussed by [3] and [4]. This work illustrates methods to the statistical modelling of spatial extremes and gives examples of their use by means of a real extremal data analysis of Switzerland precipitation levels. [1] Cressie, N. A. C. (1993). Statistics for Spatial Data. Wiley, New York. [2] de Haan, L and Ferreria A. (2006). Extreme Value Theory An Introduction. Springer, USA. [3] Padoan, S. A., Ribatet, M and Sisson, S. A. (2009). Likelihood-Based Inference for Max-Stable Processes. Journal of the American Statistical Association, Theory & Methods. In press. [4] Davison, A. C. and Gholamrezaee, M. (2009), Geostatistics of extremes. Journal of the Royal Statistical Society, Series B. To appear.

MPATHav: A software prototype for multiobjective routing in transportation risk assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ganter, J.H.; Smith, J.D.

Most routing problems depend on several important variables: transport distance, population exposure, accident rate, mandated roads (e.g., HM-164 regulations), and proximity to emergency response resources are typical. These variables may need to be minimized or maximized, and often are weighted. `Objectives` to be satisfied by the analysis are thus created. The resulting problems can be approached by combining spatial analysis techniques from geographic information systems (GIS) with multiobjective analysis techniques from the field of operations research (OR); we call this hybrid multiobjective spatial analysis` (MOSA). MOSA can be used to discover, display, and compare a range of solutions that satisfymore » a set of objectives to varying degrees. For instance, a suite of solutions may include: one solution that provides short transport distances, but at a cost of high exposure; another solution that provides low exposure, but long distances; and a range of solutions between these two extremes.« less

Hybrid stochastic and deterministic simulations of calcium blips.

PubMed

Rüdiger, S; Shuai, J W; Huisinga, W; Nagaiah, C; Warnecke, G; Parker, I; Falcke, M

2007-09-15

Intracellular calcium release is a prime example for the role of stochastic effects in cellular systems. Recent models consist of deterministic reaction-diffusion equations coupled to stochastic transitions of calcium channels. The resulting dynamics is of multiple time and spatial scales, which complicates far-reaching computer simulations. In this article, we introduce a novel hybrid scheme that is especially tailored to accurately trace events with essential stochastic variations, while deterministic concentration variables are efficiently and accurately traced at the same time. We use finite elements to efficiently resolve the extreme spatial gradients of concentration variables close to a channel. We describe the algorithmic approach and we demonstrate its efficiency compared to conventional methods. Our single-channel model matches experimental data and results in intriguing dynamics if calcium is used as charge carrier. Random openings of the channel accumulate in bursts of calcium blips that may be central for the understanding of cellular calcium dynamics.

Evaluation of the sensitivity of the Amazonian diurnal cycle to convective intensity in reanalyses

NASA Astrophysics Data System (ADS)

Itterly, Kyle F.; Taylor, Patrick C.

2017-02-01

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics—specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Evaluation of the Sensitivity of the Amazonian Diurnal Cycle to Convective Intensity in Reanalyses

NASA Technical Reports Server (NTRS)

Itterly, Kyle F.; Taylor, Patrick C.

2016-01-01

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics-specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Climate Change and Conservation Planning in California: The San Francisco Bay Area Upland Habitat Goals Approach

NASA Astrophysics Data System (ADS)

Branciforte, R.; Weiss, S. B.; Schaefer, N.

2008-12-01

Climate change threatens California's vast and unique biodiversity. The Bay Area Upland Habitat Goals is a comprehensive regional biodiversity assessment of the 9 counties surrounding San Francisco Bay, and is designing conservation land networks that will serve to protect, manage, and restore that biodiversity. Conservation goals for vegetation, rare plants, mammals, birds, fish, amphibians, reptiles, and invertebrates are set, and those goals are met using the optimization algorithm MARXAN. Climate change issues are being considered in the assessment and network design in several ways. The high spatial variability at mesoclimatic and topoclimatic scales in California creates high local biodiversity, and provides some degree of local resiliency to macroclimatic change. Mesoclimatic variability from 800 m scale PRISM climatic norms is used to assess "mesoclimate spaces" in distinct mountain ranges, so that high mesoclimatic variability, especially local extremes that likely support range limits of species and potential climatic refugia, can be captured in the network. Quantitative measures of network resiliency to climate change include the spatial range of key temperature and precipitation variables within planning units. Topoclimatic variability provides a finer-grained spatial patterning. Downscaling to the topoclimatic scale (10-50 m scale) includes modeling solar radiation across DEMs for predicting maximum temperature differentials, and topographic position indices for modeling minimum temperature differentials. PRISM data are also used to differentiate grasslands into distinct warm and cool types. The overall conservation strategy includes local and regional connectivity so that range shifts can be accommodated.

Shock drive capabilities of a 30-Joule laser at the matter in extreme conditions hutch of the Linac Coherent Light Source

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Shaughnessy Brennan; Hashim, Akel; Gleason, Arianna

In this paper, we measure the shock drive capabilities of a 30 J, nanosecond, 527 nm laser system at the matter in extreme conditions hutch of the Linac Coherent Light Source. Using a velocity interferometer system for any reflector, we ascertain the maximum instantaneous ablation pressure and characterize its dependence on a drive laser spot size, spatial profile, and temporal profile. We also examine the effects of these parameters on shock spatial and temporal uniformity. Our analysis shows the drive laser capable of generating instantaneous ablation pressures exceeding 160 GPa while maintaining a 1D shock profile. We find that slopemore » pulses provide higher instantaneous ablation pressures than plateau pulses. Our results show instantaneous ablation pressures comparable to those measured at the Omega Laser Facility in Rochester, NY under similar optical drive parameters. In conclusion, we analyze how optical laser ablation pressures are compare with known scaling relations, accounting for variable laser wavelengths.« less

Shock drive capabilities of a 30-Joule laser at the matter in extreme conditions hutch of the Linac Coherent Light Source

DOE PAGES

Brown, Shaughnessy Brennan; Hashim, Akel; Gleason, Arianna; ...

2017-10-23

In this paper, we measure the shock drive capabilities of a 30 J, nanosecond, 527 nm laser system at the matter in extreme conditions hutch of the Linac Coherent Light Source. Using a velocity interferometer system for any reflector, we ascertain the maximum instantaneous ablation pressure and characterize its dependence on a drive laser spot size, spatial profile, and temporal profile. We also examine the effects of these parameters on shock spatial and temporal uniformity. Our analysis shows the drive laser capable of generating instantaneous ablation pressures exceeding 160 GPa while maintaining a 1D shock profile. We find that slopemore » pulses provide higher instantaneous ablation pressures than plateau pulses. Our results show instantaneous ablation pressures comparable to those measured at the Omega Laser Facility in Rochester, NY under similar optical drive parameters. In conclusion, we analyze how optical laser ablation pressures are compare with known scaling relations, accounting for variable laser wavelengths.« less

Comparison of spatial interpolation of rainfall with emphasis on extreme events

NASA Astrophysics Data System (ADS)

Amin, Kanwal; Duan, Zheng; Disse, Markus

2017-04-01

The sparse network of rain-gauges has always motivated the scientists to find more robust ways to include the spatial variability of precipitation. Turning Bands Simulation, External Drift Kriging, Copula and Random Mixing are amongst one of them. Remote sensing Technologies i.e., radar and satellite estimations are widely known to provide a spatial profile of the precipitation, however during extreme events the accuracy of the resulted areal precipitation is still under discussion. The aim is to compare the areal hourly precipitation results of a flood event from RADOLAN (Radar online adjustment) with the gridded rainfall obtained via Turning Bands Simulation (TBM) and Inverse Distance Weighting (IDW) method. The comparison is mainly focused on performing the uncertainty analysis of the areal precipitation through the said simulation and remote sensing technique for the Upper Main Catchment. The comparison of the results obtained from TBM, IDW and RADOLAN show considerably similar results near the rain gauge stations, but the degree of ambiguity elevates with the increasing distance from the gauge stations. Future research will be carried out to compare the forecasted gridded precipitation simulations with the real-time rainfall forecast system (RADVOR) to make the flood evacuation process more robust and efficient.

Seasonality of semi-arid and savanna-type ecosystems in an Earth system model

NASA Astrophysics Data System (ADS)

Dahlin, K.; Swenson, S. C.; Lombardozzi, D.; Kamoske, A.

2016-12-01

Recent work has identified semi-arid and savanna-type (SAST) ecosystems as a critical component of interannual variability in the Earth system (Poulter et al. 2014, Ahlström et al. 2015), yet our understanding of the spatial and temporal patterns present in these systems remains limited. There are three major factors that contribute to the complex behavior of SAST ecosystems, globally. First is leaf phenology, the timing of the appearance, presence, and senescence of plant leaves. Plants grow and drop their leaves in response to a variety of cues, including soil moisture, rainfall, day length, and relative humidity, and alternative phenological strategies might often co-exist in the same location. The second major factor in savannas is soil moisture. The complex nature of soil behavior under extremely dry, then extremely wet conditions is critical to our understanding of how savannas function. The third factor is fire. Globally, virtually all savanna-type ecosystems operate with some non-zero fire return interval. Here we compare model output from the Community Land Model (CLM5-BGC) in SAST regions to remotely sensed data on these three variables - phenology (MODIS LAI), soil moisture (SMAP), and fire (GFED4) - assessing both annual spatial patterns and intra-annual variability, which is critical in these highly variable systems. We present new SAST-specific first- and second-order benchmarks, including numbers of annual LAI peaks (often >1 in SAST systems) and correlations between soil moisture, LAI, and fire. Developing a better understanding of how plants respond to seasonal patterns is a critical first step in understanding how SAST ecosystems will respond to and influence climate under future scenarios.

Attribution of the 1995 and 2006 storm surge events in the southern Baltic Sea

NASA Astrophysics Data System (ADS)

Klehmet, K.; Rockel, B.; von Storch, H.

2016-12-01

In November 1995 and 2006, the German Baltic Sea coast experienced severe storm surge conditions. Exceptional water level heights of about 1.8m above mean sea level were measured at German tide gauges. Extreme event attribution poses unique challenges trying to distinguish the role of anthropogenic influence, as e.g. greenhouse gas emissions or land-use changes, from natural variability. This study, which is part of the EUCLEIA project (EUropean CLimate and weather Events: Interpretation and Attribution, www. eucleia.eu), aims to estimate how the contribution of anthropogenic drivers has altered the probability of single extreme events such as the 1995 and 2006 storm surge events. We explore these aspects using two 7-member ensembles of Hadley Centre Global Environmental Model version 3-A (HadGEM3-A), the atmosphere only component of the HadGEM3, provided by the Met Office Hadley Centre. The ensemble of HadGEM3-A consists of two multi-decadal experiments from 1960-2013 - one with anthropogenic forcing factors and natural forcings representing the actual climate. The second experiment represents the natural climate including only natural forcing factors. These two 7-member ensembles of about 60km spatial resolution are used as atmospheric forcing data to drive the regional ocean model TRIM-NP in order to calculate water level in the Baltic Sea in 12.8km spatial resolution. Findings indicate some limitations of the regional model ensemble to reproduce the magnitude of extreme water levels well. It is tested whether increased spatial resolution of atmospheric forcing fields can improve the representation of Baltic Sea extreme water levels along the coast and thus add value in the attribution analysis.

Detection of Historical and Future Precipitation Variations and Extremes Over the Continental United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anderson, Bruce T.

2015-12-11

Problem: The overall goal of this proposal is to detect observed seasonal-mean precipitation variations and extreme event occurrences over the United States. Detection, e.g. the process of demonstrating that an observed change in climate is unusual, first requires some means of estimating the range of internal variability absent any external drivers. Ideally, the internal variability would be derived from the observations themselves, however generally the observed variability is a confluence of both internal variability and variability in response to external drivers. Further, numerical climate models—the standard tool for detection studies—have their own estimates of intrinsic variability, which may differ substantiallymore » from that found in the observed system as well as other model systems. These problems are further compounded for weather and climate extremes, which as singular events are particularly ill-suited for detection studies because of their infrequent occurrence, limited spatial range, and underestimation within global and even regional numerical models. Rationale: As a basis for this research we will show how stochastic daily-precipitation models—models in which the simulated interannual-to-multidecadal precipitation variance is purely the result of the random evolution of daily precipitation events within a given time period—can be used to address many of these issues simultaneously. Through the novel application of these well-established models, we can first estimate the changes/trends in various means and extremes that can occur even with fixed daily-precipitation characteristics, e.g. that can occur simply as a result of the stochastic evolution of daily weather events within a given climate. Detection of a change in the observed climate—either naturally or anthropogenically forced—can then be defined as any change relative to this stochastic variability, e.g. as changes/trends in the means and extremes that could only have occurred through a change in the underlying climate. As such, this method is capable of detecting “hot spot” regions—as well as “flare ups” within the hot spot regions—that have experienced interannual to multi-decadal scale variations and trends in seasonal-mean precipitation and extreme events. Further by applying the same methods to numerical climate models we can discern the fidelity of the current-generation climate models in representing detectability within the observed climate system. In this way, we can objectively determine the utility of these model systems for performing detection studies of historical and future climate change.« less

Nonparametric Bayesian models through probit stick-breaking processes

PubMed Central

Rodríguez, Abel; Dunson, David B.

2013-01-01

We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology. PMID:24358072

Nonparametric Bayesian models through probit stick-breaking processes.

PubMed

Rodríguez, Abel; Dunson, David B

2011-03-01

We describe a novel class of Bayesian nonparametric priors based on stick-breaking constructions where the weights of the process are constructed as probit transformations of normal random variables. We show that these priors are extremely flexible, allowing us to generate a great variety of models while preserving computational simplicity. Particular emphasis is placed on the construction of rich temporal and spatial processes, which are applied to two problems in finance and ecology.

Effects Of Spatial Variability In Marshes On Coastal Erosion Under Storm Conditions

NASA Astrophysics Data System (ADS)

Lunghino, B.; Suckale, J.; Fringer, O. B.; Maldonado, S.; Ferreira, C.; Marras, S.; Mandel, T.

2016-12-01

To quantify the contribution of marshes in protecting coastlines, engineers and planners need to evaluate how variability in marsh characteristics and storm conditions affect erosion in the inundation zone. Previous studies show that spatial patterns in marshes significantly affect flow and sediment transport under normal tidal conditions [1, 2]. This study investigates the effect of spatial variability on floodplain sediment transport under a range of extreme hydrodynamic conditions that occur during storm events. We model the hydrodynamics of storm surge conditions on an idealized coastal floodplain by solving the 2D shallow water equations. We approximate the effect of vegetation on hydrodynamics as a constant drag coefficient. The model calculates suspended sediment transport with the advection-diffusion equation and updates morphology with erosional and depositional fluxes. We conduct numerical experiments in which we vary both the scale of the storm event and the spatial patterns of vegetation and evaluate the impact on erosion and deposition on the floodplain. We find that the alongshore extent of the vegetation is the primary control on the net volume of sediment eroded. Scour occurs in narrow channels between vegetated areas, but this does not significantly alter the net volume of sediment transported. Deposition occurs in vegetated areas under the full range of flow velocities we test. These results suggest that resolving all variability in vegetation is not necessary to quantify net sediment transport volumes at the floodplain scale. Increasing the scale of the storm event does not alter the role of spatial variability. References [1] Meire, D. W., Kondziolka, J. M., and Nepf, H. M. Interaction between neighboring vegetation patches: Impact on flow and deposition. Water Resources Research 50, 5 (2014), 3809-3825. [2] Temmerman, S., Bouma, T., Govers, G., Wang, Z., De Vries, M., and Her- man, P. Impact of vegetation on flow routing and sedimentation patterns: Three-dimensional modeling for a tidal marsh. Journal of Geophysical Research: Earth Surface 110, F4 (2005).

Spatial-temporal analysis of climate variations in mid-17th through 19th centuries in East China and the possible relationships with Monsoon climate

NASA Astrophysics Data System (ADS)

Lin, K. H. E.; Wang, P. K.; Liao, Y. C.; Lee, S. Y.; Tan, P.

2016-12-01

IPCC AR5 has revealed more frequent extreme climate events and higher climate variability in the near future. Regardless of all the improvements, East Asia monsoon climate is still less understood and/or poorly projected due partly to insufficient records. Most areas of the Asian region lack sufficient observational records to draw conclusions about trends in annual precipitation over the past century (i.e. WGIAR5 Chapter 2). Precipitation trends, including extremes, are characterized by strong variability, with both increasing and decreasing observed in different parts and seasons of Asia. Understanding the variations of the monsoon climate in historical time may bring significant insights to reveal its spatial and temporal patterns embedded in the atmospheric dynamics at different decadal or centennial scales. This study presents some preliminary research results of high resolution climate reconstruction, in both time and space coverage, in east China, by using RCEC historical climate dataset that is developed under interdisciplinary collaboration led by Research Center for Environmental Changes at Academia Sinica, Taiwan. The present research results are derived from chronological meteorological records in the RCEC dataset in Qing dynasty labeling mid-17th to 19th centuries. In total, the dataset comprises more than 1,300 cities/counties in China that has had more than sixty thousands meteorological records in the period. The analysis comprises three parts. Firstly, the frequency of extreme temperature, precipitation, drought, and flood in every recorded cities/counties were computed to depicting climate variabilities in northeast, central-east and southeast China. Secondly, the multivariate regression model was conducted to estimate the coefficients among the climatic index (temperature, precipitation, and drought). It is found that the temperature and wet-dry characteristics have great seasonal and yearly variations; northeast China compared with central-east or southeast tends to have higher variability. Thirdly, those data was used to conduct empirical orthogonal function (EOF) analysis to decompose possible mechanisms that might have cause changes in East Asia monsoon regime during the time period. The reconstructed data were also compared against paleoclimate simulation.

A hydro-meteorological model chain to assess the influence of natural variability and impacts of climate change on extreme events and propose optimal water management

NASA Astrophysics Data System (ADS)

von Trentini, F.; Willkofer, F.; Wood, R. R.; Schmid, F. J.; Ludwig, R.

2017-12-01

The ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) focuses on the effects of climate change on hydro-meteorological extreme events and their implications for water management in Bavaria and Québec. Therefore, a hydro-meteorological model chain is applied. It employs high performance computing capacity of the Leibniz Supercomputing Centre facility SuperMUC to dynamically downscale 50 members of the Global Circulation Model CanESM2 over European and Eastern North American domains using the Canadian Regional Climate Model (RCM) CRCM5. Over Europe, the unique single model ensemble is conjointly analyzed with the latest information provided through the CORDEX-initiative, to better assess the influence of natural climate variability and climatic change in the dynamics of extreme events. Furthermore, these 50 members of a single RCM will enhance extreme value statistics (extreme return periods) by exploiting the available 1500 model years for the reference period from 1981 to 2010. Hence, the RCM output is applied to drive the process based, fully distributed, and deterministic hydrological model WaSiM in high temporal (3h) and spatial (500m) resolution. WaSiM and the large ensemble are further used to derive a variety of hydro-meteorological patterns leading to severe flood events. A tool for virtual perfect prediction shall provide a combination of optimal lead time and management strategy to mitigate certain flood events following these patterns.

Attribution of extreme precipitation in the lower reaches of the Yangtze River during May 2016

NASA Astrophysics Data System (ADS)

Li, Chunxiang; Tian, Qinhua; Yu, Rong; Zhou, Baiquan; Xia, Jiangjiang; Burke, Claire; Dong, Buwen; Tett, Simon F. B.; Freychet, Nicolas; Lott, Fraser; Ciavarella, Andrew

2018-01-01

May 2016 was the third wettest May on record since 1961 over central eastern China based on station observations, with total monthly rainfall 40% more than the climatological mean for 1961-2013. Accompanying disasters such as waterlogging, landslides and debris flow struck part of the lower reaches of the Yangtze River. Causal influence of anthropogenic forcings on this event is investigated using the newly updated Met Office Hadley Centre system for attribution of extreme weather and climate events. Results indicate that there is a significant increase in May 2016 rainfall in model simulations relative to the climatological period, but this increase is largely attributable to natural variability. El Niño years have been found to be correlated with extreme rainfall in the Yangtze River region in previous studies—the strong El Niño of 2015-2016 may account for the extreme precipitation event in 2016. However, on smaller spatial scales we find that anthropogenic forcing has likely played a role in increasing the risk of extreme rainfall to the north of the Yangtze and decreasing it to the south.

Extreme weather and climate events with ecological relevance: a review

PubMed Central

Meehl, Gerald A.

2017-01-01

Robust evidence exists that certain extreme weather and climate events, especially daily temperature and precipitation extremes, have changed in regard to intensity and frequency over recent decades. These changes have been linked to human-induced climate change, while the degree to which climate change impacts an individual extreme climate event (ECE) is more difficult to quantify. Rapid progress in event attribution has recently been made through improved understanding of observed and simulated climate variability, methods for event attribution and advances in numerical modelling. Attribution for extreme temperature events is stronger compared with other event types, notably those related to the hydrological cycle. Recent advances in the understanding of ECEs, both in observations and their representation in state-of-the-art climate models, open new opportunities for assessing their effect on human and natural systems. Improved spatial resolution in global climate models and advances in statistical and dynamical downscaling now provide climatic information at appropriate spatial and temporal scales. Together with the continued development of Earth System Models that simulate biogeochemical cycles and interactions with the biosphere at increasing complexity, these make it possible to develop a mechanistic understanding of how ECEs affect biological processes, ecosystem functioning and adaptation capabilities. Limitations in the observational network, both for physical climate system parameters and even more so for long-term ecological monitoring, have hampered progress in understanding bio-physical interactions across a range of scales. New opportunities for assessing how ECEs modulate ecosystem structure and functioning arise from better scientific understanding of ECEs coupled with technological advances in observing systems and instrumentation. This article is part of the themed issue ‘Behavioural, ecological and evolutionary responses to extreme climatic events’. PMID:28483866

Extreme weather and climate events with ecological relevance: a review.

PubMed

Ummenhofer, Caroline C; Meehl, Gerald A

2017-06-19

Robust evidence exists that certain extreme weather and climate events, especially daily temperature and precipitation extremes, have changed in regard to intensity and frequency over recent decades. These changes have been linked to human-induced climate change, while the degree to which climate change impacts an individual extreme climate event (ECE) is more difficult to quantify. Rapid progress in event attribution has recently been made through improved understanding of observed and simulated climate variability, methods for event attribution and advances in numerical modelling. Attribution for extreme temperature events is stronger compared with other event types, notably those related to the hydrological cycle. Recent advances in the understanding of ECEs, both in observations and their representation in state-of-the-art climate models, open new opportunities for assessing their effect on human and natural systems. Improved spatial resolution in global climate models and advances in statistical and dynamical downscaling now provide climatic information at appropriate spatial and temporal scales. Together with the continued development of Earth System Models that simulate biogeochemical cycles and interactions with the biosphere at increasing complexity, these make it possible to develop a mechanistic understanding of how ECEs affect biological processes, ecosystem functioning and adaptation capabilities. Limitations in the observational network, both for physical climate system parameters and even more so for long-term ecological monitoring, have hampered progress in understanding bio-physical interactions across a range of scales. New opportunities for assessing how ECEs modulate ecosystem structure and functioning arise from better scientific understanding of ECEs coupled with technological advances in observing systems and instrumentation.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'. © 2017 The Author(s).

Continuous-variable quantum computing in optical time-frequency modes using quantum memories.

PubMed

Humphreys, Peter C; Kolthammer, W Steven; Nunn, Joshua; Barbieri, Marco; Datta, Animesh; Walmsley, Ian A

2014-09-26

We develop a scheme for time-frequency encoded continuous-variable cluster-state quantum computing using quantum memories. In particular, we propose a method to produce, manipulate, and measure two-dimensional cluster states in a single spatial mode by exploiting the intrinsic time-frequency selectivity of Raman quantum memories. Time-frequency encoding enables the scheme to be extremely compact, requiring a number of memories that are a linear function of only the number of different frequencies in which the computational state is encoded, independent of its temporal duration. We therefore show that quantum memories can be a powerful component for scalable photonic quantum information processing architectures.

On the nonlinearity of spatial scales in extreme weather attribution statements

NASA Astrophysics Data System (ADS)

Angélil, Oliver; Stone, Daíthí; Perkins-Kirkpatrick, Sarah; Alexander, Lisa V.; Wehner, Michael; Shiogama, Hideo; Wolski, Piotr; Ciavarella, Andrew; Christidis, Nikolaos

2018-04-01

In the context of ongoing climate change, extreme weather events are drawing increasing attention from the public and news media. A question often asked is how the likelihood of extremes might have changed by anthropogenic greenhouse-gas emissions. Answers to the question are strongly influenced by the model used, duration, spatial extent, and geographic location of the event—some of these factors often overlooked. Using output from four global climate models, we provide attribution statements characterised by a change in probability of occurrence due to anthropogenic greenhouse-gas emissions, for rainfall and temperature extremes occurring at seven discretised spatial scales and three temporal scales. An understanding of the sensitivity of attribution statements to a range of spatial and temporal scales of extremes allows for the scaling of attribution statements, rendering them relevant to other extremes having similar but non-identical characteristics. This is a procedure simple enough to approximate timely estimates of the anthropogenic contribution to the event probability. Furthermore, since real extremes do not have well-defined physical borders, scaling can help quantify uncertainty around attribution results due to uncertainty around the event definition. Results suggest that the sensitivity of attribution statements to spatial scale is similar across models and that the sensitivity of attribution statements to the model used is often greater than the sensitivity to a doubling or halving of the spatial scale of the event. The use of a range of spatial scales allows us to identify a nonlinear relationship between the spatial scale of the event studied and the attribution statement.

On the nonlinearity of spatial scales in extreme weather attribution statements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Angélil, Oliver; Stone, Daíthí; Perkins-Kirkpatrick, Sarah

In the context of continuing climate change, extreme weather events are drawing increasing attention from the public and news media. A question often asked is how the likelihood of extremes might have changed by anthropogenic greenhouse-gas emissions. Answers to the question are strongly influenced by the model used, duration, spatial extent, and geographic location of the event—some of these factors often overlooked. Using output from four global climate models, we provide attribution statements characterised by a change in probability of occurrence due to anthropogenic greenhouse-gas emissions, for rainfall and temperature extremes occurring at seven discretised spatial scales and three temporalmore » scales. An understanding of the sensitivity of attribution statements to a range of spatial and temporal scales of extremes allows for the scaling of attribution statements, rendering them relevant to other extremes having similar but non-identical characteristics. This is a procedure simple enough to approximate timely estimates of the anthropogenic contribution to the event probability. Furthermore, since real extremes do not have well-defined physical borders, scaling can help quantify uncertainty around attribution results due to uncertainty around the event definition. Results suggest that the sensitivity of attribution statements to spatial scale is similar across models and that the sensitivity of attribution statements to the model used is often greater than the sensitivity to a doubling or halving of the spatial scale of the event. The use of a range of spatial scales allows us to identify a nonlinear relationship between the spatial scale of the event studied and the attribution statement.« less

On the nonlinearity of spatial scales in extreme weather attribution statements

DOE PAGES

Angélil, Oliver; Stone, Daíthí; Perkins-Kirkpatrick, Sarah; ...

2017-06-17

In the context of continuing climate change, extreme weather events are drawing increasing attention from the public and news media. A question often asked is how the likelihood of extremes might have changed by anthropogenic greenhouse-gas emissions. Answers to the question are strongly influenced by the model used, duration, spatial extent, and geographic location of the event—some of these factors often overlooked. Using output from four global climate models, we provide attribution statements characterised by a change in probability of occurrence due to anthropogenic greenhouse-gas emissions, for rainfall and temperature extremes occurring at seven discretised spatial scales and three temporalmore » scales. An understanding of the sensitivity of attribution statements to a range of spatial and temporal scales of extremes allows for the scaling of attribution statements, rendering them relevant to other extremes having similar but non-identical characteristics. This is a procedure simple enough to approximate timely estimates of the anthropogenic contribution to the event probability. Furthermore, since real extremes do not have well-defined physical borders, scaling can help quantify uncertainty around attribution results due to uncertainty around the event definition. Results suggest that the sensitivity of attribution statements to spatial scale is similar across models and that the sensitivity of attribution statements to the model used is often greater than the sensitivity to a doubling or halving of the spatial scale of the event. The use of a range of spatial scales allows us to identify a nonlinear relationship between the spatial scale of the event studied and the attribution statement.« less

500-year April-September droughts in the Czech Lands based on documentary data and instrumental records

NASA Astrophysics Data System (ADS)

Řezníčková, Ladislava; Brázdil, Rudolf; Trnka, Miroslav; Dobrovolný, Petr; Kotyza, Oldřich; Štěpánek, Petr; Zahradníček, Pavel; Valášek, Hubert

2013-04-01

This paper analyses temporal and spatial variability of April-September (the vegetation period) droughts in the Czech Lands over the last 500 years. The study is based on different types of documentary data (e.g. chronicles, newspapers, economic sources, weather diaries) covering the pre-instrumental period AD 1501-1804 and on the systematic instrumental meteorological measurements afterwards. Historical-climatological database of the Czech Lands is used for the study of the duration and intensity of drought episodes based on the series of precipitation indices created from documentary data in a 7-degree scale from -3 (extremely dry) to +3 (extremely wet). For the instrumental period of 1805-2012 Palmer's Z-index and PDSI series for mean Czech temperature and precipitation series are used (they were calculated from homogeneous series of 10 and 14 stations respectively). Consequently the 500-year chronology of drought episodes derived from documentary and instrumental data is compiled and the temporal (frequency, seasonality and intensity) and spatial variability of droughts in the Czech Lands from AD 1501 is analysed. The most outstanding drought events are selected and analysed in detail also with respect to their human impacts. The results obtained for the Czech Lands are compared with drought episodes known in Central Europe from other studies and are evaluated with respect to climate variability in Central Europe during the last 500 years (this research is supported by projects InterDrought no. CZ.1.07/2.3.00/20.0248, and GA CR no. P209/11/0956).

Circulation controls of the spatial structure of maximum daily precipitation over Poland

NASA Astrophysics Data System (ADS)

Stach, Alfred

2015-04-01

Among forecasts made on the basis of global and regional climatic models is one of a high probability of an increase in the frequency and intensity of extreme precipitation events. Learning the regularities underlying the recurrence and spatial extent of extreme precipitation is obviously of great importance, both economic and social. The main goal of the study was to analyse regularities underlying spatial and temporal variations in monthly Maximum Daily Precipitation Totals (MDPTs) observed in Poland over the years 1956-1980. These data are specific because apart from being spatially discontinuous, which is typical of precipitation, they are also non-synchronic. The main aim of the study was accomplished via several detailed goals: • identification and typology of the spatial structure of monthly MDPTs, • determination of the character and probable origin of events generating MDPTs, and • quantitative assessment of the contribution of the particular events to the overall MDPT figures. The analysis of the spatial structure of MDPTs was based on 300 models of spatial structure, one for each of the analysed sets of monthly MDPTs. The models were built on the basis of empirical anisotropic semivariograms of normalised data. In spite of their spatial discontinuity and asynchronicity, the MDPT data from Poland display marked regularities in their spatial pattern that yield readily to mathematical modelling. The MDPT field in Poland is usually the sum of the outcomes of three types of processes operating at various spatial scales: local (<10-20 km), regional (50-150 km), and supra-regional (>200 km). The spatial scales are probably connected with a convective/ orographic, a frontal and a 'planetary waves' genesis of high precipitation. Their contributions are highly variable. Generally predominant, however, are high daily precipitation totals with a spatial extent of 50 to 150 km connected with mesoscale phenomena and the migration of atmospheric fronts (35-38%). The spatial extent of areas of high local-scale precipitation usually varies at random, especially in the warm season. At supra-local scales, structures of repetitive size predominate. Eight types of anisotropic structures of monthly MDPTs were distinguished. To identify them, an analysis was made of semivariance surface similarities. The types differ not only in the level and direction of anisotropy, but also in the number and type of elementary components, which is evidence of genetic differences in precipitation. Their appearance shows a significant seasonal variability, so the most probable supposition was that temporal variations in the MDPT pattern were connected with circulation conditions: the type and direction of inflow of air masses. This hypothesis was validated by testing differences in the frequency of occurrence of Grosswetterlagen circulation situations in the months belonging to the distinguished types of the spatial MDPT pattern.

Can animal habitat use patterns influence their vulnerability to extreme climate events? An estuarine sportfish case study.

PubMed

Boucek, Ross E; Heithaus, Michael R; Santos, Rolando; Stevens, Philip; Rehage, Jennifer S

2017-10-01

Global climate forecasts predict changes in the frequency and intensity of extreme climate events (ECEs). The capacity for specific habitat patches within a landscape to modulate stressors from extreme climate events, and animal distribution throughout habitat matrices during events, could influence the degree of population level effects following the passage of ECEs. Here, we ask (i) does the intensity of stressors of an ECE vary across a landscape? And (ii) Do habitat use patterns of a mobile species influence their vulnerability to ECEs? Specifically, we measured how extreme cold spells might interact with temporal variability in habitat use to affect populations of a tropical, estuarine-dependent large-bodied fish Common Snook, within Everglades National Park estuaries (FL US). We examined temperature variation across the estuary during cold disturbances with different degrees of severity, including an extreme cold spell. Second, we quantified Snook distribution patterns when the passage of ECEs is most likely to occur from 2012 to 2016 using passive acoustic tracking. Our results revealed spatial heterogeneity in the intensity of temperature declines during cold disturbances, with some habitats being consistently 3-5°C colder than others. Surprisingly, Snook distributions during periods of greatest risk to experience an extreme cold event varied among years. During the winters of 2013-2014 and 2014-2015 a greater proportion of Snook occurred in the colder habitats, while the winters of 2012-2013 and 2015-2016 featured more Snook observed in the warmest habitats. This study shows that Snook habitat use patterns could influence vulnerability to extreme cold events, however, whether Snook habitat use increases or decreases their vulnerability to disturbance depends on the year, creating temporally dynamic vulnerability. Faunal global change research should address the spatially explicit nature of extreme climate events and animal habitat use patterns to identify potential mechanisms that may influence population effects following these disturbances. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Towards a unified study of extreme events using universality concepts and transdisciplinary analysis methods

NASA Astrophysics Data System (ADS)

Balasis, George; Donner, Reik V.; Donges, Jonathan F.; Radebach, Alexander; Eftaxias, Konstantinos; Kurths, Jürgen

2013-04-01

The dynamics of many complex systems is characterized by the same universal principles. In particular, systems which are otherwise quite different in nature show striking similarities in their behavior near tipping points (bifurcations, phase transitions, sudden regime shifts) and associated extreme events. Such critical phenomena are frequently found in diverse fields such as climate, seismology, or financial markets. Notably, the observed similarities include a high degree of organization, persistent behavior, and accelerated energy release, which are common to (among others) phenomena related to geomagnetic variability of the terrestrial magnetosphere (intense magnetic storms), seismic activity (electromagnetic emissions prior to earthquakes), solar-terrestrial physics (solar flares), neurophysiology (epileptic seizures), and socioeconomic systems (stock market crashes). It is an open question whether the spatial and temporal complexity associated with extreme events arises from the system's structural organization (geometry) or from the chaotic behavior inherent to the nonlinear equations governing the dynamics of these phenomena. On the one hand, the presence of scaling laws associated with earthquakes and geomagnetic disturbances suggests understanding these events as generalized phase transitions similar to nucleation and critical phenomena in thermal and magnetic systems. On the other hand, because of the structural organization of the systems (e.g., as complex networks) the associated spatial geometry and/or topology of interactions plays a fundamental role in the emergence of extreme events. Here, a few aspects of the interplay between geometry and dynamics (critical phase transitions) that could result in the emergence of extreme events, which is an open problem, will be discussed.

Spatial dependence of extreme rainfall

NASA Astrophysics Data System (ADS)

Radi, Noor Fadhilah Ahmad; Zakaria, Roslinazairimah; Satari, Siti Zanariah; Azman, Muhammad Az-zuhri

2017-05-01

This study aims to model the spatial extreme daily rainfall process using the max-stable model. The max-stable model is used to capture the dependence structure of spatial properties of extreme rainfall. Three models from max-stable are considered namely Smith, Schlather and Brown-Resnick models. The methods are applied on 12 selected rainfall stations in Kelantan, Malaysia. Most of the extreme rainfall data occur during wet season from October to December of 1971 to 2012. This period is chosen to assure the available data is enough to satisfy the assumption of stationarity. The dependence parameters including the range and smoothness, are estimated using composite likelihood approach. Then, the bootstrap approach is applied to generate synthetic extreme rainfall data for all models using the estimated dependence parameters. The goodness of fit between the observed extreme rainfall and the synthetic data is assessed using the composite likelihood information criterion (CLIC). Results show that Schlather model is the best followed by Brown-Resnick and Smith models based on the smallest CLIC's value. Thus, the max-stable model is suitable to be used to model extreme rainfall in Kelantan. The study on spatial dependence in extreme rainfall modelling is important to reduce the uncertainties of the point estimates for the tail index. If the spatial dependency is estimated individually, the uncertainties will be large. Furthermore, in the case of joint return level is of interest, taking into accounts the spatial dependence properties will improve the estimation process.

ClimEx - Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec

NASA Astrophysics Data System (ADS)

Ludwig, Ralf; Baese, Frank; Braun, Marco; Brietzke, Gilbert; Brissette, Francois; Frigon, Anne; Giguère, Michel; Komischke, Holger; Kranzlmueller, Dieter; Leduc, Martin; Martel, Jean-Luc; Ricard, Simon; Schmid, Josef; von Trentini, Fabian; Turcotte, Richard; Weismueller, Jens; Willkofer, Florian; Wood, Raul

2017-04-01

The recent accumulation of extreme hydrological events in Bavaria and Québec has stimulated scientific and also societal interest. In addition to the challenges of an improved prediction of such situations and the implications for the associated risk management, there is, as yet, no confirmed knowledge whether and how climate change contributes to the magnitude and frequency of hydrological extreme events and how regional water management could adapt to the corresponding risks. The ClimEx project (2015-2019) investigates the effects of climate change on the meteorological and hydrological extreme events and their implications for water management in Bavaria and Québec. High Performance Computing is employed to enable the complex simulations in a hydro-climatological model processing chain, resulting in a unique high-resolution and transient (1950-2100) dataset of climatological and meteorological forcing and hydrological response: (1) The climate module has developed a large ensemble of high resolution data (12km) of the CRCM5 RCM for Central Europe and North-Eastern North America, downscaled from 50 members of the CanESM2 GCM. The dataset is complemented by all available data from the Euro-CORDEX project to account for the assessment of both natural climate variability and climate change. The large ensemble with several thousand model years provides the potential to catch rare extreme events and thus improves the process understanding of extreme events with return periods of 1000+ years. (2) The hydrology module comprises process-based and spatially explicit model setups (e.g. WaSiM) for all major catchments in Bavaria and Southern Québec in high temporal (3h) and spatial (500m) resolution. The simulations form the basis for in depth analysis of hydrological extreme events based on the inputs from the large climate model dataset. The specific data situation enables to establish a new method for 'virtual perfect prediction', which assesses climate change impacts on flood risk and water resources management by identifying patterns in the data which reveal preferential triggers of hydrological extreme events. The presentation will highlight first results from the analysis of the large scale ClimEx model ensemble, showing the current and future ratio of natural variability and climate change impacts on meteorological extreme events. Selected data from the ensemble is used to drive a hydrological model experiment to illustrate the capacity to better determine the recurrence periods of hydrological extreme events under conditions of climate change. [The authors acknowledge funding for the project from the Bavarian State Ministry for the Environment and Consumer Protection].

Climatology of extreme daily precipitation in Colorado and its diverse spatial and seasonal variability

USGS Publications Warehouse

Mahoney, Kelly M.; Ralph, F. Martin; Walter, Klaus; Doesken, Nolan; Dettinger, Michael; Gottas, Daniel; Coleman, Timothy; White, Allen

2015-01-01

The climatology of Colorado’s historical extreme precipitation events shows a remarkable degree of seasonal and regional variability. Analysis of the largest historical daily precipitation totals at COOP stations across Colorado by season indicates that the largest recorded daily precipitation totals have ranged from less than 60 mm day−1 in some areas to more than 250 mm day−1 in others. East of the Continental Divide, winter events are rarely among the top 10 events at a given site, but spring events dominate in and near the foothills; summer events are most common across the lower-elevation eastern plains, while fall events are most typical for the lower elevations west of the Divide. The seasonal signal in Colorado’s central mountains is complex; high-elevation intense precipitation events have occurred in all months of the year, including summer, when precipitation is more likely to be liquid (as opposed to snow), which poses more of an instantaneous flood risk. Notably, the historic Colorado Front Range daily rainfall totals that contributed to the damaging floods in September 2013 occurred outside of that region’s typical season for most extreme precipitation (spring–summer). That event and many others highlight the fact that extreme precipitation in Colorado has occurred historically during all seasons and at all elevations, emphasizing a year-round statewide risk.

Assessment of the uncertainty in future projection for summer climate extremes over the East Asia

NASA Astrophysics Data System (ADS)

Park, Changyong; Min, Seung-Ki; Cha, Dong-Hyun

2017-04-01

Future projections of climate extremes in regional and local scales are essential information needed for better adapting to climate changes. However, future projections hold larger uncertainty factors arising from internal and external processes which reduce the projection confidence. Using CMIP5 (Coupled Model Intercomparison Project Phase 5) multi-model simulations, we assess uncertainties in future projections of the East Asian temperature and precipitation extremes focusing on summer. In examining future projection, summer mean and extreme projections of the East Asian temperature and precipitation would be larger as time. Moreover, uncertainty cascades represent wider scenario difference and inter-model ranges with increasing time. A positive mean-extreme relation is found in projections for both temperature and precipitation. For the assessment of uncertainty factors for these projections, dominant uncertainty factors from temperature and precipitation change as time. For uncertainty of mean and extreme temperature, contributions of internal variability and model uncertainty declines after mid-21st century while role of scenario uncertainty grows rapidly. For uncertainty of mean precipitation projections, internal variability is more important than the scenario uncertainty. Unlike mean precipitation, extreme precipitation shows that the scenario uncertainty is expected to be a dominant factor in 2090s. The model uncertainty holds as an important factor for both mean and extreme precipitation until late 21st century. The spatial changes for the uncertainty factors of mean and extreme projections generally are expressed according to temporal changes of the fraction of total variance from uncertainty factors in many grids of the East Asia. ACKNOWLEDGEMENTS The research was supported by the Korea Meteorological Administration Research and Development program under grant KMIPA 2015-2083 and the National Research Foundation of Korea Grant funded by the Ministry of Science, ICT and Future Planning of Korea (NRF-2016M3C4A7952637) for its support and assistant in completion of the study.

ISM DUST GRAINS AND N-BAND SPECTRAL VARIABILITY IN THE SPATIALLY RESOLVED SUBARCSECOND BINARY UY Aur

DOE Office of Scientific and Technical Information (OSTI.GOV)

Skemer, Andrew J.; Close, Laird M.; Hinz, Philip M.

2010-03-10

The 10 {mu}m silicate feature is an essential diagnostic of dust-grain growth and planet formation in young circumstellar disks. The Spitzer Space Telescope has revolutionized the study of this feature, but due to its small (85 cm) aperture, it cannot spatially resolve small/medium-separation binaries ({approx}<3''; {approx}< 420 AU) at the distances of the nearest star-forming regions ({approx}140 pc). Large, 6-10 m ground-based telescopes with mid-infrared instruments can resolve these systems. In this paper, we spatially resolve the 0.''88 binary, UY Aur, with MMTAO/BLINC-MIRAC4 mid-infrared spectroscopy. We then compare our spectra to Spitzer/IRS (unresolved) spectroscopy, and resolved images from IRTF/MIRAC2, Keck/OSCIR,more » and Gemini/Michelle, which were taken over the past decade. We find that UY Aur A has extremely pristine, interstellar medium (ISM)-like grains and that UY Aur B has an unusually shaped silicate feature, which is probably the result of blended emission and absorption from foreground extinction in its disk. We also find evidence for variability in both UY Aur A and UY Aur B by comparing synthetic photometry from our spectra with resolved imaging from previous epochs. The photometric variability of UY Aur A could be an indication that the silicate emission itself is variable, as was recently found in EX Lupi. Otherwise, the thermal continuum is variable, and either the ISM-like dust has never evolved, or it is being replenished, perhaps by UY Aur's circumbinary disk.« less

Long-term Observations of Intense Precipitation Small-scale Spatial Variability in a Semi-arid Catchment

NASA Astrophysics Data System (ADS)

Cropp, E. L.; Hazenberg, P.; Castro, C. L.; Demaria, E. M.

2017-12-01

In the southwestern US, the summertime North American Monsoon (NAM) provides about 60% of the region's annual precipitation. Recent research using high-resolution atmospheric model simulations and retrospective predictions has shown that since the 1950's, and more specifically in the last few decades, the mean daily precipitation in the southwestern U.S. during the NAM has followed a decreasing trend. Furthermore, days with more extreme precipitation have intensified. The current work focuses the impact of these long-term changes on the observed small-scale spatial variability of intense precipitation. Since limited long-term high-resolution observational data exist to support such climatological-induced spatial changes in precipitation frequency and intensity, the current work utilizes observations from the USDA-ARS Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona. Within this 150 km^2 catchment over 90 rain gauges have been installed since the 1950s, measuring at sub-hourly resolution. We have applied geospatial analyses and the kriging interpolation technique to identify long-term changes in the spatial and temporal correlation and anisotropy of intense precipitation. The observed results will be compared with the previously model simulated results, as well as related to large-scale variations in climate patterns, such as the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO).

Modeling α- and β-diversity in a tropical forest from remotely sensed and spatial data

NASA Astrophysics Data System (ADS)

Hernández-Stefanoni, J. Luis; Gallardo-Cruz, J. Alberto; Meave, Jorge A.; Rocchini, Duccio; Bello-Pineda, Javier; López-Martínez, J. Omar

2012-10-01

Comprehensive information on species distribution and species composition patterns of plant communities is required for effective conservation and management of biodiversity. Remote sensing offers an inexpensive means of attaining complete spatial coverage for large areas, at regular time intervals, and can therefore be extremely useful for estimating both species richness and spatial variation of species composition (α- and β-diversity). An essential step to map such attributes is to identify and understand their main drivers. We used remotely sensed data as a surrogate of plant productivity and habitat structure variables for explaining α- and β-diversity, and evaluated the relative roles of productivity-habitat structure and spatial variables in explaining observed patterns of α- and β-diversity by using a Principal Coordinates of Neighbor Matrices analysis. We also examined the relationship between remotely sensed and field data, in order to map α- and β-diversity at the landscape-level in the Yucatan Peninsula, using a regression kriging procedure. These two procedures integrate the relationship of species richness and spatial species turnover both with remotely sensed data and spatial structure. The empirical models so obtained can be used to predict species richness and variation in species composition, and they can be regarded as valuable tools not only for identifying areas with high local species richness (α-diversity), but also areas with high species turnover (β-diversity). Ultimately, information obtained in this way can help maximize the number of species preserved in a landscape.

A Comparison of Latent Heat Fluxes over Global Oceans for Four Flux Products

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Nelkin, Eric; Ardizzone, Joe; Atlas, Robert M.

2003-01-01

To improve our understanding of global energy and water cycle variability, and to improve model simulations of climate variations, it is vital to have accurate latent heat fluxes (LHF) over global oceans. Monthly LHF, 10-m wind speed (U10m), 10-m specific humidity (Q10h), and sea-air humidity difference (Qs-Q10m) of GSSTF2 (version 2 Goddard Satellite-based Surface Turbulent Fluxes) over global Oceans during 1992-93 are compared with those of HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data), NCEP (NCEP/NCAR reanalysis). The mean differences, standard deviations of differences, and temporal correlation of these monthly variables over global Oceans during 1992-93 between GSSTF2 and each of the three datasets are analyzed. The large-scale patterns of the 2yr-mean fields for these variables are similar among these four datasets, but significant quantitative differences are found. The temporal correlation is higher in the northern extratropics than in the south for all variables, with the contrast being especially large for da Silva as a result of more missing ship data in the south. The da Silva has extremely low temporal correlation and large differences with GSSTF2 for all variables in the southern extratropics, indicating that da Silva hardly produces a realistic variability in these variables. The NCEP has extremely low temporal correlation (0.27) and large spatial variations of differences with GSSTF2 for Qs-Q10m in the tropics, which causes the low correlation for LHF. Over the tropics, the HOAPS LHF is significantly smaller than GSSTF2 by approx. 31% (37 W/sq m), whereas the other two datasets are comparable to GSSTF2. This is because the HOAPS has systematically smaller LHF than GSSTF2 in space, while the other two datasets have very large spatial variations of large positive and negative LHF differences with GSSTF2 to cancel and to produce smaller regional-mean differences. Our analyses suggest that the GSSTF2 latent heat flux, surface air humidity, and winds are likely to be more realistic than the other three flux datasets examined, although those of GSSTF2 are still subject to regional biases.

Permafrost as an additional driving factor for the extreme fire event in the boreal Baikal region in 2003

NASA Astrophysics Data System (ADS)

Forkel, M.; Thonicke, K.; Beer, C.; Cramer, W.; Bartalev, S.; Schmullius, C.

2012-04-01

Wildfires are a natural and important element in the functioning of boreal forests. However, in some years, fires with extreme spread and severity occur. Such severe fires degrade the forest, affect human values, emit huge amount of carbon and aerosols and alter the land surface albedo. Usually, wind, slope, and dry conditions have been recognized as factors determining fire spread. In the Baikal region, 127,000 km2 burned in 2003, while the annual average burned area is approx. 8100 km2. In average years, 16% of the burned area occurred in the continuous permafrost zone but in 2003, 33% of these burned areas coincide with the existence of permanently frozen grounds. Permafrost and the associated upper active layer, which thaws during summer and refreezes during winter, is an important supply for soil moisture in boreal ecosystems. This leads to the question if permafrost hydrology is a potential additional driving factor for extreme fire events in boreal forests. Using temperature and precipitation data, we calculated the Nesterov index as indicator for fire weather conditions. Further, we used satellite observations of burned area and surface moisture, a digital elevation model, a land cover and a permafrost map to evaluate drivers for the temporal dynamic and spatial variability of surface moisture conditions and burned area in spring 2003. On the basis of time series decomposition, we separated the effect of drivers for fire activity on different time scales. We next computed cross-correlations to identify potential time lags between weather conditions, surface moisture and fire activity. Finally, we assessed the predictive capability of different combinations of driving variables for surface moisture conditions and burned area using multivariate spatial-temporal regression models. The results from this study demonstrate that permafrost in larch-dominated ecosystems regulates the inter-annual variability of surface moisture and thus increases the inter-annual variability of burned area. The drought conditions in spring 2003 were accelerated by the presence of permafrost because less water was stored in the upper active layer from the dry previous summer 2002 and the permafrost table prevents vegetative water uptake from deeper layers. In contrast, weather conditions (precipitation anomaly, Nesterov index) are weaker predictors for the 2003 fire event. Our analysis advances the understanding of complex interactions between the atmosphere, vegetation and soil on how feedback mechanisms can lead to extreme fire events. These findings emphasize the importance of a mechanistic coupling of soil thermodynamics, hydrology, and fire activity in earth system models for projecting climate change impacts over the next century.

Drought assessment using a TRMM-derived standardized precipitation index for the upper São Francisco River basin, Brazil.

PubMed

Santos, Celso Augusto Guimarães; Brasil Neto, Reginaldo Moura; Passos, Jacqueline Sobral de Araújo; da Silva, Richarde Marques

2017-06-01

In this work, the use of Tropical Rainfall Measuring Mission (TRMM) rainfall data and the Standardized Precipitation Index (SPI) for monitoring spatial and temporal drought variabilities in the Upper São Francisco River basin is investigated. Thus, the spatiotemporal behavior of droughts and cluster regions with similar behaviors is identified. As a result, the joint analysis of clusters, dendrograms, and the spatial distribution of SPI values proved to be a powerful tool in identifying homogeneous regions. The results showed that the northeast region of the basin has the lowest rainfall indices and the southwest region has the highest rainfall depths, and that the region has well-defined dry and rainy seasons from June to August and November to January, respectively. An analysis of the drought and rain conditions showed that the studied region was homogeneous and well-distributed; however, the quantity of extreme and severe drought events in short-, medium- and long-term analysis was higher than that expected in regions with high rainfall depths, particularly in the south/southwest and southeast areas. Thus, an alternative classification is proposed to characterize the drought, which spatially categorizes the drought type (short-, medium-, and long-term) according to the analyzed drought event type (extreme, severe, moderate, and mild).

Temporal-spatial evolution of the hydrologic drought characteristics of the karst drainage basins in South China

NASA Astrophysics Data System (ADS)

He, Zhonghua; Liang, Hong; Yang, Chaohui; Huang, Fasu; Zeng, Xinbo

2018-02-01

Hydrologic drought, as a typical natural phenomenon in the context of global climate change, is the extension and development of meteorological and agricultural droughts, and it is an eventual and extreme drought. This study selects 55 hydrological control basins in Southern China as research areas. The study analyzes features, such as intensity and occurrence frequency of hydrologic droughts, and explores the spatial-temporal evolution patterns in the karst drainage basins in Southern China by virtue of Streamflow Drought Index. Results show that (1) the general hydrologic droughts from 1970s to 2010s exhibited ;an upward trend after having experienced a previous decline; in the karst drainage basins in Southern China; the trend was mainly represented by the gradual alleviation of hydrologic droughts from 1970s to 1990s and the gradual aggravation from 2000s to 2010s. (2) The spatial-temporal evolution pattern of occurrence frequency in the karst drainage basins in Southern China was consistent with the intensity of hydrologic droughts. The periods of 1970s and 2010s exhibited the highest occurrence frequency. (3) The karst drainage basins in Southern China experienced extremely complex variability of hydrologic droughts from 1970s to 2010s. Drought intensity and occurrence frequency significantly vary for different types of hydrology.

Regional flood-frequency relations for streams with many years of no flow

USGS Publications Warehouse

Hjalmarson, Hjalmar W.; Thomas, Blakemore E.; ,

1990-01-01

In the southwestern United States, flood-frequency relations for streams that drain small arid basins are difficult to estimate, largely because of the extreme temporal and spatial variability of floods and the many years of no flow. A method is proposed that is based on the station-year method. The new method produces regional flood-frequency relations using all available annual peak-discharge data. The prediction errors for the relations are directly assessed using randomly selected subsamples of the annual peak discharges.

"EWS Matrix" and "EWG Matrix": "De-sign for All" tools referred to the development of a enabling communication system for public spaces.

PubMed

Di Bucchianico, Giuseppe; Camplone, Stefania; Picciani, Stefano; Vallese, Valeria

2012-01-01

The widespread sense of spatial disorientation that can be experienced in many public places (buildings and open spaces),generally depends on a design approach that doesn't take into account both the "communication skills" of the different parts of the spatial organization, both the variability of people and their ways of interacting with environments, orienteering themselves. Nevertheless, "not find the way" often has some obvious practical costs (loss of time, failure to achieve a target) and some more intangible, but no less important, emotional costs. That's why the design of signage systems must take into account both the specificities of places and the extreme variability of its users. The paper presents the results of a study on this specific issue. In particular, the study focuses on the description of some tools useful for the analysis and design of a signage system that is truly "for All".

Climate network analysis of regional precipitation extremes: The true story told by event synchronization

NASA Astrophysics Data System (ADS)

Odenweller, Adrian; Donner, Reik V.

2017-04-01

Over the last decade, complex network methods have been frequently used for characterizing spatio-temporal patterns of climate variability from a complex systems perspective, yielding new insights into time-dependent teleconnectivity patterns and couplings between different components of the Earth climate. Among the foremost results reported, network analyses of the synchronicity of extreme events as captured by the so-called event synchronization have been proposed to be powerful tools for disentangling the spatio-temporal organization of particularly extreme rainfall events and anticipating the timing of monsoon onsets or extreme floodings. Rooted in the analysis of spike train synchrony analysis in the neurosciences, event synchronization has the great advantage of automatically classifying pairs of events arising at two distinct spatial locations as temporally close (and, thus, possibly statistically - or even dynamically - interrelated) or not without the necessity of selecting an additional parameter in terms of a maximally tolerable delay between these events. This consideration is conceptually justified in case of the original application to spike trains in electroencephalogram (EEG) recordings, where the inter-spike intervals show relatively narrow distributions at high temporal sampling rates. However, in case of climate studies, precipitation extremes defined by daily precipitation sums exceeding a certain empirical percentile of their local distribution exhibit a distinctively different type of distribution of waiting times between subsequent events. This raises conceptual concerns if event synchronization is still appropriate for detecting interlinkages between spatially distributed precipitation extremes. In order to study this problem in more detail, we employ event synchronization together with an alternative similarity measure for event sequences, event coincidence rates, which requires a manual setting of the tolerable maximum delay between two events to be considered potentially related. Both measures are then used to generate climate networks from parts of the satellite-based TRMM precipitation data set at daily resolution covering the Indian and East Asian monsoon domains, respectively, thereby reanalysing previously published results. The obtained spatial patterns of degree densities and local clustering coefficients exhibit marked differences between both similarity measures. Specifically, we demonstrate that there exists a strong relationship between the fraction of extremes occurring at subsequent days and the degree density in the event synchronization based networks, suggesting that the spatial patterns obtained using this approach are strongly affected by the presence of serial dependencies between events. Given that a manual selection of the maximally tolerable delay between two events can be guided by a priori climatological knowledge and even used for systematic testing of different hypotheses on climatic processes underlying the emergence of spatio-temporal patterns of extreme precipitation, our results provide evidence that event coincidence rates are a more appropriate statistical characteristic for similarity assessment and network construction for climate extremes, while results based on event synchronization need to be interpreted with great caution.

Future Extreme Heat Scenarios to Enable the Assessment of Climate Impacts on Public Health over the Coterminous U.S

NASA Astrophysics Data System (ADS)

Quattrochi, D. A.; Crosson, W. L.; Al-Hamdan, M. Z.; Estes, M. G., Jr.

2013-12-01

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981-2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a ';heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km) much finer than that of GCMs, to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices, and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S.

Future Extreme Heat Scenarios to Enable the Assessment of Climate Impacts on Public Health over the Coterminous U.S.

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Crosson, William L.; Al-Hamdan, Mohammad Z.; Estes, Maurice G., Jr.

2013-01-01

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981- 2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a 'heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km), to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wideranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S

Towards integrated assessment of the northern Adriatic Sea sediment budget using remote sensing

NASA Astrophysics Data System (ADS)

Taramelli, A.; Filipponi, F.; Valentini, E.; Zucca, F.; Gutierrez, O. Q.; Liberti, L.; Cordella, M.

2014-12-01

Understanding the factors influencing sediment fluxes is a key issue to interpret the evolution of coastal sedimentation under natural and human impact and relevant for the natural resources management. Despite river plumes represent one of the major gain in sedimentary budget of littoral cells, knowledge of factors influencing complex behavior of coastal plumes, like river discharge characteristics, wind stress and hydro-climatic variables, has not been yet fully investigated. Use of Earth Observation data allows the identification of spatial and temporal variations of suspended sediments related to river runoff, seafloor erosion, sediment transport and deposition processes. Objective of the study is to investigate sediment fluxes in northern Adriatic Sea by linking suspended sediment patterns of coastal plumes to hydrologic and climatic forcing regulating the sedimentary cell budget and geomorphological evolution in coastal systems and continental shelf waters. Analysis of Total Suspended Matter (TSM) product, derived from 2002-2012 MERIS time series, was done to map changes in spatial and temporal dimension of suspended sediments, focusing on turbid plume waters and intense wind stress conditions. From the generated multi temporal TSM maps, dispersal patterns of major freshwater runoff plumes in northern Adriatic Sea were evaluated through spatial variability of coastal plumes shape and extent. Additionally, sediment supply from river distributary mouths was estimated from TSM and correlated with river discharge rates, wind field and wave field through time. Spatial based methodology has been developed to identify events of wave-generated resuspension of sediments, which cause variation in water column turbidity, occurring during intense wind stress and extreme metocean conditions, especially in the winter period. The identified resuspension events were qualitatively described and compared with to hydro-climatic variables. The identification of spatial and temporal pattern variability highlighted the presence of seasonal sediment dynamics linked to the seasonal cycle in river discharge and wind stress. Results suggest that sediment fluxes generate geomorphological variations in northern Adriatic Sea, which are mainly controlled by river discharge rates and modulated by the winds.

Bayesian hierarchical modelling of North Atlantic windiness

NASA Astrophysics Data System (ADS)

Vanem, E.; Breivik, O. N.

2013-03-01

Extreme weather conditions represent serious natural hazards to ship operations and may be the direct cause or contributing factor to maritime accidents. Such severe environmental conditions can be taken into account in ship design and operational windows can be defined that limits hazardous operations to less extreme conditions. Nevertheless, possible changes in the statistics of extreme weather conditions, possibly due to anthropogenic climate change, represent an additional hazard to ship operations that is less straightforward to account for in a consistent way. Obviously, there are large uncertainties as to how future climate change will affect the extreme weather conditions at sea and there is a need for stochastic models that can describe the variability in both space and time at various scales of the environmental conditions. Previously, Bayesian hierarchical space-time models have been developed to describe the variability and complex dependence structures of significant wave height in space and time. These models were found to perform reasonably well and provided some interesting results, in particular, pertaining to long-term trends in the wave climate. In this paper, a similar framework is applied to oceanic windiness and the spatial and temporal variability of the 10-m wind speed over an area in the North Atlantic ocean is investigated. When the results from the model for North Atlantic windiness is compared to the results for significant wave height over the same area, it is interesting to observe that whereas an increasing trend in significant wave height was identified, no statistically significant long-term trend was estimated in windiness. This may indicate that the increase in significant wave height is not due to an increase in locally generated wind waves, but rather to increased swell. This observation is also consistent with studies that have suggested a poleward shift of the main storm tracks.

Estimating extreme river discharges in Europe through a Bayesian network

NASA Astrophysics Data System (ADS)

Paprotny, Dominik; Morales-Nápoles, Oswaldo

2017-06-01

Large-scale hydrological modelling of flood hazards requires adequate extreme discharge data. In practise, models based on physics are applied alongside those utilizing only statistical analysis. The former require enormous computational power, while the latter are mostly limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian networks (BNs), a graphical model for dependent random variables. We use a non-parametric BN to describe the joint distribution of extreme discharges in European rivers and variables representing the geographical characteristics of their catchments. Annual maxima of daily discharges from more than 1800 river gauges (stations with catchment areas ranging from 1.4 to 807 000 km2) were collected, together with information on terrain, land use and local climate. The (conditional) correlations between the variables are modelled through copulas, with the dependency structure defined in the network. The results show that using this method, mean annual maxima and return periods of discharges could be estimated with an accuracy similar to existing studies using physical models for Europe and better than a comparable global statistical model. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and remains the same in a split-sample validation. Though discharge prediction under climate change is not the main scope of this paper, the BN was applied to a large domain covering all sizes of rivers in the continent both for present and future climate, as an example. Results show substantial variation in the influence of climate change on river discharges. The model can be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling.

Estimating and mapping ecological processes influencing microbial community assembly

PubMed Central

Stegen, James C.; Lin, Xueju; Fredrickson, Jim K.; Konopka, Allan E.

2015-01-01

Ecological community assembly is governed by a combination of (i) selection resulting from among-taxa differences in performance; (ii) dispersal resulting from organismal movement; and (iii) ecological drift resulting from stochastic changes in population sizes. The relative importance and nature of these processes can vary across environments. Selection can be homogeneous or variable, and while dispersal is a rate, we conceptualize extreme dispersal rates as two categories; dispersal limitation results from limited exchange of organisms among communities, and homogenizing dispersal results from high levels of organism exchange. To estimate the influence and spatial variation of each process we extend a recently developed statistical framework, use a simulation model to evaluate the accuracy of the extended framework, and use the framework to examine subsurface microbial communities over two geologic formations. For each subsurface community we estimate the degree to which it is influenced by homogeneous selection, variable selection, dispersal limitation, and homogenizing dispersal. Our analyses revealed that the relative influences of these ecological processes vary substantially across communities even within a geologic formation. We further identify environmental and spatial features associated with each ecological process, which allowed mapping of spatial variation in ecological-process-influences. The resulting maps provide a new lens through which ecological systems can be understood; in the subsurface system investigated here they revealed that the influence of variable selection was associated with the rate at which redox conditions change with subsurface depth. PMID:25983725

Analysis of Extreme Snow Water Equivalent Data in Central New Hampshire

NASA Astrophysics Data System (ADS)

Vuyovich, C.; Skahill, B. E.; Kanney, J. F.; Carr, M.

2017-12-01

Heavy snowfall and snowmelt-related events have been linked to widespread flooding and damages in many regions of the U.S. Design of critical infrastructure in these regions requires spatial estimates of extreme snow water equivalent (SWE). In this study, we develop station specific and spatially explicit estimates of extreme SWE using data from fifteen snow sampling stations maintained by the New Hampshire Department of Environmental Services. The stations are located in the Mascoma, Pemigewasset, Winnipesaukee, Ossipee, Salmon Falls, Lamprey, Sugar, and Isinglass basins in New Hampshire. The average record length for the fifteen stations is approximately fifty-nine years. The spatial analysis of extreme SWE involves application of two Bayesian Hierarchical Modeling methods, one that assumes conditional independence, and another which uses the Smith max-stable process model to account for spatial dependence. We also apply additional max-stable process models, albeit not in a Bayesian framework, that better model the observed dependence among the extreme SWE data. The spatial process modeling leverages readily available and relevant spatially explicit covariate data. The noted additional max-stable process models also used the nonstationary winter North Atlantic Oscillation index, which has been observed to influence snowy weather along the east coast of the United States. We find that, for this data set, SWE return level estimates are consistently higher when derived using methods which account for the observed spatial dependence among the extreme data. This is particularly significant for design scenarios of relevance for critical infrastructure evaluation.

Extreme cyclone events in the Arctic during wintertime: Variability and Trends

NASA Astrophysics Data System (ADS)

Rinke, Annette; Maturilli, Marion; Graham, Robert; Matthes, Heidrun; Handorf, Doerthe; Cohen, Lana; Hudson, Stephen; Moore, John

2017-04-01

Extreme cyclone events are of significant interest as they can transport much heat, moisture, and momentum poleward. Associated impacts are warming and sea-ice breakup. Recently, several examples of such extreme weather events occurred in winter (e.g. during the N-ICE2015 campaign north of Svalbard and the Frank North Atlantic storm during the end of December 2015). With Arctic amplification and associated reduced sea-ice cover and warmer sea surface temperatures, the occurrence of extreme cyclones events could be a plausible scenario. We calculate the spatial patterns, and changes and trends of the number of extreme cyclone events in the Arctic based on ERA-Interim six-hourly sea level pressure (SLP) data for winter (November-February) 1979-2015. Further, we analyze the SLP data from the Ny Alesund station for the same 37 year period. We define an extreme cyclone event by a extreme low central pressure (SLP below 985 hPa, which is the 5th percentile of the Ny Alesund/N-ICE2015 SLP data) and a deepening of at least 6 hPa/6 hours. Areas of highest frequency of occurrence of extreme cyclones are south/southeast of Greenland (corresponding to the Islandic low), between Norway and Svalbard and in the Barents/Kara Seas. The time series of the number of occurrence of extreme cyclone events for Ny Alesund/N-ICE show considerable interannual variability. The trend is not consistent through the winter, but we detect an increase in early winter and a slight decrease in late winter. The former is due to the increased occurrence of longer events at the expense of short events. Furthermore, the difference patterns of the frequency of events for months following the September with high and low Arctic sea-ice extent ("Low minus high sea ice") conforms with the change patterns of extreme cyclones numbers (frequency of events "2000-2015 minus 1979-1994") and with the trend patterns. This indicates that the changes in extreme cyclone occurrence in early winter are associated with sea-ice changes (regional feedback). In contrast, different mechanisms via large-scale circulation changes/teleconnections seem to play a role in late winter.

Spatial Variability of Soil Water and Soil Organic Carbon Contents Under Different Degradation Degrees of Alpine Meadow Soil over the Qinghai-Tibetan Plateau

NASA Astrophysics Data System (ADS)

Zeng, C.; Zhang, F.

2014-12-01

Alpine meadow is one of widespread vegetation types of the Qinghai-Tibetan Plateau. However, alpine meadow ecosystem is undergoing degradation in recent years. The degradation of alpine meadow can changes soil physical and chemical properties as well as it's spatial variability. However, little research has been done that address the spatial patterns of soil properties under different degradation degrees of alpine meadow of the Qinghai-Tibetan Plateau although these changes were important to water and heat study and modelling of land surface. 296 soil surface (0-10 cm) samples were collected using grid sampling design from three different degraded alpine meadow regions (1 km2). Then soil water content (SWC) and organic carbon content (OCC) were measured. Classical statistical and geostatistical methods were employed to study the spatial heterogeneities of SWC and OCC under different degradation degrees (Non-degraded ND, moderately degraded MD, extremely degraded ED) of alpine meadow. Results show that both SWC and OCC of alpine meadow were normally distributed with the exception of SWC under ED. On average, both SWC and OCC of alpine meadow decreased in the order that ND > MD > ED. For nugget ratios, SWC and OCC of alpine meadow showed increasing spatial dependence tendency from ND to ED. For the range of spatial variation, both SWC and OCC of alpine meadow showed increasing tendency in distance with the increasing degree of degradation. In all, the degradation of alpine meadow has significant impact on spatial heterogeneities of SWC and OCC of alpine meadow. With increasing of alpine meadow degradation, soil water condition and nutrient condition become worse, and their distributions in spatial become unevenly.

Spatial and temporal analysis of extreme sea level and storm surge events around the coastline of the UK

PubMed Central

Haigh, Ivan D.; Wadey, Matthew P.; Wahl, Thomas; Ozsoy, Ozgun; Nicholls, Robert J.; Brown, Jennifer M.; Horsburgh, Kevin; Gouldby, Ben

2016-01-01

In this paper we analyse the spatial footprint and temporal clustering of extreme sea level and skew surge events around the UK coast over the last 100 years (1915–2014). The vast majority of the extreme sea level events are generated by moderate, rather than extreme skew surges, combined with spring astronomical high tides. We distinguish four broad categories of spatial footprints of events and the distinct storm tracks that generated them. There have been rare events when extreme levels have occurred along two unconnected coastal regions during the same storm. The events that occur in closest succession (<4 days) typically impact different stretches of coastline. The spring/neap tidal cycle prevents successive extreme sea level events from happening within 4–8 days. Finally, the 2013/14 season was highly unusual in the context of the last 100 years from an extreme sea level perspective. PMID:27922630

Five centuries of Central European temperature extremes reconstructed from tree-ring density and documentary evidence

NASA Astrophysics Data System (ADS)

Battipaglia, Giovanna; Frank, David; Büntgen, Ulf; Dobrovolný, Petr; Brázdil, Rudolf; Pfister, Christian; Esper, Jan

2010-06-01

Future climate change will likely influence the frequency and intensity of weather extremes. As such events are by definition rare, long records are required to understand their characteristics, drivers, and consequences on ecology and society. Herein we provide a unique perspective on regional-scale temperature extremes over the past millennium, using three tree-ring maximum latewood density (MXD) chronologies from higher elevations in the European Alps. We verify the tree-ring-based extremes using documentary evidences from Switzerland, the Czech Republic, and Central Europe that allowed the identification of 44 summer extremes over the 1550-2003 period. These events include cold temperatures in 1579, 1628, 1675, and 1816, as well as warm ones in 1811 and 2003. Prior to 1550, we provide new evidence for cold (e.g., 1068 and 1258) and warm (e.g., 1333) summers derived from the combined MXD records and thus help to characterize high-frequency temperature variability during medieval times. Spatial coherence of the reconstructed extremes is found over Switzerland, with most signatures even extending across Central Europe. We discuss potential limitations of the tree-ring and documentary archives, including the ( i) ability of MXD to particularly capture extremely warm temperatures, ( ii) methodological identification and relative definition of extremes, and ( iii) placement of those events in the millennium-long context of low-frequency climate change.

Synthetic generation of spatially high resolution extreme rainfall in Japan using Monte Carlo simulation with AMeDAS analyzed rainfall data sets

NASA Astrophysics Data System (ADS)

Haruki, W.; Iseri, Y.; Takegawa, S.; Sasaki, O.; Yoshikawa, S.; Kanae, S.

2016-12-01

Natural disasters caused by heavy rainfall occur every year in Japan. Effective countermeasures against such events are important. In 2015, a catastrophic flood occurred in Kinu river basin, which locates in the northern part of Kanto region. The remarkable feature of this flood event was not only in the intensity of rainfall but also in the spatial characteristics of heavy rainfall area. The flood was caused by continuous overlapping of heavy rainfall area over the Kinu river basin, suggesting consideration of spatial extent is quite important to assess impacts of heavy rainfall events. However, the spatial extent of heavy rainfall events cannot be properly measured through rainfall measurement by rain gauges at observation points. On the other hand, rainfall measurements by radar observations provide spatially and temporarily high resolution rainfall data which would be useful to catch the characteristics of heavy rainfall events. For long term effective countermeasure, extreme heavy rainfall scenario considering rainfall area and distribution is required. In this study, a new method for generating extreme heavy rainfall events using Monte Carlo Simulation has been developed in order to produce extreme heavy rainfall scenario. This study used AMeDAS analyzed precipitation data which is high resolution grid precipitation data made by Japan Meteorological Agency. Depth area duration (DAD) analysis has been conducted to extract extreme rainfall events in the past, considering time and spatial scale. In the Monte Carlo Simulation, extreme rainfall event is generated based on events extracted by DAD analysis. Extreme heavy rainfall events are generated in specific region in Japan and the types of generated extreme heavy rainfall events can be changed by varying the parameter. For application of this method, we focused on Kanto region in Japan. As a result, 3000 years rainfall data are generated. 100 -year probable rainfall and return period of flood in Kinu River Basin (2015) are obtained using generated data. We compared 100-year probable rainfall calculated by this method with other traditional method. New developed method enables us to generate extreme rainfall events considering time and spatial scale and produce extreme rainfall scenario.

Experimental droughts: Are precipitation variability and methodological trends hindering our understanding of ecological sensitivities to drought?

NASA Astrophysics Data System (ADS)

Hoover, D. L.; Wilcox, K.; Young, K. E.

2017-12-01

Droughts are projected to increase in frequency and intensity with climate change, which may have dramatic and prolonged effects on ecosystem structure and function. There are currently hundreds of published, ongoing, and new drought experiments worldwide aimed to assess ecosystem sensitivities to drought and identify the mechanisms governing ecological resistance and resilience. However, to date, the results from these experiments have varied widely, and thus patterns of drought sensitivities have been difficult to discern. This lack of consensus at the field scale, limits the abilities of experiments to help improve land surface models, which often fail to realistically simulate ecological responses to extreme events. This is unfortunate because models offer an alternative, yet complementary approach to increase the spatial and temporal assessment of ecological sensitivities to drought that are not possible in the field due to logistical and financial constraints. Here we examined 89 published drought experiments, along with their associated historical precipitation records to (1) identify where and how drought experiments have been imposed, (2) determine the extremity of drought treatments in the context of historical climate, and (3) assess the influence of precipitation variability on drought experiments. We found an overall bias in drought experiments towards short-term, extreme experiments in water-limited ecosystems. When placed in the context of local historical precipitation, most experimental droughts were extreme, with 61% below the 5th, and 43% below the 1st percentile. Furthermore, we found that interannual precipitation variability had a large and potentially underappreciated effect on drought experiments due to the co-varying nature of control and drought treatments. Thus detecting ecological effects in experimental droughts is strongly influenced by the interaction between drought treatment magnitude, precipitation variability, and key physiological thresholds. The results from this study have important implication for the design and interpretation of drought experiments as well as integrating field results with land surface models.

Recent advances on reconstruction of climate and extreme events in China for the past 2000 year

NASA Astrophysics Data System (ADS)

Zheng, Jingyun; Hao, Zhixin; Ge, Quansheng; Liu, Yang

2016-04-01

The study of regional climate changes for past 2000 year could present spatial pattern of climate variation and various historical analogues for the sensitivity and operation of the climate system (e.g., the modulations of internal variability, feedbacks and teleconnections, abrupt changes and regional extreme events, etc.) from inter-annual to centennial scales and provide the knowledge to predict and project climate in the near future. China is distinguished by a prominent monsoon climate in east, continental arid climate in northwest and high land cold climate in Qinghai-Tibetan Plateau located at southwest. The long history of civilization and the variety of climate in China provides an abundant and well-dated documentary records and a wide range of natural archives (e.g., tree-ring, ice core, stalagmite, varved lake sediment, etc.) for high-resolution paleoclimate reconstruction. This paper presented a review of recent advances on reconstruction of climate and extreme events in China for the past 2000 years. In recent 10 years, there were many new high-resolution paleoclimatic reconstructions reported in China, e.g., the annual and decadal resolution series of temperature and precipitation in eastern China derived from historical documents, in western China derived from tree-ring and other natural archives. These new reconstructions provided more proxies and better spatial coverage to understand the characteristics of climate change over China and the uncertainty of regional reconstructions, as well as to reconstruct the high-resolution temperature series and the spatial pattern of precipitation change for whole China in the past millenniums by synthesizing the multi-proxy together. The updated results show that, in China, the warm intervals for the past 2000 years were in AD 1-200, AD 551-760, AD 951-1320, and after AD 1921; as well as the cold intervals were in AD 201-350, AD 441-530, AD 781-950, and AD 1321-1920. The extreme cold winters occurred in periods of 1500-1900 were more frequent than that after 1950. The intensity of regional heat wave occurred in the context of recent global warming may not exceed the natural climate variability during the historical times. In the eastern monsoon region of China, the significant cycles of precipitation are 90-100a, 70-80a, 43-48a, 35a, 25-27a and 17-18a in North China Plain; 90-100a, 73-75a, 63-68a, 55a, 45a, 37a and 26a in Jiang-Huai area; and 85-100a, 75-77a, 58-65a, 37-39a, 31a and 26a in Jiang-Nan area; respectively. Whereas, the spatial pattern of drought/flood for all cold periods ensemble mean showed an east to west distribution, but for all warm periods ensemble mean showed a tri-pole pattern with drought in south of 25°N, flood in 25°-30°N, and drought in north of 30°N. The extreme drought events were more frequent at the periods of 301-400, 751-800, 1051-1150, 1501-1550 and 1601-1650, the extreme flood events were more frequent at the periods of 101-150, 251-300, 951-1000, 1701-1750, 1801-1850 and 1901-1950, and for the period of 1551-1600, the coexisting extreme drought and extreme flood events most frequently occurred. In arid area, China, it was characterized by a relatively dry in AD 1000-1350, a wet in AD 1500 to 1850 and tending to moisture in recent decades. In the northeastern Qinghai-Tibetan Plateau, there existed evident centennial oscillations for precipitation during the past 1000 years, with interruption of several multi-decadal severe drought events, which two prominent droughty events centered on AD1480s and AD 1710s. In the Southwest of China, the extreme droughts as severe as in Sichuan and Chongqing in 2006 have also been occurred during the historical times.

Unidirectional trends in annual and seasonal climate and extremes in Egypt

NASA Astrophysics Data System (ADS)

Nashwan, Mohamed Salem; Shahid, Shamsuddin; Abd Rahim, Norhan

2018-05-01

The presence of short- and long-term autocorrelations can lead to considerable change in significance of trend in hydro-climatic time series. Therefore, past findings of climatic trend studies that did not consider autocorrelations became a questionable issue. The spatial patterns in the trends of annual and seasonal temperature, rainfall, and related extremes in Egypt have been assessed in this paper using modified Mann-Kendal (MMK) trend test which can detect unidirectional trends in time series in the presence of short- and long-term autocorrelations. The trends obtained using the MMK test was compared with that obtained using standard Mann-Kendall (MK) test to show how natural variability in climate affects the trends. The daily rainfall and temperature data of Princeton Global Meteorological Forcing for the period 1948-2010 having a spatial resolution of 0.25° × 0.25° was used for this purpose. The results showed a large difference between the trends obtained using MMK and MK tests. The MMK test showed increasing trends in temperature and a number of temperature extremes in Egypt, but almost no change in rainfall and rainfall extremes. The minimum temperature was found to increase (0.08-0.29 °C/decade) much faster compared to maximum temperature (0.07-0.24 °C/decade) and therefore, a decrease in diurnal temperature range (- 0.01 to - 0.16 °C/decade) in most part of Egypt. The number of winter hot days and nights are increasing, while the number of cold days is decreasing in most part of the country. The study provides a more realistic scenario of the changes in climate and weather extremes of Egypt.

Major hydrologic shifts in northwest Florida during the Holocene from a lacustrine sediment record

NASA Astrophysics Data System (ADS)

Rodysill, J. R.; Donnelly, J. P.

2011-12-01

Recent climate extremes have threatened water resource availability and destroyed homes and infrastructure along the heavily populated northern Gulf of Mexico coast. Water resources in Northwest Florida, in particular, suffer from declining aquifer levels and salt water intrusion despite the presence of extensive river and aquifer systems. Intensive water resource management has been necessary to meet water supply demands during recent droughts. Advanced preparedness for abrupt climate events requires the ability to anticipate when hydrologic extremes are likely to occur; however, the long-term history of hydrologic extremes is not well known, and the instrumental record is too short to resolve longer-term hydrologic variability. Reconstructing the pre-instrumental hydrologic history is essential to building our understanding of the timing of and the driving forces behind wet and dry extremes. Here we present a new record of paleohydrology in northwest Florida based upon variations in sediment lithology and geochemistry from Rattlesnake Lake. We see evidence for both brief and long-lived changes in the lake environment during the Holocene. We compare our record to published pollen-based reconstructions of paleohydrology to examine the spatial and temporal patterns of paleohydrologic extremes across the northern Gulf of Mexico region during the Holocene.

Combining multiple sources of data to inform conservation of Lesser Prairie-Chicken populations

USGS Publications Warehouse

Ross, Beth; Haukos, David A.; Hagen, Christian A.; Pitman, James

2018-01-01

Conservation of small populations is often based on limited data from spatially and temporally restricted studies, resulting in management actions based on an incomplete assessment of the population drivers. If fluctuations in abundance are related to changes in weather, proper management is especially important, because extreme weather events could disproportionately affect population abundance. Conservation assessments, especially for vulnerable populations, are aided by a knowledge of how extreme events influence population status and trends. Although important for conservation efforts, data may be limited for small or vulnerable populations. Integrated population models maximize information from various sources of data to yield population estimates that fully incorporate uncertainty from multiple data sources while allowing for the explicit incorporation of environmental covariates of interest. Our goal was to assess the relative influence of population drivers for the Lesser Prairie-Chicken (Tympanuchus pallidicinctus) in the core of its range, western and southern Kansas, USA. We used data from roadside lek count surveys, nest monitoring surveys, and survival data from telemetry monitoring combined with climate (Palmer drought severity index) data in an integrated population model. Our results indicate that variability in population growth rate was most influenced by variability in juvenile survival. The Palmer drought severity index had no measurable direct effects on adult survival or mean number of offspring per female; however, there were declines in population growth rate following severe drought. Because declines in population growth rate occurred at a broad spatial scale, declines in response to drought were likely due to decreases in chick and juvenile survival rather than emigration outside of the study area. Overall, our model highlights the importance of accounting for environmental and demographic sources of variability, and provides a thorough method for simultaneously evaluating population demography in response to long-term climate effects.

Impact of spectral nudging on regional climate simulation over CORDEX East Asia using WRF

NASA Astrophysics Data System (ADS)

Tang, Jianping; Wang, Shuyu; Niu, Xiaorui; Hui, Pinhong; Zong, Peishu; Wang, Xueyuan

2017-04-01

In this study, the impact of the spectral nudging method on regional climate simulation over the Coordinated Regional Climate Downscaling Experiment East Asia (CORDEX-EA) region is investigated using the Weather Research and Forecasting model (WRF). Driven by the ERA-Interim reanalysis, five continuous simulations covering 1989-2007 are conducted by the WRF model, in which four runs adopt the interior spectral nudging with different wavenumbers, nudging variables and nudging coefficients. Model validation shows that WRF has the ability to simulate spatial distributions and temporal variations of the surface climate (air temperature and precipitation) over CORDEX-EA domain. Comparably the spectral nudging technique is effective in improving the model's skill in the following aspects: (1), the simulated biases and root mean square errors of annual mean temperature and precipitation are obviously reduced. The SN3-UVT (spectral nudging with wavenumber 3 in both zonal and meridional directions applied to U, V and T) and SN6 (spectral nudging with wavenumber 6 in both zonal and meridional directions applied to U and V) experiments give the best simulations for temperature and precipitation respectively. The inter-annual and seasonal variances produced by the SN experiments are also closer to the ERA-Interim observation. (2), the application of spectral nudging in WRF is helpful for simulating the extreme temperature and precipitation, and the SN3-UVT simulation shows a clear advantage over the other simulations in depicting both the spatial distributions and inter-annual variances of temperature and precipitation extremes. With the spectral nudging, WRF is able to preserve the variability in the large scale climate information, and therefore adjust the temperature and precipitation variabilities toward the observation.

Improved Hourly and Sub-Hourly Gauge Data for Assessing Precipitation Extremes in the U.S.

NASA Astrophysics Data System (ADS)

Lawrimore, J. H.; Wuertz, D.; Palecki, M. A.; Kim, D.; Stevens, S. E.; Leeper, R.; Korzeniewski, B.

2017-12-01

The NOAA/National Weather Service (NWS) Fischer-Porter (F&P) weighing bucket precipitation gauge network consists of approximately 2000 stations that comprise a subset of the NWS Cooperative Observers Program network. This network has operated since the mid-20th century, providing one of the longest records of hourly and 15-minute precipitation observations in the U.S. The lengthy record of this dataset combined with its relatively high spatial density, provides an important source of data for many hydrological applications including understanding trends and variability in the frequency and intensity of extreme precipitation events. In recent years NOAA's National Centers for Environmental Information initiated an upgrade of its end-to-end processing and quality control system for these data. This involved a change from a largely manual review and edit process to a fully automated system that removes the subjectivity that was previously a necessary part of dataset quality control and processing. An overview of improvements to this dataset is provided along with the results of an analysis of observed variability and trends in U.S. precipitation extremes since the mid-20th century. Multi-decadal trends in many parts of the nation are consistent with model projections of an increase in the frequency and intensity of heavy precipitation in a warming world.

Extreme infrared variables from UKIDSS - II. An end-of-survey catalogue of eruptive YSOs and unusual stars

NASA Astrophysics Data System (ADS)

Lucas, P. W.; Smith, L. C.; Contreras Peña, C.; Froebrich, D.; Drew, J. E.; Kumar, M. S. N.; Borissova, J.; Minniti, D.; Kurtev, R.; Monguió, M.

2017-12-01

We present a catalogue of 618 high-amplitude infrared variable stars (1 < ΔK < 5 mag) detected by the two widely separated epochs of 2.2 μm data in the UKIDSS Galactic plane survey, from searches covering ∼1470 deg2. Most were discovered by a search of all fields at 30 < l < 230°. Sources include new dusty Mira variables, three new cataclysmic variable candidates, a blazar and a peculiar source that may be an interacting binary system. However, ∼60 per cent are young stellar obbjects (YSOs), based on spatial association with star-forming regions at distances ranging from 300 pc to over 10 kpc. This confirms our initial result in Contreras Peña et al. (Paper I) that YSOs dominate the high-amplitude infrared variable sky in the Galactic disc. It is also supported by recently published VISTA Variables in the Via Lactea (VVV) results at 295 < l < 350°. The spectral energy distributions of the YSOs indicate class I or flat-spectrum systems in most cases, as in the VVV sample. A large number of variable YSOs are associated with the Cygnus X complex and other groups are associated with the North America/Pelican nebula, the Gemini OB1 molecular cloud, the Rosette complex, the Cone nebula, the W51 star-forming region and the S86 and S236 H II regions. Most of the YSO variability is likely due to variable/episodic accretion on time-scales of years, albeit usually less extreme than classical FUors and EXors. Luminosities at the 2010 Wide-field Infrared Survey Explorer epoch range from ∼0.1 to 103 L⊙ but only rarely exceed 102.5 L⊙.

Towards a monitoring system of temperature extremes in Europe

NASA Astrophysics Data System (ADS)

Lavaysse, Christophe; Cammalleri, Carmelo; Dosio, Alessandro; van der Schrier, Gerard; Toreti, Andrea; Vogt, Jürgen

2018-01-01

Extreme-temperature anomalies such as heat and cold waves may have strong impacts on human activities and health. The heat waves in western Europe in 2003 and in Russia in 2010, or the cold wave in southeastern Europe in 2012, generated a considerable amount of economic loss and resulted in the death of several thousands of people. Providing an operational system to monitor extreme-temperature anomalies in Europe is thus of prime importance to help decision makers and emergency services to be responsive to an unfolding extreme event. In this study, the development and the validation of a monitoring system of extreme-temperature anomalies are presented. The first part of the study describes the methodology based on the persistence of events exceeding a percentile threshold. The method is applied to three different observational datasets, in order to assess the robustness and highlight uncertainties in the observations. The climatology of extreme events from the last 21 years is then analysed to highlight the spatial and temporal variability of the hazard, and discrepancies amongst the observational datasets are discussed. In the last part of the study, the products derived from this study are presented and discussed with respect to previous studies. The results highlight the accuracy of the developed index and the statistical robustness of the distribution used to calculate the return periods.

Modeling Spatial Dependence of Rainfall Extremes Across Multiple Durations

NASA Astrophysics Data System (ADS)

Le, Phuong Dong; Leonard, Michael; Westra, Seth

2018-03-01

Determining the probability of a flood event in a catchment given that another flood has occurred in a nearby catchment is useful in the design of infrastructure such as road networks that have multiple river crossings. These conditional flood probabilities can be estimated by calculating conditional probabilities of extreme rainfall and then transforming rainfall to runoff through a hydrologic model. Each catchment's hydrological response times are unlikely to be the same, so in order to estimate these conditional probabilities one must consider the dependence of extreme rainfall both across space and across critical storm durations. To represent these types of dependence, this study proposes a new approach for combining extreme rainfall across different durations within a spatial extreme value model using max-stable process theory. This is achieved in a stepwise manner. The first step defines a set of common parameters for the marginal distributions across multiple durations. The parameters are then spatially interpolated to develop a spatial field. Storm-level dependence is represented through the max-stable process for rainfall extremes across different durations. The dependence model shows a reasonable fit between the observed pairwise extremal coefficients and the theoretical pairwise extremal coefficient function across all durations. The study demonstrates how the approach can be applied to develop conditional maps of the return period and return level across different durations.

Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment

NASA Astrophysics Data System (ADS)

Widmann, Martin; Bedia, Joaquin; Gutiérrez, Jose Manuel; Maraun, Douglas; Huth, Radan; Fischer, Andreas; Keller, Denise; Hertig, Elke; Vrac, Mathieu; Wibig, Joanna; Pagé, Christian; Cardoso, Rita M.; Soares, Pedro MM; Bosshard, Thomas; Casado, Maria Jesus; Ramos, Petra

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. Within VALUE a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods has been developed. In the first validation experiment the downscaling methods are validated in a setup with perfect predictors taken from the ERA-interim reanalysis for the period 1997 - 2008. This allows to investigate the isolated skill of downscaling methods without further error contributions from the large-scale predictors. One aspect of the validation is the representation of spatial variability. As part of the VALUE validation we have compared various properties of the spatial variability of downscaled daily temperature and precipitation with the corresponding properties in observations. We have used two test validation datasets, one European-wide set of 86 stations, and one higher-density network of 50 stations in Germany. Here we present results based on three approaches, namely the analysis of i.) correlation matrices, ii.) pairwise joint threshold exceedances, and iii.) regions of similar variability. We summarise the information contained in correlation matrices by calculating the dependence of the correlations on distance and deriving decorrelation lengths, as well as by determining the independent degrees of freedom. Probabilities for joint threshold exceedances and (where appropriate) non-exceedances are calculated for various user-relevant thresholds related for instance to extreme precipitation or frost and heat days. The dependence of these probabilities on distance is again characterised by calculating typical length scales that separate dependent from independent exceedances. Regionalisation is based on rotated Principal Component Analysis. The results indicate which downscaling methods are preferable if the dependency of variability at different locations is relevant for the user.

ISM Dust Grains and N-band Spectral Variability in the Spatially Resolved Subarcsecond Binary UY Aur

NASA Astrophysics Data System (ADS)

Skemer, Andrew J.; Close, Laird M.; Hinz, Philip M.; Hoffmann, William F.; Greene, Thomas P.; Males, Jared R.; Beck, Tracy L.

2010-03-01

The 10 μm silicate feature is an essential diagnostic of dust-grain growth and planet formation in young circumstellar disks. The Spitzer Space Telescope has revolutionized the study of this feature, but due to its small (85 cm) aperture, it cannot spatially resolve small/medium-separation binaries (lsim3''; <~ 420 AU) at the distances of the nearest star-forming regions (~140 pc). Large, 6-10 m ground-based telescopes with mid-infrared instruments can resolve these systems. In this paper, we spatially resolve the 0farcs88 binary, UY Aur, with MMTAO/BLINC-MIRAC4 mid-infrared spectroscopy. We then compare our spectra to Spitzer/IRS (unresolved) spectroscopy, and resolved images from IRTF/MIRAC2, Keck/OSCIR, and Gemini/Michelle, which were taken over the past decade. We find that UY Aur A has extremely pristine, interstellar medium (ISM)-like grains and that UY Aur B has an unusually shaped silicate feature, which is probably the result of blended emission and absorption from foreground extinction in its disk. We also find evidence for variability in both UY Aur A and UY Aur B by comparing synthetic photometry from our spectra with resolved imaging from previous epochs. The photometric variability of UY Aur A could be an indication that the silicate emission itself is variable, as was recently found in EX Lupi. Otherwise, the thermal continuum is variable, and either the ISM-like dust has never evolved, or it is being replenished, perhaps by UY Aur's circumbinary disk. The observations reported here were partially obtained at the Infrared Telescope Facility, which is operated by the University of Hawaii under Cooperative Agreement no. NCC 5-538 with the National Aeronautics and Space Administration, Science Mission Directorate, Planetary Astronomy Program.

Incorporating climate change and morphological uncertainty into coastal change hazard assessments

USGS Publications Warehouse

Baron, Heather M.; Ruggiero, Peter; Wood, Nathan J.; Harris, Erica L.; Allan, Jonathan; Komar, Paul D.; Corcoran, Patrick

2015-01-01

Documented and forecasted trends in rising sea levels and changes in storminess patterns have the potential to increase the frequency, magnitude, and spatial extent of coastal change hazards. To develop realistic adaptation strategies, coastal planners need information about coastal change hazards that recognizes the dynamic temporal and spatial scales of beach morphology, the climate controls on coastal change hazards, and the uncertainties surrounding the drivers and impacts of climate change. We present a probabilistic approach for quantifying and mapping coastal change hazards that incorporates the uncertainty associated with both climate change and morphological variability. To demonstrate the approach, coastal change hazard zones of arbitrary confidence levels are developed for the Tillamook County (State of Oregon, USA) coastline using a suite of simple models and a range of possible climate futures related to wave climate, sea-level rise projections, and the frequency of major El Niño events. Extreme total water levels are more influenced by wave height variability, whereas the magnitude of erosion is more influenced by sea-level rise scenarios. Morphological variability has a stronger influence on the width of coastal hazard zones than the uncertainty associated with the range of climate change scenarios.

Fabrication of Extremely Short Length Fiber Bragg Gratings for Sensor Applications

NASA Technical Reports Server (NTRS)

Wu, Meng-Chou; Rogowski, Robert S.; Tedjojuwono, Ken K.

2002-01-01

A new technique and a physical model for writing extremely short length Bragg gratings in optical fibers have been developed. The model describes the effects of diffraction on the spatial spectra and therefore, the wavelength spectra of the Bragg gratings. Using an interferometric technique and a variable aperture, short gratings of various lengths and center wavelengths were written in optical fibers. By selecting the related parameters, the Bragg gratings with typical length of several hundred microns and bandwidth of several nanometers can be obtained. These short gratings can be apodized with selected diffraction patterns and hence their broadband spectra have a well-defined bell shape. They are suitable for use as miniaturized distributed strain sensors, which have broad applications to aerospace research and industry as well.

Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds

USGS Publications Warehouse

O'Connell, Allan F.; Gardner, Beth; Oppel, Steffen; Meirinho, Ana; Ramírez, Iván; Miller, Peter I.; Louzao, Maite

2012-01-01

Knowledge about the spatial distribution of seabirds at sea is important for conservation. During marine conservation planning, logistical constraints preclude seabird surveys covering the complete area of interest and spatial distribution of seabirds is frequently inferred from predictive statistical models. Increasingly complex models are available to relate the distribution and abundance of pelagic seabirds to environmental variables, but a comparison of their usefulness for delineating protected areas for seabirds is lacking. Here we compare the performance of five modelling techniques (generalised linear models, generalised additive models, Random Forest, boosted regression trees, and maximum entropy) to predict the distribution of Balearic Shearwaters (Puffinus mauretanicus) along the coast of the western Iberian Peninsula. We used ship transect data from 2004 to 2009 and 13 environmental variables to predict occurrence and density, and evaluated predictive performance of all models using spatially segregated test data. Predicted distribution varied among the different models, although predictive performance varied little. An ensemble prediction that combined results from all five techniques was robust and confirmed the existence of marine important bird areas for Balearic Shearwaters in Portugal and Spain. Our predictions suggested additional areas that would be of high priority for conservation and could be proposed as protected areas. Abundance data were extremely difficult to predict, and none of five modelling techniques provided a reliable prediction of spatial patterns. We advocate the use of ensemble modelling that combines the output of several methods to predict the spatial distribution of seabirds, and use these predictions to target separate surveys assessing the abundance of seabirds in areas of regular use.

A New Integrated Threshold Selection Methodology for Spatial Forecast Verification of Extreme Events

NASA Astrophysics Data System (ADS)

Kholodovsky, V.

2017-12-01

Extreme weather and climate events such as heavy precipitation, heat waves and strong winds can cause extensive damage to the society in terms of human lives and financial losses. As climate changes, it is important to understand how extreme weather events may change as a result. Climate and statistical models are often independently used to model those phenomena. To better assess performance of the climate models, a variety of spatial forecast verification methods have been developed. However, spatial verification metrics that are widely used in comparing mean states, in most cases, do not have an adequate theoretical justification to benchmark extreme weather events. We proposed a new integrated threshold selection methodology for spatial forecast verification of extreme events that couples existing pattern recognition indices with high threshold choices. This integrated approach has three main steps: 1) dimension reduction; 2) geometric domain mapping; and 3) thresholds clustering. We apply this approach to an observed precipitation dataset over CONUS. The results are evaluated by displaying threshold distribution seasonally, monthly and annually. The method offers user the flexibility of selecting a high threshold that is linked to desired geometrical properties. The proposed high threshold methodology could either complement existing spatial verification methods, where threshold selection is arbitrary, or be directly applicable in extreme value theory.

Modeling land surface hydrology sensitivity in the Colorado River Basin to historical climate variability

NASA Astrophysics Data System (ADS)

Whitney, K. M.; Bohn, T. J.; Vivoni, E. R.

2017-12-01

Over the past century, the Colorado River Basin (CRB) has experienced substantial warming and interannual climate variations, including prolonged drought periods. These patterns are projected to accelerate in the 21st century, with major consequences for water resources in the southwestern U.S. and northwestern Mexico. To evaluate future projections appropriately, however, it is important to first quantify the regional hydrologic response to historical climate variability in the CRB. In the current effort, we force the Variable Infiltration Capacity (VIC) land surface hydrology model and a river routing model with historical meteorological data to estimate water balance components and naturalized streamflow response in the CRB at 1/16o spatial resolution and at an hourly time step over the period 1950-2013. We utilize data products from satellite remote sensing to specify spatiotemporal variations in vegetation parameters and include an irrigation scheme to account for evapotranspiration from croplands in the CRB. Furthermore, we apply recent modifications in VIC to more properly account for bare soil evaporation in arid and semiarid ecosystems. Analyses of the historical model simulations are focused on quantifying the spatiotemporal variability of the soil moisture, evapotranspiration, streamflow and snowmelt response and their linkages to extreme meteorological events. Here we characterize the annual and monthly distributions, trends, and statistical extremes and central tendencies of water balance terms averaged over the CRB and its sub-basins for the entire study period 1950-2013. By building a model-based hydrologic climatology and catalog of historical extreme events for the CRB, we aim to construct a basis for future activities that analyze the impact of statistically downscaled climate change projections on the hydrology of the CRB and its urban areas.

A nested observation and model approach to non linear groundwater surface water interactions.

NASA Astrophysics Data System (ADS)

van der Velde, Y.; Rozemeijer, J. C.; de Rooij, G. H.

2009-04-01

Surface water quality measurements in The Netherlands are scattered in time and space. Therefore, water quality status and its variations and trends are difficult to determine. In order to reach the water quality goals according to the European Water Framework Directive, we need to improve our understanding of the dynamics of surface water quality and the processes that affect it. In heavily drained lowland catchment groundwater influences the discharge towards the surface water network in many complex ways. Especially a strong seasonal contracting and expanding system of discharging ditches and streams affects discharge and solute transport. At a tube drained field site the tube drain flux and the combined flux of all other flow routes toward a stretch of 45 m of surface water have been measured for a year. Also the groundwater levels at various locations in the field and the discharge at two nested catchment scales have been monitored. The unique reaction of individual flow routes on rainfall events at the field site allowed us to separate the discharge at a 4 ha catchment and at a 6 km2 into flow route contributions. The results of this nested experimental setup combined with the results of a distributed hydrological model has lead to the formulation of a process model approach that focuses on the spatial variability of discharge generation driven by temporal and spatial variations in groundwater levels. The main idea of this approach is that discharge is not generated by catchment average storages or groundwater heads, but is mainly generated by points scale extremes i.e. extreme low permeability, extreme high groundwater heads or extreme low surface elevations, all leading to catchment discharge. We focused on describing the spatial extremes in point scale storages and this led to a simple and measurable expression that governs the non-linear groundwater surface water interaction. We will present the analysis of the field site data to demonstrate the potential of nested-scale, high frequency observations. The distributed hydrological model results will be used to show transient catchment scale relations between groundwater levels and discharges. These analyses lead to a simple expression that can describe catchment scale groundwater surface water interactions.

Spatial and temporal variability of cv. Tempranillo phenology and grape quality within the Ribera del Duero DO (Spain) and relationships with climate.

PubMed

Ramos, M C; Jones, G V; Yuste, J

2015-12-01

The aim of this work was to analyze spatial phenology and grape quality variability related to the climatic characteristics within the Ribera del Duero Designation of Origin (DO). Twenty plots planted with cv. Tempranillo and distributed within the DO were analyzed for phenology from 2004 to 2013. Grape quality parameters at ripening (berry weight, sugar content, acidity and pH, and anthocyanins) were analyzed in 26 plots for the period 2003-2013. The relationships between phenology and grape parameters with different climatic variables were confirmed with a multivariate analysis. On average, bud break was April 27(th), bloom June 17(th), and veraison August 12th. However, phenology during the time period showed high variability, with differences between years of up to 21 days for a phenology stage. The earliest dates were observed in dry years (2005, 2006, and to a lesser degree in 2009) while the later phenology dates occurred in the wettest year of the period (2008). High correlations were found between veraison date and temperature variables as well as with precipitation-evapotranspiration recorded during the bloom-veraison period. These effects tended to be higher in the central part of the DO. Grape quality parameters also showed high variability among the dry and the wet years, and the influence of extreme temperatures on color development as well as the effect of available water on acidity were observed.

Spatial and temporal variability of cv. Tempranillo phenology and grape quality within the Ribera del Duero DO (Spain) and relationships with climate

NASA Astrophysics Data System (ADS)

Ramos, M. C.; Jones, G. V.; Yuste, J.

2015-12-01

The aim of this work was to analyze spatial phenology and grape quality variability related to the climatic characteristics within the Ribera del Duero Designation of Origin (DO). Twenty plots planted with cv. Tempranillo and distributed within the DO were analyzed for phenology from 2004 to 2013. Grape quality parameters at ripening (berry weight, sugar content, acidity and pH, and anthocyanins) were analyzed in 26 plots for the period 2003-2013. The relationships between phenology and grape parameters with different climatic variables were confirmed with a multivariate analysis. On average, bud break was April 27th, bloom June 17th, and veraison August 12th. However, phenology during the time period showed high variability, with differences between years of up to 21 days for a phenology stage. The earliest dates were observed in dry years (2005, 2006, and to a lesser degree in 2009) while the later phenology dates occurred in the wettest year of the period (2008). High correlations were found between veraison date and temperature variables as well as with precipitation-evapotranspiration recorded during the bloom-veraison period. These effects tended to be higher in the central part of the DO. Grape quality parameters also showed high variability among the dry and the wet years, and the influence of extreme temperatures on color development as well as the effect of available water on acidity were observed.

Evaluation of Drought Occurrence and Climate Change in the Pearl River Basin in South China

NASA Astrophysics Data System (ADS)

DU, Y.; Chen, J.; Wang, K.; Shi, H.

2015-12-01

This study uses the Variable Infiltration Capacity (VIC) Model to simulate the hydrological processes over the Pearl River basin in South China. The observed streamflow data in the Pearl River Basin for the period 1951-2000 are used to evaluate the model simulation results. Further, in this study, the 55 datasets of climate projection from 18 General Circulation Models (GCMs) for the IPCC AR4 (SRES A2/A1B/B1) and AR5 (RCP 2.6/4.5/6.0/8.5) are used to drive the VIC model at 0.5°× 0.5°spatial resolution and daily temporal resolution. Then, the monthly Standard Precipitation Index (SPI) and standardized runoff index (SRI) are generated to detect the drought occurrence. This study validates the GCMs projection through comparing the observed precipitation for the period of 2000-2013. Then, spatial variation of the frequency change of moderate drought, severe drought and extreme drought are analyzed for the 21st century. The study reveals that the frequencies of severe drought and extreme drought occurrences over the Pearl River Basin increase along with time. Specifically, for the scenario of AR5 RCP 8.5, the east and west parts of the Pearl River Basin most likely suffer from severe drought and extreme drought with an increased frequency throughout the 21st century.

A variable resolution x-ray detector for computed tomography: II. Imaging theory and performance.

PubMed

DiBianca, F A; Zou, P; Jordan, L M; Laughter, J S; Zeman, H D; Sebes, J

2000-08-01

A computed tomography (CT) imaging technique called variable resolution x-ray (VRX) detection provides variable image resolution ranging from that of clinical body scanning (1 cy/mm) to that of microscopy (100 cy/mm). In this paper, an experimental VRX CT scanner based on a rotating subject table and an angulated storage phosphor screen detector is described and tested. The measured projection resolution of the scanner is > or = 20 lp/mm. Using this scanner, 4.8-s CT scans are made of specimens of human extremities and of in vivo hamsters. In addition, the system's projected spatial resolution is calculated to exceed 100 cy/mm for a future on-line CT scanner incorporating smaller focal spots (0.1 mm) than those currently used and a 1008-channel VRX detector with 0.6-mm cell spacing.

Radar-rain-gauge rainfall estimation for hydrological applications in small catchments

NASA Astrophysics Data System (ADS)

Gabriele, Salvatore; Chiaravalloti, Francesco; Procopio, Antonio

2017-07-01

The accurate evaluation of the precipitation's time-spatial structure is a critical step for rainfall-runoff modelling. Particularly for small catchments, the variability of rainfall can lead to mismatched results. Large errors in flow evaluation may occur during convective storms, responsible for most of the flash floods in small catchments in the Mediterranean area. During such events, we may expect large spatial and temporal variability. Therefore, using rain-gauge measurements only can be insufficient in order to adequately depict extreme rainfall events. In this work, a double-level information approach, based on rain gauges and weather radar measurements, is used to improve areal rainfall estimations for hydrological applications. In order to highlight the effect that precipitation fields with different level of spatial details have on hydrological modelling, two kinds of spatial rainfall fields were computed for precipitation data collected during 2015, considering both rain gauges only and their merging with radar information. The differences produced by these two precipitation fields in the computation of the areal mean rainfall accumulation were evaluated considering 999 basins of the region Calabria, southern Italy. Moreover, both of the two precipitation fields were used to carry out rainfall-runoff simulations at catchment scale for main precipitation events that occurred during 2015 and the differences between the scenarios obtained in the two cases were analysed. A representative case study is presented in detail.

Assessment of extreme value distributions for maximum temperature in the Mediterranean area

NASA Astrophysics Data System (ADS)

Beck, Alexander; Hertig, Elke; Jacobeit, Jucundus

2015-04-01

Extreme maximum temperatures highly affect the natural as well as the societal environment Heat stress has great effects on flora, fauna and humans and culminates in heat related morbidity and mortality. Agriculture and different industries are severely affected by extreme air temperatures. Even more under climate change conditions, it is necessary to detect potential hazards which arise from changes in the distributional parameters of extreme values, and this is especially relevant for the Mediterranean region which is characterized as a climate change hot spot. Therefore statistical approaches are developed to estimate these parameters with a focus on non-stationarities emerging in the relationship between regional climate variables and their large-scale predictors like sea level pressure, geopotential heights, atmospheric temperatures and relative humidity. Gridded maximum temperature data from the daily E-OBS dataset (Haylock et al., 2008) with a spatial resolution of 0.25° x 0.25° from January 1950 until December 2012 are the predictands for the present analyses. A s-mode principal component analysis (PCA) has been performed in order to reduce data dimension and to retain different regions of similar maximum temperature variability. The grid box with the highest PC-loading represents the corresponding principal component. A central part of the analyses is the model development for temperature extremes under the use of extreme value statistics. A combined model is derived consisting of a Generalized Pareto Distribution (GPD) model and a quantile regression (QR) model which determines the GPD location parameters. The QR model as well as the scale parameters of the GPD model are conditioned by various large-scale predictor variables. In order to account for potential non-stationarities in the predictors-temperature relationships, a special calibration and validation scheme is applied, respectively. Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones, and M. New (2008), A European daily high-resolution gridded data set of surface temperature and precipitation for 1950 - 2006, J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.

Spatial extremes modeling applied to extreme precipitation data in the state of Paraná

NASA Astrophysics Data System (ADS)

Olinda, R. A.; Blanchet, J.; dos Santos, C. A. C.; Ozaki, V. A.; Ribeiro, P. J., Jr.

2014-11-01

Most of the mathematical models developed for rare events are based on probabilistic models for extremes. Although the tools for statistical modeling of univariate and multivariate extremes are well developed, the extension of these tools to model spatial extremes includes an area of very active research nowadays. A natural approach to such a modeling is the theory of extreme spatial and the max-stable process, characterized by the extension of infinite dimensions of multivariate extreme value theory, and making it possible then to incorporate the existing correlation functions in geostatistics and therefore verify the extremal dependence by means of the extreme coefficient and the Madogram. This work describes the application of such processes in modeling the spatial maximum dependence of maximum monthly rainfall from the state of Paraná, based on historical series observed in weather stations. The proposed models consider the Euclidean space and a transformation referred to as space weather, which may explain the presence of directional effects resulting from synoptic weather patterns. This method is based on the theorem proposed for de Haan and on the models of Smith and Schlather. The isotropic and anisotropic behavior of these models is also verified via Monte Carlo simulation. Estimates are made through pairwise likelihood maximum and the models are compared using the Takeuchi Information Criterion. By modeling the dependence of spatial maxima, applied to maximum monthly rainfall data from the state of Paraná, it was possible to identify directional effects resulting from meteorological phenomena, which, in turn, are important for proper management of risks and environmental disasters in countries with its economy heavily dependent on agribusiness.

Spatial and Temporal Patterns of Throughfall Amounts and Solutes in a Tropical Montane Forest - Comparisons with Findings From Lowland Rain Forests

NASA Astrophysics Data System (ADS)

Zimmermann, A.

2007-05-01

The diverse tree species composition, irregular shaped tree crowns and a multi-layered forest structure affect the redistribution of rainfall in lower montane rain forests. In addition, abundant epiphyte biomass and associated canopy humus influence spatial patterns of throughfall. The spatial variability of throughfall amounts controls spatial patterns of solute concentrations and deposition. Moreover, the living and dead biomass interacts with the rainwater during the passage through the canopy and creates a chemical variability of its own. Since spatial and temporal patterns are intimately linked, the analysis of temporal solute concentration dynamics is an important step to understand the emerging spatial patterns. I hypothesized that: (1) the spatial variability of volumes and chemical composition of throughfall is particularly high compared with other forests because of the high biodiversity and epiphytism, (2) the temporal stability of the spatial pattern is high because of stable structures in the canopy (e.g. large epiphytes) that show only minor changes during the short term observation period, and (3) the element concentrations decrease with increasing rainfall because of exhausting element pools in the canopy. The study area at 1950 m above sea level is located in the south Ecuadorian Andes far away from anthropogenic emission sources and marine influences. Rain and throughfall were collected from August to October 2005 on an event and within-event basis for five precipitation periods and analyzed for pH, K, Na, Ca, Mg, NH4+, Cl-, NO3-, PO43-, TN, TP and TOC. Throughfall amounts and most of the solutes showed a high spatial variability, thereby the variability of H+, K, Ca, Mg, Cl- and NO3- exceeded those from a Brazilian tropical rain forest. The temporal persistence of the spatial patterns was high for throughfall amounts and varied depending on the solute. Highly persistent time stability patterns were detected for K, Mg and TOC concentrations. Time stability patterns of solute deposition were somewhat weaker than for concentrations for most of the solutes. Epiphytes strongly affected time stability patterns in that collectors situated below thick moss mats or arboreal bromeliads were in large part responsible for the extreme persistence with low throughfall amounts and high ion concentrations (H+ showed low concentrations). Rainfall solute concentrations were low compared with a variety of other tropical lowland and montane forest sites and showed a small temporal variability during the study period for both between and within-event dynamics, respectively. Throughfall solute concentrations were more within the range when compared with other sites and showed highly variable within-event dynamics. For most of the solutes, within-event concentrations did not reach low, constant concentrations in later event stages, rather concentrations fluctuated (e.g. Cl-) or increased (e.g. K and TOC). The within-event throughfall solute concentration dynamics in this lower montane rain forest contrast to recent observations from lowland tropical rain forests in Panama and Brazil. The observed within-event patterns are attributed (1) to the influence of epiphytes and associated canopy humus, and (2) to low rainfall intensities.

An assessment of precipitation and surface air temperature over China by regional climate models

NASA Astrophysics Data System (ADS)

Wang, Xueyuan; Tang, Jianping; Niu, Xiaorui; Wang, Shuyu

2016-12-01

An analysis of a 20-year summer time simulation of present-day climate (1989-2008) over China using four regional climate models coupled with different land surface models is carried out. The climatic means, interannual variability, linear trends, and extremes are examined, with focus on precipitation and near surface air temperature. The models are able to reproduce the basic features of the observed summer mean precipitation and temperature over China and the regional detail due to topographic forcing. Overall, the model performance is better for temperature than that of precipitation. The models reasonably grasp the major anomalies and standard deviations over China and the five subregions studied. The models generally reproduce the spatial pattern of high interannual variability over wet regions, and low variability over the dry regions. The models also capture well the variable temperature gradient increase to the north by latitude. Both the observed and simulated linear trend of precipitation shows a drying tendency over the Yangtze River Basin and wetting over South China. The models capture well the relatively small temperature trends in large areas of China. The models reasonably simulate the characteristics of extreme precipitation indices of heavy rain days and heavy precipitation fraction. Most of the models also performed well in capturing both the sign and magnitude of the daily maximum and minimum temperatures over China.

Small scale currents and ocean wave heights: from today's models to future satellite observations with CFOSAT and SKIM

NASA Astrophysics Data System (ADS)

Ardhuin, Fabrice; Gille, Sarah; Menemenlis, Dimitris; Rocha, Cesar; Rascle, Nicolas; Gula, Jonathan; Chapron, Bertrand

2017-04-01

Tidal currents and large oceanic currents, such as the Agulhas, Gulf Stream and Kuroshio, are known to modify ocean wave properties, causing extreme sea states that are a hazard to navigation. Recent advances in the understanding and modeling capability of ocean currents at scales of 10 km or less have revealed the ubiquitous presence of fronts and filaments. Based on realistic numerical models, we show that these structures can be the main source of variability in significant wave heights at scales less than 200 km, including important variations at 10 km. This current-induced variability creates gradients in wave heights that were previously overlooked and are relevant for extreme wave heights and remote sensing. The spectrum of significant wave heights is found to be of the order of 70⟨Hs ⟩2/(g2⟨Tm0,-1⟩2) times the current spectrum, where ⟨Hs ⟩ is the spatially-averaged significant wave height, ⟨Tm0,-1⟩ is the average energy period, and g is the gravity acceleration. This small scale variability is consistent with Jason-3 and SARAL along-track variability. We will discuss how future satellite mission with wave spectrometers can help observe these wave-current interactions. CFOSAT is due for launch in 2018, and SKIM is a proposal for ESA Earth Explorer 9.

Extremophiles in Household Water Heaters

NASA Astrophysics Data System (ADS)

Wilpiszeski, R.; House, C. H.

2016-12-01

A significant fraction of Earth's microbial diversity comes from species living in extreme environments, but natural extreme environments can be difficult to access. Manmade systems like household water heaters serve as an effective proxy for thermophilic environments that are otherwise difficult to sample directly. As such, we are investigating the biogeography, taxonomic distribution, and evolution of thermophiles growing in domestic water heaters. Citizen scientists collected hot tap water culture- and filter- samples from 101 homes across the United States. We recovered a single species of thermophilic heterotroph from culture samples inoculated from water heaters across the United States, Thermus scotoductus. Whole-genome sequencing was conducted to better understand the distribution and evolution of this single species. We have also sequenced hyper-variable regions of the 16S rRNA gene from whole-community filter samples to identify the broad diversity and distribution of microbial cells captured from each water heater. These results shed light on the processes that shape thermophilic populations and genomes at a spatial resolution that is difficult to access in naturally occurring extreme ecosystems.

Biological Extreme Events - Past, Present, and Future

NASA Astrophysics Data System (ADS)

Gutschick, V. P.

2010-12-01

Biological extreme events span wide ranges temporally and spatially and in type - population dieoffs, extinctions, ecological reorganizations, changes in biogeochemical fluxes, and more. Driving variables consist in meteorology, tectonics, orbital changes, anthropogenic changes (land-use change, species introductions, reactive N injection into the biosphere), and evolution (esp. of diseases). However, the mapping of extremes in the drivers onto biological extremes as organismal responses is complex, as laid out originally in the theoretical framework of Gutschick and BassiriRad (New Phytologist [2003] 100:21-42). Responses are nonlinear and dependent on (mostly unknown and) complex temporal sequences - often of multiple environmental variables. The responses are species- and genotype specific. I review extreme events over from past to present over wide temporal scales, while noting that they are not wholly informative of responses to the current and near-future drivers for at least two reasons: 1) the current combination of numerous environmental extremes - changes in CO2, temperature, precipitation, reactive N, land fragmentation, O3, etc. -is unprecedented in scope, and 2) adaptive genetic variation for organismal responses is constrained by poorly-characterized genetic structures (in organisms and populations) and by loss of genetic variation by genetic drift over long periods. We may expect radical reorganizations of ecosystem and biogeochemical functions. These changes include many ecosystem services in flood control, crop pollination and insect/disease control, C-water-mineral cycling, and more, as well as direct effects on human health. Predictions of such changes will necessarily be very weak in the critical next few decades, given the great deal of observation, experimentation, and theory construction that will be necessary, on both organisms and drivers. To make the research efforts most effective will require extensive, insightful planning, beginning immediately. Massive dieoff of conifers in the US Southwest, an extreme event driven by a remarkably uncommon co-occurrence of high temperature, drought, and long active season for insects

Anticipating Future Extreme Climate Events for Alaska Using Dynamical Downscaling and Quantile Mapping

NASA Astrophysics Data System (ADS)

Lader, R.; Walsh, J. E.

2016-12-01

Alaska is projected to experience major changes in extreme climate during the 21st century, due to greenhouse warming and exacerbated by polar amplification, wherein the Arctic is warming at twice the rate compared to the Northern Hemisphere. Given its complex topography, Alaska displays extreme gradients of temperature and precipitation. However, global climate models (GCMs), which typically have a spatial resolution on the order of 100km, struggle to replicate these extremes. To help resolve this issue, this study employs dynamically downscaled regional climate simulations and quantile-mapping methodologies to provide a full suite of daily model variables at 20 km spatial resolution for Alaska, from 1970 to 2100. These data include downscaled products of the: ERA-Interim reanalysis from 1979 to 2015, GFDL-CM3 historical from 1970 to 2005, and GFDL-CM3 RCP 8.5 from 2006 to 2100. Due to the limited nature of long-term observations and high-resolution modeling in Alaska, these data enable a broad expansion of extremes analysis. This study uses these data to highlight a subset of the 27 climate extremes indices, previously defined by the Expert Team on Climate Change Detection and Indices, as they pertain to climate change in Alaska. These indices are based on the statistical distributions of daily surface temperature and precipitation and focus on threshold exceedance, and percentiles. For example, the annual number of days with a daily maximum temperature greater than 25°C is anticipated to triple in many locations in Alaska by the end of the century. Climate extremes can also refer to long duration events, such as the record-setting warmth that defined the 2015-16 cold season in Alaska. The downscaled climate model simulations indicate that this past winter will be considered normal by as early as the mid-2040s, if we continue to warm according to the business-as-usual RCP 8.5 emissions scenario. This represents an accelerated warming as compared to projections form the coarse scale GCMs, and this greater rate of change in the downscaled products is noted with other extremes indices as well.

Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods

NASA Astrophysics Data System (ADS)

Werner, A. T.; Cannon, A. J.

2015-06-01

Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis datasets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical datasets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.

Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods

NASA Astrophysics Data System (ADS)

Werner, Arelia T.; Cannon, Alex J.

2016-04-01

Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis data sets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical data sets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.

Climate change impacts on crop yield in the Euro-Mediterranean region

NASA Astrophysics Data System (ADS)

Toreti, Andrea; Ceglar, Andrej; Dentener, Frank; Niemeyer, Stefan; Dosio, Alessandro; Fumagalli, Davide

2017-04-01

Agriculture is strongly influenced by climate variability, climate extremes and climate changes. Recent studies on past decades have identified and analysed the effects of climate variability and extremes on crop yields in the Euro-Mediterranean region. As these effects could be amplified in a changing climate context, it is essential to analyse available climate projections and investigate the possible impacts on European agriculture in terms of crop yield. In this study, five model runs from the Euro-CORDEX initiative under two scenarios (RCP4.5 and RCP8.5) have been used. Climate model data have been bias corrected and then used to feed a mechanistic crop growth model. The crop model has been run under different settings to better sample the intrinsic uncertainties. Among the main results, it is worth to report a weak but significant and spatially homogeneous increase in potential wheat yield at mid-century (under a CO2 fertilisation effect scenario). While more complex changes seem to characterise potential maize yield, with large areas in the region showing a weak-to-moderate decrease.

Evaluation of Statistical Downscaling Skill at Reproducing Extreme Events

NASA Astrophysics Data System (ADS)

McGinnis, S. A.; Tye, M. R.; Nychka, D. W.; Mearns, L. O.

2015-12-01

Climate model outputs usually have much coarser spatial resolution than is needed by impacts models. Although higher resolution can be achieved using regional climate models for dynamical downscaling, further downscaling is often required. The final resolution gap is often closed with a combination of spatial interpolation and bias correction, which constitutes a form of statistical downscaling. We use this technique to downscale regional climate model data and evaluate its skill in reproducing extreme events. We downscale output from the North American Regional Climate Change Assessment Program (NARCCAP) dataset from its native 50-km spatial resolution to the 4-km resolution of University of Idaho's METDATA gridded surface meterological dataset, which derives from the PRISM and NLDAS-2 observational datasets. We operate on the major variables used in impacts analysis at a daily timescale: daily minimum and maximum temperature, precipitation, humidity, pressure, solar radiation, and winds. To interpolate the data, we use the patch recovery method from the Earth System Modeling Framework (ESMF) regridding package. We then bias correct the data using Kernel Density Distribution Mapping (KDDM), which has been shown to exhibit superior overall performance across multiple metrics. Finally, we evaluate the skill of this technique in reproducing extreme events by comparing raw and downscaled output with meterological station data in different bioclimatic regions according to the the skill scores defined by Perkins et al in 2013 for evaluation of AR4 climate models. We also investigate techniques for improving bias correction of values in the tails of the distributions. These techniques include binned kernel density estimation, logspline kernel density estimation, and transfer functions constructed by fitting the tails with a generalized pareto distribution.

It's the Heat AND the Humidity -- Assessment of Extreme Heat Scenarios to Enable the Assessment of Climate Impacts on Public Health

NASA Technical Reports Server (NTRS)

Crosson, William L; Al-Hamdan, Mohammad Z.; Economou, Sigrid, A.; Estes, Maurice G.; Estes, Sue M.; Puckett, Mark; Quattrochi, Dale A

2013-01-01

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. In a NASA-funded project supporting the National Climate Assessment, we are providing historical and future measures of extreme heat to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The project s emphasis is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM output, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons, 2040 and 2090, are the focus of future assessments; these are compared to the recent past period of 1981-2000. We are characterizing regional-scale temperature and humidity conditions using GCM output for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM output have been analyzed to develop a heat stress climatology based on statistics of extreme heat indicators. Differences between the two future and past periods have been used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes, combined with hourly historical meteorological data at a spatial scale (12 km) much finer than that of GCMs, enable us to create future climate realizations, from which we compute the daily heat stress measures and related spatially-specific climatological fields. These include the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices and a new heat stress variable that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. All output is being provided at the 12 km spatial scale and will also be aggregated to the county level, which is a popular scale of analysis for public health researchers. County-level statistics will be made available by our collaborators at the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. CDC WONDER makes the information resources of the CDC available to public health professionals and the general public. This addition of heat stress measures to CDC WONDER will allow decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. It will also allow public health researchers and policy makers to better include such heat stress measures in the context of national health data available in the CDC WONDER system. The users will be able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S.

Analysis of satellite precipitation over East Africa during last decades

NASA Astrophysics Data System (ADS)

Cattani, Elsa; Wenhaji Ndomeni, Claudine; Merino, Andrés; Levizzani, Vincenzo

2016-04-01

Daily accumulated precipitation time series from satellite retrieval algorithms (e.g., ARC2 and TAMSAT) are exploited to extract the spatial and temporal variability of East Africa (EA - 5°S-20°N, 28°E-52°E) precipitation during last decades (1983-2013). The Empirical Orthogonal Function (EOF) analysis is applied to precipitation time series to investigate the spatial and temporal variability in particular for October-November-December referred to as the short rain season. Moreover, the connection among EA's precipitation, sea surface temperature, and soil moisture is analyzed through the correlation with the dominant EOF modes of variability. Preliminary results concern the first two EOF's modes for the ARC2 data set. EOF1 is characterized by an inter-annual variability and a positive correlation between precipitation and El Niño, positive Indian Ocean Dipole mode, and soil moisture, while EOF2 shows a dipole structure of spatial variability associated with a longer scale temporal variability. This second dominant mode is mostly linked to sea surface temperature variations in the North Atlantic Ocean. Further analyses are carried out by computing the time series of the joint CCI/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI, http://etccdi.pacificclimate.org/index.shtml), i.e. RX1day, RX5day, CDD, CDD, CWD, SDII, PRCPTOT, R10, R20. The purpose is to identify the occurrenes of extreme events (droughts and floods) and extract precipitation temporal variation by trend analysis (Mann-Kendall technique). Results for the ARC2 data set demonstrate the existence of a dipole spatial pattern in the linear trend of the time series of PRCPTOT (annual precipitation considering days with a rain rate > 1 mm) and SDII (average precipitation on wet days over a year). A negative trend is mainly present over West Ethiopia and Sudan, whereas a positive trend is exhibited over East Ethiopia and Somalia. CDD (maximum number of consecutive dry days) and CWD (maximum number of consecutive wet days) time series do not exhibit a similar behavior and trends are generally weaker with a lower significance level with respect to PRCPTOT and SDII.

Practical way to avoid spurious geometrical contributions in Brillouin light scattering experiments at variable scattering angles.

PubMed

Battistoni, Andrea; Bencivenga, Filippo; Fioretto, Daniele; Masciovecchio, Claudio

2014-10-15

In this Letter, we present a simple method to avoid the well-known spurious contributions in the Brillouin light scattering (BLS) spectrum arising from the finite aperture of collection optics. The method relies on the use of special spatial filters able to select the scattered light with arbitrary precision around a given value of the momentum transfer (Q). We demonstrate the effectiveness of such filters by analyzing the BLS spectra of a reference sample as a function of scattering angle. This practical and inexpensive method could be an extremely useful tool to fully exploit the potentiality of Brillouin acoustic spectroscopy, as it will easily allow for effective Q-variable experiments with unparalleled luminosity and resolution.

Linking Excessive Heat with Daily Heat-Related Mortality over the Coterminous United States

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Crosson, William L.; Al-Hamdan, Mohammad Z.; Estes, Maurice G., Jr.

2014-01-01

In the United States, extreme heat is the most deadly weather-related hazard. In the face of a warming climate and urbanization, which contributes to local-scale urban heat islands, it is very likely that extreme heat events (EHEs) will become more common and more severe in the U.S. This research seeks to provide historical and future measures of climate-driven extreme heat events to enable assessments of the impacts of heat on public health over the coterminous U.S. We use atmospheric temperature and humidity information from meteorological reanalysis and from Global Climate Models (GCMs) to provide data on past and future heat events. The focus of research is on providing assessments of the magnitude, frequency and geographic distribution of extreme heat in the U.S. to facilitate public health studies. In our approach, long-term climate change is captured with GCM outputs, and the temporal and spatial characteristics of short-term extremes are represented by the reanalysis data. Two future time horizons for 2040 and 2090 are compared to the recent past period of 1981- 2000. We characterize regional-scale temperature and humidity conditions using GCM outputs for two climate change scenarios (A2 and A1B) defined in the Special Report on Emissions Scenarios (SRES). For each future period, 20 years of multi-model GCM outputs are analyzed to develop a 'heat stress climatology' based on statistics of extreme heat indicators. Differences between the two future and the past period are used to define temperature and humidity changes on a monthly time scale and regional spatial scale. These changes are combined with the historical meteorological data, which is hourly and at a spatial scale (12 km) much finer than that of GCMs, to create future climate realizations. From these realizations, we compute the daily heat stress measures and related spatially-specific climatological fields, such as the mean annual number of days above certain thresholds of maximum and minimum air temperatures, heat indices, and a new heat stress variable developed as part of this research that gives an integrated measure of heat stress (and relief) over the course of a day. Comparisons are made between projected (2040 and 2090) and past (1990) heat stress statistics. Outputs are aggregated to the county level, which is a popular scale of analysis for public health interests. County-level statistics are made available to public health researchers by the Centers for Disease Control and Prevention (CDC) via the Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This addition of heat stress measures to CDC WONDER allows decision and policy makers to assess the impact of alternative approaches to optimize the public health response to EHEs. Through CDC WONDER, users are able to spatially and temporally query public health and heat-related data sets and create county-level maps and statistical charts of such data across the coterminous U.S.

Multivariate spatial analysis of a heavy rain event in a densely populated delta city

NASA Astrophysics Data System (ADS)

Gaitan, Santiago; ten Veldhuis, Marie-claire; Bruni, Guenda; van de Giesen, Nick

2014-05-01

Delta cities account for half of the world's population and host key infrastructure and services for the global economic growth. Due to the characteristic geography of delta areas, these cities face high vulnerability to extreme weather and pluvial flooding risks, that are expected to increase as climate change drives heavier rain events. Besides, delta cities are subjected to fast urban densification processes that progressively make them more vulnerable to pluvial flooding. Delta cities need to be adapted to better cope with this threat. The mechanism leading to damage after heavy rains is not completely understood. For instance, current research has shown that rain intensities and volumes can only partially explain the occurrence and localization of rain-related insurance claims (Spekkers et al., 2013). The goal of this paper is to provide further insights into spatial characteristics of the urban environment that can significantly be linked to pluvial-related flooding impacts. To that end, a study-case has been selected: on October 12 to 14 2013, a heavy rain event triggered pluvial floods in Rotterdam, a densely populated city which is undergoing multiple climate adaptation efforts and is located in the Meuse river Delta. While the average yearly precipitation in this city is around 800 mm, local rain gauge measurements ranged from aprox. 60 to 130 mm just during these three days. More than 600 citizens' telephonic complaints reported impacts related to rainfall. The registry of those complaints, which comprises around 300 calls made to the municipality and another 300 to the fire brigade, was made available for research. Other accessible information about this city includes a series of rainfall measurements with up to 1 min time-step at 7 different locations around the city, ground-based radar rainfall data (1 Km^2 spatial resolution and 5 min time-step), a digital elevation model (50 cm of horizontal resolution), a model of overland-flow paths, cadastral maps describing individual location and types of buildings, and maps on categorical socioeconomic statistics (1 Ha of spatial resolution). On the basis of the quality and availability of the mentioned information, spatial and temporal units of analysis will be discussed and defined. Aggregation of single occurrences for binary variables will be performed, while simple interpolations or averages will be used in case of continuous or categorical data. To determine spatial clustering within each variable, Nearest Neighbor Distance and Spatial Autocorrelation tests will be carried out. When appropriate, the Getis-Ord Gi* test will be used to identify single variable clusters. Finally, with the purpose of inferring possible associations between the available spatially distributed variables, a Mantel test will be applied to variables with a probed non-random spatial pattern. The results of this paper will allow to determine if the environmental characteristics described by the available data can provide additional explanation of the variability of rain-related damage in a delta city which is willing to become climate-proof.

Estimating and mapping ecological processes influencing microbial community assembly

DOE PAGES

Stegen, James C.; Lin, Xueju; Fredrickson, Jim K.; ...

2015-05-01

Ecological community assembly is governed by a combination of (i) selection resulting from among-taxa differences in performance; (ii) dispersal resulting from organismal movement; and (iii) ecological drift resulting from stochastic changes in population sizes. The relative importance and nature of these processes can vary across environments. Selection can be homogeneous or variable, and while dispersal is a rate, we conceptualize extreme dispersal rates as two categories; dispersal limitation results from limited exchange of organisms among communities, and homogenizing dispersal results from high levels of organism exchange. To estimate the influence and spatial variation of each process we extend a recentlymore » developed statistical framework, use a simulation model to evaluate the accuracy of the extended framework, and use the framework to examine subsurface microbial communities over two geologic formations. For each subsurface community we estimate the degree to which it is influenced by homogeneous selection, variable selection, dispersal limitation, and homogenizing dispersal. Our analyses revealed that the relative influences of these ecological processes vary substantially across communities even within a geologic formation. We further identify environmental and spatial features associated with each ecological process, which allowed mapping of spatial variation in ecological-process-influences. The resulting maps provide a new lens through which ecological systems can be understood; in the subsurface system investigated here they revealed that the influence of variable selection was associated with the rate at which redox conditions change with subsurface depth.« less

Dynamically-downscaled projections of changes in temperature extremes over China

NASA Astrophysics Data System (ADS)

Guo, Junhong; Huang, Guohe; Wang, Xiuquan; Li, Yongping; Lin, Qianguo

2018-02-01

In this study, likely changes in extreme temperatures (including 16 indices) over China in response to global warming throughout the twenty-first century are investigated through the PRECIS regional climate modeling system. The PRECIS experiment is conducted at a spatial resolution of 25 km and is driven by a perturbed-physics ensemble to reflect spatial variations and model uncertainties. Simulations of present climate (1961-1990) are compared with observations to validate the model performance in reproducing historical climate over China. Results indicate that the PRECIS demonstrates reasonable skills in reproducing the spatial patterns of observed extreme temperatures over the most regions of China, especially in the east. Nevertheless, the PRECIS shows a relatively poor performance in simulating the spatial patterns of extreme temperatures in the western mountainous regions, where its driving GCM exhibits more uncertainties due to lack of insufficient observations and results in more errors in climate downscaling. Future spatio-temporal changes of extreme temperature indices are then analyzed for three successive periods (i.e., 2020s, 2050s and 2080s). The projected changes in extreme temperatures by PRECIS are well consistent with the results of the major global climate models in both spatial and temporal patterns. Furthermore, the PRECIS demonstrates a distinct superiority in providing more detailed spatial information of extreme indices. In general, all extreme indices show similar changes in spatial pattern: large changes are projected in the north while small changes are projected in the south. In contrast, the temporal patterns for all indices vary differently over future periods: the warm indices, such as SU, TR, WSDI, TX90p, TN90p and GSL are likely to increase, while the cold indices, such as ID, FD, CSDI, TX10p and TN10p, are likely to decrease with time in response to global warming. Nevertheless, the magnitudes of changes in all indices tend to decrease gradually with time, indicating the projected warming will begin to slow down in the late of this century. In addition, the projected range of changes for all indices would become larger with time, suggesting more uncertainties would be involved in long-term climate projections.

Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery

NASA Astrophysics Data System (ADS)

Beguet, Benoit; Guyon, Dominique; Boukir, Samia; Chehata, Nesrine

2014-10-01

The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pléiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ˜ 1.1 m is expected on crown diameter, ˜ 0.9 m on tree spacing, ˜ 3 m on height and ˜ 0.06 m on diameter at breast height.

Long-term changes and spatio-temporal variability of the growing season temperature in Europe during the last Millennium

NASA Astrophysics Data System (ADS)

Guiot, Joel; Corona, Christophe

2010-05-01

A gridded reconstruction of April to September temperature was produced for Europe based on tree-rings, documentaries, pollen and ice cores. The majority of the proxy series have an annual resolution. For a better inference of long-term climate variations, they were completed by number of low resolution data (decadal or more), mostly on pollen and ice-core data. An original spectral analogue method was devised to deal with this heterogeneous dataset, and especially to preserve the long-term variations and the variability of the temperature series. It is the condition is to make pertinent the comparison of the recent climate changes to a broader context of 1400 years. The reconstruction of the April-September temperature was validated with a Jack-knife technique, and it was also compared with other spatially gridded temperature reconstructions, literature data, and glacier advance and retreat curves. We also attempted to relate the spatial distribution of European temperature anomalies to known solar and volcanic forcings. We found that (1) our results are sound back to A.D. 750; (2) conditions during the last decade have exceeded all those known during the last millennium; (3) before the 20th century, cold periods can partly be explained by low solar activity and/or high volcanic activity and that Medieval Warm Period (MWP) is consistent with a high solar activity; (4) during the 20th century, however only anthropogenic forcing can explain the exceptionally high temperature rise; (5) based on an analysis of the distribution of extreme temperatures, the maximum event of the Medieval Period (1.1°C higher than the 1960-1990 reference period) had a return period of more than 1000 years, but this recently fell to less than 26 years; (6) all decades before AD 1350 were warm on average but relatively heterogeneous, while the last decade was homogeneously warmer. These results support the fact that we are facing an unprecedented changing climate in Europe unlike any known in the last 1000 years, as pointed out previously. The new result is that this anthropogenic change is characterised by spatial homogeneity and changes as well in average temperatures than in distribution of extreme events, while natural climate forcings induce warm periods with heterogeneous spatial patterns and less frequent extreme events. This study demonstrates that recent changes in temperature differ substantially from temperature changes reconstructed in the past and are well in excess of normal variations experienced in previous centuries and caused by natural forcings.

Decoding the spatial signatures of multi-scale climate variability - a climate network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Jajcay, N.; Wiedermann, M.; Ekhtiari, N.; Palus, M.

2017-12-01

During the last years, the application of complex networks as a versatile tool for analyzing complex spatio-temporal data has gained increasing interest. Establishing this approach as a new paradigm in climatology has already provided valuable insights into key spatio-temporal climate variability patterns across scales, including novel perspectives on the dynamics of the El Nino Southern Oscillation or the emergence of extreme precipitation patterns in monsoonal regions. In this work, we report first attempts to employ network analysis for disentangling multi-scale climate variability. Specifically, we introduce the concept of scale-specific climate networks, which comprises a sequence of networks representing the statistical association structure between variations at distinct time scales. For this purpose, we consider global surface air temperature reanalysis data and subject the corresponding time series at each grid point to a complex-valued continuous wavelet transform. From this time-scale decomposition, we obtain three types of signals per grid point and scale - amplitude, phase and reconstructed signal, the statistical similarity of which is then represented by three complex networks associated with each scale. We provide a detailed analysis of the resulting connectivity patterns reflecting the spatial organization of climate variability at each chosen time-scale. Global network characteristics like transitivity or network entropy are shown to provide a new view on the (global average) relevance of different time scales in climate dynamics. Beyond expected trends originating from the increasing smoothness of fluctuations at longer scales, network-based statistics reveal different degrees of fragmentation of spatial co-variability patterns at different scales and zonal shifts among the key players of climate variability from tropically to extra-tropically dominated patterns when moving from inter-annual to decadal scales and beyond. The obtained results demonstrate the potential usefulness of systematically exploiting scale-specific climate networks, whose general patterns are in line with existing climatological knowledge, but provide vast opportunities for further quantifications at local, regional and global scales that are yet to be explored.

Are extreme hydro-meteorological events a prerequisite for extreme water quality impacts? Exploring climate impacts on inland and coastal waters

NASA Astrophysics Data System (ADS)

Michalak, A. M.; Balaji, V.; Del Giudice, D.; Sinha, E.; Zhou, Y.; Ho, J. C.

2017-12-01

Questions surrounding water sustainability, climate change, and extreme events are often framed around water quantity - whether too much or too little. The massive impacts of extreme water quality impairments are equally compelling, however. Recent years have provided a host of compelling examples, with unprecedented harmful algal blooms developing along the West coast, in Utah Lake, in Lake Erie, and off the Florida coast, and huge hypoxic dead zones continuing to form in regions such as Lake Erie, the Chesapeake Bay, and the Gulf of Mexico. Linkages between climate change, extreme events, and water quality impacts are not well understood, however. Several factors explain this lack of understanding, including the relative complexity of underlying processes, the spatial and temporal scale mismatch between hydrologists and climatologists, and observational uncertainty leading to ambiguities in the historical record. Here, we draw on a number of recent studies that aim to quantitatively link meteorological variability and water quality impacts to test the hypothesis that extreme water quality impairments are the result of extreme hydro-meteorological events. We find that extreme hydro-meteorological events are neither always a necessary nor a sufficient condition for the occurrence of extreme water quality impacts. Rather, extreme water quality impairments often occur in situations where multiple contributing factors compound, which complicates both attribution of historical events and the ability to predict the future incidence of such events. Given the critical societal importance of water quality projections, a concerted program of uncertainty reduction encompassing observational and modeling components will be needed to examine situations where extreme weather plays an important, but not solitary, role in the chain of cause and effect.

Application of a symbolic motion structure representation algorithm to identify upper extremity kinematic changes during a repetitive task.

PubMed

Whittaker, Rachel L; Park, Woojin; Dickerson, Clark R

2018-04-27

Efficient and holistic identification of fatigue-induced movement strategies can be limited by large between-subject variability in descriptors of joint angle data. One promising alternative to traditional, or computationally intensive methods is the symbolic motion structure representation algorithm (SMSR), which identifies the basic spatial-temporal structure of joint angle data using string descriptors of temporal joint angle trajectories. This study attempted to use the SMSR to identify changes in upper extremity time series joint angle data during a repetitive goal directed task causing muscle fatigue. Twenty-eight participants (15 M, 13 F) performed a seated repetitive task until fatigued. Upper extremity joint angles were extracted from motion capture for representative task cycles. SMSRs, averages and ranges of several joint angles were compared at the start and end of the repetitive task to identify kinematic changes with fatigue. At the group level, significant increases in the range of all joint angle data existed with large between-subject variability that posed a challenge to the interpretation of these fatigue-related changes. However, changes in the SMSRs across participants effectively summarized the adoption of adaptive movement strategies. This establishes SMSR as a viable, logical, and sensitive method of fatigue identification via kinematic changes, with novel application and pragmatism for visual assessment of fatigue development. Copyright © 2018 Elsevier Ltd. All rights reserved.

Generalist feeding strategies in Arctic freshwater fish: A mechanism for dealing with extreme environments

USGS Publications Warehouse

Laske, Sarah M.; Rosenberger, Amanda E.; Wipfli, Mark S.; Zimmerman, Christian E.

2018-01-01

Generalist feeding strategies are favoured in stressful or variable environments where flexibility in ecological traits is beneficial. Species that feed across multiple habitat types and trophic levels may impart stability on food webs through the use of readily available, alternative energy pools. In lakes, generalist fish species may take advantage of spatially and temporally variable prey by consuming both benthic and pelagic prey to meet their energy demands. Using stomach content and stable isotope analyses, we examined the feeding habits of fish species in Alaska's Arctic Coastal Plain (ACP) lakes to determine the prevalence of generalist feeding strategies as a mechanism for persistence in extreme environments (e.g. low productivity, extreme cold and short growing season). Generalist and flexible feeding strategies were evident in five common fish species. Fish fed on benthic and pelagic (or nektonic) prey and across trophic levels. Three species were clearly omnivorous, feeding on fish and their shared invertebrate prey. Dietary differences based on stomach content analysis often exceeded 70%, and overlap in dietary niches based on shared isotopic space varied from zero to 40%. Metrics of community‐wide trophic structure varied with the number and identity of species involved and on the dietary overlap and niche size of individual fishes. Accumulation of energy from shared carbon sources by Arctic fishes creates redundancy in food webs, increasing likely resistance to perturbations or stochastic events. Therefore, the generalist and omnivorous feeding strategies employed by ACP fish may maintain energy flow and food web stability in extreme environments.

Impacts of multiple global environmental changes on African crop yield and water use efficiency: Implications to food and water security

NASA Astrophysics Data System (ADS)

Pan, S.; Yang, J.; Zhang, J.; Xu, R.; Dangal, S. R. S.; Zhang, B.; Tian, H.

2016-12-01

Africa is one of the most vulnerable regions in the world to climate change and climate variability. Much concern has been raised about the impacts of climate and other environmental factors on water resource and food security through the climate-water-food nexus. Understanding the responses of crop yield and water use efficiency to environmental changes is particularly important because Africa is well known for widespread poverty, slow economic growth and agricultural systems particularly sensitive to frequent and persistent droughts. However, the lack of integrated understanding has limited our ability to quantify and predict the potential of Africa's agricultural sustainability and freshwater supply, and to better manage the system for meeting an increasing food demand in a way that is socially and environmentally or ecologically sustainable. By using the Dynamic Land Ecosystem Model (DLEM-AG2) driven by spatially-explicit information on land use, climate and other environmental changes, we have assessed the spatial and temporal patterns of crop yield, evapotranspiration (ET) and water use efficiency across entire Africa in the past 35 years (1980-2015) and the rest of the 21st century (2016-2099). Our preliminary results indicate that African crop yield in the past three decades shows an increasing trend primarily due to cropland expansion (about 50%), elevated atmospheric CO2 concentration, and nitrogen deposition. However, crop yield shows substantially spatial and temporal variation due to inter-annual and inter-decadal climate variability and spatial heterogeneity of environmental drivers. Climate extremes especially droughts and heat wave have largely reduced crop yield in the most vulnerable regions. Our results indicate that N fertilizer could be a major driver to improve food security in Africa. Future climate warming could reduce crop yield and shift cropland distribution. Our study further suggests that improving water use efficiency through land management practices including the increased uses of fertilizers and irrigation will be the key for reducing the loss of crop yield in a warming climate and extreme weather.

Disentangling environmental correlates of vascular plant biodiversity in a Mediterranean hotspot.

PubMed

Molina-Venegas, Rafael; Aparicio, Abelardo; Pina, Francisco José; Valdés, Benito; Arroyo, Juan

2013-10-01

We determined the environmental correlates of vascular plant biodiversity in the Baetic-Rifan region, a plant biodiversity hotspot in the western Mediterranean. A catalog of the whole flora of Andalusia and northern Morocco, the region that includes most of the Baetic-Rifan complex, was compiled using recent comprehensive floristic catalogs. Hierarchical cluster analysis (HCA) and detrended correspondence analysis (DCA) of the different ecoregions of Andalusia and northern Morocco were conducted to determine their floristic affinities. Diversity patterns were studied further by focusing on regional endemic taxa. Endemic and nonendemic alpha diversities were regressed to several environmental variables. Finally, semi-partial regressions on distance matrices were conducted to extract the respective contributions of climatic, altitudinal, lithological, and geographical distance matrices to beta diversity in endemic and nonendemic taxa. We found that West Rifan plant assemblages had more similarities with Andalusian ecoregions than with other nearby northern Morocco ecoregions. The endemic alpha diversity was explained relatively well by the environmental variables related to summer drought and extreme temperature values. Of all the variables, geographical distance contributed by far the most to spatial turnover in species diversity in the Baetic-Rifan hotspot. In the Baetic range, elevation was the most significant driver of nonendemic species beta diversity, while lithology and elevation were the main drivers of endemic beta diversity. Despite the fact that Andalusia and northern Morocco are presently separated by the Atlantic Ocean and the Mediterranean Sea, the Baetic and Rifan mountain ranges have many floristic similarities - especially in their western ranges - due to past migration of species across the Strait of Gibraltar. Climatic variables could be shaping the spatial distribution of endemic species richness throughout the Baetic-Rifan hotspot. Determinants of spatial turnover in biodiversity in the Baetic-Rifan hotspot vary in importance between endemic and nonendemic species.

Developing and diagnosing climate change indictors of regional aerosol optical properties

NASA Astrophysics Data System (ADS)

Sullivan, Ryan C.; Levy, Robert C.; da Silva, Arlindo M.; Pryor, Sara C.

2017-04-01

The US Global Change Research Program has developed climate indicators (CIs) to track changes in the physical, chemical, biological, and societal components of the climate system. Given the importance of atmospheric aerosol particles to clouds and radiative forcing, human mortality and morbidity, and biogeochemical cycles, we propose new aerosol particle CIs applicable to the US National Climate Assessment (NCA). Here we define these aerosol CIs and use them to quantify temporal trends in each NCA region. Furthermore, we use a synoptic classification (e.g., meteorological variables), and gas and particle emissions inventories to diagnose and attribute causes of observed changes. Our CIs are derived using output from the satellite-constrained Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2) reanalysis. MERRA-2 provides estimates of column-integrated aerosol optical properties at 0.625° by 0.5° resolution, including aerosol optical depth (AOD), Ångström exponent (AE), and single scattering albedo (SSA), which are related to aerosol loading, relative particle size, and chemical composition, respectively. For each NCA region, and for each aerosol variable, we derive statistics that describe mean and extreme values, as well as two metrics (spatial autocorrelation and coherence) that describe the spatial scales of aerosol variability. Consistent with previous analyses of aerosol precursor emissions and near-surface fine aerosol mass concentrations in the US, analyses of our aerosol CIs show that since 2000, both mean and extreme AOD have decreased over most NCA regions. There are significant (α = 0.05, using the non-parametric Kendall's tau) decreases in AOD for the Northeast (NE), Southeast (SE), Midwest (MW), and lower Great Plains (GPl) regions, and notable but not significant decreases in the Southwest (SW). AOD has increased for the Northwest (NW; significant) and upper Great Plains (GPu; not significant). Over all regions, there is a significant positive trend in AE (relative decrease in aerosol size) along with significant negative trend in SSA (relative decrease in scattering versus absorption extinction). Negative trends in AOD and SSA are consistent with documented decreases in sulfur dioxide emissions. Conversely, increased AOD in NW and GPu may reflect a lower impact of emissions standards in more remote regions, and/or that other aerosol and precursor sources (e.g., gas and oil extraction, wildfire frequency, long-range transport) may be increasing. Low AOD days are associated with dry, cool synoptic conditions. Since 2000, the structure of the aerosol field has changed. Using the Moran's I test, all regions exhibit declining spatial autocorrelation, suggesting AOD has become less uniform. At the same time, semivariogram models show that in many regions (NW, GPl, MW, SE) spatial coherence is increasing, and is consistent with an increase in the intensity of certain synoptic conditions. These results suggest that it is the variability in local emissions that accounts for the spatial structure of the AOD fields. However, more intense synoptic features are associated with more intense regional aerosol events.

Baseline climatology of extremely high vertical wind shears' values over Europe based on ERA-Interim reanalysis

NASA Astrophysics Data System (ADS)

Palarz, Angelika; Celiński-Mysław, Daniel

2017-04-01

The dominant role in the development of deep convection is played by kinematic and thermodynamic conditions, as well as atmospheric circulation, land cover and local relief. Severe thunderstorms are considerably more likely to form in environments with large values of convective available potential energy (CAPE) and significant magnitude of vertical wind shears (VWSs). According to the most recent research, the tropospheric wind shears have an important influence on intensity, longevity and organisation of the primary convective systems - bow echoes, squall lines and supercell thunderstorms. This study, in turn, examines the role of wind structure in controlling the spatial and temporal variability of VWSs over Europe. Considering the importance of the kinematic conditions for the convective systems formation, research is limited exclusively to 0-1 km, 0-3 km and 0-6 km wind shears. In order to compute the VWS' values, the data derived from ERA-Interim reanalysis for the period 1981-2015 was applied. It consisted of U and V wind components with 12-hourly sampling and horizontal resolution of 0.75×0.75°. The VWS' values were calculated as wind difference between two levels - this entails that the hodograph's shape was not considered (e.g. Clark 2013, Pucik et. al 2015). We have analysed both VWS' mean values (MN) and frequency of VWSs exceeding assumed thresholds (FQ). Taking into account previous studies (e.g. Rasmussen & Blanchard 1998, Schneider et al. 2006, Schaumann & Przybylinski 2012), the thresholds for extremely high values of vertical wind shears were set at 10 m/s for 0-1 km shear, 15 m/s for 0-3 km shear and 18 m/s for 0-6 km shear. Both MN and FQ values were characterised by strong temporal variability, as well as significant spatial differentiation over the research area. A clear diurnal cycle was identified in the case of 0-1 km shear, while seasonal variability was typical for 0-3 km and 0-6 km shears. Regardless of the season, 0-1 km shear reached higher MN and FQ values at 00 UTC than at 12 UTC. Moreover, its spatial distribution showed distinct differences linked to the underlying surface type. Surface energy budget seems to be an important factor contributing to the diurnal and spatial variability of VWSs - it generates the formation of local air circulation leading to modification of the wind direction and speed in the boundary layer. For 0-3 km and 0-6 km shears, a noticeable spatial differentiation between land and sea areas was not recognised. The significantly higher MN and FQ values over the land were found exclusively in the case of 0-3 km shear during the winter, particularly over the Mediterranean region. In the middle troposphere, the VWS' fluctuations (0-3 and 0-6 km shears) are primarily determined by the seasonal changes in atmospheric circulation patterns over the research area.

Influence of spatial and temporal scales in identifying temperature extremes

NASA Astrophysics Data System (ADS)

van Eck, Christel M.; Friedlingstein, Pierre; Mulder, Vera L.; Regnier, Pierre A. G.

2016-04-01

Extreme heat events are becoming more frequent. Notable are severe heatwaves such as the European heatwave of 2003, the Russian heat wave of 2010 and the Australian heatwave of 2013. Surface temperature is attaining new maxima not only during the summer but also during the winter. The year of 2015 is reported to be a temperature record breaking year for both summer and winter. These extreme temperatures are taking their human and environmental toll, emphasizing the need for an accurate method to define a heat extreme in order to fully understand the spatial and temporal spread of an extreme and its impact. This research aims to explore how the use of different spatial and temporal scales influences the identification of a heat extreme. For this purpose, two near-surface temperature datasets of different temporal scale and spatial scale are being used. First, the daily ERA-Interim dataset of 0.25 degree and a time span of 32 years (1979-2010). Second, the daily Princeton Meteorological Forcing Dataset of 0.5 degree and a time span of 63 years (1948-2010). A temperature is considered extreme anomalous when it is surpassing the 90th, 95th, or the 99th percentile threshold based on the aforementioned pre-processed datasets. The analysis is conducted on a global scale, dividing the world in IPCC's so-called SREX regions developed for the analysis of extreme climate events. Pre-processing is done by detrending and/or subtracting the monthly climatology based on 32 years of data for both datasets and on 63 years of data for only the Princeton Meteorological Forcing Dataset. This results in 6 datasets of temperature anomalies from which the location in time and space of the anomalous warm days are identified. Comparison of the differences between these 6 datasets in terms of absolute threshold temperatures for extremes and the temporal and spatial spread of the extreme anomalous warm days show a dependence of the results on the datasets and methodology used. This stresses the need for a careful selection of data and methodology when identifying heat extremes.

Characteristics and present trends of wave extremes in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Pino, Cosimo; Lionello, Piero; Galati, Maria Barbara

2010-05-01

Wind generated surface waves are an important factor characterizing marine storminess and the marine environment. This contribution considers characteristics and trends of SWH (Significant Wave Height) extremes (both high and low extremes, such as dead calm duration are analyzed). The data analysis is based on a 44-year long simulation (1958-2001) of the wave field in the Mediterranean Sea. The quality of the model simulation is controlled using satellite data. The results show the different characteristics of the different parts of the basin with the variability being higher in the western (where the highest values are produced) than in the eastern areas of the basin (where absence of wave is a rare condition). In fact, both duration of storms and of dead calm episodes is larger in the east than in the west part of the Mediterranean. The African coast and the southern Ionian Sea are the areas were exceptional values of SWH are expected to occur in correspondence with exceptional meteorological events. Significant trends of storm characteristics are present only in sparse areas and suggest a decrease of both storm intensity and duration (a marginal increase of storm intensity is present in the center of the Mediterranean). The statistics of extremes and high SWH values is substantially steady during the second half of the 20th century. The influence of the large-scale teleconnection patterns (TlcP) that are known to be relevant for the Mediterranean climate on the intensity and spatial distribution of extreme SWH (Significant Wave Height) has been investigated. The analysis was focused on the monthly scale analysing the variability of links along the annual cycle. The considered TlcP are the North Atlantic Oscillation, the East-Atlantic / West-Russian pattern and the Scandinavian pattern and their effect on the intensity and the frequency of high/low SWH conditions. The results show it is difficult to establish a dominant TlcP for SWH extremes, because all 4 patterns considered are important for at least few months in the year and none of them is important for the whole year. High extremes in winter and fall are more influenced by the TlcPs than in other seasons and low extremes.

Comparing daily temperature averaging methods: the role of surface and atmosphere variables in determining spatial and seasonal variability

NASA Astrophysics Data System (ADS)

Bernhardt, Jase; Carleton, Andrew M.

2018-05-01

The two main methods for determining the average daily near-surface air temperature, twice-daily averaging (i.e., [Tmax+Tmin]/2) and hourly averaging (i.e., the average of 24 hourly temperature measurements), typically show differences associated with the asymmetry of the daily temperature curve. To quantify the relative influence of several land surface and atmosphere variables on the two temperature averaging methods, we correlate data for 215 weather stations across the Contiguous United States (CONUS) for the period 1981-2010 with the differences between the two temperature-averaging methods. The variables are land use-land cover (LULC) type, soil moisture, snow cover, cloud cover, atmospheric moisture (i.e., specific humidity, dew point temperature), and precipitation. Multiple linear regression models explain the spatial and monthly variations in the difference between the two temperature-averaging methods. We find statistically significant correlations between both the land surface and atmosphere variables studied with the difference between temperature-averaging methods, especially for the extreme (i.e., summer, winter) seasons (adjusted R2 > 0.50). Models considering stations with certain LULC types, particularly forest and developed land, have adjusted R2 values > 0.70, indicating that both surface and atmosphere variables control the daily temperature curve and its asymmetry. This study improves our understanding of the role of surface and near-surface conditions in modifying thermal climates of the CONUS for a wide range of environments, and their likely importance as anthropogenic forcings—notably LULC changes and greenhouse gas emissions—continues.

Effects of anthropogenic activity emerging as intensified extreme precipitation over China

NASA Astrophysics Data System (ADS)

Li, Huixin; Chen, Huopo; Wang, Huijun

2017-07-01

This study aims to provide an assessment of the effects of anthropogenic (ANT) forcings and other external factors on observed increases in extreme precipitation over China from 1961 to 2005. Extreme precipitation is represented by the annual maximum 1 day precipitation (RX1D) and the annual maximum 5 day consecutive precipitation (RX5D), and these variables are investigated using observations and simulations from the Coupled Model Intercomparison Project phase 5. The analyses mainly focus on the probability-based index (PI), which is derived from RX1D and RX5D by fitting generalized extreme value distributions. The results indicate that the simulations that include the ANT forcings provide the best representation of the spatial and temporal characteristics of extreme precipitation over China. We use the optimal fingerprint method to obtain the univariate and multivariate fingerprints of the responses to external forcings. The results show that only the ANT forcings are detectable at a 90% confidence level, both individually and when natural forcings are considered simultaneously. The impact of the forcing associated with greenhouse gases (GHGs) is also detectable in RX1D, but its effects cannot be separated from those of combinations of forcings that exclude the GHG forcings in the two-signal analyses. Besides, the estimated changes of PI, extreme precipitation, and events with a 20 year return period under nonstationary climate states are potentially attributable to ANT or GHG forcings, and the relationships between extreme precipitation and temperature from ANT forcings show agreement with observations.

Large wood recruitment processes and transported volumes in Swiss mountain streams during the extreme flood of August 2005

NASA Astrophysics Data System (ADS)

Steeb, Nicolas; Rickenmann, Dieter; Badoux, Alexandre; Rickli, Christian; Waldner, Peter

2017-02-01

The extreme flood event that occurred in August 2005 was the most costly (documented) natural hazard event in the history of Switzerland. The flood was accompanied by the mobilization of > 69,000 m3 of large wood (LW) throughout the affected area. As recognized afterward, wood played an important role in exacerbating the damages, mainly because of log jams at bridges and weirs. The present study aimed at assessing the risk posed by wood in various catchments by investigating the amount and spatial variability of recruited and transported LW. Data regarding LW quantities were obtained by field surveys, remote sensing techniques (LiDAR), and GIS analysis and was subsequently translated into a conceptual model of wood transport mass balance. Detailed wood budgets and transport diagrams were established for four study catchments of Swiss mountain streams, showing the spatial variability of LW recruitment and deposition. Despite some uncertainties with regard to parameter assumptions, the sum of reconstructed wood input and observed deposition volumes agree reasonably well. Mass wasting such as landslides and debris flows were the dominant recruitment processes in headwater streams. In contrast, LW recruitment from lateral bank erosion became significant in the lower part of mountain streams where the catchment reached a size of about 100 km2. According to our analysis, 88% of the reconstructed total wood input was fresh, i.e., coming from living trees that were recruited from adjacent areas during the event. This implies an average deadwood contribution of 12%, most of which was estimated to have been in-channel deadwood entrained during the flood event.

Multiple approaches to detect outliers in a genome scan for selection in ocellated lizards (Lacerta lepida) along an environmental gradient.

PubMed

Nunes, Vera L; Beaumont, Mark A; Butlin, Roger K; Paulo, Octávio S

2011-01-01

Identification of loci with adaptive importance is a key step to understand the speciation process in natural populations, because those loci are responsible for phenotypic variation that affects fitness in different environments. We conducted an AFLP genome scan in populations of ocellated lizards (Lacerta lepida) to search for candidate loci influenced by selection along an environmental gradient in the Iberian Peninsula. This gradient is strongly influenced by climatic variables, and two subspecies can be recognized at the opposite extremes: L. lepida iberica in the northwest and L. lepida nevadensis in the southeast. Both subspecies show substantial morphological differences that may be involved in their local adaptation to the climatic extremes. To investigate how the use of a particular outlier detection method can influence the results, a frequentist method, DFDIST, and a Bayesian method, BayeScan, were used to search for outliers influenced by selection. Additionally, the spatial analysis method was used to test for associations of AFLP marker band frequencies with 54 climatic variables by logistic regression. Results obtained with each method highlight differences in their sensitivity. DFDIST and BayeScan detected a similar proportion of outliers (3-4%), but only a few loci were simultaneously detected by both methods. Several loci detected as outliers were also associated with temperature, insolation or precipitation according to spatial analysis method. These results are in accordance with reported data in the literature about morphological and life-history variation of L. lepida subspecies along the environmental gradient. © 2010 Blackwell Publishing Ltd.

Atmospheric conditions associated with extreme fire activity in the Western Mediterranean region.

PubMed

Amraoui, Malik; Pereira, Mário G; DaCamara, Carlos C; Calado, Teresa J

2015-08-15

Active fire information provided by TERRA and AQUA instruments on-board sun-synchronous polar MODIS platform is used to describe fire activity in the Western Mediterranean and to identify and characterize the synoptic patterns of several meteorological fields associated with the occurrence of extreme fire activity episodes (EEs). The spatial distribution of the fire pixels during the period of 2003-2012 leads to the identification of two most affected sub-regions, namely the Northern and Western parts of the Iberian Peninsula (NWIP) and Northern Africa (NAFR). The temporal distribution of the fire pixels in these two sub-regions is characterized by: (i) high and non-concurrent inter- and intra-annual variability with maximum values during the summer of 2003 and 2005 in NWIP and 2007 and 2012 in NAFR; and, (ii) high intra-annual variability dominated by a prominent annual cycle with a main peak centred in August in both sub-regions and a less pronounced secondary peak in March only evident in NWIP region. The 34 EEs identified were grouped according to the location, period of occurrence and spatial configuration of the associated synoptic patterns into 3 clusters (NWIP-summer, NWIP-winter and NAFR-summer). Results from the composite analysis reveal similar fire weather conditions (statistically significant positive anomalies of air temperature and negative anomalies of air relative humidity) but associated with different circulation patterns at lower and mid-levels of the atmosphere associated with the occurrence of EEs in each cluster of the Western Mediterranean region. Copyright © 2015 Elsevier B.V. All rights reserved.

Regional model simulations of New Zealand climate

NASA Astrophysics Data System (ADS)

Renwick, James A.; Katzfey, Jack J.; Nguyen, Kim C.; McGregor, John L.

1998-03-01

Simulation of New Zealand climate is examined through the use of a regional climate model nested within the output of the Commonwealth Scientific and Industrial Research Organisation nine-level general circulation model (GCM). R21 resolution GCM output is used to drive a regional model run at 125 km grid spacing over the Australasian region. The 125 km run is used in turn to drive a simulation at 50 km resolution over New Zealand. Simulations with a full seasonal cycle are performed for 10 model years. The focus is on the quality of the simulation of present-day climate, but results of a doubled-CO2 run are discussed briefly. Spatial patterns of mean simulated precipitation and surface temperatures improve markedly as horizontal resolution is increased, through the better resolution of the country's orography. However, increased horizontal resolution leads to a positive bias in precipitation. At 50 km resolution, simulated frequency distributions of daily maximum/minimum temperatures are statistically similar to those of observations at many stations, while frequency distributions of daily precipitation appear to be statistically different to those of observations at most stations. Modeled daily precipitation variability at 125 km resolution is considerably less than observed, but is comparable to, or exceeds, observed variability at 50 km resolution. The sensitivity of the simulated climate to changes in the specification of the land surface is discussed briefly. Spatial patterns of the frequency of extreme temperatures and precipitation are generally well modeled. Under a doubling of CO2, the frequency of precipitation extremes changes only slightly at most locations, while air frosts become virtually unknown except at high-elevation sites.

Distributed watershed modeling of design storms to identify nonpoint source loading areas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endreny, T.A.; Wood, E.F.

1999-03-01

Watershed areas that generate nonpoint source (NPS) polluted runoff need to be identified prior to the design of basin-wide water quality projects. Current watershed-scale NPS models lack a variable source area (VSA) hydrology routine, and are therefore unable to identify spatially dynamic runoff zones. The TOPLATS model used a watertable-driven VSA hydrology routine to identify runoff zones in a 17.5 km{sup 2} agricultural watershed in central Oklahoma. Runoff areas were identified in a static modeling framework as a function of prestorm watertable depth and also in a dynamic modeling framework by simulating basin response to 2, 10, and 25 yrmore » return period 6 h design storms. Variable source area expansion occurred throughout the duration of each 6 h storm and total runoff area increased with design storm intensity. Basin-average runoff rates of 1 mm h{sup {minus}1} provided little insight into runoff extremes while the spatially distributed analysis identified saturation excess zones with runoff rates equaling effective precipitation. The intersection of agricultural landcover areas with these saturation excess runoff zones targeted the priority potential NPS runoff zones that should be validated with field visits. These intersected areas, labeled as potential NPS runoff zones, were mapped within the watershed to demonstrate spatial analysis options available in TOPLATS for managing complex distributions of watershed runoff. TOPLATS concepts in spatial saturation excess runoff modelling should be incorporated into NPS management models.« less

Random Distribution Pattern and Non-adaptivity of Genome Size in a Highly Variable Population of Festuca pallens

PubMed Central

Šmarda, Petr; Bureš, Petr; Horová, Lucie

2007-01-01

Background and Aims The spatial and statistical distribution of genome sizes and the adaptivity of genome size to some types of habitat, vegetation or microclimatic conditions were investigated in a tetraploid population of Festuca pallens. The population was previously documented to vary highly in genome size and is assumed as a model for the study of the initial stages of genome size differentiation. Methods Using DAPI flow cytometry, samples were measured repeatedly with diploid Festuca pallens as the internal standard. Altogether 172 plants from 57 plots (2·25 m2), distributed in contrasting habitats over the whole locality in South Moravia, Czech Republic, were sampled. The differences in DNA content were confirmed by the double peaks of simultaneously measured samples. Key Results At maximum, a 1·115-fold difference in genome size was observed. The statistical distribution of genome sizes was found to be continuous and best fits the extreme (Gumbel) distribution with rare occurrences of extremely large genomes (positive-skewed), as it is similar for the log-normal distribution of the whole Angiosperms. Even plants from the same plot frequently varied considerably in genome size and the spatial distribution of genome sizes was generally random and unautocorrelated (P > 0·05). The observed spatial pattern and the overall lack of correlations of genome size with recognized vegetation types or microclimatic conditions indicate the absence of ecological adaptivity of genome size in the studied population. Conclusions These experimental data on intraspecific genome size variability in Festuca pallens argue for the absence of natural selection and the selective non-significance of genome size in the initial stages of genome size differentiation, and corroborate the current hypothetical model of genome size evolution in Angiosperms (Bennetzen et al., 2005, Annals of Botany 95: 127–132). PMID:17565968

Geographical distribution of the association between El Niño South Oscillation and dengue fever in the Americas: a continental analysis using geographical information system-based techniques.

PubMed

Ferreira, Marcos C

2014-11-01

El Niño South Oscillation (ENSO) is one climatic phenomenon related to the inter-annual variability of global meteorological patterns influencing sea surface temperature and rainfall variability. It influences human health indirectly through extreme temperature and moisture conditions that may accelerate the spread of some vector-borne viral diseases, like dengue fever (DF). This work examines the spatial distribution of association between ENSO and DF in the countries of the Americas during 1995-2004, which includes the 1997-1998 El Niño, one of the most important climatic events of 20(th) century. Data regarding the South Oscillation index (SOI), indicating El Niño-La Niña activity, were obtained from Australian Bureau of Meteorology. The annual DF incidence (AIy) by country was computed using Pan-American Health Association data. SOI and AIy values were standardised as deviations from the mean and plotted in bars-line graphics. The regression coefficient values between SOI and AIy (rSOI,AI) were calculated and spatially interpolated by an inverse distance weighted algorithm. The results indicate that among the five years registering high number of cases (1998, 2002, 2001, 2003 and 1997), four had El Niño activity. In the southern hemisphere, the annual spatial weighted mean centre of epidemics moved southward, from 6° 31' S in 1995 to 21° 12' S in 1999 and the rSOI,AI values were negative in Cuba, Belize, Guyana and Costa Rica, indicating a synchrony between higher DF incidence rates and a higher El Niño activity. The rSOI,AI map allows visualisation of a graded surface with higher values of ENSO-DF associations for Mexico, Central America, northern Caribbean islands and the extreme north-northwest of South America.

Changes in US extreme sea levels and the role of large scale climate variations

NASA Astrophysics Data System (ADS)

Wahl, T.; Chambers, D. P.

2015-12-01

We analyze a set of 20 tide gauge records covering the contiguous United States (US) coastline and the period from 1929 to 2013 to identify long-term trends and multi-decadal variations in extreme sea levels (ESLs) relative to changes in mean sea level (MSL). Significant but small long-term trends in ESLs above/below MSL are found at individual sites along most coastline stretches, but are mostly confined to the southeast coast and the winter season when storm surges are primarily driven by extra-tropical cyclones. We identify six regions with broadly coherent and considerable multi-decadal ESL variations unrelated to MSL changes. Using a quasi-non-stationary extreme value analysis approach we show that the latter would have caused variations in design relevant return water levels (RWLs; 50 to 200 year return periods) ranging from ~10 cm to as much as 110 cm across the six regions. To explore the origin of these temporal changes and the role of large-scale climate variability we develop different sets of simple and multiple linear regression models with RWLs as dependent variables and climate indices, or tailored (toward the goal of predicting multi-decadal RWL changes) versions of them, and wind stress curl as independent predictors. The models, after being tested for spatial and temporal stability, explain up to 97% of the observed variability at individual sites and almost 80% on average. Using the model predictions as covariates for the quasi-non-stationary extreme value analysis also significantly reduces the range of change in the 100-year RWLs over time, turning a non-stationary process into a stationary one. This highlights that the models - when used with regional and global climate model output of the predictors - should also be capable of projecting future RWL changes to be used by decision makers for improved flood preparedness and long-term resiliency.

Knowledge discovery and nonlinear modeling can complement climate model simulations for predictive insights about climate extremes and their impacts

NASA Astrophysics Data System (ADS)

Ganguly, A. R.; Steinbach, M.; Kumar, V.

2009-12-01

The IPCC AR4 not only provided conclusive evidence about anticipated global warming at century scales, but also indicated with a high level of certainty that the warming is caused by anthropogenic emissions. However, an outstanding knowledge-gap is to develop credible projections of climate extremes and their impacts. Climate extremes are defined in this context as extreme weather and hydrological events, as well as changes in regional hydro-meteorological patterns, especially at decadal scales. While temperature extremes from climate models have relatively better skills, hydrological variables and their extremes have significant shortcomings. Credible projections about tropical storms, sea level rise, coastal storm surge, land glacier melts, and landslides remain elusive. The next generation of climate models is expected to have higher precision. However, their ability to provide more accurate projections of climate extremes remains to be tested. Projections of observed trends into the future may not be reliable in non-stationary environments like climate change, even though functional relationships derived from physics may hold. On the other hand, assessments of climate change impacts which are useful for stakeholders and policy makers depend critically on regional and decadal scale projections of climate extremes. Thus, climate impacts scientists often need to develop qualitative inferences about the not so-well predicted climate extremes based on insights from observations (e.g., increased hurricane intensity) or conceptual understanding (e.g., relation of wildfires to regional warming or drying and hurricanes to SST). However, neither conceptual understanding nor observed trends may be reliable when extrapolating in a non-stationary environment. These urgent societal priorities offer fertile grounds for nonlinear modeling and knowledge discovery approaches. Thus, qualitative inferences on climate extremes and impacts may be transformed into quantitative predictive insights based on a combination of hypothesis-guided data analysis and relatively hypothesis-free but data-guided discovery processes. The analysis and discovery approaches need to be cognizant of climate data characteristics like nonlinear processes, low-frequency variability, long-range spatial dependence and long-memory temporal processes; the value of physically-motivated conceptual understanding and functional associations; as well as possible thresholds and tipping points in the impacted natural, engineered or human systems. Case studies focusing on new methodologies as well as novel climate insights are discussed with a focus on stakeholder requirements.

A Geographical Analysis of Emergency Medical Service Calls and Extreme Heat in King County, WA, USA (2007-2012).

PubMed

DeVine, Aubrey C; Vu, Phuong T; Yost, Michael G; Seto, Edmund Y W; Busch Isaksen, Tania M

2017-08-20

This research analyzed the relationship between extreme heat and Emergency Medical Service (EMS) calls in King County, WA, USA between 2007 and 2012, including the effect of community-level characteristics. Extreme heat thresholds for the Basic Life Support (BLS) data and the Advanced Life Support (ALS) data were found using a piecewise generalized linear model with Akaike Information Criterion (AIC). The association between heat exposure and EMS call rates was investigated using a generalized estimating equations with Poisson mean model, while adjusting for community-level indicators of poverty, impervious surface, and elderly population (65+). In addition, we examined the effect modifications of these community-level factors. Extreme-heat thresholds of 31.1 °C and 33.5 °C humidex were determined for the BLS and ALS data, respectively. After adjusting for other variables in the model, increased BLS call volume was significantly associated with occurring on a heat day (relative rate (RR) = 1.080, p < 0.001), as well as in locations with higher percent poverty (RR = 1.066, p < 0.001). No significant effect modification was identified for the BLS data on a heat day. Controlling for other variables, higher ALS call volume was found to be significantly associated with a heat day (RR = 1.067, p < 0.001), as well as in locations with higher percent impervious surface (RR = 1.015, p = 0.039), higher percent of the population 65 years or older (RR = 1.057, p = 0.005), and higher percent poverty (RR = 1.041, p = 0.016). Furthermore, percent poverty and impervious surface were found to significantly modify the relative rate of ALS call volumes between a heat day and non-heat day. We conclude that EMS call volume increases significantly on a heat day compared to non-heat day for both call types. While this study shows that there is some effect modification between the community-level variables and call volume on a heat day, further research is necessary. Our findings also suggest that with adequate power, spatially refined analyses may not be necessary to accurately estimate the extreme-heat effect on health.

The Relationship between Spatial and Temporal Magnitude Estimation of Scientific Concepts at Extreme Scales

NASA Astrophysics Data System (ADS)

Price, Aaron; Lee, H.

2010-01-01

Many astronomical objects, processes, and events exist and occur at extreme scales of spatial and temporal magnitudes. Our research draws upon the psychological literature, replete with evidence of linguistic and metaphorical links between the spatial and temporal domains, to compare how students estimate spatial and temporal magnitudes associated with objects and processes typically taught in science class.. We administered spatial and temporal scale estimation tests, with many astronomical items, to 417 students enrolled in 12 undergraduate science courses. Results show that while the temporal test was more difficult, students’ overall performance patterns between the two tests were mostly similar. However, asymmetrical correlations between the two tests indicate that students think of the extreme ranges of spatial and temporal scales in different ways, which is likely influenced by their classroom experience. When making incorrect estimations, students tended to underestimate the difference between the everyday scale and the extreme scales on both tests. This suggests the use of a common logarithmic mental number line for both spatial and temporal magnitude estimation. However, there are differences between the two tests in the errors student make in the everyday range. Among the implications discussed is the use of spatio-temporal reference frames, instead of smooth bootstrapping, to help students maneuver between scales of magnitude and the use of logarithmic transformations between reference frames. Implications for astronomy range from learning about spectra to large scale galaxy structure.

Post-Fire Recovery of Eco-Hydrologic Behavior Given Historic and Projected Climate Variability in California Mediterranean Type Environments

NASA Astrophysics Data System (ADS)

Seaby, L. P.; Tague, C. L.; Hope, A. S.

2006-12-01

The Mediterranean type environments (MTEs) of California are characterized by a distinct wet and dry season and high variability in inter-annual climate. Water limitation in MTEs makes eco-hydrological processes highly sensitive to both climate variability and frequent fire disturbance. This research modeled post-fire eco- hydrologic behavior under historical and moderate and extreme scenarios of future climate in a semi-arid chaparral dominated southern California MTE. We used a physically-based, spatially-distributed, eco- hydrological model (RHESSys - Regional Hydro-Ecologic Simulation System), to capture linkages between water and vegetation response to the combined effects of fire and historic and future climate variability. We found post-fire eco-hydrologic behavior to be strongly influenced by the episodic nature of MTE climate, which intensifies under projected climate change. Higher rates of post-fire net primary productivity were found under moderate climate change, while more extreme climate change produced water stressed conditions which were less favorable for vegetation productivity. Precipitation variability in the historic record follows the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), and these inter-annual climate characteristics intensify under climate change. Inter-annual variation in streamflow follows these precipitation patterns. Post-fire streamflow and carbon cycling trajectories are strongly dependent on climate characteristics during the first 5 years following fire, and historic intra-climate variability during this period tends to overwhelm longer term trends and variation that might be attributable to climate change. Results have implications for water resource availability, vegetation type conversion from shrubs to grassland, and changes in ecosystem structure and function.

A new large initial condition ensemble to assess avoided impacts in a climate mitigation scenario

NASA Astrophysics Data System (ADS)

Sanderson, B. M.; Tebaldi, C.; Knutti, R.; Oleson, K. W.

2014-12-01

It has recently been demonstrated that when considering timescales of up to 50 years, natural variability may play an equal role to anthropogenic forcing on subcontinental trends for a variety of climate indicators. Thus, for many questions assessing climate impacts on such time and spatial scales, it has become clear that a significant number of ensemble members may be required to produce robust statistics (and especially so for extreme events). However, large ensemble experiments to date have considered the role of variability in a single scenario, leaving uncertain the relationship between the forced climate trajectory and the variability about that path. To address this issue, we present a new, publicly available, 15 member initial condition ensemble of 21st century climate projections for the RCP 4.5 scenario using the CESM1.1 Earth System Model, which we propose as a companion project to the existing 40 member CESM large ensemble which uses the higher greenhouse gas emission future of RCP8.5. This provides a valuable data set for assessing what societal and ecological impacts might be avoided through a moderate mitigation strategy in contrast to a fossil fuel intensive future. We present some early analyses of these combined ensembles to assess to what degree the climate variability can be considered to combine linearly with the underlying forced response. In regions where there is no detectable relationship between the mean state and the variability about the mean trajectory, then linear assumptions can be trivially exploited to utilize a single ensemble or control simulation to characterize the variability in any scenario of interest. We highlight regions where there is a detectable nonlinearity in extreme event frequency, how far in the future they will be manifested and propose mechanisms to account for these effects.

High northern latitude temperature extremes, 1400-1999

NASA Astrophysics Data System (ADS)

Tingley, M. P.; Huybers, P.; Hughen, K. A.

2009-12-01

There is often an interest in determining which interval features the most extreme value of a reconstructed climate field, such as the warmest year or decade in a temperature reconstruction. Previous approaches to this type of question have not fully accounted for the spatial and temporal covariance in the climate field when assessing the significance of extreme values. Here we present results from applying BARSAT, a new, Bayesian approach to reconstructing climate fields, to a 600 year multiproxy temperature data set that covers land areas between 45N and 85N. The end result of the analysis is an ensemble of spatially and temporally complete realizations of the temperature field, each of which is consistent with the observations and the estimated values of the parameters that define the assumed spatial and temporal covariance functions. In terms of the spatial average temperature, 1990-1999 was the warmest decade in the 1400-1999 interval in each of 2000 ensemble members, while 1995 was the warmest year in 98% of the ensemble members. A similar analysis at each node of a regular 5 degree grid gives insight into the spatial distribution of warm temperatures, and reveals that 1995 was anomalously warm in Eurasia, whereas 1998 featured extreme warmth in North America. In 70% of the ensemble members, 1601 featured the coldest spatial average, indicating that the eruption of Huaynaputina in Peru in 1600 (with a volcanic explosivity index of 6) had a major cooling impact on the high northern latitudes. Repeating this analysis at each node reveals the varying impacts of major volcanic eruptions on the distribution of extreme cooling. Finally, we use the ensemble to investigate extremes in the time evolution of centennial temperature trends, and find that in more than half the ensemble members, the greatest rate of change in the spatial mean time series was a cooling centered at 1600. The largest rate of centennial scale warming, however, occurred in the 20th Century in more than 98% of the ensemble members.

Modelling extreme dry spells in the Mediterranean region in connection with atmospheric circulation

NASA Astrophysics Data System (ADS)

Tramblay, Yves; Hertig, Elke

2018-04-01

Long droughts periods can affect the Mediterranean region during the winter season, when most of annual precipitation occurs, and consequently have strong impacts on agriculture, groundwater levels and water resources. The goal of this study is to model annual maximum dry spells lengths (AMDSL) that occur during the extended winter season (October to April). The spatial patterns of extreme dry spells and their relationships with large-scale atmospheric circulation were first investigated. Then, AMDSL were modelled using Generalized Extreme Value (GEV) distributions incorporating climatic covariates, to evaluate the dependences of extreme dry spells to synoptic patterns using an analogue approach. The data from a network of 160 rain gauges having daily precipitation measurements between 1960 and 2009 are considered together with the ERA-20C reanalysis of the 20th century to provide atmospheric variables (geopotential heights, humidity, winds). A regional classification of both the occurrence and the duration of AMDSL helped to distinguish three spatially contiguous regions in which the regional distributions were found homogeneous. From composite analysis, significant positive anomalies in geopotential height (Z500) and negative anomalies in zonal wind (U850) and relative and specific humidity (S850, R850) were found to be associated with AMDSL in the three regions and provided the reference to build analogue days. Finally, non-stationary GEV models have been compared, in which the location and scale parameters are related to different atmospheric indices. Results indicates, at the whole Mediterranean scale, that positives anomalies of the North Atlantic Oscillation index and to a lesser extent the Mediterranean Oscillation index are linked to longer extreme dry spells in the majority of stations. For the three regions identified, the frequency of U850 negative anomalies over North Africa is significantly associated with the magnitude of AMDSL. AMDL are also associated with the frequency of S850 negative anomalies for the southeastern region, and with positive Z500 anomalies for the Western and North-eastern Mediterranean regions.

Hindcast of extreme sea states in North Atlantic extratropical storms

NASA Astrophysics Data System (ADS)

Ponce de León, Sonia; Guedes Soares, Carlos

2015-02-01

This study examines the variability of freak wave parameters around the eye of northern hemisphere extratropical cyclones. The data was obtained from a hindcast performed with the WAve Model (WAM) model forced by the wind fields of the Climate Forecast System Reanalysis (CFSR). The hindcast results were validated against the wave buoys and satellite altimetry data showing a good correlation. The variability of different wave parameters was assessed by applying the empirical orthogonal functions (EOF) technique on the hindcast data. From the EOF analysis, it can be concluded that the first empirical orthogonal function (V1) accounts for greater share of variability of significant wave height (Hs), peak period (Tp), directional spreading (SPR) and Benjamin-Feir index (BFI). The share of variance in V1 varies for cyclone and variable: for the 2nd storm and Hs V1 contains 96 % of variance while for the 3rd storm and BFI V1 accounts only for 26 % of variance. The spatial patterns of V1 show that the variables are distributed around the cyclones centres mainly in a lobular fashion.

Population exposure to heat-related extremes: Demographic change vs climate change

NASA Astrophysics Data System (ADS)

Jones, B.; O'Neill, B. C.; Tebaldi, C.; Oleson, K. W.

2014-12-01

Extreme heat events are projected to increase in frequency and intensity in the coming decades [1]. The physical effects of extreme heat on human populations are well-documented, and anticipating changes in future exposure to extreme heat is a key component of adequate planning/mitigation [2, 3]. Exposure to extreme heat depends not only on changing climate, but also on changes in the size and spatial distribution of the human population. Here we focus on systematically quantifying exposure to extreme heat as a function of both climate and population change. We compare exposure outcomes across multiple global climate and spatial population scenarios, and characterize the relative contributions of each to population exposure to extreme heat. We consider a 2 x 2 matrix of climate and population output, using projections of heat extremes corresponding to RCP 4.5 and RCP 8.5 from the NCAR community land model, and spatial population projections for SSP 3 and SSP 5 from the NCAR spatial population downscaling model. Our primary comparison is across RCPs - exposure outcomes from RCP 4.5 versus RCP 8.5 - paying particular attention to how variation depends on the choice of SSP in terms of aggregate global and regional exposure, as well as the spatial distribution of exposure. We assess how aggregate exposure changes based on the choice of SSP, and which driver is more important, population or climate change (i.e. does that outcome vary more as a result of RCP or SSP). We further decompose the population component to analyze the contributions of total population change, migration, and changes in local spatial structure. Preliminary results from a similar study of the US suggests a four-to-six fold increase in total exposure by the latter half of the 21st century. Changes in population are as important as changes in climate in driving this outcome, and there is regional variation in the relative importance of each. Aggregate population growth, as well as redistribution of the population across larger US regions, strongly affects outcomes while smaller-scale spatial patterns of population change have smaller effects. [1] Collins, M. et al. (2013) Contribution of WG I to the 5th AR of the IPCC[2] Romero-Lankao, P. et al (2014) Contribution of WG II to the 5th AR of the IPCC[3] Walsh, J. et al. (2014) The 3rd National Climate Assessment

Partitioning into hazard subregions for regional peaks-over-threshold modeling of heavy precipitation

NASA Astrophysics Data System (ADS)

Carreau, J.; Naveau, P.; Neppel, L.

2017-05-01

The French Mediterranean is subject to intense precipitation events occurring mostly in autumn. These can potentially cause flash floods, the main natural danger in the area. The distribution of these events follows specific spatial patterns, i.e., some sites are more likely to be affected than others. The peaks-over-threshold approach consists in modeling extremes, such as heavy precipitation, by the generalized Pareto (GP) distribution. The shape parameter of the GP controls the probability of extreme events and can be related to the hazard level of a given site. When interpolating across a region, the shape parameter should reproduce the observed spatial patterns of the probability of heavy precipitation. However, the shape parameter estimators have high uncertainty which might hide the underlying spatial variability. As a compromise, we choose to let the shape parameter vary in a moderate fashion. More precisely, we assume that the region of interest can be partitioned into subregions with constant hazard level. We formalize the model as a conditional mixture of GP distributions. We develop a two-step inference strategy based on probability weighted moments and put forward a cross-validation procedure to select the number of subregions. A synthetic data study reveals that the inference strategy is consistent and not very sensitive to the selected number of subregions. An application on daily precipitation data from the French Mediterranean shows that the conditional mixture of GPs outperforms two interpolation approaches (with constant or smoothly varying shape parameter).

Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning

PubMed Central

Officer, Rick; Clarke, Maurice; Reid, David G.; Brophy, Deirdre

2017-01-01

Boosted Regression Trees. Excellent for data-poor spatial management but hard to use Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage. BRTs automated and simplified for accessible general use with rich feature set We have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it) with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs), based on stakeholder priorities, such as the minimisation of fishing effort displacement. Gbm.auto for management in various settings By bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these tools can allow improved spatial abundance predictions, and therefore better management, decision-making, and conservation. Although this package was built to support spatial management of a data-limited marine elasmobranch fishery, it should be equally applicable to spatial abundance modelling, area protection, and stakeholder engagement in various scenarios. PMID:29216310

Interpretation of heavy rainfall spatial distribution in mountain watersheds by copula functions

NASA Astrophysics Data System (ADS)

Grossi, Giovanna; Balistrocchi, Matteo

2016-04-01

The spatial distribution of heavy rainfalls can strongly influence flood dynamics in mountain watersheds, depending on their geomorphologic features, namely orography, slope, land covers and soil types. Unfortunately, the direct observation of rainfall fields by meteorological radar is very difficult in this situation, so that interpolation of rain gauge observations or downscaling of meteorological predictions must be adopted to derive spatial rainfall distributions. To do so, various stochastic and physically based approaches are already available, even though the first one is the most familiar in hydrology. Indeed, Kriging interpolation procedures represent very popular techniques to face this problem by means of a stochastic approach. A certain number of restrictive assumptions and parameter uncertainties however affects Kriging. Many alternative formulations and additional procedures were therefore developed during the last decades. More recently, copula functions (Joe, 1997; Nelsen, 2006; Salvadori et al. 2007) were suggested to provide a more straightforward solution to carry out spatial interpolations of hydrologic variables (Bardossy & Pegram; 2009). Main advantages lie in the possibility of i) assessing the dependence structure relating to rainfall variables independently of marginal distributions, ii) expressing the association degree through rank correlation coefficients, iii) implementing marginal distributions and copula functions belonging to different models to develop complex joint distribution functions, iv) verifying the model reliability by effective statistical tests (Genest et al., 2009). A suitable case study to verify these potentialities is provided by the Taro River, a right-bank tributary of the Po River (northern Italy), whose contributing area amounts to about 2˙000 km2. The mountain catchment area is divided into two similar watersheds, so that spatial distribution is crucial in extreme flood event generation. A quite well diffused hydro-meteorological network, consisting of about 30 rain gauges and 10 hydrometers, monitors this medium-size watershed. A decade of rainfall-runoff event observations are available. Severe rainfall events were identified with reference to a main raingauge station, by using an interevent time definition and a depth threshold. Rainfall depths were thus derived and the spatial variability of their association degree was represented by using the Kendall coefficient. A unique copula model based on Gumbel copula function was finally found to be suitable to represent the dependence structure relating to rainfall depths observed in distinct raingauges. Bardossy A., Pegram G. (2009), Copula based multisite model for daily precipitation simulation, Hydrol. Earth Syst. Sci., 13, 2299-2314. Genest C., Rémilland B., Beaudoin D. (2009), Goodness-of-fit tests for copulas: a review and a power study, Insur. Math. Econ., 44(2), 199-213. Joe H. (1997), Multivariate models and dependence concepts, Chapman and Hall, London. Nelsen R. B. (2006), An introduction to copulas, second ed., Springer, New York. Salvadori G., De Michele C., Kottegoda N. T., Rosso R. (2007), Extremes in nature: an approach using copulas, Springer, Dordrecht, The Nederlands.

The Spatial Coherence of Interannual Temperature Variations in the Antarctic Peninsula

NASA Technical Reports Server (NTRS)

King, John C.; Comiso, Josefino C.; Koblinsky, Chester J. (Technical Monitor)

2002-01-01

Over 50 years of observations from climate stations on the west coast of the Antarctic Peninsula show that this is a region of extreme interannual variability in near-surface temperatures. The region has also experienced more rapid warming than any other part of the Southern Hemisphere. In this paper we use a new dataset of satellite-derived surface temperatures to define the extent of the region of extreme variability more clearly than was possible using the sparse station data. The region in which satellite surface temperatures correlate strongly with west Peninsula station temperatures is found to be quite small and is largely confined to the seas just west of the Peninsula, with a northward and eastward extension into the Scotia Sea and a southward extension onto the western slopes of Palmer Land. Correlation of Peninsula surface temperatures with surface temperatures over the rest of continental Antarctica is poor confirming that the west Peninsula is in a different climate regime. The analysis has been used to identify sites where ice core proxy records might be representative of variations on the west coast of the Peninsula. Of the five existing core sites examined, only one is likely to provide a representative record for the west coast.

NASA Contributions to Improve Understanding of Extreme Events in the Global Energy and Water Cycle

NASA Technical Reports Server (NTRS)

Lapenta, William M.

2008-01-01

The U.S. Climate Change Science Program (CCSP) has established the water cycle goals of the Nation's climate change program. Accomplishing these goals will require, in part, an accurate accounting of the key reservoirs and fluxes associated with the global water and energy cycle, including their spatial and temporal variability. through integration of all necessary observations and research tools, To this end, in conjunction with NASA's Earth science research strategy, the overarching long-term NASA Energy and Water Cycle Study (NEWS) grand challenge can he summarized as documenting and enabling improved, observationally based, predictions of water and energy cycle consequences of Earth system variability and change. This challenge requires documenting and predicting trends in the rate of the Earth's water and energy cycling that corresponds to climate change and changes in the frequency and intensity of naturally occurring related meteorological and hydrologic events, which may vary as climate may vary in the future. The cycling of water and energy has obvious and significant implications for the health and prosperity of our society. The importance of documenting and predicting water and energy cycle variations and extremes is necessary to accomplish this benefit to society.

Adaptations to Climate-Mediated Selective Pressures in Humans

PubMed Central

Hancock, Angela M.; Witonsky, David B.; Alkorta-Aranburu, Gorka; Beall, Cynthia M.; Gebremedhin, Amha; Sukernik, Rem; Utermann, Gerd; Pritchard, Jonathan K.; Coop, Graham; Di Rienzo, Anna

2011-01-01

Humans inhabit a remarkably diverse range of environments, and adaptation through natural selection has likely played a central role in the capacity to survive and thrive in extreme climates. Unlike numerous studies that used only population genetic data to search for evidence of selection, here we scan the human genome for selection signals by identifying the SNPs with the strongest correlations between allele frequencies and climate across 61 worldwide populations. We find a striking enrichment of genic and nonsynonymous SNPs relative to non-genic SNPs among those that are strongly correlated with these climate variables. Among the most extreme signals, several overlap with those from GWAS, including SNPs associated with pigmentation and autoimmune diseases. Further, we find an enrichment of strong signals in gene sets related to UV radiation, infection and immunity, and cancer. Our results imply that adaptations to climate shaped the spatial distribution of variation in humans. PMID:21533023

Extreme dissolved oxygen variability in urbanised tropical wetlands: The need for detailed monitoring to protect nursery ground values

NASA Astrophysics Data System (ADS)

Dubuc, Alexia; Waltham, Nathan; Malerba, Martino; Sheaves, Marcus

2017-11-01

Little is known about levels of dissolved oxygen fish are exposed to daily in typical urbanised tropical wetlands found along the Great Barrier Reef coastline. This study investigates diel dissolved oxygen (DO) dynamics in one of these typical urbanised wetlands, in tropical North Queensland, Australia. High frequency data loggers (DO, temperature, depth) were deployed for several days over the summer months in different tidal pools and channels that fish use as temporal or permanent refuges. DO was extremely variable over a 24 h cycle, and across the small-scale wetland. The high spatial and temporal DO variability measured was affected by time of day and tidal factors, namely water depth, tidal range and tidal direction (flood vs ebb). For the duration of the logging time, DO was mainly above the adopted threshold for hypoxia (50% saturation), however, for around 11% of the time, and on almost every logging day, DO values fell below the threshold, including a severe hypoxic event (<5% saturation) that continued for several hours. Fish still use this wetland intensively, so must be able to cope with low DO periods. Despite the ability of fish to tolerate extreme conditions, continuing urban expansion is likely to lead to further water quality degradation and so potential loss of nursery ground value. There is a substantial discontinuity between the recommended DO values in the Australian and New Zealand Guidelines for Fresh and Marine Water Quality and the values observed in this wetland, highlighting the limited value of these guidelines for management purposes. Local and regional high frequency data monitoring programs, in conjunction with local exposure risk studies are needed to underpin the development of the management that will ensure the sustainability of coastal wetlands.

Meteorological risks are drivers of environmental innovation in agro-ecosystem management

NASA Astrophysics Data System (ADS)

Gobin, Anne; Van de Vyver, Hans; Vanwindekens, Frédéric; Planchon, Viviane; Verspecht, Ann; Frutos de Cachorro, Julia; Buysse, Jeroen

2016-04-01

Extreme weather events such as droughts, heat waves and rain storms are projected to increase both in frequency and magnitude with climate change. The research hypothesis of the MERINOVA project is that meteorological risks act as drivers of environmental innovation in agro-ecosystem management which is being tested using a chain of risk approach. The project comprises of five major parts that reflect the chain of risks: the hazard, its impact on different agro-ecosystems, vulnerability, risk management and risk communication. Generalized Extreme Value (GEV) theory was used to model annual maxima of meteorological variables based on a location-, scale- and shape-parameter that determine the center of the distribution, the deviation of the location-parameter and the upper tail decay, respectively. Spatial interpolation of GEV-derived return levels has yielded maps of temperature extremes, precipitation deficits and wet periods. The degree of temporal overlap between extreme weather conditions and sensitive periods in the agro-ecosystem was determined using a bio-physically based modelling framework that couples phenological models, a soil water balance, crop growth and environmental models. 20-year return values for frost, heat stress, drought, waterlogging and field access during different crop stages were related to arable yields. The spatial extent of vulnerability is developed on different layers of spatial information that include inter alia meteorology, soil-landscapes, crop cover and management. The level of vulnerability and resilience of an agro-ecosystem is also determined by risk management. The types of agricultural risk and their relative importance differ across sectors and farm types as elucidated by questionnaires and focus groups. Risk types are distinguished according to production, market, institutional, financial and liability risks. A portfolio of potential strategies was identified at farm, market and policy level. In conclusion, MERINOVA provides for a robust and flexible framework by demonstrating its performance across Belgian agro-ecosystems, and by ensuring its relevance to policy makers and practitioners. A strong expert and end-user network is established to help disseminate and exploit project results to meet user needs.

Quantile regression models of animal habitat relationships

USGS Publications Warehouse

Cade, Brian S.

2003-01-01

Typically, all factors that limit an organism are not measured and included in statistical models used to investigate relationships with their environment. If important unmeasured variables interact multiplicatively with the measured variables, the statistical models often will have heterogeneous response distributions with unequal variances. Quantile regression is an approach for estimating the conditional quantiles of a response variable distribution in the linear model, providing a more complete view of possible causal relationships between variables in ecological processes. Chapter 1 introduces quantile regression and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of estimates for homogeneous and heterogeneous regression models. Chapter 2 evaluates performance of quantile rankscore tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). A permutation F test maintained better Type I errors than the Chi-square T test for models with smaller n, greater number of parameters p, and more extreme quantiles τ. Both versions of the test required weighting to maintain correct Type I errors when there was heterogeneity under the alternative model. An example application related trout densities to stream channel width:depth. Chapter 3 evaluates a drop in dispersion, F-ratio like permutation test for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). Chapter 4 simulates from a large (N = 10,000) finite population representing grid areas on a landscape to demonstrate various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable (spatially and not spatially structured). Depending on whether interactions of the measured habitat and unmeasured variable were negative (interference interactions) or positive (facilitation interactions), either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Sampling (n = 20 - 300) simulations demonstrated that confidence intervals constructed by inverting rankscore tests provided valid coverage of these biased parameters. Quantile regression was used to estimate effects of physical habitat resources on a bivalve mussel (Macomona liliana) in a New Zealand harbor by modeling the spatial trend surface as a cubic polynomial of location coordinates.

Assessing Global Water Storage Variability from GRACE: Trends, Seasonal Cycle, Subseasonal Anomalies and Extremes.

PubMed

Humphrey, Vincent; Gudmundsson, Lukas; Seneviratne, Sonia I

Throughout the past decade, the Gravity Recovery and Climate Experiment (GRACE) has given an unprecedented view on global variations in terrestrial water storage. While an increasing number of case studies have provided a rich overview on regional analyses, a global assessment on the dominant features of GRACE variability is still lacking. To address this, we survey key features of temporal variability in the GRACE record by decomposing gridded time series of monthly equivalent water height into linear trends, inter-annual, seasonal, and subseasonal (intra-annual) components. We provide an overview of the relative importance and spatial distribution of these components globally. A correlation analysis with precipitation and temperature reveals that both the inter-annual and subseasonal anomalies are tightly related to fluctuations in the atmospheric forcing. As a novelty, we show that for large regions of the world high-frequency anomalies in the monthly GRACE signal, which have been partly interpreted as noise, can be statistically reconstructed from daily precipitation once an adequate averaging filter is applied. This filter integrates the temporally decaying contribution of precipitation to the storage changes in any given month, including earlier precipitation. Finally, we also survey extreme dry anomalies in the GRACE record and relate them to documented drought events. This global assessment sets regional studies in a broader context and reveals phenomena that had not been documented so far.

Extreme events following bifurcation to spatiotemporal chaos in a spatially extended microcavity laser

NASA Astrophysics Data System (ADS)

Coulibaly, S.; Clerc, M. G.; Selmi, F.; Barbay, S.

2017-02-01

The occurrence of extreme events in a spatially extended microcavity laser has been recently reported [Selmi et al., Phys. Rev. Lett. 116, 013901 (2016), 10.1103/PhysRevLett.116.013901] to be correlated to emergence of spatiotemporal chaos. In this dissipative system, the role of spatial coupling through diffraction is essential to observe the onset of spatiotemporal complexity. We investigate further the formation mechanism of extreme events by comparing the statistical and dynamical analyses. Experimental measurements together with numerical simulations allow us to assign the quasiperiodicity mechanism as the route to spatiotemporal chaos in this system. Moreover, by investigating the fine structure of the maximum Lyapunov exponent, of the Lyapunov spectrum, and of the Kaplan-Yorke dimension of the chaotic attractor, we are able to deduce that intermittency plays a key role in the proportion of extreme events measured. We assign the observed mechanism of generation of extreme events to quasiperiodic extended spatiotemporal intermittency.

Variability of Extreme Precipitation Events in Tijuana, Mexico During ENSO Years

NASA Astrophysics Data System (ADS)

Cavazos, T.; Rivas, D.

2007-05-01

We present the variability of daily precipitation extremes (top 10 percecnt) in Tijuana, Mexico during 1950-2000. Interannual rainfall variability is significantly modulated by El Nino/Southern Oscillation. The interannual precipitation variability exhibits a large change with a relatively wet period and more variability during 1976- 2000. The wettest years and the largest frequency of daily extremes occurred after 1976-1977, with 6 out of 8 wet years characterized by El Nino episodes and 2 by neutral conditions. However, more than half of the daily extremes during 1950-2000 occurred in non-ENSO years, evidencing that neutral conditions also contribute significantly to extreme climatic variability in the region. Extreme events that occur in neutral (strong El Nino) conditions are associated with a pineapple express and a neutral PNA (negative TNH) teleconnection pattern that links an anomalous tropical convective forcing west (east) of the date line with a strong subtropical jet over the study area. At regional scale, both types of extremes are characterized by a trough in the subtropical jet over California/Baja California, which is further intensified by thermal interaction with an anomalous warm California Current off Baja California, low-level moisture advection from the subtropical warm sea-surface region, intense convective activity over the study area and extreme rainfall from southern California to Baja California.

Spatio-temporal variability of several eco-precipitation indicators in China

NASA Astrophysics Data System (ADS)

Guo, B. B.; Zhang, J.; Wang, F.

2016-12-01

Climate change is expected to have large impacts on the eco-hydrological processes. Precipitation as one of the most important meteorological factors is a significant parameter in ecohydrology. Many studies and precipitation indexes focused on the long-term precipitation variability have been put forward. However, these former studies did not consider the vegetation response and these indexes could not reflect it efficiently. Eco-precipitation indicators reflecting the features and patterns of precipitations and serving as significant input parameters of eco-hydrological models are of paramount significance to the studies of these models. Therefore we proposed 4 important eco-precipitation indicators—Precipitation Variability Index (PVI), Precipitation Occurrence Rate (λ), Mean Precipitation Depth (1/θ) and Annual Precipitation (AP). The PVI index depicts the precipitation variability with a value of zero for perfectly uniform and increases as precipitation events become more sporadic. The λ, 1/θ and AP depict the precipitation frequency, intensity and annual amount, respectively. With large precipitation and vegetation discrepancies, China is selected as a study area. Firstly, these indicators are calculated separately with 55-years (1961-2015) daily precipitation time-series from 693 weather stations in China. Then, the temporal trend is analyzed through Mann-Kendall (MK) test and parametric t-test in annual time scale. Furthermore, the spatial distribution is analyzed through the spatial interpolation tools ANUsplin. The result shows that: (1) 1/θ increased significantly (4.59cm/10yr) while λ decreased significantly (1.54 days/10yr), which means there is an increasing trend of extreme precipitation events; (2)there is a significant downward trend of PVI, which means the rhythm of precipitation has a uniform and concentrated trend; (3) AP increased insignificantly (0.57mm/10yr); and (4)the MK test of these indicators shows that there is saltation of λ and 1/θ with a saltation point in the year 1997 and 1992, respectively. This study indicates that uniform and concentrated extreme precipitation significantly increased in China under the climate change, which brings severer challenge in constructing eco-hydrological models to make rational countermeasures.

Conditional probability of rainfall extremes across multiple durations

NASA Astrophysics Data System (ADS)

Le, Phuong Dong; Leonard, Michael; Westra, Seth

2017-04-01

The conditional probability that extreme rainfall will occur at one location given that it is occurring at another location is critical in engineering design and management circumstances including planning of evacuation routes and the sitting of emergency infrastructure. A challenge with this conditional simulation is that in many situations the interest is not so much the conditional distributions of rainfall of the same duration at two locations, but rather the conditional distribution of flooding in two neighbouring catchments, which may be influenced by rainfall of different critical durations. To deal with this challenge, a model that can consider both spatial and duration dependence of extremes is required. The aim of this research is to develop a model that can take account both spatial dependence and duration dependence into the dependence structure of extreme rainfalls. To achieve this aim, this study is a first attempt at combining extreme rainfall for multiple durations within a spatial extreme model framework based on max-stable process theory. Max-stable processes provide a general framework for modelling multivariate extremes with spatial dependence for just a single duration extreme rainfall. To achieve dependence across multiple timescales, this study proposes a new approach that includes addition elements representing duration dependence of extremes to the covariance matrix of max-stable model. To improve the efficiency of calculation, a re-parameterization proposed by Koutsoyiannis et al. (1998) is used to reduce the number of parameters necessary to be estimated. This re-parameterization enables the GEV parameters to be represented as a function of timescale. A stepwise framework has been adopted to achieve the overall aims of this research. Firstly, the re-parameterization is used to define a new set of common parameters for marginal distribution across multiple durations. Secondly, spatial interpolation of the new parameter set is used to estimate marginal parameters across the full spatial domain. Finally, spatial interpolation result is used as initial condition to estimate dependence parameters via a likelihood function of max-stable model for multiple durations. The Hawkesbury-Nepean catchment near Sydney in Australia was selected as case study for this research. This catchment has 25 sub-daily rain gauges with the minimum record length of 24 years over a region of 300 km × 300 km area. The re-parameterization was applied for each station for durations from 1 hour to 24 hours and then is evaluated by comparing with the at-site fitted GEV. The evaluation showed that the average R2 for all station is around 0.80 with the range from 0.26 to 1.0. The output of re-parameterization then was used to construct the spatial surface based on covariates including longitude, latitude, and elevation. The dependence model showed good agreements between empirical extremal coefficient and theoretical extremal coefficient for multiple durations. For the overall model, a leave-one-out cross-validation for all stations showed it works well for 20 out of 25 stations. The potential application of this model framework was illustrated through a conditional map of return period and return level across multiple durations, both of which are important for engineering design and management.

Decadal variability of the position and strength of the South Atlantic Convergence Zone and its relationship to precipitation variability and extremes over Southeastern Brazil

NASA Astrophysics Data System (ADS)

Zilli, M. T.; Carvalho, L. V.; Lintner, B. R.

2016-12-01

The South Atlantic Convergence Zone (SACZ) is a diagonally oriented zone of low-level convergence, convective cloudiness, and rainfall originating over South America and extending to the southeast over the Atlantic Ocean. The objective of this study is to investigate the role of variability in the position and strength of the SACZ in causing precipitation variability and extremes over the southeastern Brazilian coast (SE). To that end, we perform Empirical Orthogonal Function (EOF) analysis of total summer (DJF) precipitation from 1979 to 2013, using the National Center for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), at 0.5° spatial resolution. The first mode (EOF-1) explains 22.7% of total variance and is characterized by a dipole-like structure, with opposite signs over central South America and over central South Atlantic along the northern margin of the SACZ. The time-coefficient or principal component of EOF-1 indicates a transition from a predominantly negative phase over 1999 to 2005 to a predominantly positive phase after 2006. The positive phase is associated with an increase in total precipitation over the continent and a reduction over the central South Atlantic, along the northern margin of the SACZ. These results provide evidence of the poleward shift of the SACZ and weakening of convergence along its northern margin over the past decade, consistent with the observed recent trends in extreme precipitation over SE. Compositing reanalysis fields with respect to the PC of EOF-1 suggests changes in moisture availability and circulation that could have affected precipitation regimes over SE. In particular, an increase in available precipitable water may have enhanced convective activity over the southern portion of SE Brazil, whereas the weakening of the northerly winds may be responsible for the weakening of convergence over the northern flank of the SACZ, inhibiting convection in this region.

Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics

NASA Astrophysics Data System (ADS)

Rice, Joshua S.; Emanuel, Ryan E.; Vose, James M.; Nelson, Stacy A. C.

2015-08-01

Changes in streamflow are an important area of ongoing research in the hydrologic sciences. To better understand spatial patterns in past changes in streamflow, we examined relationships between watershed-scale spatial characteristics and trends in streamflow. Trends in streamflow were identified by analyzing mean daily flow observations between 1940 and 2009 from 967 U.S. Geological Survey stream gages. Results indicated that streamflow across the continental U.S., as a whole, increased while becoming less extreme between 1940 and 2009. However, substantial departures from the continental U.S. (CONUS) scale pattern occurred at the regional scale, including increased annual maxima, decreased annual minima, overall drying trends, and changes in streamflow variability. A subset of watersheds belonging to a reference data set exhibited significantly smaller trend magnitudes than those observed in nonreference watersheds. Boosted regression tree models were applied to examine the influence of watershed characteristics on streamflow trend magnitudes at both the CONUS and regional scale. Geographic location was found to be of particular importance at the CONUS scale while local variability in hydroclimate and topography tended to have a strong influence on regional-scale patterns in streamflow trends. This methodology facilitates detailed, data-driven analyses of how the characteristics of individual watersheds interact with large-scale hydroclimate forces to influence how changes in streamflow manifest.

A nonstationary analysis for the Northern Adriatic extreme sea levels

NASA Astrophysics Data System (ADS)

Masina, Marinella; Lamberti, Alberto

2013-09-01

The historical data from the Trieste, Venice, Porto Corsini, and Rimini tide gauges have been used to investigate the spatial and temporal changes in extreme high water levels in the Northern Adriatic. A detailed analysis of annual mean sea level evolution at the three longest operating stations shows a coherent behavior both on a regional and global scale. A slight increase in magnitude of extreme water elevations, after the removal of the regularized annual mean sea level necessary to eliminate the effect of local subsidence and sea level rise, is found at the Venice and Porto Corsini stations. It seems to be mainly associated with a wind regime change occurred in the 1990s, due to an intensification of Bora wind events after their decrease in frequency and intensity during the second half of the 20th century. The extreme values, adjusted for the annual mean sea level trend, are modeled using a time-dependent GEV distribution. The inclusion of seasonality in the GEV parameters considerably improves the data fitting. The interannual fluctuations of the detrended monthly maxima exhibit a significant correlation with the variability of the large-scale atmospheric circulation represented by the North Atlantic Oscillation and Arctic Oscillation indices. The different coast exposure to the Bora and Sirocco winds and their seasonal character explain the various seasonal patterns of extreme sea levels observed at the tide gauges considered in the present analysis.

Effects of Climate Change on Extreme Streamflow Risks in the Olympic National Park

NASA Astrophysics Data System (ADS)

Tohver, I. M.; Lee, S.; Hamlet, A.

2011-12-01

Conventionally, natural resource management practices are designed within the framework that past conditions serve as a baseline for future conditions. However, the warmer future climate projected for the Pacific Northwest will alter the region's flood and low flow risks, posing considerable challenges to resource managers in the Olympic National Forest (ONF) and Olympic National Park (ONP). Shifts in extreme streamflow will influence two key management objectives in the ONF and ONP: the protection of wildlife and the maintenance of road infrastructure. The ONF is charged with managing habitat for species listed under the Endangered Species Act (ESA), and with maintaining the network of forest roads and culverts. Climate-induced increases in flood severity will introduce additional challenges in road and culvert design. Furthermore, the aging road infrastructure and more extreme summer low flows will compromise aquatic habitats, intrinsic to the health of threatened and endangered fish species listed under the ESA. Current practice uses estimates of Q100 (or the peak flow with an estimated 100 year return frequency) as the standard metric for stream crossing design. Simple regression models relating annual precipitation and basin area to Q100 are used in the design process. Low flow estimates are based on historical streamflow data to calculate the 7-day consecutive lowest flow with a 10-year return interval, or 7Q10. Under the projections a changing climate, these methods for estimating extreme flows are ill equipped to capture the complex and spatially varying effects of seasonal changes in temperature, precipitation, and snowpack on extreme flow risk. As an alternative approach, this study applies a physically-based hydrologic model to estimate historical and future flood risk at 1/16th degree (latitude/longitude) resolution (about 32 km2). We downscaled climate data derived from 10 global climate models to use as input for the Variable Infiltration Capacity (VIC) model, a macro-scale hydrologic model, which simulates various hydrologic variables at a daily time step. Using the VIC estimates for baseflow and run-off, we calculated Q100 and 7Q10 for the historical period and under two emission scenarios, A1B and B1, at three future time intervals: the 2020s, the 2040s and the 2080s. We also calculated Q100 and 7Q10 at the spatial scale of the 12-digit hydrologic unit codes (HUCs) as delineated by the United States Geologic Survey. The results demonstrate the sensitivity of snowpack at mid-elevation basins to a warmer climate, resulting in more severe winter flooding and lower streamflows in the summertime. These ensemble estimates of extreme streamflows will serve as a tool for management practices by providing high-resolution maps of changing risk over the ONF and ONP.

The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Zhenong; Zhuang, Qianlai; Wang, Jiali

Heat and drought stresses are two emerging climatic threats to the US maize and soybean production, yet their impacts on yields are collectively determined by the magnitude of climate change and rising atmospheric CO2 concentration. Here we present a study that quantified the current and future yield responses of US rainfed maize and soybean to climate extremes, and for the first time characterized spatial shifts in the relative importance of temperature, heat and drought stress. Crop yields are simulated using the Agricultural Production Systems sIMulator (APSIM), driven by the high-resolution (12 km) Weather Research and Forecasting (WRF) Model downscaled futuremore » climate scenarios at two time slices (1995-2005 and 2085-2094). Our results show that climatic yield gaps and interannual variability are greater in the core production area than in the remaining US by the late 21st century under both Representative Concentration Pathway (RCP) 4.5 and RCP8.5 scenarios, and the magnitude of change is highly dependent on the current climate sensitivity and vulnerability. Elevated CO2 partially offsets the climatic yield gaps and reduces interannual yield variability, and effect is more prominent in soybean than in maize. We demonstrate that drought will continue to be the largest threat to US rainfed maize and soybean production, although its dominant role gradually gives way to other impacts of heat extremes. We also reveal that shifts in the geographic distributions of dominant stressors are characterized by increases in the concurrent stress, especially for the US Midwest. These findings imply the importance of considering drought and extreme heat simultaneously for future agronomic adaptation and mitigation strategies, particularly for breeding programs and crop management.« less

Nonstationary Intensity-Duration-Frequency Curves for Drainge Infrastructure Coping with Climate Change

NASA Astrophysics Data System (ADS)

Kim, Byung Sik; Jeung, Se Jin; Lee, Dong Seop; Han, Woo Suk

2015-04-01

As the abnormal rainfall condition has been more and more frequently happen and serious by climate change and variabilities, the question whether the design of drainage system could be prepared with abnormal rainfall condition or not has been on the rise. Usually, the drainage system has been designed by rainfall I-D-F (Intensity-Duration-Frequency) curve with assumption that I-D-F curve is stationary. The design approach of the drainage system has limitation not to consider the extreme rainfall condition of which I-D-F curve is non-stationary by climate change and variabilities. Therefore, the assumption that the I-D-F curve is stationary to design drainage system maybe not available in the climate change period, because climate change has changed the characteristics of extremes rainfall event to be non-stationary. In this paper, design rainfall by rainfall duration and non-stationary I-D-F curve are derived by the conditional GEV distribution considering non-stationary of rainfall characteristics. Furthermore, the effect of designed peak flow with increase of rainfall intensity was analyzed by distributed rainfall-runoff model, S-RAT(Spatial Runoff Assessment Tool). Although there are some difference by rainfall duration, the traditional I-D-F curves underestimates the extreme rainfall events for high-frequency rainfall condition. As a result, this paper suggest that traditional I-D-F curves could not be suitable for the design of drainage system under climate change condition. Keywords : Drainage system, Climate Change, non-stationary, I-D-F curves This research was supported by a grant 'Development of multi-function debris flow control technique considering extreme rainfall event' [NEMA-Natural-2014-74] from the Natural Hazard Mitigation Research Group, National Emergency Management Agency of KOREA

The Role of Low-Level, Terrain-Induced Jets in Rainfall Variability in Tigris Euphrates Headwaters

NASA Technical Reports Server (NTRS)

Dezfuli, Amin K.; Zaitchik, Benjamin F.; Badr, Hamada S.; Evans, Jason; Peters-Lidard, Christa D.

2017-01-01

Rainfall variability in the Tigris Euphrates headwaters is a result of interaction between topography and meteorological features at a range of spatial scales. Here, the Weather Research and Forecasting (WRF) Model, driven by the NCEP-DOE AMIP-II reanalysis (R-2), has been implemented to better understand these interactions. Simulations were performed over a domain covering most of the Middle East. The extended simulation period (1983 - 2013) enables us to study seasonality, interannual variability, spatial variability, and extreme events of rainfall. Results showed that the annual cycle of precipitation produced by WRF agrees much more closely with observations than does R-2. This was particularly evident during the transition months of April and October, which were further examined to study the underlying physical mechanisms. In both months, WRF improves representation of interannual variability relative to R-2, with a substantially larger benefit in April. This improvement results primarily from WRFs ability to resolve two low-level, terrain-induced flows in the region that are either absent or weak in R-2: one parallel to the western edge of the Zagros Mountains, and one along the east Turkish highlands. The first shows a complete reversal in its direction during wet and dry days, when flowing southeasterly it transports moisture from the Persian Gulf to the region, and when flowing northwesterly it blocks moisture and transports it away from the region. The second is more directly related to synoptic-scale systems and carries moist, warm air from the Mediterranean and Red Seas toward the region. The combined contribution of these flows explains about 50 of interannual variability in both WRF and observations for April and October precipitation.

The role of low-level terrain-induced jets in rainfall variability in Tigris-Euphrates Headwaters

PubMed Central

Zaitchik, Benjamin F.; Badr, Hamada S.; Evans, Jason; Peters-Lidard, Christa D.

2018-01-01

Rainfall variability in the Tigris-Euphrates Headwaters is a result of interaction between topography and meteorological features at a range of spatial scales. Here, we have implemented the Weather Research and Forecasting (WRF) model, driven by NCEP/DOE R2, to better understand these interactions. Simulations were performed over a domain covering most of the Middle-East. The extended simulation period (1983–2013) enables us to study seasonality, interannual variability, spatial variability and extreme events of rainfall. Results showed that the annual cycle of precipitation produced by WRF agrees much more closely with observations than does R2. This was particularly evident during the transition months of April and October, which were further examined to study the underlying physical mechanisms. In both months, WRF improves representation of interannual variability relative to R2, with a substantially larger benefit in April. This improvement results primarily from WRF’s ability to resolve two low-level terrain-induced flows in the region that are either absent or weak in NCEP/DOE: one parallel to western edge of the Zagros Mountains, and one along the East Turkish Highlands. The first shows a complete reversal in its direction during wet and dry days: when flowing southeasterly it transports moisture from the Persian Gulf to the region, and when flowing northwesterly it blocks moisture and transports it away from the region. The second is more directly related to synoptic-scale systems and carries moist, warm air from the Mediterranean and Red Seas toward the region. The combined contribution of these flows explains about 50% of interannual variability in both WRF and observations for April and October precipitation. PMID:29726552

The Role of Low-Level Terrain-Induced Jets in Rainfall Variability in Tigris-Euphrates Headwaters

NASA Technical Reports Server (NTRS)

Dezfuli, Amin K.; Zaitchik, Benjamin F.; Badr, Hamada S.; Evans, Jason; Peters-Lidard, Christa D.

2017-01-01

Rainfall variability in the Tigris-Euphrates headwaters is a result of interaction between topography and meteorological features at a range of spatial scales. Here, the Weather Research and Forecasting (WRF) Model, driven by the NCEPDOE AMIP-II reanalysis (R-2), has been implemented to better understand these interactions. Simulations were performed over a domain covering most of the Middle East. The extended simulation period (19832013) enables us to study seasonality, interannual variability, spatial variability, and extreme events of rainfall. Results showed that the annual cycle of precipitation produced by WRF agrees much more closely with observations than does R-2. This was particularly evident during the transition months of April and October, which were further examined to study the underlying physical mechanisms. In both months, WRF improves representation of interannual variability relative to R-2, with a substantially larger benefit in April. This improvement results primarily from WRFs ability to resolve two low-level, terrain-induced flows in the region that are either absent or weak in R-2: one parallel to the western edge of the Zagros Mountains, and one along the east Turkish highlands. The first shows a complete reversal in its direction during wet and dry days: when flowing southeasterly it transports moisture from the Persian Gulf to the region, and when flowing northwesterly it blocks moisture and transports it away from the region. The second is more directly related to synoptic-scale systems and carries moist, warm air from the Mediterranean and Red Seas toward the region. The combined contribution of these flows explains about 50 of interannual variability in both WRF and observations for April and October precipitation.

Novel Atmospheric and Sea State Modeling in Ocean Energy Applications

NASA Astrophysics Data System (ADS)

Kallos, George; Galanis, George; Kalogeri, Christina; Larsen, Xiaoli Guo

2013-04-01

The rapidly increasing use of renewable energy sources poses new challenges for the research and technological community today. The integration of the, usually, highly variable wind and wave energy amounts into the general grid, the optimization of energy transition and the forecast of extreme values that could lead to instabilities and failures of the system can be listed among them. In the present work, novel methodologies based on state of the art numerical wind/wave simulation systems and advanced statistical techniques addressing such type of problems are discussed. In particular, extremely high resolution modeling systems simulating the atmospheric and sea state conditions with spatial resolution of 100 meters or less and temporal discretization of a few seconds are utilized in order to simulate in the most detailed way the combined wind-wave energy potential at offshore sites. In addition, a statistical analysis based on a variety of mean and variation measures as well as univariate and bivariate probability distributions is used for the estimation of the variability of the power potential revealing the advantages of the use of combined forms of energy by offshore platforms able to produce wind and wave power simultaneously. The estimation and prediction of extreme wind/wave conditions - a critical issue both for site assessment and infrastructure maintenance - is also studied by means of the 50-year return period over areas with increased power potential. This work has been carried out within the framework of the FP7 project MARINA Platform (http://www.marina-platform.info/index.aspx).

Birth at 22 gestational weeks: case report of cognitive resilience.

PubMed

Hopp, Crista A; Baron, Ida Sue

2017-02-01

Children delivered at the edge of viability are at greatest risk of medical and neuropsychological disability, their adverse outcomes overshadowing extremely preterm survivors with more optimal outcomes. We aimed to describe an exceptionally early-born extremely preterm (EEEP) preschooler whose neurobiological, familial, and socioeconomic factors likely influenced her unexpected cognitive resilience. Baby G was a 3-years 10-months-old, English-speaking, Caucasian, singleton girl born weighing 435 g at 22 5/7  weeks' gestation to well-educated married parents. Neonatal complications of extremely premature birth included sepsis, severe respiratory distress syndrome, patent ductus arteriosus requiring ligation, necrotizing enterocolitis not requiring surgical intervention, and retinopathy of prematurity. Intellectual and neuropsychological testing was administered. Baby G performed age-appropriately in nearly all domains and did not exhibit intellectual deficits. Her general conceptual ability was above average for both her chronological and adjusted ages. She had below average performance on tests of motor function, working memory, and delayed recall of spatial locations. Standardized parental behavioral questionnaires indicated no concern in emotional or attentional functioning except in relation to mental shifting capacity and signs of anxiety. Report of persistent adverse neurodevelopmental/neuropsychological disabilities following EEEP birth is a counterpoint to the more optimal outcomes in some vulnerable EEEP survivors. This case emphasizes that decisions about aggressive resuscitation and prognostication for infants born EEEP may be enhanced by consideration of individual variability, and of pertinent medical, socioeconomic, and sociodemographic variables that may be more predictive of neuropsychological outcomes than birth weight and gestational age.

Normal-incidence EXtreme-Ultraviolet imaging Spectrometer - NEXUS

NASA Astrophysics Data System (ADS)

Dere, K. P.

2003-05-01

NEXUS is the result of a breakthrough optical design that incorporates new technologies to achieve high optical throughput at high spatial (1 arcsec) and spectral (1-2 km s-1) resolution over a wide field of view in an optimal extreme-ultraviolet spectral band. This achievement was made possible primarily by two technical developments. First, a coating of boron-carbide deposited onto a layer of iridium provided a greatly enhanced reflectivity at EUV wavelengths that would enable NEXUS to observe the Sun over a wide temperature range at high cadence. The reflectivity of these coatings have been measured and demonstrated in the laboratory. The second key development was the use of a variable-line-spaced toroidal grating spectrometer. The spectrometer design allowed the Sun to be imaged at high spatial and spectral resolution along a 1 solar radius-long slit and over a wavelength range from 450 to 800 Å, nearly an entire spectral order. Because the spectrograph provided a magnification of about a factor of 6, only 2 optical elements are required to achieved the desired imaging performance. Throughput was enhanced by the use of only 2 reflections. The could all be accomodated within a total instrument length of 1.5m. We would like to acknowledge support from ONR

Evolution of sediment plumes in the Chesapeake bay and implications of climate variability.

PubMed

Zheng, Guangming; DiGiacomo, Paul M; Kaushal, Sujay S; Yuen-Murphy, Marilyn A; Duan, Shuiwang

2015-06-02

Fluvial sediment transport impacts fisheries, marine ecosystems, and human health. In the upper Chesapeake Bay, river-induced sediment plumes are generally known as either a monotonic spatial shape or a turbidity maximum. Little is known about plume evolution in response to variation in streamflow and extreme discharge of sediment. Here we propose a typology of sediment plumes in the upper Chesapeake Bay using a 17 year time series of satellite-derived suspended sediment concentration. On the basis of estimated fluvial and wind contributions, we define an intermittent/wind-dominated type and a continuous type, the latter of which is further divided into four subtypes based on spatial features of plumes, which we refer to as Injection, Transport, Temporary Turbidity-Maximum, and Persistent Turbidity-Maximum. The four continuous types exhibit a consistent sequence of evolution within 1 week to 1 month following flood events. We also identify a "shift" in typology with increased frequency of Turbidity-Maximum types before and after Hurricane Ivan (2004), which implies that extreme events have longer-lasting effects upon estuarine suspended sediment than previously considered. These results can serve as a diagnostic tool to better predict distribution and impacts of estuarine suspended sediment in response to changes in climate and land use.

Examining global extreme sea level variations on the coast from in-situ and remote observations

NASA Astrophysics Data System (ADS)

Menendez, Melisa; Benkler, Anna S.

2017-04-01

The estimation of extreme water level values on the coast is a requirement for a wide range of engineering and coastal management applications. In addition, climate variations of extreme sea levels on the coastal area result from a complex interacting of oceanic, atmospheric and terrestrial processes across a wide range of spatial and temporal scales. In this study, variations of extreme sea level return values are investigated from two available sources of information: in-situ tide-gauge records and satellite altimetry data. Long time series of sea level from tide-gauge records are the most valuable observations since they directly measure water level in a specific coastal location. They have however a number of sources of in-homogeneities that may affect the climate description of extremes when this data source is used. Among others, the presence of gaps, historical time in-homogeneities and jumps in the mean sea level signal are factors that can provide uncertainty in the characterization of the extreme sea level behaviour. Moreover, long records from tide-gauges are sparse and there are many coastal areas worldwide without in-situ available information. On the other hand, with the accumulating altimeter records of several satellite missions from the 1990s, approaching 25 recorded years at the time of writing, it is becoming possible the analysis of extreme sea level events from this data source. Aside the well-known issue of altimeter measurements very close to the coast (mainly due to corruption by land, wet troposphere path delay errors and local tide effects on the coastal area), there are other aspects that have to be considered when sea surface height values estimated from satellite are going to be used in a statistical extreme model, such as the use of a multi-mission product to get long observed periods and the selection of the maxima sample, since altimeter observations do not provide values uniform in time and space. Here, we have compared the extreme values of 'still water level' and 'non-tidal-residual' of in-situ records from the GESLA2 dataset (Woodworth et al. 2016) against the novel coastal altimetry datasets (Cipollini et al. 2016). Seasonal patterns, inter-annual variability and long-term trends are analyzed. Then, a time-dependent extreme model (Menendez et al. 2009) is applied to characterize extreme sea level return values and their variability on the coastal area around the world.

A stochastic-geometric model of soil variation in Pleistocene patterned ground

NASA Astrophysics Data System (ADS)

Lark, Murray; Meerschman, Eef; Van Meirvenne, Marc

2013-04-01

In this paper we examine the spatial variability of soil in parent material with complex spatial structure which arises from complex non-linear geomorphic processes. We show that this variability can be better-modelled by a stochastic-geometric model than by a standard Gaussian random field. The benefits of the new model are seen in the reproduction of features of the target variable which influence processes like water movement and pollutant dispersal. Complex non-linear processes in the soil give rise to properties with non-Gaussian distributions. Even under a transformation to approximate marginal normality, such variables may have a more complex spatial structure than the Gaussian random field model of geostatistics can accommodate. In particular the extent to which extreme values of the variable are connected in spatially coherent regions may be misrepresented. As a result, for example, geostatistical simulation generally fails to reproduce the pathways for preferential flow in an environment where coarse infill of former fluvial channels or coarse alluvium of braided streams creates pathways for rapid movement of water. Multiple point geostatistics has been developed to deal with this problem. Multiple point methods proceed by sampling from a set of training images which can be assumed to reproduce the non-Gaussian behaviour of the target variable. The challenge is to identify appropriate sources of such images. In this paper we consider a mode of soil variation in which the soil varies continuously, exhibiting short-range lateral trends induced by local effects of the factors of soil formation which vary across the region of interest in an unpredictable way. The trends in soil variation are therefore only apparent locally, and the soil variation at regional scale appears random. We propose a stochastic-geometric model for this mode of soil variation called the Continuous Local Trend (CLT) model. We consider a case study of soil formed in relict patterned ground with pronounced lateral textural variations arising from the presence of infilled ice-wedges of Pleistocene origin. We show how knowledge of the pedogenetic processes in this environment, along with some simple descriptive statistics, can be used to select and fit a CLT model for the apparent electrical conductivity (ECa) of the soil. We use the model to simulate realizations of the CLT process, and compare these with realizations of a fitted Gaussian random field. We show how statistics that summarize the spatial coherence of regions with small values of ECa, which are expected to have coarse texture and so larger saturated hydraulic conductivity, are better reproduced by the CLT model than by the Gaussian random field. This suggests that the CLT model could be used to generate an unlimited supply of training images to allow multiple point geostatistical simulation or prediction of this or similar variables.

Meteorological risks are drivers of environmental innovation in agro-ecosystem management

NASA Astrophysics Data System (ADS)

Gobin, Anne; Van de Vijver, Hans; Vanwindekens, Frédéric; de Frutos Cachorro, Julia; Verspecht, Ann; Planchon, Viviane; Buyse, Jeroen

2017-04-01

Agricultural crop production is to a great extent determined by weather conditions. The research hypothesis is that meteorological risks act as drivers of environmental innovation in agro-ecosystem management. The methodology comprised five major parts: the hazard, its impact on different agro-ecosystems, vulnerability, risk management and risk communication. Generalized Extreme Value (GEV) theory was used to model annual maxima of meteorological variables based on a location-, scale- and shape-parameter that determine the center of the distribution, the deviation of the location-parameter and the upper tail decay, respectively. Spatial interpolation of GEV-derived return levels resulted in spatial temperature extremes, precipitation deficits and wet periods. The temporal overlap between extreme weather conditions and sensitive periods in the agro-ecosystem was realised using a bio-physically based modelling framework that couples phenology, a soil water balance and crop growth. 20-year return values for drought and waterlogging during different crop stages were related to arable yields. The method helped quantify agricultural production risks and rate both weather and crop-based agricultural insurance. The spatial extent of vulnerability is developed on different layers of geo-information to include meteorology, soil-landscapes, crop cover and management. Vulnerability of agroecosystems was mapped based on rules set by experts' knowledge and implemented by Fuzzy Inference System modelling and Geographical Information System tools. The approach was applied for cropland vulnerability to heavy rain and grassland vulnerability to drought. The level of vulnerability and resilience of an agro-ecosystem was also determined by risk management which differed across sectors and farm types. A calibrated agro-economic model demonstrated a marked influence of climate adapted land allocation and crop management on individual utility. The "chain of risk" approach allowed for investigating the hypothesis that meteorological risks act as drivers for agricultural innovation. Risk types were quantified in terms of probability and distribution, and further distinguished according to production type. Examples of strategies and options were provided at field, farm and policy level using different modelling methods.

West Coast atmospheric river climatology in CMIP5 climate models

NASA Astrophysics Data System (ADS)

Warner, M.; Mass, C.; Salathe, E. P.

2015-12-01

In recent years, there has been a flurry of research on how atmospheric river events (ARs) will respond to anthropogenic global warming. This study uses 10 CMIP5 RCP 8.5 climate models to focus on changes in AR frequency, seasonality, and synoptic conditions along the west coast of the United States and is a follow-up to previous work by the same authors (Warner et al. 2015) which investigated expected changes in AR intensity in the same region. There are only very slight changes in annual AR climatology from the end of the last century to the end of this century when considering the most extreme integrated water vapor transport (IVT) events (99th percentile). However, when evaluating by the number of future days exceeding a historical threshold, there are significant increases in extreme IVT events in all months, especially during months when the majority of events take place. The peaks in historical and future frequency occur in similar months given the amount of model variability. Extreme IVT events appear to be occurring slightly earlier in the season, particularly along the northern US coast, and these results are similar to other studies. Spatially, 10-model mean historical composites of IVT reveal canonical AR conditions. At locations farther south, there is less model agreement on the spatial extent and intensity of AR events; whereas farther north, the various models are in agreement. Composites of future events indicate very little spatial change from historical events. The location and orientation of AR events in the historical and future time periods are similar, and the upper-level winds change little over that time period (Warner et al. 2015). This suggests little change in synoptic conditions for approaching ARs. The future-historical difference plots highlight the largest changes expected in the future, namely increases in IVT intensity which are primarily associated with thermodynamic changes related to future integrated water vapor increases due to a warming atmosphere.

Influence of an extreme high water event on survival, reproduction, and distribution of snail kites in Florida, USA

USGS Publications Warehouse

Bennetts, R.E.; Kitchens, W.M.; Dreitz, V.J.

2002-01-01

Hydrology frequently has been reported as the environmental variable having the greatest influence on Florida snail kite (Rostrhamus sociabilis) populations. Although drought has received the most attention, high-water conditions also have been reported to affect kites. Years of high water generally have been reported to be favorable for nesting, although prolonged high water may be detrimental to sustaining suitable habitat. During 1994 and 1995, southern Florida experienced an extreme high water event. This event enabled us to compare survival, nesting success, number of young per successful nest, and spatial distribution of nesting before, during, and after the event. We found no evidence of an effect (either negative or positive) on survival of adult kites. In contrast, juvenile kites experienced the highest survival during the event, although our data suggest greater annual variability than can be explained by the event alone. We found no evidence of an effect of the high water event on nest success or number of young per successful nest. Nest success was highest during the event in the southern portion of the range but was quite similar to other years, both before and after the event. Our data do indicate a substantial shift in the spatial distribution of nesting birds. During the event, nesting activity shifted to higher elevations (i.e., shallower water) in the major nesting areas of the Everglades region. Nesting also occurred in Big Cypress National Preserve during the event, which is typically too dry to support nesting kites. Thus, our data indicate a potential short-term benefit of increased juvenile survival and an expansion of nesting habitat. However, the deterioration of habitat quality from prolonged high water precludes any recommendation for such conditions to be maintained for extended periods. ?? 2002, The Society of Wetland Scientists.

Butterflies, Black swans and Dragon kings: How to use the Dynamical Systems Theory to build a "zoology" of mid-latitude circulation atmospheric extremes?

NASA Astrophysics Data System (ADS)

Faranda, D.; Yiou, P.; Alvarez-Castro, M. C. M.

2015-12-01

A combination of dynamical systems and statistical techniques allows for a robust assessment of the dynamical properties of the mid-latitude atmospheric circulation. Extremes at different spatial and time scales are not only associated to exceptionally intense weather structures (e.g. extra-tropical cyclones) but also to rapid changes of circulation regimes (thunderstorms, supercells) or the extreme persistence of weather structure (heat waves, cold spells). We will show how the dynamical systems theory of recurrence combined to the extreme value theory can take into account the spatial and temporal dependence structure of the mid-latitude circulation structures and provide information on the statistics of extreme events.

Evaluation of climatic changes in South-Asia

NASA Astrophysics Data System (ADS)

Kjellstrom, Erik; Rana, Arun; Grigory, Nikulin; Renate, Wilcke; Hansson, Ulf; Kolax, Michael

2016-04-01

Literature has sufficient evidences of climate change impact all over the world and its impact on various sectors. In light of new advancements made in climate modeling, availability of several climate downscaling approaches, the more robust bias correction methods with varying complexities and strengths, in the present study we performed a systematic evaluation of climate change impact over South-Asia region. We have used different Regional Climate Models (RCMs) (from CORDEX domain), (Global Climate Models GCMs) and gridded observations for the study area to evaluate the models in historical/control period (1980-2010) and changes in future period (2010-2099). Firstly, GCMs and RCMs are evaluated against the Gridded observational datasets in the area using precipitation and temperature as indicative variables. Observational dataset are also evaluated against the reliable set of observational dataset, as pointed in literature. Bias, Correlation, and changes (among other statistical measures) are calculated for the entire region and both the variables. Eventually, the region was sub-divided into various smaller domains based on homogenous precipitation zones to evaluate the average changes over time period. Spatial and temporal changes for the region are then finally calculated to evaluate the future changes in the region. Future changes are calculated for 2 Representative Concentration Pathways (RCPs), the middle emission (RCP4.5) and high emission (RCP8.5) and for both climatic variables, precipitation and temperature. Lastly, Evaluation of Extremes is performed based on precipitation and temperature based indices for whole region in future dataset. Results have indicated that the whole study region is under extreme stress in future climate scenarios for both climatic variables i.e. precipitation and temperature. Precipitation variability is dependent on the location in the area leading to droughts and floods in various regions in future. Temperature is hinting towards a constant increase throughout the region regardless of location.

Holographic photolysis of caged neurotransmitters

PubMed Central

Lutz, Christoph; Otis, Thomas S.; DeSars, Vincent; Charpak, Serge; DiGregorio, David A.; Emiliani, Valentina

2009-01-01

Stimulation of light-sensitive chemical probes has become a powerful tool for the study of dynamic signaling processes in living tissue. Classically, this approach has been constrained by limitations of lens–based and point-scanning illumination systems. Here we describe a novel microscope configuration that incorporates a nematic liquid crystal spatial light modulator (LC-SLM) to generate holographic patterns of illumination. This microscope can produce illumination spots of variable size and number and patterns shaped to precisely match user-defined elements in a specimen. Using holographic illumination to photolyse caged glutamate in brain slices, we demonstrate that shaped excitation on segments of neuronal dendrites and simultaneous, multi-spot excitation of different dendrites enables precise spatial and rapid temporal control of glutamate receptor activation. By allowing the excitation volume shape to be tailored precisely, the holographic microscope provides an extremely flexible method for activation of various photosensitive proteins and small molecules. PMID:19160517

Monitoring of oceanographic properties of Glacier Bay, Alaska 2004

USGS Publications Warehouse

Madison, Erica N.; Etherington, Lisa L.

2005-01-01

Glacier Bay is a recently (300 years ago) deglaciated fjord estuarine system that has multiple sills, very deep basins, tidewater glaciers, and many streams. Glacier Bay experiences a large amount of runoff, high sedimentation, and large tidal variations. High freshwater discharge due to snow and ice melt and the presence of the tidewater glaciers makes the bay extremely cold. There are many small- and large-scale mixing and upwelling zones at sills, glacial faces, and streams. The complex topography and strong currents lead to highly variable salinity, temperature, sediment, primary productivity, light penetration, stratification levels, and current patterns within a small area. The oceanographic patterns within Glacier Bay drive a large portion of the spatial and temporal variability of the ecosystem. It has been widely recognized by scientists and resource managers in Glacier Bay that a program to monitor oceanographic patterns is essential for understanding the marine ecosystem and to differentiate between anthropogenic disturbance and natural variation. This year’s sampling marks the 12th continuous year of monitoring the oceanographic conditions at 23 stations along the primary axes within Glacier Bay, AK, making this a very unique and valuable data set in terms of its spatial and temporal coverage.

Extreme Radio Flares and Associated X-Ray Variability from Young Stellar Objects in the Orion Nebula Cluster

DOE Office of Scientific and Technical Information (OSTI.GOV)

Forbrich, Jan; Reid, Mark J.; Wolk, Scott J.

Young stellar objects are known to exhibit strong radio variability on timescales of weeks to months, and a few reports have documented extreme radio flares with at least an order of magnitude change in flux density on timescales of hours to days. However, there have been few constraints on the occurrence rate of such radio flares or on the correlation with pre-main sequence X-ray flares, although such correlations are known for the Sun and nearby active stars. Here we report simultaneous deep VLA radio and Chandra X-ray observations of the Orion Nebula Cluster, targeting hundreds of sources to look formore » the occurrence rate of extreme radio variability and potential correlation with the most extreme X-ray variability. We identify 13 radio sources with extreme radio variability, with some showing an order of magnitude change in flux density in less than 30 minutes. All of these sources show X-ray emission and variability, but we find clear correlations with extreme radio flaring only on timescales <1 hr. Strong X-ray variability does not predict the extreme radio sources and vice versa. Radio flares thus provide us with a new perspective on high-energy processes in YSOs and the irradiation of their protoplanetary disks. Finally, our results highlight implications for interferometric imaging of sources violating the constant-sky assumption.« less

The National Extreme Events Data and Research Center (NEED)

NASA Astrophysics Data System (ADS)

Gulledge, J.; Kaiser, D. P.; Wilbanks, T. J.; Boden, T.; Devarakonda, R.

2014-12-01

The Climate Change Science Institute at Oak Ridge National Laboratory (ORNL) is establishing the National Extreme Events Data and Research Center (NEED), with the goal of transforming how the United States studies and prepares for extreme weather events in the context of a changing climate. NEED will encourage the myriad, distributed extreme events research communities to move toward the adoption of common practices and will develop a new database compiling global historical data on weather- and climate-related extreme events (e.g., heat waves, droughts, hurricanes, etc.) and related information about impacts, costs, recovery, and available research. Currently, extreme event information is not easy to access and is largely incompatible and inconsistent across web sites. NEED's database development will take into account differences in time frames, spatial scales, treatments of uncertainty, and other parameters and variables, and leverage informatics tools developed at ORNL (i.e., the Metadata Editor [1] and Mercury [2]) to generate standardized, robust documentation for each database along with a web-searchable catalog. In addition, NEED will facilitate convergence on commonly accepted definitions and standards for extreme events data and will enable integrated analyses of coupled threats, such as hurricanes/sea-level rise/flooding and droughts/wildfires. Our goal and vision is that NEED will become the premiere integrated resource for the general study of extreme events. References: [1] Devarakonda, Ranjeet, et al. "OME: Tool for generating and managing metadata to handle BigData." Big Data (Big Data), 2014 IEEE International Conference on. IEEE, 2014. [2] Devarakonda, Ranjeet, et al. "Mercury: reusable metadata management, data discovery and access system." Earth Science Informatics 3.1-2 (2010): 87-94.

Instrumenting an upland research catchment in Canterbury, New Zealand to study controls on variability of soil moisture, shallow groundwater and streamflow

NASA Astrophysics Data System (ADS)

McMillan, Hilary; Srinivasan, Ms

2015-04-01

Hydrologists recognise the importance of vertical drainage and deep flow paths in runoff generation, even in headwater catchments. Both soil and groundwater stores are highly variable over multiple scales, and the distribution of water has a strong control on flow rates and timing. In this study, we instrumented an upland headwater catchment in New Zealand to measure the temporal and spatial variation in unsaturated and saturated-zone responses. In NZ, upland catchments are the source of much of the water used in lowland agriculture, but the hydrology of such catchments and their role in water partitioning, storage and transport is poorly understood. The study area is the Langs Gully catchment in the North Branch of the Waipara River, Canterbury: this catchment was chosen to be representative of the foothills environment, with lightly managed dryland pasture and native Matagouri shrub vegetation cover. Over a period of 16 months we measured continuous soil moisture at 32 locations and near-surface water table (< 2 m) at 14 locations, as well as measuring flow at 3 stream gauges. The distributed measurement sites were located to allow comparisons between North and South facing locations, near-stream versus hillslope locations, and convergent versus divergent hillslopes. We found that temporal variability is strongly controlled by the climatic seasonal cycle, for both soil moisture and water table, and for both the mean and extremes of their distributions. Groundwater is a larger water storage component than soil moisture, and the difference increases with catchment wetness. The spatial standard deviation of both soil moisture and groundwater is larger in winter than in summer. It peaks during rainfall events due to partial saturation of the catchment, and also rises in spring as different locations dry out at different rates. The most important controls on spatial variability are aspect and distance from stream. South-facing and near-stream locations have higher water tables and more, larger soil moisture wetting events. Typical hydrological models do not explicitly account for aspect, but our results suggest that it is an important factor in hillslope runoff generation. Co-measurement of soil moisture and water table level allowed us to identify interrelationships between the two. Locations where water tables peaked closest to the surface had consistently wetter soils and higher water tables. These wetter sites were the same across seasons. However, temporary patterns of strong soil moisture response to summer storms did not correspond to the wetter sites. Total catchment spatial variability is composed of multiple variability sources, and the dominant type is sensitive to those stores that are close to a threshold such as field capacity or saturation. Therefore, we classified spatial variability as 'summer mode' or 'winter mode'. In summer mode, variability is controlled by shallow processes e.g. interactions of water with soils and vegetation. In winter mode, variability is controlled by deeper processes e.g. groundwater movement and bypass flow. Double flow peaks observed during some events show the direct impact of groundwater variability on runoff generation. Our results suggest that emergent catchment behaviour depends on the combination of these multiple, time varying components of variability.

Can Regional Climate Modeling Capture the Observed Changes in Spatial Organization of Extreme Storms at Higher Temperatures?

NASA Astrophysics Data System (ADS)

Li, J.; Wasko, C.; Johnson, F.; Evans, J. P.; Sharma, A.

2018-05-01

The spatial extent and organization of extreme storm events has important practical implications for flood forecasting. Recently, conflicting evidence has been found on the observed changes of storm spatial extent with increasing temperatures. To further investigate this question, a regional climate model assessment is presented for the Greater Sydney region, in Australia. Two regional climate models were considered: the first a convection-resolving simulation at 2-km resolution, the second a resolution of 10 km with three different convection parameterizations. Both the 2- and the 10-km resolutions that used the Betts-Miller-Janjic convective scheme simulate decreasing storm spatial extent with increasing temperatures for 1-hr duration precipitation events, consistent with the observation-based study in Australia. However, other observed relationships of extreme rainfall with increasing temperature were not well represented by the models. Improved methods for considering storm organization are required to better understand potential future changes.

Variability in precipitation in a watershed in the altiplano, Peru and modes of variation

NASA Astrophysics Data System (ADS)

Mazzarino, M.; Brown, C. M.

2012-12-01

This research examines system linkages between climate, water availability, pasture availability, camelids (llamas and alpacas) and indigenous herders in an Andean watershed in southern Peru. In this region, extreme meteorological events such as drought and flood, occur often and have the potential to negatively impact herding livelihoods. Predictability in the system is paramount to reducing risks associated with these events. In the altiplano, a large portion of variability in precipitation has been attributed to the influence of El Nino Southern Oscillation (ENSO). In light of climate change and observations by herders, this research returns to the question of teleconnections in the altiplano. We use December through March precipitation totals obtained from eight meteorological stations for 43 years (1964-2006) and sea surface temperatures (SSTs) in the equatorial Pacific and Atlantic to characterize the hydroclimatology in the watershed and determine modes of variability. Following principal components analysis, prevailing periodicities in regional precipitation were determined using wavelet analysis and spatial correlation and regression analysis were used to determine the relationship between SST anomalies (SSTA's) and precipitation events in the watershed. Results suggest a non-linear and non-stationary mode of variability. We draw three conclusions from the results: 1) Positive precipitation extremes are dominated by an ENSO signal in the Nino 2 region; 2) Post 1987 there is a weak relationship, if any, between anomalously dry years in the precipitation record and SSTA's in the equatorial Pacific; 3) There is a stronger relationship (inverse) between precipitation in the region and SSTA's in the tropical Atlantic than previously believed.

Spatiotemporal characteristics of heat waves over China in regional climate simulations within the CORDEX-EA project

NASA Astrophysics Data System (ADS)

Wang, Pinya; Tang, Jianping; Sun, Xuguang; Liu, Jianyong; Juan, Fang

2018-03-01

Using the Weather Research and Forecasting (WRF) model, this paper analyzes the spatiotemporal features of heat waves in 20-year regional climate simulations over East Asia, and investigates the capability of WRF to reproduce observational heat waves in China. Within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX), the WRF model is driven by the ERA-Interim (ERAIN) reanalysis, and five continuous simulations are conducted from 1989 to 2008. Of these, four runs apply the interior spectral nudging (SN) technique with different wavenumbers, nudging variables and nudging coefficients. Model validations show that WRF can reasonably reproduce the spatiotemporal features of heat waves in China. Compared with the experiment without SN, the application of SN is effectie on improving the skill of the model in simulating both the spatial distributions and temporal variations of heat waves of different intensities. The WRF model shows advantages in reproducing the synoptic circulations with SN and therefore yields better representations for heat wave events. Besides, the SN method is able to preserve the variability of large-scale circulations quite well, which in turn adjusts the extreme temperature variability towards the observation. Among the four SN experiments, those with stronger nudging coefficients perform better in modulating both the spatial and temporal features of heat waves. In contrast, smaller nudging coefficients weaken the effects of SN on improving WRF's performances.

Spatial Intensity Duration Frequency Relationships Using Hierarchical Bayesian Analysis for Urban Areas

NASA Astrophysics Data System (ADS)

Rupa, Chandra; Mujumdar, Pradeep

2016-04-01

In urban areas, quantification of extreme precipitation is important in the design of storm water drains and other infrastructure. Intensity Duration Frequency (IDF) relationships are generally used to obtain design return level for a given duration and return period. Due to lack of availability of extreme precipitation data for sufficiently large number of years, estimating the probability of extreme events is difficult. Typically, a single station data is used to obtain the design return levels for various durations and return periods, which are used in the design of urban infrastructure for the entire city. In an urban setting, the spatial variation of precipitation can be high; the precipitation amounts and patterns often vary within short distances of less than 5 km. Therefore it is crucial to study the uncertainties in the spatial variation of return levels for various durations. In this work, the extreme precipitation is modeled spatially using the Bayesian hierarchical analysis and the spatial variation of return levels is studied. The analysis is carried out with Block Maxima approach for defining the extreme precipitation, using Generalized Extreme Value (GEV) distribution for Bangalore city, Karnataka state, India. Daily data for nineteen stations in and around Bangalore city is considered in the study. The analysis is carried out for summer maxima (March - May), monsoon maxima (June - September) and the annual maxima rainfall. In the hierarchical analysis, the statistical model is specified in three layers. The data layer models the block maxima, pooling the extreme precipitation from all the stations. In the process layer, the latent spatial process characterized by geographical and climatological covariates (lat-lon, elevation, mean temperature etc.) which drives the extreme precipitation is modeled and in the prior level, the prior distributions that govern the latent process are modeled. Markov Chain Monte Carlo (MCMC) algorithm (Metropolis Hastings algorithm within a Gibbs sampler) is used to obtain the samples of parameters from the posterior distribution of parameters. The spatial maps of return levels for specified return periods, along with the associated uncertainties, are obtained for the summer, monsoon and annual maxima rainfall. Considering various covariates, the best fit model is selected using Deviance Information Criteria. It is observed that the geographical covariates outweigh the climatological covariates for the monsoon maxima rainfall (latitude and longitude). The best covariates for summer maxima and annual maxima rainfall are mean summer precipitation and mean monsoon precipitation respectively, including elevation for both the cases. The scale invariance theory, which states that statistical properties of a process observed at various scales are governed by the same relationship, is used to disaggregate the daily rainfall to hourly scales. The spatial maps of the scale are obtained for the study area. The spatial maps of IDF relationships thus generated are useful in storm water designs, adequacy analysis and identifying the vulnerable flooding areas.

Spatial dependence and correlation of rainfall in the Danube catchment and its role in flood risk assessment.

NASA Astrophysics Data System (ADS)

Martina, M. L. V.; Vitolo, R.; Todini, E.; Stephenson, D. B.; Cook, I. M.

2009-04-01

The possibility that multiple catastrophic events occur within a given timespan and affect the same portfolio of insured properties may induce enhanced risk. For this reason, in the insurance industry it is of interest to characterise not only the point probability of catastrophic events, but also their spatial structure. As far as floods are concerned it is important to determine the probability of having multiple simultaneous events in different parts of the same basin: in this case, indeed, the loss in a portfolio can be significantly different. Understanding the spatial structure of the precipitation field is a necessary step for the proper modelling of the spatial dependence and correlation of river discharge. Several stochastic models are available in the scientific literature for the multi-site generation of precipitation. Although most models achieve good performance in modelling mean values, temporal variability and inter-site dependence of extremes are still delicate issues. In this work we aim at identifying the main spatial characteristics of the precipitation structure and then at analysing them in a real case. We consider data from a large network of raingauges in the Danube catchment. This catchment is a good example of a large-scale catchment where the spatial correlation of flood events can radically change the effect in term of flood damage.

Contribution of Anthropogenic Warming to California Drought During 2012-2014

NASA Technical Reports Server (NTRS)

Williams, A. Park; Seager, Richard; Abatzoglou, John T.; Cook, Benjamin I.; Smerdon, Jason E.; Cook, Edward R.

2015-01-01

A suite of climate data sets and multiple representations of atmospheric moisture demand are used to calculate many estimates of the self-calibrated Palmer Drought Severity Index, a proxy for near-surface soil moisture, across California from 1901 to 2014 at high spatial resolution. Based on the ensemble of calculations, California drought conditions were record breaking in 2014, but probably not record breaking in 2012-2014, contrary to prior findings. Regionally, the 2012-2014 drought was record breaking in the agriculturally important southern Central Valley and highly populated coastal areas. Contributions of individual climate variables to recent drought are also examined, including the temperature component associated with anthropogenic warming. Precipitation is the primary driver of drought variability but anthropogenic warming is estimated to have accounted for 8-27 percent of the observed drought anomaly in 2012-2014 and 5-18 percent in 2014. Although natural variability dominates, anthropogenic warming has substantially increased the overall likelihood of extreme California droughts.

Drought effects on US maize and soybean production: spatiotemporal patterns and historical changes

NASA Astrophysics Data System (ADS)

Zipper, Samuel C.; Qiu, Jiangxiao; Kucharik, Christopher J.

2016-09-01

Maximizing agricultural production on existing cropland is one pillar of meeting future global food security needs. To close crop yield gaps, it is critical to understand how climate extremes such as drought impact yield. Here, we use gridded, daily meteorological data and county-level annual yield data to quantify meteorological drought sensitivity of US maize and soybean production from 1958 to 2007. Meteorological drought negatively affects crop yield over most US crop-producing areas, and yield is most sensitive to short-term (1-3 month) droughts during critical development periods from July to August. While meteorological drought is associated with 13% of overall yield variability, substantial spatial variability in drought effects and sensitivity exists, with central and southeastern US becoming increasingly sensitive to drought over time. Our study illustrates fine-scale spatiotemporal patterns of drought effects, highlighting where variability in crop production is most strongly associated with drought, and suggests that management strategies that buffer against short-term water stress may be most effective at sustaining long-term crop productivity.

Characterization of extreme precipitation within atmospheric river events over California

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jeon, S.; Prabhat,; Byna, S.

Atmospheric rivers (ARs) are large, spatially coherent weather systems with high concentrations of elevated water vapor. These systems often cause severe downpours and flooding over the western coastal United States – and with the availability of more atmospheric moisture in the future under global warming we expect ARs to play an important role as potential causes of extreme precipitation changes. Therefore, we aim to investigate changes in extreme precipitation properties correlated with AR events in a warmer climate, which are large-scale meteorological patterns affecting the weather and climate of California. We have recently developed the TECA (Toolkit for Extreme Climatemore » Analysis) software for automatically identifying and tracking features in climate data sets. Specifically, we can now identify ARs that make landfall on the western coast of North America. Based on this detection procedure, we can investigate the impact of ARs by exploring the spatial extent of AR precipitation using climate model (CMIP5) simulations and characterize spatial patterns of dependence for future projections between AR precipitation extremes under climate change within the statistical framework. Our results show that AR events in the future RCP (Representative Concentration Pathway)8.5 scenario (2076–2100) tend to produce heavier rainfall with higher frequency and longer days than events from the historical run (1981–2005). We also find that the dependence between extreme precipitation events has a shorter spatial range, within localized areas in California, under the high future emissions scenario than under the historical run.« less

Characterization of extreme precipitation within atmospheric river events over California

DOE PAGES

Jeon, S.; Prabhat,; Byna, S.; ...

2015-11-17

Atmospheric rivers (ARs) are large, spatially coherent weather systems with high concentrations of elevated water vapor. These systems often cause severe downpours and flooding over the western coastal United States – and with the availability of more atmospheric moisture in the future under global warming we expect ARs to play an important role as potential causes of extreme precipitation changes. Therefore, we aim to investigate changes in extreme precipitation properties correlated with AR events in a warmer climate, which are large-scale meteorological patterns affecting the weather and climate of California. We have recently developed the TECA (Toolkit for Extreme Climatemore » Analysis) software for automatically identifying and tracking features in climate data sets. Specifically, we can now identify ARs that make landfall on the western coast of North America. Based on this detection procedure, we can investigate the impact of ARs by exploring the spatial extent of AR precipitation using climate model (CMIP5) simulations and characterize spatial patterns of dependence for future projections between AR precipitation extremes under climate change within the statistical framework. Our results show that AR events in the future RCP (Representative Concentration Pathway)8.5 scenario (2076–2100) tend to produce heavier rainfall with higher frequency and longer days than events from the historical run (1981–2005). We also find that the dependence between extreme precipitation events has a shorter spatial range, within localized areas in California, under the high future emissions scenario than under the historical run.« less

Extreme cyclone events in the Arctic: Wintertime variability and trends

NASA Astrophysics Data System (ADS)

Rinke, A.; Maturilli, M.; Graham, R. M.; Matthes, H.; Handorf, D.; Cohen, L.; Hudson, S. R.; Moore, J. C.

2017-12-01

Extreme cyclone events often occur during Arctic winters, and are of concern as they transport heat and moisture into the Arctic, which is associated with mixed-phase clouds and increased longwave downward radiation, and can cause temperatures to rise above freezing resulting in wintertime sea-ice melting or retarded sea-ice growth. With Arctic amplification and associated reduced sea-ice cover and warmer sea surface temperatures, the occurrence of extreme cyclones events could be a plausible scenario. We calculate the spatial patterns, and changes and trends of the number of extreme cyclone events in the Arctic based on ERA-Interim six-hourly sea level pressure (SLP) data for winter (November-February) 1979-2015. Further, we analyze the SLP data from the Ny-Ålesund station for the same 37 year period. We define an extreme cyclone event by an extreme low central pressure (SLP below 985 hPa, which is the 5th percentile of the Ny-Ålesund/N-ICE2015 SLP data). Typically 20-40 extreme cyclone events (sometimes called `weather bombs') occur in the Arctic North Atlantic per winter season, with an increasing trend of 6 events/decade, according to the Ny-Ålesund data. This increased frequency of extreme cyclones drive considerable warming in that region, consistent with the observed significant winter warming of 3 K/decade. The positive winter trend in extreme cyclones is dominated by a positive monthly trend of about 3-4 events/decade in November-December, due mainly to an increasing persistence of extreme cyclone events. A negative trend in January opposes this, while there is no significant trend in February. We relate the regional patterns of the trend in extreme cyclones to anomalously low sea-ice conditions in recent years, together with associated large-scale atmospheric circulation changes such as "blocking-like" circulation patterns (e.g. Scandinavian blocking in December and Ural blocking during January-February).

[Temporal and spatial variations of extreme climatic events in Songnen Grassland, Northeast China during 1960-2014].

PubMed

Ma, Qi Yun; Zhang, Ji Quan; Lai, Quan; Zhang, Feng; Dong, Zhen Hua; A, Lu Si

2017-06-18

Fourteen extreme climatic indices related with main regional meteorological disasters and vegetation growth were calculated based on daily data from 13 meteorological stations during 1960-2014 in Songnen Grassland, Northeast China. Then, the variation trend and the spatial and temporal patterns of climatic extreme events were analyzed by using regression analysis, break trend analy-sis, Mann-Kendall test, Sen's slope estimator and moving t-test method. The results indicated that summer days (SU25), warm days (TX90P), warm nights (TN90P) and warm spell duration (WSDI) representing extremely high temperatures showed significant increasing trends (P<0.05). Meanwhile, frost days (FD0), cold days (TX10P), cold nights (TN10P) and cold spell duration indicator (CSDI) representing extremely low temperatures showed obviously decreasing trends. The magnitudes of changes in cold indices (FD0, TX10P, TN10P and CSDI) were clearly greater than those of warm indices (SU25, TX90P, TN90P and WSDI), and that changes in night indices were larger than those of day indices. Regional climate warming trend was obvious from 1970 to 2009, and the most occurrences of the abrupt changes in these indices were identified in this period. The extreme precipitation indices did not show obvious trend, in general, SDII and CDD experienced a slightly decreasing trend while RX5D, R95P, PRCPTOT and CWD witnessed a mildly increasing trend. It may be concluded that regional climate changed towards warming and slightly wetting in Songnen Grassland. The most sensitive region for extreme temperature was distributed in the south and north region. Additionally, the extreme temperature indices showed clearly spatial difference between the south and the north. As for the spatial variations of extreme precipitation indices, the climate could be characterized by becoming wetter in northern region, and getting drier in southern region, especially in southwestern region with a high drought risk.

Modelling the Influence of Long-Term Hydraulic Conditions on Juvenile Salmon Habitats in AN Upland Scotish River

NASA Astrophysics Data System (ADS)

Fabris, L.; Malcolm, I.; Millidine, K. J.; Buddendorf, B.; Tetzlaff, D.; Soulsby, C.

2015-12-01

Wild Atlantic salmon populations in Scottish rivers constitute an important economic and recreational resource, as well as being a key component of biodiversity. Salmon have very specific habitat requirements at different life stages and their distribution is therefore strongly influenced by a complex suite of biological and physical controls. Previous research has shown that stream hydrodynamics and channel morphology have a strong influence on the distribution and density of juvenile salmon. Here, we utilise a unique 20 year data set of spatially distributed juvenile salmon densities derived from annual electro-fishing surveys in an upland Scottish river. We examine to what extent the spatial and temporal variability of in-stream hydraulics regulates the spatial and temporal variability in the performance and density of juvenile salmon. A 2-D hydraulic model (River2D) is used to simulate water velocity and water depth under different flow conditions for seven different electro-fishing sites. The selected sites represent different hydromorphological environments including plane-bed, step-pool and pool riffle reaches. The bathymetry of each site was characterised using a total station providing an accurate DTM of the bed, and hydraulic simulations were driven by 20 year stream flow records. Habitat suitability curves, based on direct observations during electro-fishing surveys, were produced for a range of hydraulic indices for juvenile salmon. The hydraulic simulations showed marked spatial differences in juvenile habitat quality both within and between reaches. They also showed marked differences both within and between years. This is most evident in extreme years with wet summers when salmon feeding opportunities may be constrained. Integration of hydraulic habitat models, with fish preference curves and the long term hydrological data allows us to assess whether long-term changes in hydroclimate may be affecting juvenile salmonid populations in the study stream.Wild Atlantic salmon populations in Scottish rivers constitute an important economic and recreational resource, as well as being a key component of biodiversity. Salmon have very specific habitat requirements at different life stages and their distribution is therefore strongly influenced by a complex suite of biological and physical controls. Previous research has shown that stream hydrodynamics and channel morphology have a strong influence on the distribution and density of juvenile salmon. Here, we utilise a unique 20 year data set of spatially distributed juvenile salmon densities derived from annual electro-fishing surveys in an upland Scottish river. We examine to what extent the spatial and temporal variability of in-stream hydraulics regulates the spatial and temporal variability in the performance and density of juvenile salmon. A 2-D hydraulic model (River2D) is used to simulate water velocity and water depth under different flow conditions for seven different electro-fishing sites. The selected sites represent different hydromorphological environments including plane-bed, step-pool and pool riffle reaches. The bathymetry of each site was characterised using a total station providing an accurate DTM of the bed, and hydraulic simulations were driven by 20 year stream flow records. Habitat suitability curves, based on direct observations during electro-fishing surveys, were produced for a range of hydraulic indices for juvenile salmon. The hydraulic simulations showed marked spatial differences in juvenile habitat quality both within and between reaches. They also showed marked differences both within and between years. This is most evident in extreme years with wet summers when salmon feeding opportunities may be constrained. Integration of hydraulic habitat models, with fish preference curves and the long term hydrological data allows us to assess whether long-term changes in hydroclimate may be affecting juvenile salmonid populations in the study stream.

Characterization of extreme years in Central Europe between 2000 and 2016 according to specific vegetation characteristics based on Earth Observatory data

NASA Astrophysics Data System (ADS)

Kern, Anikó; Marjanović, Hrvoje; Barcza, Zoltán

2017-04-01

Extreme weather events frequently occur in Central Europe, affecting the state of the vegetation in large areas. Droughts and heat-waves affect all plant functional types, but the response of the vegetation is not uniform and depends on other parameters, plant strategies and the antecedent meteorological conditions as well. Meteorologists struggle with the definition of extreme events and selection of years that can be considered as extreme in terms of meteorological conditions due to the large variability of the meteorological parameters both in time and space. One way to overcome this problem is the definition of extreme weather based on its observed effect on plant state. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Leaf Area Index (LAI), the Fraction of Photosynthetically Active Radiation (FPAR) and the Gross Primary Production (GPP) are different measures of the land vegetation derived from remote sensing data, providing information about the plant state, but it is less known how weather anomalies affect these measures. We used the vegetation related official products created from the measurements of the MODerate resolution Imaging Spectroradiometer (MODIS) on board satellite Terra to select and characterize the extreme years in Central European countries during the 2000-2016 time period. The applied Collection-6 MOD13 NDVI/EVI, MOD15 LAI/FPAR and MOD17 GPP datasets have 500 m × 500 m spatial resolution covering the region of the Carpathian-Basin. After quality and noise filtering (and temporal interpolation in case of MOD13) 8-day anomaly values were derived to investigate the different years. The freely available FORESEE meteorological database was used to study climate variability in the region. Daily precipitation and maximum/minimum temperature fields at 1/12° × 1/12° grid were resampled to the 8-day temporal and 500 m × 500 m spatial resolution of the MODIS products. To discriminate the different behavior of the various plant functional types MODIS (MCD12) and CORINE (CLC2012) land cover datasets were applied and handled together. Based on the determination of the reliable pixels with different plant types the response of broadleaf forests, coniferous forests, grasslands and croplands were discriminated and investigated. Characteristic time periods were selected based on the remote sensing data to define anomalies, and then the meteorological data were used to define critical time periods within the year that has the strongest effect on the observed anomalies. Similarities/dissimilarities between the behaviors of the different remotely sensed measures are also studied to elucidate the consistency of the indices. The results indicate that the diverse remote sensing indices typically co-vary but reveal strong plant functional type dependency. The study suggest that the selection of extreme years based on annual data is not the best choice, as shorter time periods within the years explain the anomalies to a higher degree than annual data. The results can be used to select anomalous years outside of the satellite era as well. Keywords: Remote sensing, meteorology; extreme years; MODIS, NDVI; EVI; LAI; FPAR; GPP; phenology

Spatio-temporal variability and trends of precipitation and extreme rainfall events in Ethiopia in 1980-2010

NASA Astrophysics Data System (ADS)

Gummadi, Sridhar; Rao, K. P. C.; Seid, Jemal; Legesse, Gizachew; Kadiyala, M. D. M.; Takele, Robel; Amede, Tilahun; Whitbread, Anthony

2017-12-01

This article summarizes the results from an analysis conducted to investigate the spatio-temporal variability and trends in the rainfall over Ethiopia over a period of 31 years from 1980 to 2010. The data is mostly observed station data supplemented by bias-corrected AgMERRA climate data. Changes in annual and Belg (March-May) and Kiremt (June to September) season rainfalls and rainy days have been analysed over the entire Ethiopia. Rainfall is characterized by high temporal variability with coefficient of variation (CV, %) varying from 9 to 30% in the annual, 9 to 69% during the Kiremt season and 15-55% during the Belg season rainfall amounts. Rainfall variability increased disproportionately as the amount of rainfall declined from 700 to 100 mm or less. No significant trend was observed in the annual rainfall amounts over the country, but increasing and decreasing trends were observed in the seasonal rainfall amounts in some areas. A declining trend is also observed in the number of rainy days especially in Oromia, Benishangul-Gumuz and Gambella regions. Trends in seasonal rainfall indicated a general decline in the Belg season and an increase in the Kiremt season rainfall amounts. The increase in rainfall during the main Kiremt season along with the decrease in the number of rainy days leads to an increase in extreme rainfall events over Ethiopia. The trends in the 95th-percentile rainfall events illustrate that the annual extreme rainfall events are increasing over the eastern and south-western parts of Ethiopia covering Oromia and Benishangul-Gumuz regions. During the Belg season, extreme rainfall events are mostly observed over central Ethiopia extending towards the southern part of the country while during the Kiremt season, they are observed over parts of Oromia, (covering Borena, Guji, Bali, west Harerge and east Harerge), Somali, Gambella, southern Tigray and Afar regions. Changes in the intensity of extreme rainfall events are mostly observed over south-eastern parts of Ethiopia extending to the south-west covering Somali and Oromia regions. Similar trends are also observed in the greatest 3-, 5- and 10-day rainfall amounts. Changes in the consecutive dry and wet days showed that consecutive wet days during Belg and Kiremt seasons decreased significantly in many areas in Ethiopia while consecutive dry days increased. The consistency in the trends over large spatial areas confirms the robustness of the trends and serves as a basis for understanding the projected changes in the climate. These results were discussed in relation to their significance to agriculture.

Effects of biotic and abiotic factors on resistance versus resilience of Douglas fir to drought.

PubMed

Carnwath, Gunnar; Nelson, Cara

2017-01-01

Significant increases in tree mortality due to drought-induced physiological stress have been documented worldwide. This trend is likely to continue with increased frequency and severity of extreme drought events in the future. Therefore, understanding the factors that influence variability in drought responses among trees will be critical to predicting ecosystem responses to climate change and developing effective management actions. In this study, we used hierarchical mixed-effects models to analyze drought responses of Pseudotsuga menziesii in 20 unmanaged forests stands across a broad range of environmental conditions in northeastern Washington, USA. We aimed to 1) identify the biotic and abiotic attributes most closely associated with the responses of individual trees to drought and 2) quantify the variability in drought responses at different spatial scales. We found that growth rates and competition for resources significantly affected resistance to a severe drought event in 2001: slow-growing trees and trees growing in subordinate canopy positions and/or with more neighbors suffered greater declines in radial growth during the drought event. In contrast, the ability of a tree to return to normal growth when climatic conditions improved (resilience) was unaffected by competition or relative growth rates. Drought responses were significantly influenced by tree age: older trees were more resistant but less resilient than younger trees. Finally, we found differences between resistance and resilience in spatial scale: a significant proportion (approximately 50%) of the variability in drought resistance across the study area was at broad spatial scales (i.e. among different forest types), most likely due to differences in the total amount of precipitation received at different elevations; in contrast, variation in resilience was overwhelmingly (82%) at the level of individual trees within stands and there was no difference in drought resilience among forest types. Our results suggest that for Pseudotsuga menziesii resistance and resilience to drought are driven by different factors and vary at different spatial scales.

Landscape genetic approaches to guide native plant restoration in the Mojave Desert

USGS Publications Warehouse

Shryock, Daniel F.; Havrilla, Caroline A.; DeFalco, Lesley; Esque, Todd C.; Custer, Nathan; Wood, Troy E.

2016-01-01

Restoring dryland ecosystems is a global challenge due to synergistic drivers of disturbance coupled with unpredictable environmental conditions. Dryland plant species have evolved complex life-history strategies to cope with fluctuating resources and climatic extremes. Although rarely quantified, local adaptation is likely widespread among these species and potentially influences restoration outcomes. The common practice of reintroducing propagules to restore dryland ecosystems, often across large spatial scales, compels evaluation of adaptive divergence within these species. Such evaluations are critical to understanding the consequences of large-scale manipulation of gene flow and to predicting success of restoration efforts. However, genetic information for species of interest can be difficult and expensive to obtain through traditional common garden experiments. Recent advances in landscape genetics offer marker-based approaches for identifying environmental drivers of adaptive genetic variability in non-model species, but tools are still needed to link these approaches with practical aspects of ecological restoration. Here, we combine spatially-explicit landscape genetics models with flexible visualization tools to demonstrate how cost-effective evaluations of adaptive genetic divergence can facilitate implementation of different seed sourcing strategies in ecological restoration. We apply these methods to Amplified Fragment Length Polymorphism (AFLP) markers genotyped in two Mojave Desert shrub species of high restoration importance: the long-lived, wind-pollinated gymnosperm Ephedra nevadensis, and the short-lived, insect-pollinated angiosperm Sphaeralcea ambigua. Mean annual temperature was identified as an important driver of adaptive genetic divergence for both species. Ephedra showed stronger adaptive divergence with respect to precipitation variability, while temperature variability and precipitation averages explained a larger fraction of adaptive divergence in Sphaeralcea. We describe multivariate statistical approaches for interpolating spatial patterns of adaptive divergence while accounting for potential bias due to neutral genetic structure. Through a spatial bootstrapping procedure, we also visualize patterns in the magnitude of model uncertainty. Finally, we introduce an interactive, distance-based mapping approach that explicitly links marker-based models of adaptive divergence with local or admixture seed sourcing strategies, promoting effective native plant restoration.

Effects of biotic and abiotic factors on resistance versus resilience of Douglas fir to drought

PubMed Central

Nelson, Cara

2017-01-01

Significant increases in tree mortality due to drought-induced physiological stress have been documented worldwide. This trend is likely to continue with increased frequency and severity of extreme drought events in the future. Therefore, understanding the factors that influence variability in drought responses among trees will be critical to predicting ecosystem responses to climate change and developing effective management actions. In this study, we used hierarchical mixed-effects models to analyze drought responses of Pseudotsuga menziesii in 20 unmanaged forests stands across a broad range of environmental conditions in northeastern Washington, USA. We aimed to 1) identify the biotic and abiotic attributes most closely associated with the responses of individual trees to drought and 2) quantify the variability in drought responses at different spatial scales. We found that growth rates and competition for resources significantly affected resistance to a severe drought event in 2001: slow-growing trees and trees growing in subordinate canopy positions and/or with more neighbors suffered greater declines in radial growth during the drought event. In contrast, the ability of a tree to return to normal growth when climatic conditions improved (resilience) was unaffected by competition or relative growth rates. Drought responses were significantly influenced by tree age: older trees were more resistant but less resilient than younger trees. Finally, we found differences between resistance and resilience in spatial scale: a significant proportion (approximately 50%) of the variability in drought resistance across the study area was at broad spatial scales (i.e. among different forest types), most likely due to differences in the total amount of precipitation received at different elevations; in contrast, variation in resilience was overwhelmingly (82%) at the level of individual trees within stands and there was no difference in drought resilience among forest types. Our results suggest that for Pseudotsuga menziesii resistance and resilience to drought are driven by different factors and vary at different spatial scales. PMID:28973008

Spatial climate patterns explain negligible variation in strength of compensatory density feedbacks in birds and mammals.

PubMed

Herrando-Pérez, Salvador; Delean, Steven; Brook, Barry W; Cassey, Phillip; Bradshaw, Corey J A

2014-01-01

The use of long-term population data to separate the demographic role of climate from density-modified demographic processes has become a major topic of ecological investigation over the last two decades. Although the ecological and evolutionary mechanisms that determine the strength of density feedbacks are now well understood, the degree to which climate gradients shape those processes across taxa and broad spatial scales remains unclear. Intuitively, harsh or highly variable environmental conditions should weaken compensatory density feedbacks because populations are hypothetically unable to achieve or maintain densities at which social and trophic interactions (e.g., competition, parasitism, predation, disease) might systematically reduce population growth. Here we investigate variation in the strength of compensatory density feedback, from long-term time series of abundance over 146 species of birds and mammals, in response to spatial gradients of broad-scale temperature precipitation variables covering 97 localities in 28 countries. We use information-theoretic metrics to rank phylogenetic generalized least-squares regression models that control for sample size (time-series length) and phylogenetic non-independence. Climatic factors explained < 1% of the remaining variation in density-feedback strength across species, with the highest non-control, model-averaged effect sizes related to extreme precipitation variables. We could not link our results directly to other published studies, because ecologists use contrasting responses, predictors and statistical approaches to correlate density feedback and climate--at the expense of comparability in a macroecological context. Censuses of multiple populations within a given species, and a priori knowledge of the spatial scales at which density feedbacks interact with climate, seem to be necessary to determine cross-taxa variation in this phenomenon. Despite the availability of robust modelling tools, the appropriate data have not yet been gathered for most species, meaning that we cannot yet make any robust generalisations about how demographic feedbacks interact with climate.

Spatial Climate Patterns Explain Negligible Variation in Strength of Compensatory Density Feedbacks in Birds and Mammals

PubMed Central

Herrando-Pérez, Salvador; Delean, Steven; Brook, Barry W.; Cassey, Phillip; Bradshaw, Corey J. A.

2014-01-01

The use of long-term population data to separate the demographic role of climate from density-modified demographic processes has become a major topic of ecological investigation over the last two decades. Although the ecological and evolutionary mechanisms that determine the strength of density feedbacks are now well understood, the degree to which climate gradients shape those processes across taxa and broad spatial scales remains unclear. Intuitively, harsh or highly variable environmental conditions should weaken compensatory density feedbacks because populations are hypothetically unable to achieve or maintain densities at which social and trophic interactions (e.g., competition, parasitism, predation, disease) might systematically reduce population growth. Here we investigate variation in the strength of compensatory density feedback, from long-term time series of abundance over 146 species of birds and mammals, in response to spatial gradients of broad-scale temperature precipitation variables covering 97 localities in 28 countries. We use information-theoretic metrics to rank phylogenetic generalized least-squares regression models that control for sample size (time-series length) and phylogenetic non-independence. Climatic factors explained < 1% of the remaining variation in density-feedback strength across species, with the highest non-control, model-averaged effect sizes related to extreme precipitation variables. We could not link our results directly to other published studies, because ecologists use contrasting responses, predictors and statistical approaches to correlate density feedback and climate – at the expense of comparability in a macroecological context. Censuses of multiple populations within a given species, and a priori knowledge of the spatial scales at which density feedbacks interact with climate, seem to be necessary to determine cross-taxa variation in this phenomenon. Despite the availability of robust modelling tools, the appropriate data have not yet been gathered for most species, meaning that we cannot yet make any robust generalisations about how demographic feedbacks interact with climate. PMID:24618822

Rapid localized spread and immunologic containment define Herpes simplex virus-2 reactivation in the human genital tract.

PubMed

Schiffer, Joshua T; Swan, David; Al Sallaq, Ramzi; Magaret, Amalia; Johnston, Christine; Mark, Karen E; Selke, Stacy; Ocbamichael, Negusse; Kuntz, Steve; Zhu, Jia; Robinson, Barry; Huang, Meei-Li; Jerome, Keith R; Wald, Anna; Corey, Lawrence

2013-04-16

Herpes simplex virus-2 (HSV-2) is shed episodically, leading to occasional genital ulcers and efficient transmission. The biology explaining highly variable shedding patterns, in an infected person over time, is poorly understood. We sampled the genital tract for HSV DNA at several time intervals and concurrently at multiple sites, and derived a spatial mathematical model to characterize dynamics of HSV-2 reactivation. The model reproduced heterogeneity in shedding episode duration and viral production, and predicted rapid early viral expansion, rapid late decay, and wide spatial dispersion of HSV replication during episodes. In simulations, HSV-2 spread locally within single ulcers to thousands of epithelial cells in <12 hr, but host immune responses eliminated infected cells in <24 hr; secondary ulcers formed following spatial propagation of cell-free HSV-2, allowing for episode prolongation. We conclude that HSV-2 infection is characterized by extremely rapid virological growth and containment at multiple contemporaneous sites within genital epithelium. DOI:http://dx.doi.org/10.7554/eLife.00288.001.

Rapid localized spread and immunologic containment define Herpes simplex virus-2 reactivation in the human genital tract

PubMed Central

Schiffer, Joshua T; Swan, David; Al Sallaq, Ramzi; Magaret, Amalia; Johnston, Christine; Mark, Karen E; Selke, Stacy; Ocbamichael, Negusse; Kuntz, Steve; Zhu, Jia; Robinson, Barry; Huang, Meei-Li; Jerome, Keith R; Wald, Anna; Corey, Lawrence

2013-01-01

Herpes simplex virus-2 (HSV-2) is shed episodically, leading to occasional genital ulcers and efficient transmission. The biology explaining highly variable shedding patterns, in an infected person over time, is poorly understood. We sampled the genital tract for HSV DNA at several time intervals and concurrently at multiple sites, and derived a spatial mathematical model to characterize dynamics of HSV-2 reactivation. The model reproduced heterogeneity in shedding episode duration and viral production, and predicted rapid early viral expansion, rapid late decay, and wide spatial dispersion of HSV replication during episodes. In simulations, HSV-2 spread locally within single ulcers to thousands of epithelial cells in <12 hr, but host immune responses eliminated infected cells in <24 hr; secondary ulcers formed following spatial propagation of cell-free HSV-2, allowing for episode prolongation. We conclude that HSV-2 infection is characterized by extremely rapid virological growth and containment at multiple contemporaneous sites within genital epithelium. DOI: http://dx.doi.org/10.7554/eLife.00288.001 PMID:23606943

Editorial for Journal of Hydrology: Regional Studies

USGS Publications Warehouse

Willems, Patrick; Batelaan, Okke; Hughes, Denis A.; Swarzenski, Peter W.

2014-01-01

Hydrological regimes and processes show strong regional differences. While some regions are affected by extreme drought and desertification, others are under threat of increased fluvial and/or pluvial floods. Changes to hydrological systems as a consequence of natural variations and human activities are region-specific. Many of these changes have significant interactions with and implications for human life and ecosystems. Amongst others, population growth, improvements in living standards and other demographic and socio-economic trends, related changes in water and energy demands, change in land use, water abstractions and returns to the hydrological system (UNEP, 2008), introduce temporal and spatial changes to the system and cause contamination of surface and ground waters. Hydro-meteorological boundary conditions are also undergoing spatial and temporal changes. Climate change has been shown to increase temporal and spatial variations of rainfall, increase temperature and cause changes to evapotranspiration and other hydro-meteorological variables (IPCC, 2013). However, these changes are also region specific. In addition to these climate trends, (multi)-decadal oscillatory changes in climatic conditions and large variations in meteorological conditions will continue to occur.

Meteorological risks as drivers of innovation for agroecosystem management

NASA Astrophysics Data System (ADS)

Gobin, Anne; Van de Vyver, Hans; Zamani, Sepideh; Curnel, Yannick; Planchon, Viviane; Verspecht, Ann; Van Huylenbroeck, Guido

2015-04-01

Devastating weather-related events recorded in recent years have captured the interest of the general public in Belgium. The MERINOVA project research hypothesis is that meteorological risks act as drivers of environmental innovation in agro-ecosystem management which is being tested using a "chain of risk" approach. The major objectives are to (1) assess the probability of extreme meteorological events by means of probability density functions; (2) analyse the extreme events impact of on agro-ecosystems using process-based bio-physical modelling methods; (3) identify the most vulnerable agro-ecosystems using fuzzy multi-criteria and spatial analysis; (4) uncover innovative risk management and adaptation options using actor-network theory and economic modelling; and, (5) communicate to research, policy and practitioner communities using web-based techniques. Generalized Extreme Value (GEV) theory was used to model annual rainfall maxima based on location-, scale- and shape-parameters that determine the centre of the distribution, the deviation of the location-parameter and the upper tail decay, respectively. Likewise the distributions of consecutive rainy days, rainfall deficits and extreme 24-hour rainfall were modelled. Spatial interpolation of GEV-derived return levels resulted in maps of extreme precipitation, precipitation deficits and wet periods. The degree of temporal overlap between extreme weather conditions and sensitive periods in the agro-ecosystem was determined using a bio-physically based modelling framework that couples phenological models, a soil water balance, crop growth and environmental models. 20-year return values were derived for frost, heat stress, drought, waterlogging and field access during different sensitive stages for different arable crops. Extreme yield values were detected from detrended long term arable yields and relationships were found with soil moisture conditions, heat stress or other meteorological variables during the season. A methodology for identifying agro-ecosystem vulnerability was developed using spatially explicit information and was tested for arable crop production in Belgium. The different components of vulnerability for a region include spatial information on meteorology, soil available water content, soil erosion, the degree of waterlogging, crop share and the diversity of potato varieties. The level of vulnerability and resilience of an agro-ecosystem is also determined by risk management. The types of agricultural risk and their relative importance differ across sectors and farm types. Risk types are further distinguished according to production, market, institutional, financial and liability risks. Strategies are often combined in the risk management strategy of a farmer and include reduction and prevention, mitigation, coping and impact reduction. Based on an extensive literature review, a portfolio of potential strategies was identified at farm, market and policy level. Research hypotheses were tested using an on-line questionnaire on knowledge of agricultural risk, measuring the general risk aversion of the farmer and risk management strategies. The "chain of risk" approach adopted as a research methodology allows for investigating the hypothesis that meteorological risks act as drivers for agricultural innovation. Risks related to extreme weather events in Belgium are mainly caused by heat, frost, excess rainfall, drought and storms, and their impact is predominantly felt by arable, horticultural and extensive dairy farmers. Quantification of the risk is evaluated in terms of probability of occurrence, magnitude, frequency and extent of impact on several agro-ecosystems services. The spatial extent of vulnerability is developed by integrating different layers of geo-information, while risk management is analysed using questionnaires and economic modelling methods. Future work will concentrate on the further development and testing of the currently developed modelling methodologies. https://merinova.vito.be The research is funded by the Belgian Science Policy Organisation (Belspo) under contract nr SD/RI/03A.

Evaluation of downscaled, gridded climate data for the conterminous United States

USGS Publications Warehouse

Robert J. Behnke,; Stephen J. Vavrus,; Andrew Allstadt,; Thomas P. Albright,; Thogmartin, Wayne E.; Volker C. Radeloff,

2016-01-01

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

Time-Variable Transit Time Distributions in the Hyporheic Zone of a Headwater Mountain Stream

NASA Astrophysics Data System (ADS)

Ward, Adam S.; Schmadel, Noah M.; Wondzell, Steven M.

2018-03-01

Exchange of water between streams and their hyporheic zones is known to be dynamic in response to hydrologic forcing, variable in space, and to exist in a framework with nested flow cells. The expected result of heterogeneous geomorphic setting, hydrologic forcing, and between-feature interaction is hyporheic transit times that are highly variable in both space and time. Transit time distributions (TTDs) are important as they reflect the potential for hyporheic processes to impact biogeochemical transformations and ecosystems. In this study we simulate time-variable transit time distributions based on dynamic vertical exchange in a headwater mountain stream with observed, heterogeneous step-pool morphology. Our simulations include hyporheic exchange over a 600 m river corridor reach driven by continuously observed, time-variable hydrologic conditions for more than 1 year. We found that spatial variability at an instance in time is typically larger than temporal variation for the reach. Furthermore, we found reach-scale TTDs were marginally variable under all but the most extreme hydrologic conditions, indicating that TTDs are highly transferable in time. Finally, we found that aggregation of annual variation in space and time into a "master TTD" reasonably represents most of the hydrologic dynamics simulated, suggesting that this aggregation approach may provide a relevant basis for scaling from features or short reaches to entire networks.

Synergistic and antagonistic interactions of future land use and climate change on river fish assemblages.

PubMed

Radinger, Johannes; Hölker, Franz; Horký, Pavel; Slavík, Ondřej; Dendoncker, Nicolas; Wolter, Christian

2016-04-01

River ecosystems are threatened by future changes in land use and climatic conditions. However, little is known of the influence of interactions of these two dominant global drivers of change on ecosystems. Does the interaction amplify (synergistic interaction) or buffer (antagonistic interaction) the impacts and does their interaction effect differ in magnitude, direction and spatial extent compared to single independent pressures. In this study, we model the impact of single and interacting effects of land use and climate change on the spatial distribution of 33 fish species in the Elbe River. The varying effects were modeled using step-wise boosted regression trees based on 250 m raster grid cells. Species-specific models were built for both 'moderate' and 'extreme' future land use and climate change scenarios to assess synergistic, additive and antagonistic interaction effects on species losses, species gains and diversity indices and to quantify their spatial distribution within the Elbe River network. Our results revealed species richness is predicted to increase by 0.7-2.9 species by 2050 across the entire river network. Changes in species richness are likely to be spatially variable with significant changes predicted for 56-85% of the river network. Antagonistic interactions would dominate species losses and gains in up to 75% of the river network. In contrast, synergistic and additive effects would occur in only 20% and 16% of the river network, respectively. The magnitude of the interaction was negatively correlated with the magnitudes of the single independent effects of land use and climate change. Evidence is provided to show that future land use and climate change effects are highly interactive resulting in species range shifts that would be spatially variable in size and characteristic. These findings emphasize the importance of adaptive river management and the design of spatially connected conservation areas to compensate for these high species turnovers and range shifts. © 2015 John Wiley & Sons Ltd.

High resolution modeling of a small urban catchment

NASA Astrophysics Data System (ADS)

Skouri-Plakali, Ilektra; Ichiba, Abdellah; Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel

2016-04-01

Flooding is one of the most complex issues that urban environments have to deal with. In France, flooding remains the first natural risk with 72% of decrees state of natural disaster issued between October 1982 and mid-November 2014. Flooding is a result of meteorological extremes that are usually aggravated by the hydrological behavior of urban catchments and human factors. The continuing urbanization process is indeed changing the whole urban water cycle by limiting the infiltration and promoting runoff. Urban environments are very complex systems due to their extreme variability, the interference between human activities and natural processes but also the effect of the ongoing urbanization process that changes the landscape and hardly influences their hydrologic behavior. Moreover, many recent works highlight the need to simulate all urban water processes at their specific temporal and spatial scales. However, considering urban catchments heterogeneity still challenging for urban hydrology, even after advances noticed in term of high-resolution data collection and computational resources. This issue is more to be related to the architecture of urban models being used and how far these models are ready to take into account the extreme variability of urban catchments. In this work, high spatio-temporal resolution modeling is performed for a small and well-equipped urban catchment. The aim of this work is to identify urban modeling needs in terms of spatial and temporal resolution especially for a very small urban area (3.7 ha urban catchment located in the Perreux-sur-Marne city at the southeast of Paris) MultiHydro model was selected to carry out this work, it is a physical based and fully distributed model that interacts four existing modules each of them representing a portion of the water cycle in urban environments. MultiHydro was implemented at 10m, 5m and 2m resolution. Simulations were performed at different spatio-temporal resolutions and analyzed with respect to real flow measurements. First Results coming out show improvements obtained in terms of the model performance at high spatio-temporal resolution.

Nonsingular, big-bounce cosmology from spinor-torsion coupling

NASA Astrophysics Data System (ADS)

Popławski, Nikodem

2012-05-01

The Einstein-Cartan-Sciama-Kibble theory of gravity removes the constraint of general relativity that the affine connection be symmetric by regarding its antisymmetric part, the torsion tensor, as a dynamical variable. The minimal coupling between the torsion tensor and Dirac spinors generates a spin-spin interaction which is significant in fermionic matter at extremely high densities. We show that such an interaction averts the unphysical big-bang singularity, replacing it with a cusp-like bounce at a finite minimum scale factor, before which the Universe was contracting. This scenario also explains why the present Universe at largest scales appears spatially flat, homogeneous and isotropic.

Temporal and spatial scaling impacts on extreme precipitation

NASA Astrophysics Data System (ADS)

Eggert, B.; Berg, P.; Haerter, J. O.; Jacob, D.; Moseley, C.

2015-01-01

Both in the current climate and in the light of climate change, understanding of the causes and risk of precipitation extremes is essential for protection of human life and adequate design of infrastructure. Precipitation extreme events depend qualitatively on the temporal and spatial scales at which they are measured, in part due to the distinct types of rain formation processes that dominate extremes at different scales. To capture these differences, we first filter large datasets of high-resolution radar measurements over Germany (5 min temporally and 1 km spatially) using synoptic cloud observations, to distinguish convective and stratiform rain events. In a second step, for each precipitation type, the observed data are aggregated over a sequence of time intervals and spatial areas. The resulting matrix allows a detailed investigation of the resolutions at which convective or stratiform events are expected to contribute most to the extremes. We analyze where the statistics of the two types differ and discuss at which resolutions transitions occur between dominance of either of the two precipitation types. We characterize the scales at which the convective or stratiform events will dominate the statistics. For both types, we further develop a mapping between pairs of spatially and temporally aggregated statistics. The resulting curve is relevant when deciding on data resolutions where statistical information in space and time is balanced. Our study may hence also serve as a practical guide for modelers, and for planning the space-time layout of measurement campaigns. We also describe a mapping between different pairs of resolutions, possibly relevant when working with mismatched model and observational resolutions, such as in statistical bias correction.

Visuo-Spatial Processing in Autism--Testing the Predictions of Extreme Male Brain Theory

ERIC Educational Resources Information Center

Falter, Christine M.; Plaisted, Kate C.; Davis, Greg

2008-01-01

It has been hypothesised that autism is an extreme version of the male brain, caused by high levels of prenatal testosterone (Baron-Cohen 1999). To test this proposal, associations were assessed between three visuo-spatial tasks and prenatal testosterone, indexed in second-to-fourth digit length ratios (2D:4D). The study included children with…

Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Han, Yuefeng; Stein, Michael L.

2016-02-10

The Weather Research and Forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximummore » temperature through comparison with North American Regional Reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting bootstrap resampling. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.« less

Uncertainties in data-model comparisons: Spatio-temporal scales for past climates

NASA Astrophysics Data System (ADS)

Lohmann, G.

2016-12-01

Data-model comparisons are hindered by uncertainties like varying reservoir ages or potential seasonality bias of the recorder systems, but also due to the models' difficulty to represent the spatio-temporal variability patterns. For the Holocene we detect a sensitivity to horizontal resolution in the atmosphere, the representation of atmospheric dynamics, as well as the dynamics of the western boundary currents in the ocean. These features can create strong spatial heterogeneity in the North Atlantic and Pacific Oceans over long timescales (unlike a diffusive spatio-temporal scale separation). Futhermore, it is shown that such non-linear mechanisms could create a non-trivial response to seasonal insolation forcing via an atmospheric bridge inducing non-uniform temperature anomalies over the northern continents on multi-millennial time scales. Through the fluctuation-dissipation-theorem, climate variability and sensitivity are ultimately coupled. It is argued that some obvious biases between models and data may be linked to the missing key persistent component of the atmospheric dynamics, the North Atlantic blocking activity. It is shown that blocking is also linked to Atlantic multidecadal ocean variability and to extreme events. Interestingly, several proxies provide a measure of the frequency of extreme events, and a proper representation is a true challenge for climate models. Finally, case studies from deep paleo are presented in which changes in land-sea distribution or subscale parameterizations can cause relatively large effects on surface temperature. Such experiments can explore the phase space of solutions, but show the limitation of past climates to constrain climate sensitivity.

Observed Temporal and Spatial Variability in the Marine Environment at the Sub-Antarctic Prince Edward Islands - Evidence of a Changing Climate?

NASA Astrophysics Data System (ADS)

Asdar, S.; Deshayes, J.; Ansorge, I. J.

2016-02-01

The sub-Antarctic Prince Edward Islands (PEI) (47°S,38°E) are classified as isolated, hostile, impoverished regions, in which the terrestrial and marine ecosystems are relatively simple and extremely sensitive to perturbations. Their location between the Sub-Antarctic Front (SAF) and the Antarctic Polar Front (APF), bordering the Antarctic Circumpolar Current (ACC) provides an ideal natural laboratory for studying how organisms, ecological processes and ecosystems respond to a changing ocean climate in the Southern Ocean. Recent studies have proposed that climate changes reported at the PEI may correspond in time to a southward shift of the ACC and in particular of the SAF. This southward migration in the geographic position is likely to coincide with dramatic changes in the distribution of species and total productivity of this region. This study focuses on the inter-comparison of observations available at these islands. Using spectral analysis which is a study of the frequency domain characteristics of a process, we first determine the dominant characteristics of both the temporal and spatial variability of physical and biogeochemical properties. In doing so the authors are able to determine whether and how these indices of variability interact with one another in order to understand better the mechanisms underpinning this variability, i.e. the seasonal zonal migrations associated with the SAF. Additionally, we include in our analysis recent data from 2 ADCP moorings deployed between the islands from 2014 to 2015. These in-situ observations of circulation and hydrography in the vicinity of the islands provide a unique opportunity to establish a better understanding of how large scale climatic variability may impact local conditions, and more importantly its influence on the fragile ecosystem surrounding the PEI.

Variability in soybean yield in Brazil stemming from the interaction of heterogeneous management and climate variability

NASA Astrophysics Data System (ADS)

Cohn, A.; Bragança, A.; Jeffries, G. R.

2017-12-01

An increasing share of global agricultural production can be found in the humid tropics. Therefore, an improved understanding of the mechanisms governing variability in the output of tropical agricultural systems is of increasing importance for food security including through climate change adaptation. Yet, the long window over which many tropical crops can be sown, the diversity of crop varieties and management practices combine to challenge inference into climate risk to cropping output in analyses of tropical crop-climate sensitivity employing administrative data. In this paper, we leverage a newly developed spatially explicit dataset of soybean yields in Brazil to combat this problem. The dataset was built by training a model of remotely-sensed vegetation index data and land cover classification data using a rich in situ dataset of soybean yield and management variables collected over the period 2006 to 2016. The dataset contains soybean yields by plant date, cropping frequency, and maturity group for each 5km grid cell in Brazil. We model variation in these yields using an approach enabling the estimation of the influence of management factors on the sensitivity of soybean yields to variability in: cumulative solar radiation, extreme degree days, growing degree days, flooding rain in the harvest period, and dry spells in the rainy season. We find strong variation in climate sensitivity by management class. Planting date and maturity group each explained a great deal more variation in yield sensitivity than did cropping frequency. Brazil collects comparatively fine spatial resolution yield data. But, our attempt to replicate our results using administrative soy yield data revealed substantially lesser crop-climate sensitivity; suggesting that previous analyses employing administrative data may have underestimated climate risk to tropical soy production.

Evaluation of a spatial rainfall generator and an interpolation methods for the creation of future gridded data sets over complex terrains

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Michaelides, Silas; Lange, Manfred A.

2015-04-01

Space-time variability of precipitation plays a key role as a driver of many processes in different environmental fields like hydrology, ecology, biology, agriculture, and natural hazards. The objective of this study was to compare two approaches for statistical downscaling of precipitation from climate models. The study was applied to the island of Cyprus, an orographically complex terrain. The first approach makes use of a spatial temporal Neyman-Scott Rectangular Pulses (NSRP) model and a previously tested interpolation scheme (Camera et al., 2014). The second approach is based on the use of the single site NSRP model and a simplified gridded scheme based on scaling coefficients obtained from past observations. The rainfall generators were evaluated on the period 1980-2010. Both approaches were subsequently used to downscale three RCMs from the EU ENSEMBLE project to calculate climate projections (2020-2050). The main advantage of the spatial-temporal approach is that it allows creating spatially consistent daily maps of precipitation. On the other hand, due to the assumptions made using a stochastic generator based on homogeneous Poisson processes, it shows a smoothing out of all the rainfall statistics (except mean and variance) all over the study area. This leads to high errors when analyzing indices related to extremes. Examples are the number of days with rainfall over 50 mm (R50 - mean error 65%), the 95th percentile value of rainy days (RT95 - mean error 19%), and the mean annual rainfall recorded on days with rainfall above the 95th percentile (RA95 - mean error 22%). The single site approach excludes the possibility of using the created gridded data sets for case studies involving spatial connection between grid cells (e.g. hydrologic modelling), but it leads to a better reproduction of rainfall statistics and properties. The errors for the extreme indices are in fact much lower: 17% for R50, 4% for RT95, and 2% for RA95. Future projections show a decrease of the mean annual rainfall (for both approaches) over the study area between 70 mm (≈15%) and 5 mm (≈1%), in comparison to the reference period 1980-2010. Regarding extremes, calculated only with the single site approach, the projections show a decrease of the R50 index between 25% and 7%, and of the RT95 between 8% and 0%. Thus, these projections indicate that a slight reduction in the number and intensity of extremes can be expected. Further research will be done to adapt and evaluate the use of a spatial-temporal generator with nonhomogeneous spatial activation of raincells (Burton et al., 2010) to the study area. Burton, A., Fowler, H.J., Kilsby, C.G., O'Connell, P. E., 2010a. A stochastic model for the spatial-temporal simulation of non-homogeneous rainfall occurrence and amounts, Water Resour. Res. 46, W11501. DOI: 10.1029/2009WR008884 Camera, C., Bruggeman, A., Hadjinicolaou, P., Pashiardis, S., Lange, M. A., 2014. Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980-2010. J. Geophys. Res. Atmos., 119, 693-712. DOI: 10.1002/2013JD020611.

Assessing changes in extreme convective precipitation from a damage perspective

NASA Astrophysics Data System (ADS)

Schroeer, K.; Tye, M. R.

2016-12-01

Projected increases in high-intensity short-duration convective precipitation are expected even in regions that are likely to become more arid. Such high intensity precipitation events can trigger hazardous flash floods, debris flows and landslides that put people and local assets at risk. However, the assessment of local scale precipitation extremes is hampered by its high spatial and temporal variability. In addition to which, not only are extreme events rare, but such small scale events are likely to be underreported where they don't coincide with the observation network. Rather than focus solely on the convective precipitation, understanding the characteristics of these extremes which drive damage may be more effective to assess future risks. Two sources of data are used in this study. First, sub-daily precipitation observations over the Southern Alps enable an examination of seasonal and regional patterns in high-intensity convective precipitation and their relationship with weather types. Secondly, reports of private loss and damage on a household scale are used to identify which events are most damaging, or what conditions potentially enhance the vulnerability to these extremes.This study explores the potential added value from including recorded loss and damage data to understand the risks from summertime convective precipitation events. By relating precipitation generating weather types to the severity of damage we hope to develop a mechanism to assess future risks. A further benefit would be to identify from damage reports the likely occurrence of precipitation extremes where no direct observations are available and use this information to validate remotely sensed observations.

Extreme coastal erosion enhanced by anomalous extratropical storm wave direction.

PubMed

Harley, Mitchell D; Turner, Ian L; Kinsela, Michael A; Middleton, Jason H; Mumford, Peter J; Splinter, Kristen D; Phillips, Matthew S; Simmons, Joshua A; Hanslow, David J; Short, Andrew D

2017-07-20

Extratropical cyclones (ETCs) are the primary driver of large-scale episodic beach erosion along coastlines in temperate regions. However, key drivers of the magnitude and regional variability in rapid morphological changes caused by ETCs at the coast remain poorly understood. Here we analyze an unprecedented dataset of high-resolution regional-scale morphological response to an ETC that impacted southeast Australia, and evaluate the new observations within the context of an existing long-term coastal monitoring program. This ETC was characterized by moderate intensity (for this regional setting) deepwater wave heights, but an anomalous wave direction approximately 45 degrees more counter-clockwise than average. The magnitude of measured beach volume change was the largest in four decades at the long-term monitoring site and, at the regional scale, commensurate with that observed due to extreme North Atlantic hurricanes. Spatial variability in morphological response across the study region was predominantly controlled by alongshore gradients in storm wave energy flux and local coastline alignment relative to storm wave direction. We attribute the severity of coastal erosion observed due to this ETC primarily to its anomalous wave direction, and call for greater research on the impacts of changing storm wave directionality in addition to projected future changes in wave heights.

Are temporal characteristics of fast repetitive oscillating movement invariant?

PubMed

Gutnik, B J; Nicholson, J; Go, W; Gale, D; Nash, D

2003-06-01

Validation of the proportional duration model was attempted using very fast single-joint repetitive horizontal abductive-adductive movements of the stretched upper extremity with minimal cognitive input. Participants drew oscillating horizontal lines during 20 sec. over relatively short distances as quickly as possible without visual feedback. Spatial, temporal, and kinetic parameters were analysed. The amplitude and the time spent accelerating, decelerating, and reversing in both directions of each experimental line were recorded and related to the centre of gravity of the upper extremity. The accelerations of the centre of mass of the upper extremity were calculated and used to calculate the forces involved. The ratios of durations were compared and intercorrelated for the two fastest, two average, and two slowest cycles from each participant. Results exhibited significant standard deviations and variability of temporal and kinetic parameters within individual trials. The number of significant coefficients of correlation within individual trials was small despite the controlling influence of the same generalised motor program. The proportional duration model did not hold for our data. Peripheral factors (probably the length-tension relationship rule for skeletal muscles and viscosity of muscle) may be important in this type of action.

Spatiotemporal Variability of Drought in Pakistan through High-Resolution Daily Gridded In-Situ Observations

NASA Astrophysics Data System (ADS)

Bashir, F.; Zeng, X.; Gupta, H. V.; Hazenberg, P.

2017-12-01

Drought as an extreme event may have far reaching socio-economic impacts on agriculture based economies like Pakistan. Effective assessment of drought requires high resolution spatiotemporally continuous hydrometeorological information. For this purpose, new in-situ daily observations based gridded analyses of precipitation, maximum, minimum and mean temperature and diurnal temperature range are developed, that covers whole Pakistan on 0.01º latitude-longitude for a 54-year period (1960-2013). The number of participating meteorological observatories used in these gridded analyses is 2 to 6 times greater than any other similar product available. This data set is used to identify extreme wet and dry periods and their spatial patterns across Pakistan using Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI). Periodicity of extreme events is estimated at seasonal to decadal scales. Spatiotemporal signatures of drought incidence indicating its extent and longevity in different areas may help water resource managers and policy makers to mitigate the severity of the drought and its impact on food security through suitable adaptive techniques. Moreover, this high resolution gridded in-situ observations of precipitation and temperature is used to evaluate other coarser-resolution gridded products.

Spatial Structure of a Braided River: Metric Resolution Hydrodynamic Modeling Reveals What SWOT Might See

NASA Astrophysics Data System (ADS)

Schubert, J.; Sanders, B. F.; Andreadis, K.

2013-12-01

The Surface Water and Ocean Topography (SWOT) mission, currently under study by NASA (National Aeronautics and Space Administration) and CNES (Centre National d'Etudes Spatiales), is designed to provide global spatial measurements of surface water properties at resolutions better than 10 m and with centimetric accuracy. The data produced by SWOT will include irregularly spaced point clouds of the water surface height, with point spacings from roughly 2-50 m depending on a point's location within SWOT's swath. This could offer unprecedented insight into the spatial structure of rivers. Features that may be resolved include backwater profiles behind dams, drawdown profiles, uniform flow sections, critical flow sections, and even riffle-pool flow structures. In the event that SWOT scans a river during a major flood, it becomes possible to delineate the limits of the flood as well as the spatial structure of the water surface elevation, yielding insight into the dynamic interaction of channels and flood plains. The Platte River in Nebraska, USA, is a braided river with a width and slope of approximately 100 m and 100 cm/km, respectively. A 1 m resolution Digital Terrain Model (DTM) of the river basin, based on airborne lidar collected during low-flow conditions, was used to parameterize a two-dimensional, variable resolution, unstructured grid, hydrodynamic model that uses 3 m resolution triangles in low flow channels and 10 m resolution triangles in the floodplain. Use of a fine resolution mesh guarantees that local variability in topography is resolved, and after applying the hydrodynamic model, the effects of topographic variability are expressed as variability in the water surface height, depth-averaged velocity and flow depth. Flow is modeled over a reach length of 10 km for multi-day durations to capture both frequent (diurnal variations associated with regulated flow) and infrequent (extreme flooding) flow phenomena. Model outputs reveal a number of interesting features, including a high degree of variability in the water depth and velocity and lesser variability in the free-surface profile and river discharge. Hydraulic control sections are also revealed, and shown to depend on flow stage. Reach-averaging of model output is applied to study the macro-scale balance of forces in this system, and the scales at which such a force balance is appropriate. We find that the reach-average slope exhibits a declining reach-length dependence with increasing reach length, up to reach lengths of 1 km. Hence, 1 km appears to be the minimum appropriate length for reach-averaging, and at this scale, a diffusive-wave momentum balance is a reasonable approximation suitable for emerging models of discharge estimation that rely only on SWOT-observable river properties (width, height, slope, etc.).

Extreme Events and Energy Providers: Science and Innovation

NASA Astrophysics Data System (ADS)

Yiou, P.; Vautard, R.

2012-04-01

Most socio-economic regulations related to the resilience to climate extremes, from infrastructure or network design to insurance premiums, are based on a present-day climate with an assumption of stationarity. Climate extremes (heat waves, cold spells, droughts, storms and wind stilling) affect in particular energy production, supply, demand and security in several ways. While national, European or international projects have generated vast amounts of climate projections for the 21st century, their practical use in long-term planning remains limited. Estimating probabilistic diagnostics of energy user relevant variables from those multi-model projections will help the energy sector to elaborate medium to long-term plans, and will allow the assessment of climate risks associated to those plans. The project "Extreme Events for Energy Providers" (E3P) aims at filling a gap between climate science and its practical use in the energy sector and creating in turn favourable conditions for new business opportunities. The value chain ranges from addressing research questions directly related to energy-significant climate extremes to providing innovative tools of information and decision making (including methodologies, best practices and software) and climate science training for the energy sector, with a focus on extreme events. Those tools will integrate the scientific knowledge that is developed by scientific communities, and translate it into a usable probabilistic framework. The project will deliver projection tools assessing the probabilities of future energy-relevant climate extremes at a range of spatial scales varying from pan-European to local scales. The E3P project is funded by the Knowledge and Innovation Community (KIC Climate). We will present the mechanisms of interactions between academic partners, SMEs and industrial partners for this project. Those mechanisms are elementary bricks of a climate service.

The role of the subtropical North Atlantic water cycle in recent US extreme precipitation events

NASA Astrophysics Data System (ADS)

Li, Laifang; Schmitt, Raymond W.; Ummenhofer, Caroline C.

2018-02-01

The role of the oceanic water cycle in the record-breaking 2015 warm-season precipitation in the US is analyzed. The extreme precipitation started in the Southern US in the spring and propagated northward to the Midwest and the Great Lakes in the summer of 2015. This seasonal evolution of precipitation anomalies represents a typical mode of variability of US warm-season precipitation. Analysis of the atmospheric moisture flux suggests that such a rainfall mode is associated with moisture export from the subtropical North Atlantic. In the spring, excessive precipitation in the Southern US is attributable to increased moisture flux from the northwestern portion of the subtropical North Atlantic. The North Atlantic moisture flux interacts with local soil moisture which enables the US Midwest to draw more moisture from the Gulf of Mexico in the summer. Further analysis shows that the relationship between the rainfall mode and the North Atlantic water cycle has become more significant in recent decades, indicating an increased likelihood of extremes like the 2015 case. Indeed, two record-high warm-season precipitation events, the 1993 and 2008 cases, both occurred in the more recent decades of the 66 year analysis period. The export of water from the North Atlantic leaves a marked surface salinity signature. The salinity signature appeared in the spring preceding all three extreme precipitation events analyzed in this study, i.e. a saltier-than-normal subtropical North Atlantic in spring followed by extreme Midwest precipitation in summer. Compared to the various sea surface temperature anomaly patterns among the 1993, 2008, and 2015 cases, the spatial distribution of salinity anomalies was much more consistent during these extreme flood years. Thus, our study suggests that preseason salinity patterns can be used for improved seasonal prediction of extreme precipitation in the Midwest.

Come rain or shine: Multi-model Projections of Climate Hazards affecting Transportation in the South Central United States

NASA Astrophysics Data System (ADS)

Mullens, E.; Mcpherson, R. A.

2016-12-01

This work develops detailed trends in climate hazards affecting the Department of Transportation's Region 6, in the South Central U.S. Firstly, a survey was developed to gather information regarding weather and climate hazards in the region from the transportation community, identifying key phenomena and thresholds to evaluate. Statistically downscaled datasets were obtained from the Multivariate Adaptive Constructed Analogues (MACA) project, and the Asynchronous Regional Regression Model (ARRM), for a total of 21 model projections, two coupled model intercomparisons (CMIP3, and CMIP5), and four emissions pathways (A1Fi, B1, RCP8.5, RCP4.5). Specific hazards investigated include winter weather, freeze-thaw cycles, hot and cold extremes, and heavy precipitation. Projections for each of these variables were calculated for the region, utilizing spatial mapping, and time series analysis at the climate division level. The results indicate that cold-season phenomena such as winter weather, freeze-thaw, and cold extremes, decrease in intensity and frequency, particularly with the higher emissions pathways. Nonetheless, specific model and downscaling method yields variability in magnitudes, with the most notable decreasing trends late in the 21st century. Hot days show a pronounced increase, particularly with greater emissions, producing annual mean 100oF day frequencies by late 21st century analogous to the 2011 heatwave over the central Southern Plains. Heavy precipitation, evidenced by return period estimates and counts-over-thresholds, also show notable increasing trends, particularly between the recent past through mid-21st Century. Conversely, mean precipitation does not show significant trends and is regionally variable. Precipitation hazards (e.g., winter weather, extremes) diverge between downscaling methods and their associated model samples much more substantially than temperature, suggesting that the choice of global model and downscaled data is particularly important when considering region-specific impacts for precipitation. These results are intended to inform region transportation professionals of the susceptibility of the area to climate extremes, and to be a resource for assessing and incorporating changing risk probabilities into their planning processes.

Effects of ice storm on forest ecosystem of southern China in 2008 Shaoqiang Wang1, Lei Zhou1, Weimin Ju2, Kun Huang1 1Key Lab of Ecosystem Network Observation and Modeling, Institute of Geographical Sciences and Natural Resources Research, Beijing, 10010

NASA Astrophysics Data System (ADS)

Wang, Shaoqiang

2014-05-01

Evidence is mounting that an increase in extreme climate events has begun to occur worldwide during the recent decades, which affect biosphere function and biodiversity. Ecosystems returned to its original structures and functions to maintain its sustainability, which was closely dependent on ecosystem resilience. Understanding the resilience and recovery capacity of ecosystem to extreme climate events is essential to predicting future ecosystem responses to climate change. Given the overwhelming importance of this region in the overall carbon cycle of forest ecosystems in China, south China suffered a destructive ice storm in 2008. In this study, we used the number of freezing day and a process-based model (Boreal Ecosystem Productivity Simulator, BEPS) to characterize the spatial distribution of ice storm region in southeastern China and explore the impacts on carbon cycle of forest ecosystem over the past decade. The ecosystem variables, i.e. Net primary productivity (NPP), Evapotranspiration (ET), and Water use efficiency (WUE, the ratio of NPP to ET) from the outputs of BEPS models were used to detect the resistance and resilience of forest ecosystem in southern China. The pattern of ice storm-induced forest productivity widespread decline was closely related to the number of freezing day during the ice storm period. The NPP of forest area suffered heavy ice storm returned to normal status after five months with high temperature and ample moisture, indicated a high resilience of subtropical forest in China. The long-term changes of forest WUE remain stable, behaving an inherent sensitivity of ecosystem to extreme climate events. In addition, ground visits suggested that the recovery of forest productivity was attributed to rapid growth of understory. Understanding the variability and recovery threshold of ecosystem following extreme climate events help us to better simulate and predict the variability of ecosystem structure and function under current and future climate change.

Role of resolution in regional climate change projections over China

NASA Astrophysics Data System (ADS)

Shi, Ying; Wang, Guiling; Gao, Xuejie

2017-11-01

This paper investigates the sensitivity of projected future climate changes over China to the horizontal resolution of a regional climate model RegCM4.4 (RegCM), using RCP8.5 as an example. Model validation shows that RegCM performs better in reproducing the spatial distribution and magnitude of present-day temperature, precipitation and climate extremes than the driving global climate model HadGEM2-ES (HadGEM, at 1.875° × 1.25° degree resolution), but little difference is found between the simulations at 50 and 25 km resolutions. Comparison with observational data at different resolutions confirmed the added value of the RCM and finer model resolutions in better capturing the probability distribution of precipitation. However, HadGEM and RegCM at both resolutions project a similar pattern of significant future warming during both winter and summer, and a similar pattern of winter precipitation changes including dominant increase in most areas of northern China and little change or decrease in the southern part. Projected precipitation changes in summer diverge among the three models, especially over eastern China, with a general increase in HadGEM, little change in RegCM at 50 km, and a mix of increase and decrease in RegCM at 25 km resolution. Changes of temperature-related extremes (annual total number of daily maximum temperature > 25 °C, the maximum value of daily maximum temperature, the minimum value of daily minimum temperature in the three simulations especially in the two RegCM simulations are very similar to each other; so are the precipitation-related extremes (maximum consecutive dry days, maximum consecutive 5-day precipitation and extremely wet days' total amount). Overall, results from this study indicate a very low sensitivity of projected changes in this region to model resolution. While fine resolution is critical for capturing the spatial variability of the control climate, it may not be as important for capturing the climate response to homogeneous forcing (in this case greenhouse gas concentration changes).

Characterizing the Spatial Contiguity of Extreme Precipitation over the US in the Recent Past

NASA Astrophysics Data System (ADS)

Touma, D. E.; Swain, D. L.; Diffenbaugh, N. S.

2016-12-01

The spatial characteristics of extreme precipitation over an area can define the hydrologic response in a basin, subsequently affecting the flood risk in the region. Here, we examine the spatial extent of extreme precipitation in the US by defining its "footprint": a contiguous area of rainfall exceeding a certain threshold (e.g., 90th percentile) on a given day. We first characterize the climatology of extreme rainfall footprint sizes across the US from 1980-2015 using Daymet, a high-resolution observational gridded rainfall dataset. We find that there are distinct regional and seasonal differences in average footprint sizes of extreme daily rainfall. In the winter, the Midwest shows footprints exceeding 500,000 sq. km while the Front Range exhibits footprints of 10,000 sq. km. Alternatively, the summer average footprint size is generally smaller and more uniform across the US, ranging from 10,000 sq. km in the Southwest to 100,000 sq. km in Montana and North Dakota. Moreover, we find that there are some significant increasing trends of average footprint size between 1980-2015, specifically in the Southwest in the winter and the Northeast in the spring. While gridded daily rainfall datasets allow for a practical framework in calculating footprint size, this calculation heavily depends on the interpolation methods that have been used in creating the dataset. Therefore, we assess footprint size using the GHCN-Daily station network and use geostatistical methods to define footprints of extreme rainfall directly from station data. Compared to the findings from Daymet, preliminary results using this method show fewer small daily footprint sizes over the US while large footprints are of similar number and magnitude to Daymet. Overall, defining the spatial characteristics of extreme rainfall as well as observed and expected changes in these characteristics allows us to better understand the hydrologic response to extreme rainfall and how to better characterize flood risks.

Characteristics of atmospheric circulation patterns associated with extreme temperatures over North America in observations and climate models

NASA Astrophysics Data System (ADS)

Loikith, Paul C.

Motivated by a desire to understand the physical mechanisms involved in future anthropogenic changes in extreme temperature events, the key atmospheric circulation patterns associated with extreme daily temperatures over North America in the current climate are identified. Several novel metrics are used to systematically identify and describe these patterns for the entire continent. The orientation, physical characteristics, and spatial scale of these circulation patterns vary based on latitude, season, and proximity to important geographic features (i.e., mountains, coastlines). The anomaly patterns associated with extreme cold events tend to be similar to, but opposite in sign of, those associated with extreme warm events, especially within the westerlies, and tend to scale with temperature in the same locations. The influence of the Pacific North American (PNA) pattern, the Northern Annular Mode (NAM), and the El Niño-Southern Oscillation (ENSO) on extreme temperature days and months shows that associations between extreme temperatures and the PNA and NAM are stronger than associations with ENSO. In general, the association with extremes tends to be stronger on monthly than daily time scales. Extreme temperatures are associated with the PNA and NAM in locations typically influenced by these circulation patterns; however many extremes still occur on days when the amplitude and polarity of these patterns do not favor their occurrence. In winter, synoptic-scale, transient weather disturbances are important drivers of extreme temperature days; however these smaller-scale events are often concurrent with amplified PNA or NAM patterns. Associations are weaker in summer when other physical mechanisms affecting the surface energy balance, such as anomalous soil moisture content, are associated with extreme temperatures. Analysis of historical runs from seventeen climate models from the CMIP5 database suggests that most models simulate realistic circulation patterns associated with extreme temperature days in most places. Model-simulated patterns tend to resemble observed patterns better in the winter than the summer and at 500 hPa than at the surface. There is substantial variability among the suite of models analyzed and most models simulate circulation patterns more realistically away from influential features such as large bodies of water and complex topography.

Extreme oceanographic forcing and coastal response due to the 2015–2016 El Niño

PubMed Central

Barnard, Patrick L.; Hoover, Daniel; Hubbard, David M.; Snyder, Alex; Ludka, Bonnie C.; Allan, Jonathan; Kaminsky, George M.; Ruggiero, Peter; Gallien, Timu W.; Gabel, Laura; McCandless, Diana; Weiner, Heather M.; Cohn, Nicholas; Anderson, Dylan L.; Serafin, Katherine A.

2017-01-01

The El Niño-Southern Oscillation is the dominant mode of interannual climate variability across the Pacific Ocean basin, with influence on the global climate. The two end members of the cycle, El Niño and La Niña, force anomalous oceanographic conditions and coastal response along the Pacific margin, exposing many heavily populated regions to increased coastal flooding and erosion hazards. However, a quantitative record of coastal impacts is spatially limited and temporally restricted to only the most recent events. Here we report on the oceanographic forcing and coastal response of the 2015–2016 El Niño, one of the strongest of the last 145 years. We show that winter wave energy equalled or exceeded measured historical maxima across the US West Coast, corresponding to anomalously large beach erosion across the region. Shorelines in many areas retreated beyond previously measured landward extremes, particularly along the sediment-starved California coast. PMID:28195580

Extreme oceanographic forcing and coastal response due to the 2015–2016 El Niño

USGS Publications Warehouse

Barnard, Patrick; Hoover, Daniel J.; Hubbard, David M.; Snyder, Alexander; Ludka, Bonnie C.; Allan, Jonathan; Kaminsky, George M.; Ruggiero,; Gallien, Timu W.; Gabel, Laura; McCandless, Diana; Weiner, Heather M.; Cohn, Nicholas; Anderson, Dylan L.; Serafin, Katherine A.

2017-01-01

The El Niño-Southern Oscillation is the dominant mode of interannual climate variability across the Pacific Ocean basin, with influence on the global climate. The two end members of the cycle, El Niño and La Niña, force anomalous oceanographic conditions and coastal response along the Pacific margin, exposing many heavily populated regions to increased coastal flooding and erosion hazards. However, a quantitative record of coastal impacts is spatially limited and temporally restricted to only the most recent events. Here we report on the oceanographic forcing and coastal response of the 2015–2016 El Niño, one of the strongest of the last 145 years. We show that winter wave energy equalled or exceeded measured historical maxima across the US West Coast, corresponding to anomalously large beach erosion across the region. Shorelines in many areas retreated beyond previously measured landward extremes, particularly along the sediment-starved California coast.

Extreme oceanographic forcing and coastal response due to the 2015-2016 El Niño.

PubMed

Barnard, Patrick L; Hoover, Daniel; Hubbard, David M; Snyder, Alex; Ludka, Bonnie C; Allan, Jonathan; Kaminsky, George M; Ruggiero, Peter; Gallien, Timu W; Gabel, Laura; McCandless, Diana; Weiner, Heather M; Cohn, Nicholas; Anderson, Dylan L; Serafin, Katherine A

2017-02-14

The El Niño-Southern Oscillation is the dominant mode of interannual climate variability across the Pacific Ocean basin, with influence on the global climate. The two end members of the cycle, El Niño and La Niña, force anomalous oceanographic conditions and coastal response along the Pacific margin, exposing many heavily populated regions to increased coastal flooding and erosion hazards. However, a quantitative record of coastal impacts is spatially limited and temporally restricted to only the most recent events. Here we report on the oceanographic forcing and coastal response of the 2015-2016 El Niño, one of the strongest of the last 145 years. We show that winter wave energy equalled or exceeded measured historical maxima across the US West Coast, corresponding to anomalously large beach erosion across the region. Shorelines in many areas retreated beyond previously measured landward extremes, particularly along the sediment-starved California coast.

Statistical structure of intrinsic climate variability under global warming

NASA Astrophysics Data System (ADS)

Zhu, Xiuhua; Bye, John; Fraedrich, Klaus

2017-04-01

Climate variability is often studied in terms of fluctuations with respect to the mean state, whereas the dependence between the mean and variability is rarely discussed. We propose a new climate metric to measure the relationship between means and standard deviations of annual surface temperature computed over non-overlapping 100-year segments. This metric is analyzed based on equilibrium simulations of the Max Planck Institute-Earth System Model (MPI-ESM): the last millennium climate (800-1799), the future climate projection following the A1B scenario (2100-2199), and the 3100-year unforced control simulation. A linear relationship is globally observed in the control simulation and thus termed intrinsic climate variability, which is most pronounced in the tropical region with negative regression slopes over the Pacific warm pool and positive slopes in the eastern tropical Pacific. It relates to asymmetric changes in temperature extremes and associates fluctuating climate means with increase or decrease in intensity and occurrence of both El Niño and La Niña events. In the future scenario period, the linear regression slopes largely retain their spatial structure with appreciable changes in intensity and geographical locations. Since intrinsic climate variability describes the internal rhythm of the climate system, it may serve as guidance for interpreting climate variability and climate change signals in the past and the future.

Modeling surface response of the Greenland Ice Sheet to interglacial climate

NASA Astrophysics Data System (ADS)

Rau, Dominik; Rogozhina, Irina

2013-04-01

We present a new parameterization of surface mass balance (SMB) of the Greenland Ice Sheet (GIS) under interglacial climate conditions validated against recent satellite observations on a regional scale. Based on detailed analysis of the modeled surface melting and refreezing rates, we conclude that the existing SMB parameterizations fail to capture either spatial pattern or amplitude of the observed surface response of the GIS. This is due to multiple simplifying assumptions adopted by the majority of modeling studies within the frame of the positive degree day method. Modeled spatial distribution of surface melting is found to be highly sensitive to a choice of daily temperature standard deviation (SD) and degree-day factors, which are generally assumed to have uniform distribution across the entire Greenland region. However, the use of uniform SD distribution and the range of commonly used SD values are absolutely unsupported by the ERA-40 and ERA-Interim climate data. In this region, SD distribution is highly inhomogeneous and characterized by low amplitudes during the summer months in the areas where most surface ice melting occurs. In addition, the use of identical degree day factors on both the eastern and western slopes of the GIS results in overestimation of surface runoff along the western coast of Greenland and significant underestimation along its eastern coast. Our approach is to make use of (i) spatially and seasonally variable SDs derived from ERA-40 and ERA-Interim time series, and (ii) spatially variable degree-day factors, measured across Greenland, Arctic Canada, Norway, Spitsbergen and Iceland. We demonstrate that the new approach is extremely efficient for modeling the evolution of the GIS during the observational period and the entire Holocene interglacial.

Flash floods in Europe: state of the art and research perspectives

NASA Astrophysics Data System (ADS)

Gaume, Eric

2014-05-01

Flash floods, i.e. floods induced by severe rainfall events generally affecting watersheds of limited area, are the most frequent, destructive and deadly kind of natural hazard known in Europe and throughout the world. Flash floods are especially intense across the Mediterranean zone, where rainfall accumulations exceeding 500 mm within a few hours may be observed. Despite this state of facts, the study of extremes in hydrology has essentially gone unexplored until the recent past, with the exception of some rare factual reports on individual flood events, with the sporadic inclusion of isolated estimated peak discharges. Floods of extraordinary magnitude are in fact hardly ever captured by existing standard measurement networks, either because they are too heavily concentrated in space and time or because their discharges greatly exceed the design and calibration ranges of the measurement devices employed (stream gauges). This situation has gradually evolved over the last decade for two main reasons. First, the expansion and densification of weather radar networks, combined with improved radar quantitative precipitation estimates, now provide ready access to rainfall measurements at spatial and temporal scales that, while not perfectly accurate, are compatible with the study of extreme events. Heavy rainfall events no longer fail to be recorded by existing rain gauge and radar networks. Second, pioneering research efforts on extreme floods, based on precise post-flood surveys, have helped overcome the limitations imposed by a small base of available direct measured data. This activity has already yielded significant progress in expanding the knowledge and understanding of extreme flash floods. This presentation will provide a review of the recent research progresses in the area of flash flood studies, mainly based on the outcomes of the European research projects FLOODsite, HYDRATE and Hymex. It will show how intensive collation of field data helped better define the possible magnitudes of flood volumes and discharges during flash floods, their spatial distribution and rates of occurrence, as well as the factors that control the hydrological response of watersheds to heavy rainfalls explaining the large spatial variability in flood hazard. Developments in the fields of flood frequency analyses and flood forecasting based on the recently acquired data or adapted for the valuation of this specific data will also be presented. The presentation will end suggesting some perspectives for future research activities on flash floods.

Spatial Variability of Snowpack Properties On Small Slopes

NASA Astrophysics Data System (ADS)

Pielmeier, C.; Kronholm, K.; Schneebeli, M.; Schweizer, J.

The spatial variability of alpine snowpacks is created by a variety of parameters like deposition, wind erosion, sublimation, melting, temperature, radiation and metamor- phism of the snow. Spatial variability is thought to strongly control the avalanche initi- ation and failure propagation processes. Local snowpack measurements are currently the basis for avalanche warning services and there exist contradicting hypotheses about the spatial continuity of avalanche active snow layers and interfaces. Very little about the spatial variability of the snowpack is known so far, therefore we have devel- oped a systematic and objective method to measure the spatial variability of snowpack properties, layering and its relation to stability. For a complete coverage, the analysis of the spatial variability has to entail all scales from mm to km. In this study the small to medium scale spatial variability is investigated, i.e. the range from centimeters to tenths of meters. During the winter 2000/2001 we took systematic measurements in lines and grids on a flat snow test field with grid distances from 5 cm to 0.5 m. Fur- thermore, we measured systematic grids with grid distances between 0.5 m and 2 m in undisturbed flat fields and on small slopes above the tree line at the Choerbschhorn, in the region of Davos, Switzerland. On 13 days we measured the spatial pattern of the snowpack stratigraphy with more than 110 snow micro penetrometer measure- ments at slopes and flat fields. Within this measuring grid we placed 1 rutschblock and 12 stuffblock tests to measure the stability of the snowpack. With the large num- ber of measurements we are able to use geostatistical methods to analyse the spatial variability of the snowpack. Typical correlation lengths are calculated from semivari- ograms. Discerning the systematic trends from random spatial variability is analysed using statistical models. Scale dependencies are shown and recurring scaling patterns are outlined. The importance of the small and medium scale spatial variability for the larger (kilometer) scale spatial variability as well as for the avalanche formation are discussed. Finally, an outlook on spatial models for the snowpack variability is given.

Forecasting of extreme events in Andes mountain basins using CFSv2

NASA Astrophysics Data System (ADS)

Castro, L.

2017-12-01

As has been shown in several recent studies related with climate change, there has been an increase in heavy daily precipitation events, and this is expected to continue in almost all areas of the globe. In central Chile, where the hydrological regime is influenced by snow accumulation, an increase in temperatures is expected due to CC, which in turn may cause an elevation of the freezing level. The impact on the freezing level increase is also significant because a larger area of the basin will be exposed to liquid precipitation rather than snow, and afterwards will have a strong impact on streamflow. The frequency of extreme precipitation events and freezing level increases have recently affected the north and central parts of Chile. In order to predict the severity of an extreme hydrometeorological event in a mountainous basin affected by rainfall and freezing level variations, this paper pose that it is necessary to know in advance the expected meteorology and the way it will affect the hydrological response of the basin. To achieve this purpose, it will be necessary to have meteorological forecasts of a numerical model for short-term prediction, corrected and disaggregated at local scale. The methodological process is as follows. First, we consider the generation of daily forecasts at local scale using statistical downscaling methods for the forecasts obtained from an NWP model. Second, we pose to improve our knowledge the spatial-temporal distribution of the meteorological forcings using a dense network of meteorological stations in a mountain basin. With the above, the statistical methods used to represent the spatial-temporal variability of the meteorological forcings at basin scale will be evaluated.

Land-atmosphere coupling strength determines impact of land cover change in South-East Asia

NASA Astrophysics Data System (ADS)

Toelle, M. H.

2017-12-01

In a previous modeling study of large-scale deforestation in South-East Asia, between 20° S and 20° N, a decrease of latent heat flux and an increase of sensible heat flux is found. This induced higher temperatures, and ultimately deepened the boundary layer with leading to less rainfall, but higher rainfall amounts and extreme temperatures. In order to attribute these differences to a feedback mechanism, a correlation analysis is performed. Therefore, the land-atmosphere coupling strength is compared with the impact of land cover change during seasonal periods and ENSO events. Hereby, ERA-Interim-driven COSMO-CLM simulations are analyzed for the period 1990 to 2004. The regional climate model is able to reproduce the overall soil moisture spatial pattern suggested by the observational Global Land Evaporation Amsterdam Model. However, COSMO-CLM shows more spatial variability and strength. By deforestation, the coupling strength between land and atmosphere is increased. Major changes in coupling strength occur during La Niña events. The impact due to deforestation depends non-linearly on the coupling strength exemplified by maximum temperature and evapotranspiration. It is shown that the magnitude of change in extreme temperature due to deforestation depends on the former coupling strength over the region. The rise in extreme temperatures due to deforestation occurs mainly over the mainland, where the coupling strength is strongest. The impact is less pronounced over the maritime islands due to the oceanic influence. It is suggested that the regional-scale impact depends on the model-specific coupling strength besides the physical reasoning over this region. Deforestation over South-East Asia will likely have consequences for the agricultural output and increase socio-economic vulnerability.

Variability in winter climate and winter extremes reduces population growth of an alpine butterfly.

PubMed

Roland, Jens; Matter, Stephen F

2013-01-01

We examined the long-term, 15-year pattern of population change in a network of 21 Rocky Mountain populations of Parnassius smintheus butterflies in response to climatic variation. We found that winter values of the broadscale climate variable, the Pacific Decadal Oscillation (PDO) index, were a strong predictor of annual population growth, much more so than were endogenous biotic factors related to population density. The relationship between PDO and population growth was nonlinear. Populations declined in years with extreme winter PDO values, when there were either extremely warm or extremely cold sea surface temperatures in the eastern Pacific relative to that in the western Pacific. Results suggest that more variable winters, and more frequent extremely cold or warm winters, will result in more frequent decline of these populations, a pattern exacerbated by the trend for increasingly variable winters seen over the past century.

Development of a heat vulnerability index for New York State.

PubMed

Nayak, S G; Shrestha, S; Kinney, P L; Ross, Z; Sheridan, S C; Pantea, C I; Hsu, W H; Muscatiello, N; Hwang, S A

2017-12-01

The frequency and intensity of extreme heat events are increasing in New York State (NYS) and have been linked with increased heat-related morbidity and mortality. But these effects are not uniform across the state and can vary across large regions due to regional sociodemographic and environmental factors which impact an individual's response or adaptive capacity to heat and in turn contribute to vulnerability among certain populations. We developed a heat vulnerability index (HVI) to identify heat-vulnerable populations and regions in NYS. Census tract level environmental and sociodemographic heat-vulnerability variables were used to develop the HVI to identify heat-vulnerable populations and areas. Variables were identified from a comprehensive literature review and climate-health research in NYS. We obtained data from 2010 US Census Bureau and 2011 National Land Cover Database. We used principal component analysis to reduce correlated variables to fewer uncorrelated components, and then calculated the cumulative HVI for each census tract by summing up the scores across the components. The HVI was then mapped across NYS (excluding New York City) to display spatial vulnerability. The prevalence rates of heat stress were compared across HVI score categories. Thirteen variables were reduced to four meaningful components representing 1) social/language vulnerability; 2) socioeconomic vulnerability; 3) environmental/urban vulnerability; and 4) elderly/ social isolation. Vulnerability to heat varied spatially in NYS with the HVI showing that metropolitan areas were most vulnerable, with language barriers and socioeconomic disadvantage contributing to the most vulnerability. Reliability of the HVI was supported by preliminary results where higher rates of heat stress were collocated in the regions with the highest HVI. The NYS HVI showed spatial variability in heat vulnerability across the state. Mapping the HVI allows quick identification of regions in NYS that could benefit from targeted interventions. The HVI will be used as a planning tool to help allocate appropriate adaptation measures like cooling centers and issue heat alerts to mitigate effects of heat in vulnerable areas. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Advancing the adaptive capacity of social-ecological systems to absorb climate extremes

NASA Astrophysics Data System (ADS)

Thonicke, Kirsten; Bahn, Michael; Bardgett, Richard; Bloemen, Jasper; Chabay, Ilan; Erb, Karlheinz; Giamberini, Mariasilvia; Gingrich, Simone; Lavorel, Sandra; Liehr, Stefan; Rammig, Anja

2017-04-01

The recent and projected increases in climate variability and the frequency of climate extremes are posing a profound challenge to society and the biosphere (IPCC 2012, IPCC 2013). Climate extremes can affect natural and managed ecosystems more severely than gradual warming. The ability of ecosystems to resist and recover from climate extremes is therefore of fundamental importance for society, which strongly relies on their ability to supply provisioning, regulating, supporting and cultural services. Society in turn triggers land-use and management decisions that affect ecosystem properties. Thus, ecological and socio-economic conditions are tightly coupled in what has been referred to as the social-ecological system. For ensuring human well-being in the light of climate extremes it is crucial to enhance the resilience of the social-ecological system (SES) across spatial, temporal and institutional scales. Stakeholders, such as resource managers, urban, landscape and conservation planners, decision-makers in agriculture and forestry, as well as natural hazards managers, require an improved knowledge base for better-informed decision making. To date the vulnerability and adaptive capacity of SESs to climate extremes is not well understood and large uncertainties exist as to the legacies of climate extremes on ecosystems and on related societal structures and processes. Moreover, we lack empirical evidence and incorporation of simulated future ecosystem and societal responses to support pro-active management and enhance social-ecological resilience. In our presentation, we outline the major research gaps and challenges to be addressed for understanding and enhancing the adaptive capacity of SES to absorb and adapt to climate extremes, including acquisition and elaboration of long-term monitoring data and improvement of ecological models to better project climate extreme effects and provide model uncertainties. We highlight scientific challenges and discuss conceptual and observational gaps that need to be overcome to advance this inter- and transdisciplinary topic.

Insight into the Pacific Sea Surface Temperature- North American Hydroclimate Connection from an Eastern Tropical North Pacific Coral Record

NASA Astrophysics Data System (ADS)

Sanchez, S. C.; Charles, C. D.; Carriquiry, J. D.

2015-12-01

The last few years of record-breaking climate anomalies across North America--a resilient atmospheric ridge and extreme drought over the West Coast, and severe winters across the Midwest and East Coast regions--have been linked to anomalous Pacific sea surface temperatures (Seager et al. 2014, Wang et al. 2014, Hartmann 2015). The synoptic associations prompt important questions on the relation between these unusual phenomena and extreme expressions of known Pacific decadal modes, such as the North Pacific Gyre Oscillation (NPGO). These questions motivate our pursuit to document multiple realizations of decadal variability in the Pacific-North American region through periods of varied radiative forcing. Here we introduce a 178 year, seasonally resolved Porites coral record from Clarion Island (18N, 115W), the westernmost island of the Revillagigedo Archipelago, a region both highly influenced by NPGO SST and SSS variability and critical for NPGO tropical-extratropical communication via the Seasonal Footprinting Mechanism (Vimont et al. 2003). When coupled with tree ring records from the western United States (Griffin and Anchukaitis 2014, MacDonald and Case 2005) and coral records from the central tropical Pacific (Cobb et al. 2001), the δ18O signal from the Clarion coral offers an extended framework of coherent continental hydroclimate and oceanic variability across the Pacific basin beyond the instrumental record. Over the last 200 years, we find clear commonality in the timing, magnitude and spatial expression of variability (illustrated through the NADA Atlas, Cook et al. 2004) amongst the proxy records. The strong relationship between Northeastern Pacific Clarion and the Central Pacific Palmyra record with the North American hydroclimate records can be viewed within the mechanistic framework of the NPGO; this framework is then explored over the last millennium across intervals of varied radiative forcing.

Insight into the Pacific Sea Surface Temperature- North American Hydroclimate Connection from an Eastern Tropical North Pacific Coral Record

NASA Astrophysics Data System (ADS)

Svendsen, J. I.; Briner, J. P.; Mangerud, J.; Hughes, A. L. C.; Young, N. E.; Vasskog, K.

2014-12-01

The last few years of record-breaking climate anomalies across North America--a resilient atmospheric ridge and extreme drought over the West Coast, and severe winters across the Midwest and East Coast regions--have been linked to anomalous Pacific sea surface temperatures (Seager et al. 2014, Wang et al. 2014, Hartmann 2015). The synoptic associations prompt important questions on the relation between these unusual phenomena and extreme expressions of known Pacific decadal modes, such as the North Pacific Gyre Oscillation (NPGO). These questions motivate our pursuit to document multiple realizations of decadal variability in the Pacific-North American region through periods of varied radiative forcing. Here we introduce a 178 year, seasonally resolved Porites coral record from Clarion Island (18N, 115W), the westernmost island of the Revillagigedo Archipelago, a region both highly influenced by NPGO SST and SSS variability and critical for NPGO tropical-extratropical communication via the Seasonal Footprinting Mechanism (Vimont et al. 2003). When coupled with tree ring records from the western United States (Griffin and Anchukaitis 2014, MacDonald and Case 2005) and coral records from the central tropical Pacific (Cobb et al. 2001), the δ18O signal from the Clarion coral offers an extended framework of coherent continental hydroclimate and oceanic variability across the Pacific basin beyond the instrumental record. Over the last 200 years, we find clear commonality in the timing, magnitude and spatial expression of variability (illustrated through the NADA Atlas, Cook et al. 2004) amongst the proxy records. The strong relationship between Northeastern Pacific Clarion and the Central Pacific Palmyra record with the North American hydroclimate records can be viewed within the mechanistic framework of the NPGO; this framework is then explored over the last millennium across intervals of varied radiative forcing.

Regional frequency analysis of extreme rainfall for the Baltimore Metropolitan region based on stochastic storm transposition

NASA Astrophysics Data System (ADS)

Zhou, Z.; Smith, J. A.; Yang, L.; Baeck, M. L.; Wright, D.; Liu, S.

2017-12-01

Regional frequency analyses of extreme rainfall are critical for development of engineering hydrometeorology procedures. In conventional approaches, the assumptions that `design storms' have specified time profiles and are uniform in space are commonly applied but often not appropriate, especially over regions with heterogeneous environments (due to topography, water-land boundaries and land surface properties). In this study, we present regional frequency analyses of extreme rainfall for Baltimore study region combining storm catalogs of rainfall fields derived from weather radar and stochastic storm transposition (SST, developed by Wright et al., 2013). The study region is Dead Run, a small (14.3 km2) urban watershed, in the Baltimore Metropolitan region. Our analyses build on previous empirical and modeling studies showing pronounced spatial heterogeneities in rainfall due to the complex terrain, including the Chesapeake Bay to the east, mountainous terrain to the west and urbanization in this region. We expand the original SST approach by applying a multiplier field that accounts for spatial heterogeneities in extreme rainfall. We also characterize the spatial heterogeneities of extreme rainfall distribution through analyses of rainfall fields in the storm catalogs. We examine the characteristics of regional extreme rainfall and derive intensity-duration-frequency (IDF) curves using the SST approach for heterogeneous regions. Our results highlight the significant heterogeneity of extreme rainfall in this region. Estimates of IDF show the advantages of SST in capturing the space-time structure of extreme rainfall. We also illustrate application of SST analyses for flood frequency analyses using a distributed hydrological model. Reference: Wright, D. B., J. A. Smith, G. Villarini, and M. L. Baeck (2013), Estimating the frequency of extreme rainfall using weather radar and stochastic storm transposition, J. Hydrol., 488, 150-165.

The spatial return level of aggregated hourly extreme rainfall in Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Shaffie, Mardhiyyah; Eli, Annazirin; Wan Zin, Wan Zawiah; Jemain, Abdul Aziz

2015-07-01

This paper is intended to ascertain the spatial pattern of extreme rainfall distribution in Peninsular Malaysia at several short time intervals, i.e., on hourly basis. Motivation of this research is due to historical records of extreme rainfall in Peninsular Malaysia, whereby many hydrological disasters at this region occur within a short time period. The hourly periods considered are 1, 2, 3, 6, 12, and 24 h. Many previous hydrological studies dealt with daily rainfall data; thus, this study enables comparison to be made on the estimated performances between daily and hourly rainfall data analyses so as to identify the impact of extreme rainfall at a shorter time scale. Return levels based on the time aggregate considered are also computed. Parameter estimation using L-moment method for four probability distributions, namely, the generalized extreme value (GEV), generalized logistic (GLO), generalized Pareto (GPA), and Pearson type III (PE3) distributions were conducted. Aided with the L-moment diagram test and mean square error (MSE) test, GLO was found to be the most appropriate distribution to represent the extreme rainfall data. At most time intervals (10, 50, and 100 years), the spatial patterns revealed that the rainfall distribution across the peninsula differ for 1- and 24-h extreme rainfalls. The outcomes of this study would provide additional information regarding patterns of extreme rainfall in Malaysia which may not be detected when considering only a higher time scale such as daily; thus, appropriate measures for shorter time scales of extreme rainfall can be planned. The implementation of such measures would be beneficial to the authorities to reduce the impact of any disastrous natural event.

Comparison of Extreme Precipitation Return Levels using Spatial Bayesian Hierarchical Modeling versus Regional Frequency Analysis

NASA Astrophysics Data System (ADS)

Love, C. A.; Skahill, B. E.; AghaKouchak, A.; Karlovits, G. S.; England, J. F.; Duren, A. M.

2017-12-01

We compare gridded extreme precipitation return levels obtained using spatial Bayesian hierarchical modeling (BHM) with their respective counterparts from a traditional regional frequency analysis (RFA) using the same set of extreme precipitation data. Our study area is the 11,478 square mile Willamette River basin (WRB) located in northwestern Oregon, a major tributary of the Columbia River whose 187 miles long main stem, the Willamette River, flows northward between the Coastal and Cascade Ranges. The WRB contains approximately two ­thirds of Oregon's population and 20 of the 25 most populous cities in the state. The U.S. Army Corps of Engineers (USACE) Portland District operates thirteen dams and extreme precipitation estimates are required to support risk­ informed hydrologic analyses as part of the USACE Dam Safety Program. Our intent is to profile for the USACE an alternate methodology to an RFA that was developed in 2008 due to the lack of an official NOAA Atlas 14 update for the state of Oregon. We analyze 24-hour annual precipitation maxima data for the WRB utilizing the spatial BHM R package "spatial.gev.bma", which has been shown to be efficient in developing coherent maps of extreme precipitation by return level. Our BHM modeling analysis involved application of leave-one-out cross validation (LOO-CV), which not only supported model selection but also a comprehensive assessment of location specific model performance. The LOO-CV results will provide a basis for the BHM RFA comparison.

Variability of O2, H2S, and pH in intertidal sediments measured on a highly resolved spatial and temporal scale

NASA Astrophysics Data System (ADS)

Walpersdorf, E.; Werner, U.; Bird, P.; de Beer, D.

2003-04-01

We investigated the variability of O_2, pH, and H_2S in intertidal sediments to assess the time- and spatial scales of changes in environmental conditions and their effects on bacterial activities. Measurements were performed over the tidal cycle and at different seasons by the use of microsensors attached to an autonomous in-situ measuring device. This study was carried out at a sand- and a mixed flat in the backbarrier area of Spiekeroog (Germany) within the frame of the DFG research group "Biogeochemistry of the Wadden Sea". Results showed that O_2 variability was not pronounced in the coastal mixed flat, where only extreme weather conditions could increase O_2 penetration. In contrast, strong dynamics in O_2 availability, pH and maximum penetration depths of several cm were found at the sandflat. In these highly permeable sediments, we directly observed tidal pumping: at high tide O_2-rich water was forced into the plate and at low tide anoxic porewater drained off the sediment. From the lower part of the plate where organic rich clayey layers were embedded in the sediment anoxic water containing H_2S leaked out during low tide. Thus advective processes, driven by the tidal pump, waves and currents, control O_2 penetration and depth distribution of H_2S and pH. The effects of the resulting porewater exchange on mineralization rates and microbial activities will be discussed.

Precipitation variability as a strong determinant on tree cover across global tropics

NASA Astrophysics Data System (ADS)

Xu, X.; Medvigy, D.; Guan, K.; Trugman, A. T.; Good, S. P.; Rodriguez-Iturbe, I.

2017-12-01

Tropical and subtropical ecosystems support a significant carbon sink and storage and provide various ecosystem services. One challenge for these ecosystems is the changing precipitation variability (PV), which is likely to become more extreme under on-going climate change. However, there is a lack of consensus in the determining role of PV on tropical tree cover, which is a widely-used indicator for ecosystem state and functions in the tropics, as well as the underlying mechanism. Here, we ask whether changes in PV by themselves are likely to lead to changes in tropical tree cover. Using a combination of climate, soil and remotely-sensed tree cover data, we comprehensively assess the effects of PV on tree cover spatial variations at intra-seasonal, seasonal and inter-annual scales. We find that PV contributes 33% -56% to the total explained spatial variation (65% -79%) in tree cover. The contribution of PV depends on mean annual precipitation (MAP) and is highest under intermediate MAP (500 - 1500 mm). In general, tree cover increases with rainy day frequency and wet season length but shows mixed responses to inter-annual precipitation variability. We further use a biophysical model to show that the PV-tree cover relation can be explained by tree-grass water competition. Our results suggest that tropical tree cover can decrease by 3-5% overall and by up to 20% in Amazonia under projected changes in PV at the end of this century.

Sensitivity to acidification of subalpine ponds and lakes in north-western Colorado

USGS Publications Warehouse

Campbell, D.H.; Muths, E.; Turk, J.T.; Corn, P.S.

2004-01-01

Although acidifying deposition in western North America is lower than in many parts of the world, many high-elevation ecosystems there are extremely sensitive to acidification. Previous studies determined that the Mount Zirkel Wilderness Area (MZWA) has the most acidic snowpack and aquatic ecosystems that are among the most sensitive in the region. In this study, spatial and temporal variability of ponds and lakes in and near the MZWA were examined to determine their sensitivity to acidification and the effects of acidic deposition during and after snowmelt. Within the areas identified as sensitive to acidification based on bedrock types, there was substantial variability in acid-neutralizing capacity (ANC), which was related to differences in hydrological flowpaths that control delivery of weathering products to surface waters. Geological and topographic maps were of limited use in predicting acid sensitivity because their spatial resolution was not fine enough to capture the variability of these attributes for lakes and ponds with small catchment areas. Many of the lakes are sensitive to acidification (summer and autumn ANC < 100 µeq L−1), but none of them appeared to be threatened immediately by episodic or chronic acidification. In contrast, 22 ponds had minimum ANC < 30 µeq L−1, indicating that they are extremely sensitive to acidic deposition and could be damaged by episodic acidification, although net acidity (ANC < 0) was not measured in any of the ponds during the study. The lowest measured pH value was 5·4, and pH generally remained less than 6·0 throughout early summer in the most sensitive ponds, indicating that biological effects of acidification are possible at levels of atmospheric deposition that occurred during the study. The aquatic chemistry of lakes was dominated by atmospheric deposition and biogeochemical processes in soils and shallow ground water, whereas the aquatic chemistry of ponds was also affected by organic acids and biogeochemical processes in the water column and at the sediment–water interface. These results indicate that conceptual and mechanistic acidification models that have been developed for lakes and streams may be inadequate for predicting acidification in less-understood systems such as ponds.

The relative contribution of climatic, edaphic, and biotic drivers to risk of tree mortality from drought

NASA Astrophysics Data System (ADS)

March, R. G.; Moore, G. W.; Edgar, C. B.; Lawing, A. M.; Washington-Allen, R. A.

2015-12-01

In recorded history, the 2011 Texas Drought was comparable in severity only to a drought that occurred 300 years ago. By mid-September, 88% of the state experienced 'exceptional' conditions, with the rest experiencing 'extreme' or 'severe' drought. By recent estimates, the 2011 Texas Drought killed 6.2% of all the state's trees, at a rate nearly 9 times greater than average. The vast spatial scale and relatively uniform intensity of this drought has provided an opportunity to examine the comparative interactions among forest types, terrain, and edaphic factors across major climate gradients which in 2011 were subjected to extreme drought conditions that ultimately caused massive tree mortality. We used maximum entropy modeling (Maxent) to rank environmental landscape factors with the potential to drive drought-related tree mortality and test the assumption that the relative importance of these factors are scale-dependent. Occurrence data of dead trees were collected during the summer of 2012 from 599 field plots distributed across Texas with 30% used for model evaluation. Bioclimatic variables, ecoregions, soils characteristics, and topographic variables were modeled with drought-killed tree occurrence. Their relative contribution to the model was seen as their relative importance in driving mortality. To test determinants at a more local scale, we examined Landsat 7 scenes in East and West Texas with moderate-resolution data for the same variables above with the exception of climate. All models were significantly better than random in binomial tests of omission and receiver operating characteristic analyses. The modeled spatial distribution of probability of occurrence showed high probability of mortality in the east-central oak woodlands and the mixed pine-hardwood forest region in northeast Texas. Both regional and local models were dominated by biotic factors (ecoregion and forest type, respectively). Forest density and precipitation of driest month also contributed highly to the regional model. The local models gave more importance to available water storage at root zone and hillshade. Understanding how environmental factors drive drought-related mortality can help predict vulnerable landscapes and aid in preparing for future drought events.

Assessment of Coastal and Urban Flooding Hazards Applying Extreme Value Analysis and Multivariate Statistical Techniques: A Case Study in Elwood, Australia

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Arnbjerg-Nielsen, Karsten; Rosbjerg, Dan; Madsen, Henrik

2016-04-01

Traditionally, flood risk assessment studies have been carried out from a univariate frequency analysis perspective. However, statistical dependence between hydrological variables, such as extreme rainfall and extreme sea surge, is plausible to exist, since both variables to some extent are driven by common meteorological conditions. Aiming to overcome this limitation, multivariate statistical techniques has the potential to combine different sources of flooding in the investigation. The aim of this study was to apply a range of statistical methodologies for analyzing combined extreme hydrological variables that can lead to coastal and urban flooding. The study area is the Elwood Catchment, which is a highly urbanized catchment located in the city of Port Phillip, Melbourne, Australia. The first part of the investigation dealt with the marginal extreme value distributions. Two approaches to extract extreme value series were applied (Annual Maximum and Partial Duration Series), and different probability distribution functions were fit to the observed sample. Results obtained by using the Generalized Pareto distribution demonstrate the ability of the Pareto family to model the extreme events. Advancing into multivariate extreme value analysis, first an investigation regarding the asymptotic properties of extremal dependence was carried out. As a weak positive asymptotic dependence between the bivariate extreme pairs was found, the Conditional method proposed by Heffernan and Tawn (2004) was chosen. This approach is suitable to model bivariate extreme values, which are relatively unlikely to occur together. The results show that the probability of an extreme sea surge occurring during a one-hour intensity extreme precipitation event (or vice versa) can be twice as great as what would occur when assuming independent events. Therefore, presuming independence between these two variables would result in severe underestimation of the flooding risk in the study area.

Statistical downscaling modeling with quantile regression using lasso to estimate extreme rainfall

NASA Astrophysics Data System (ADS)

Santri, Dewi; Wigena, Aji Hamim; Djuraidah, Anik

2016-02-01

Rainfall is one of the climatic elements with high diversity and has many negative impacts especially extreme rainfall. Therefore, there are several methods that required to minimize the damage that may occur. So far, Global circulation models (GCM) are the best method to forecast global climate changes include extreme rainfall. Statistical downscaling (SD) is a technique to develop the relationship between GCM output as a global-scale independent variables and rainfall as a local- scale response variable. Using GCM method will have many difficulties when assessed against observations because GCM has high dimension and multicollinearity between the variables. The common method that used to handle this problem is principal components analysis (PCA) and partial least squares regression. The new method that can be used is lasso. Lasso has advantages in simultaneuosly controlling the variance of the fitted coefficients and performing automatic variable selection. Quantile regression is a method that can be used to detect extreme rainfall in dry and wet extreme. Objective of this study is modeling SD using quantile regression with lasso to predict extreme rainfall in Indramayu. The results showed that the estimation of extreme rainfall (extreme wet in January, February and December) in Indramayu could be predicted properly by the model at quantile 90th.

Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data

PubMed Central

Johnson, Daniel P; Wilson, Jeffrey S; Luber, George C

2009-01-01

Background Extreme heat events are the number one cause of weather-related fatalities in the United States. The current system of alert for extreme heat events does not take into account intra-urban spatial variation in risk. The purpose of this study is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature derived from thermal remote sensing data. Results Comparison of logistic regression models indicates that supplementing known sociodemographic risk factors with remote sensing estimates of land surface temperature improves the delineation of intra-urban variations in risk from extreme heat events. Conclusion Thermal remote sensing data can be utilized to improve understanding of intra-urban variations in risk from extreme heat. The refinement of current risk assessment systems could increase the likelihood of survival during extreme heat events and assist emergency personnel in the delivery of vital resources during such disasters. PMID:19835578

Regional-Scale High-Latitude Extreme Geoelectric Fields Pertaining to Geomagnetically Induced Currents

NASA Technical Reports Server (NTRS)

Pulkkinen, Antti; Bernabeu, Emanuel; Eichner, Jan; Viljanen, Ari; Ngwira, Chigomezyo

2015-01-01

Motivated by the needs of the high-voltage power transmission industry, we use data from the high-latitude IMAGE magnetometer array to study characteristics of extreme geoelectric fields at regional scales. We use 10-s resolution data for years 1993-2013, and the fields are characterized using average horizontal geoelectric field amplitudes taken over station groups that span about 500-km distance. We show that geoelectric field structures associated with localized extremes at single stations can be greatly different from structures associated with regionally uniform geoelectric fields, which are well represented by spatial averages over single stations. Visual extrapolation and rigorous extreme value analysis of spatially averaged fields indicate that the expected range for 1-in-100-year extreme events are 3-8 V/km and 3.4-7.1 V/km, respectively. The Quebec reference ground model is used in the calculations.

Evolution of precipitation extremes in two large ensembles of climate simulations

NASA Astrophysics Data System (ADS)

Martel, Jean-Luc; Mailhot, Alain; Talbot, Guillaume; Brissette, François; Ludwig, Ralf; Frigon, Anne; Leduc, Martin; Turcotte, Richard

2017-04-01

Recent studies project significant changes in the future distribution of precipitation extremes due to global warming. It is likely that extreme precipitation intensity will increase in a future climate and that extreme events will be more frequent. In this work, annual maxima daily precipitation series from the Canadian Earth System Model (CanESM2) 50-member large ensemble (spatial resolution of 2.8°x2.8°) and the Community Earth System Model (CESM1) 40-member large ensemble (spatial resolution of 1°x1°) are used to investigate extreme precipitation over the historical (1980-2010) and future (2070-2100) periods. The use of these ensembles results in respectively 1 500 (30 years x 50 members) and 1200 (30 years x 40 members) simulated years over both the historical and future periods. These large datasets allow the computation of empirical daily extreme precipitation quantiles for large return periods. Using the CanESM2 and CESM1 large ensembles, extreme daily precipitation with return periods ranging from 2 to 100 years are computed in historical and future periods to assess the impact of climate change. Results indicate that daily precipitation extremes generally increase in the future over most land grid points and that these increases will also impact the 100-year extreme daily precipitation. Considering that many public infrastructures have lifespans exceeding 75 years, the increase in extremes has important implications on service levels of water infrastructures and public safety. Estimated increases in precipitation associated to very extreme precipitation events (e.g. 100 years) will drastically change the likelihood of flooding and their extent in future climate. These results, although interesting, need to be extended to sub-daily durations, relevant for urban flooding protection and urban infrastructure design (e.g. sewer networks, culverts). Models and simulations at finer spatial and temporal resolution are therefore needed.

Intensity-Duration-Frequency curves from remote sensing datasets: direct comparison of weather radar and CMORPH over the Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Morin, Efrat; Marra, Francesco; Peleg, Nadav; Mei, Yiwen; Anagnostou, Emmanouil N.

2017-04-01

Rainfall frequency analysis is used to quantify the probability of occurrence of extreme rainfall and is traditionally based on rain gauge records. The limited spatial coverage of rain gauges is insufficient to sample the spatiotemporal variability of extreme rainfall and to provide the areal information required by management and design applications. Conversely, remote sensing instruments, even if quantitative uncertain, offer coverage and spatiotemporal detail that allow overcoming these issues. In recent years, remote sensing datasets began to be used for frequency analyses, taking advantage of increased record lengths and quantitative adjustments of the data. However, the studies so far made use of concepts and techniques developed for rain gauge (i.e. point or multiple-point) data and have been validated by comparison with gauge-derived analyses. These procedures add further sources of uncertainty and prevent from isolating between data and methodological uncertainties and from fully exploiting the available information. In this study, we step out of the gauge-centered concept presenting a direct comparison between at-site Intensity-Duration-Frequency (IDF) curves derived from different remote sensing datasets on corresponding spatial scales, temporal resolutions and records. We analyzed 16 years of homogeneously corrected and gauge-adjusted C-Band weather radar estimates, high-resolution CMORPH and gauge-adjusted high-resolution CMORPH over the Eastern Mediterranean. Results of this study include: (a) good spatial correlation between radar and satellite IDFs ( 0.7 for 2-5 years return period); (b) consistent correlation and dispersion in the raw and gauge adjusted CMORPH; (c) bias is almost uniform with return period for 12-24 h durations; (d) radar identifies thicker tail distributions than CMORPH and the tail of the distributions depends on the spatial and temporal scales. These results demonstrate the potential of remote sensing datasets for rainfall frequency analysis for management (e.g. warning and early-warning systems) and design (e.g. sewer design, large scale drainage planning)

Trends in 1970-2010 southern California surface maximum temperatures: extremes and heat waves

NASA Astrophysics Data System (ADS)

Ghebreegziabher, Amanuel T.

Daily maximum temperatures from 1970-2010 were obtained from the National Climatic Data Center (NCDC) for 28 South Coast Air Basin (SoCAB) Cooperative Network (COOP) sites. Analyses were carried out on the entire data set, as well as on the 1970-1974 and 2006-2010 sub-periods, including construction of spatial distributions and time-series trends of both summer-average and annual-maximum values and of the frequency of two and four consecutive "daytime" heat wave events. Spatial patterns of average and extreme values showed three areas consistent with climatological SoCAB flow patterns: cold coastal, warm inland low-elevation, and cool further-inland mountain top. Difference (2006-2010 minus 1970-1974) distributions of both average and extreme-value trends were consistent with the shorter period (1970-2005) study of previous study, as they showed the expected inland regional warming and a "reverse-reaction" cooling in low elevation coastal and inland areas open to increasing sea breeze flows. Annual-extreme trends generally showed cooling at sites below 600 m and warming at higher elevations. As the warming trends of the extremes were larger than those of the averages, regional warming thus impacts extremes more than averages. Spatial distributions of hot-day frequencies showed expected maximum at inland low-elevation sites. Regional warming again thus induced increases at both elevated-coastal areas, but low-elevation areas showed reverse-reaction decreases.

Into the environment of mosquito-borne disease: A spatial analysis of vector distribution using traditional and remotely sensed methods

NASA Astrophysics Data System (ADS)

Brown, Heidi E.

Spatially explicit information is increasingly available for infectious disease modeling. However, such information is reluctantly or inappropriately incorporated. My dissertation research uses spatially explicit data to assess relationships between landscape and mosquito species distribution and discusses challenges regarding accurate predictive risk modeling. The goal of my research is to use remotely sensed environmental information and spatial statistical methods to better understand mosquito-borne disease epidemiology for improvement of public health responses. In addition to reviewing the progress of spatial infectious disease modeling, I present four research projects. I begin by evaluating the biases in surveillance data and build up to predictive modeling of mosquito species presence. In the first study I explore how mosquito surveillance trap types influence estimations of mosquito populations. Then. I use county-based human surveillance data and landscape variables to identify risk factors for West Nile virus disease. The third study uses satellite-based vegetation indices to identify spatial variation among West Nile virus vectors in an urban area and relates the variability to virus transmission dynamics. Finally, I explore how information from three satellite sensors of differing spatial and spectral resolution can be used to identify and distinguish mosquito habitat across central Connecticut wetlands. Analyses presented here constitute improvements to the prediction of mosquito distribution and therefore identification of disease risk factors. Current methods for mosquito surveillance data collection are labor intensive and provide an extremely limited, incomplete picture of the species composition and abundance. Human surveillance data offers additional challenges with respect to reporting bias and resolution, but is nonetheless informative in identifying environmental risk factors and disease transmission dynamics. Remotely sensed imagery supports mosquito and human disease surveillance data by providing spatially explicit, line resolution information about environmental factors relevant to vector-borne disease processes. Together, surveillance and remotely sensed environmental data facilitate improved description and modeling of disease transmission. Remote sensing can be used to develop predictive maps of mosquito distribution in relation to disease risk. This has implications for increased accuracy of mosquito control efforts. The projects presented in this dissertation enhance current public health capacities by examining the applications of spatial modeling with respect to mosquito-borne disease.

Bayesian quantitative precipitation forecasts in terms of quantiles

NASA Astrophysics Data System (ADS)

Bentzien, Sabrina; Friederichs, Petra

2014-05-01

Ensemble prediction systems (EPS) for numerical weather predictions on the mesoscale are particularly developed to obtain probabilistic guidance for high impact weather. An EPS not only issues a deterministic future state of the atmosphere but a sample of possible future states. Ensemble postprocessing then translates such a sample of forecasts into probabilistic measures. This study focus on probabilistic quantitative precipitation forecasts in terms of quantiles. Quantiles are particular suitable to describe precipitation at various locations, since no assumption is required on the distribution of precipitation. The focus is on the prediction during high-impact events and related to the Volkswagen Stiftung funded project WEX-MOP (Mesoscale Weather Extremes - Theory, Spatial Modeling and Prediction). Quantile forecasts are derived from the raw ensemble and via quantile regression. Neighborhood method and time-lagging are effective tools to inexpensively increase the ensemble spread, which results in more reliable forecasts especially for extreme precipitation events. Since an EPS provides a large amount of potentially informative predictors, a variable selection is required in order to obtain a stable statistical model. A Bayesian formulation of quantile regression allows for inference about the selection of predictive covariates by the use of appropriate prior distributions. Moreover, the implementation of an additional process layer for the regression parameters accounts for spatial variations of the parameters. Bayesian quantile regression and its spatially adaptive extension is illustrated for the German-focused mesoscale weather prediction ensemble COSMO-DE-EPS, which runs (pre)operationally since December 2010 at the German Meteorological Service (DWD). Objective out-of-sample verification uses the quantile score (QS), a weighted absolute error between quantile forecasts and observations. The QS is a proper scoring function and can be decomposed into reliability, resolutions and uncertainty parts. A quantile reliability plot gives detailed insights in the predictive performance of the quantile forecasts.

Stochastic Generation of Spatiotemporal Rainfall Events for Flood Risk Assessment

NASA Astrophysics Data System (ADS)

Diederen, D.; Liu, Y.; Gouldby, B.; Diermanse, F.

2017-12-01

Current flood risk analyses that only consider peaks of hydrometeorological forcing variables have limitations regarding their representation of reality. Simplistic assumptions regarding antecedent conditions are required, often different sources of flooding are considered in isolation, and the complex temporal and spatial evolution of the events is not considered. Mid-latitude storms, governed by large scale climatic conditions, often exhibit a high degree of temporal dependency, for example. For sustainable flood risk management, that accounts appropriately for climate change, it is desirable for flood risk analyses to reflect reality more appropriately. Analysis of risk mitigation measures and comparison of their relative performance is therefore likely to be more robust and lead to improved solutions. We provide a new framework for the provision of boundary conditions to flood risk analyses that more appropriately reflects reality. The boundary conditions capture the temporal dependencies of complex storms whilst preserving the extreme values and associated spatial dependencies. We demonstrate the application of this framework to generate a synthetic rainfall events time series boundary condition set from reanalysis rainfall data (CFSR) on the continental scale. We define spatiotemporal clusters of rainfall as events, extract hydrological parameters for each event, generate synthetic parameter sets with a multivariate distribution with a focus on the joint tail probability [Heffernan and Tawn, 2004], and finally create synthetic events from the generated synthetic parameters. We highlight the stochastic integration of (a) spatiotemporal features, e.g. event occurrence intensity over space-time, or time to previous event, which we use for the spatial placement and sequencing of the synthetic events, and (b) value-specific parameters, e.g. peak intensity and event extent. We contrast this to more traditional approaches to highlight the significant improvements in terms of representing the reality of extreme flood events.

Tying Variability in Summertime North American Extreme Weather Regimes to the Boreal Summer Intraseasonal Oscillation

NASA Astrophysics Data System (ADS)

Jenney, A. M.; Randall, D. A.

2017-12-01

Tropical intraseasonal oscillations are known to be a source of extratropical variability. We show that subseasonal variability in observed North American epidemiologically significant regional extreme weather regimes is teleconnected to the boreal summer intraseasonal oscillation (BSISO)—a complex tropical weather system that is active during the northern summer and has a 30-50 day timescale. The dynamics of the teleconnection are examined. We also find that interannual variability of the tropical mean-state can modulate the teleconnection. Our results suggest that the BSISO may enable subseasonal to seasonal predictions of North American summertime weather extremes.

Linking crop yield anomalies to large-scale atmospheric circulation in Europe.

PubMed

Ceglar, Andrej; Turco, Marco; Toreti, Andrea; Doblas-Reyes, Francisco J

2017-06-15

Understanding the effects of climate variability and extremes on crop growth and development represents a necessary step to assess the resilience of agricultural systems to changing climate conditions. This study investigates the links between the large-scale atmospheric circulation and crop yields in Europe, providing the basis to develop seasonal crop yield forecasting and thus enabling a more effective and dynamic adaptation to climate variability and change. Four dominant modes of large-scale atmospheric variability have been used: North Atlantic Oscillation, Eastern Atlantic, Scandinavian and Eastern Atlantic-Western Russia patterns. Large-scale atmospheric circulation explains on average 43% of inter-annual winter wheat yield variability, ranging between 20% and 70% across countries. As for grain maize, the average explained variability is 38%, ranging between 20% and 58%. Spatially, the skill of the developed statistical models strongly depends on the large-scale atmospheric variability impact on weather at the regional level, especially during the most sensitive growth stages of flowering and grain filling. Our results also suggest that preceding atmospheric conditions might provide an important source of predictability especially for maize yields in south-eastern Europe. Since the seasonal predictability of large-scale atmospheric patterns is generally higher than the one of surface weather variables (e.g. precipitation) in Europe, seasonal crop yield prediction could benefit from the integration of derived statistical models exploiting the dynamical seasonal forecast of large-scale atmospheric circulation.

A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

NASA Astrophysics Data System (ADS)

Sun, Qiaohong; Miao, Chiyuan; Duan, Qingyun; Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin

2018-03-01

In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge-based, satellite-related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high-latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

NASA Astrophysics Data System (ADS)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger-)causes vegetation dynamics in most regions globally. More specifically, water availability is the most dominant vegetation driver, being the dominant vegetation driver in 54% of the vegetated surface. Furthermore, our results show that precipitation and soil moisture have prolonged impacts on vegetation in semiarid regions, with up to 10% of additional explained variance on the vegetation dynamics occurring three months later. Finally, hydro-climatic extremes seem to have a remarkable impact on vegetation, since they also explain up to 10% of additional variance of vegetation in certain regions despite their infrequent occurrence. References [1] Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate-vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.

Extreme Response Style and the Measurement of Intra-Individual Variability

ERIC Educational Resources Information Center

Deng, Sien

2017-01-01

Psychologists have become increasingly interested in the intra-individual variability of psychological measures as a meaningful distinguishing characteristic of persons. Assessments of intra-individual variability are frequently based on the repeated administration of self-report rating scale instruments, and extreme response style (ERS) has the…

Spatial distribution of precipitation extremes in Norway

NASA Astrophysics Data System (ADS)

Verpe Dyrrdal, Anita; Skaugen, Thomas; Lenkoski, Alex; Thorarinsdottir, Thordis; Stordal, Frode; Førland, Eirik J.

2015-04-01

Estimates of extreme precipitation, in terms of return levels, are crucial in planning and design of important infrastructure. Through two separate studies, we have examined the levels and spatial distribution of daily extreme precipitation over catchments in Norway, and hourly extreme precipitation in a point. The analyses were carried out through the development of two new methods for estimating extreme precipitation in Norway. For daily precipitation we fit the Generalized Extreme Value (GEV) distribution to areal time series from a gridded dataset, consisting of daily precipitation during the period 1957-today with a resolution of 1x1 km². This grid-based method is more objective and less manual and time-consuming compared to the existing method at MET Norway. In addition, estimates in ungauged catchments are easier to obtain, and the GEV approach includes a measure of uncertainty, which is a requirement in climate studies today. Further, we go into depth on the debated GEV shape parameter, which plays an important role for longer return periods. We show that it varies according to dominating precipitation types, having positive values in the southeast and negative values in the southwest. We also find indications that the degree of orographic enhancement might affect the shape parameter. For hourly precipitation, we estimate return levels on a 1x1 km² grid, by linking GEV distributions with latent Gaussian fields in a Bayesian hierarchical model (BHM). Generalized linear models on the GEV parameters, estimated from observations, are able to incorporate location-specific geographic and meteorological information and thereby accommodate these effects on extreme precipitation. Gaussian fields capture additional unexplained spatial heterogeneity and overcome the sparse grid on which observations are collected, while a Bayesian model averaging component directly assesses model uncertainty. We find that mean summer precipitation, mean summer temperature, latitude, longitude, mean annual precipitation and elevation are good covariate candidates for hourly precipitation in our model. Summer indices succeed because hourly precipitation extremes often occur during the convective season. The spatial distribution of hourly and daily precipitation differs in Norway. Daily precipitation extremes are larger along the southwestern coast, where large-scale frontal systems dominate during fall season and the mountain ridge generates strong orographic enhancement. The largest hourly precipitation extremes are mostly produced by intense convective showers during summer, and are thus found along the entire southern coast, including the Oslo-region.

Spatial analysis of extreme precipitation deficit as an index for atmospheric drought in Belgium

NASA Astrophysics Data System (ADS)

Zamani, Sepideh; Van De Vyver, Hans; Gobin, Anne

2014-05-01

The growing concern among the climate scientists is that the frequency of weather extremes will increase as a result of climate change. European society, for example, is particularly vulnerable to changes in the frequency and intensity of extreme events such as heat waves, heavy precipitation, droughts, and wind storms, as seen in recent years [1,2]. A more than 50% of the land is occupied by managed ecosystem (agriculture, forestry) in Belgium. Moreover, among the many extreme weather conditions, drought counts to have a substantial impact on the agriculture and ecosystem of the affected region, because its most immediate consequence is a fall in crop production. Besides the technological advances, a reliable estimation of weather conditions plays a crucial role in improving the agricultural productivity. The above mentioned reasons provide a strong motivation for a research on the drought and its impacts on the economical and agricultural aspects in Belgium. The main purpose of the presented work is to map atmospheric drought Return-Levels (RL), as first insight for agricultural drought, employing spatial modelling approaches. The likelihood of future drought is studied on the basis of precipitation deficit indices for four vegetation types: water (W), grass (G), deciduous (D) and coniferous forests (C) is considered. Extreme Value Theory (EVT) [3,4,5] as a branch of probability and statistics, is dedicated to characterize the behaviour of extreme observations. The tail behaviour of the EVT distributions provide important features about return levels. EVT distributions are applicable in many study areas such as: hydrology, environmental research and meteorology, insurance and finance. Spatial Generalized Extreme Value (GEV) distributions, as a branch of EVT, are applied to annual maxima of drought at 13 hydro-meteorological stations across Belgium. Superiority of the spatial GEV model is that a region can be modelled merging the individual time series of observations from isolated sites and using a common regression model based on climatological/geographical covariates. The behaviour of the fitted spatial GEV-distribution is heavy-tailed with γ ≡ 0.3 over Belgium. A comparison between the RL-maps using GEV model and the ones obtained from Universal Kriging (UK) confirms the reliability of the spatial GEV model in explaining atmospheric drought in Belgium. References [1] Beniston, M., Stephenson, D. B., Christensen, O. B., Ferro, C. A. T., Frei, C., Goyette, S., Halsnaes, K., Holt, T., Jylhü, K., Koffi, B., Palutikoff, J., Schöll, R., Semmler, T., and Woth, K. (2007), Future extreme events in European climate; an exploration of Regional Climate Model projections. Climatic Change, 81, 71-95. [2] Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (Eds.)] (2007), king Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp. [3] Coles, S. (2001), An Introduction to Statistical Modeling of Extreme Values, Springer-Verlag Heidelberg, Germany. [4] Embrechts, P., C. Klüppelberg, and T. Mikosch (1997), Modelling Extremal Events for Insurance and Finance, Springer-Verlag, Berlin. [5] Smith, R., (2004), Statistics of extremes, with application in environment, insurance and finance, in : Extreme Values in Finance, Telecommunications and the Environment, edited by: Finkenstadt, B. and Rootzen, H., 373-388, Chapman and Hall CRC Press, London.

Hydrographic Variability off the Coast of Oman

NASA Astrophysics Data System (ADS)

Belabbassi, L.; Dimarco, S. F.; Jochens, A. E.; Al Gheilani, H.; Wang, Z.

2010-12-01

Data from hydrographic transects made in 2001 and 2002 and between 2007 and 2009 were obtained from the Oman Ministry of Fisheries Wealth. Property-depth plots of temperature, salinity, and dissolved oxygen were produced for all transects and in all months for which data were available. These were analyzed for temporal and spatial variability. For all transects, there exist large variability on various timescales, with strong spatial variability. Two common features that are seen in the hydrographic data sets are the Persian Gulf Water (PGW) and a layer of continuous low oxygen concentrations in the lower part of the water column. Plots of salinity produced for transects located in the northern part of the Gulf of Oman show a one-unit increase in salinity of the water at the bottom of deepest station during the months of August and September as compared to the other months. Similarly, cross-shelf contour plots of temperature shows an increase in water temperature near the bottom station during the months of August and September. These indicate the presence of the PGW outflow in the northern part of the Gulf of Oman. For dissolved oxygen distributions, hydrographic transects that did not extend far offshore show monthly differences in the presence of water with low oxygen concentrations. For transects that do extend far offshore and also show a layer of low oxygen water throughout the year, there is generally a monthly difference on whether this water is found close to the surface or deeper in the water column. The variability seen in the data could only be explained by comparing these data to data collected from the real time cable ocean observing system installed by Lighthouse R &D Enterprise in the Oman Sea and the Arabian Sea in 2005. The analysis of these data reveal that the variability observed is related to processes such as ocean conditions, monsoonal cycle, and extreme weather events.

Diphytanyl glycerol ether distributions in sediments of the Orca Basin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pease, T.K.; VanVleet, E.S.; Barre, J.S.

1992-09-01

Archaebacterially produced diphytanyl glycerol ether (DPGE) was examined in core sediments from the Orca Basin, an anoxic hypersaline basin in the northwestern Gulf of Mexico, to observe its spatial variability and potential origin. A differential extraction protocol was employed to quantify the isopranyl glycerol ethers associated with unbound, intermediate-bound, and kerogen-bound lipid fractions. Archaebacterial lipids were evident at all depths for the unbound and intermediate-bound fractions. Concentrations of DPGE ranged from 0.51 to 2.91 [mu]g/g dry sediment at the surface and showed secondary maxima deeper in basin sediments. Intermediate-bound DPGE concentrations exhibited an inverse relationship to unbound DPGE concentrations. Kerogen-boundmore » DPGE concentrations were normally below detection limits. Earlier studies describing the general homogeneity of lipid components within the overlying brine and at the brine/seawater interface suggest that the large-scale sedimentary DPGE variations observed in this study result from spatial and temporal variations in in-situ production by methanogenic or extremely halophilic archaebacteria.« less

Deltas, freshwater discharge, and waves along the Young Sound, NE Greenland.

PubMed

Kroon, Aart; Abermann, Jakob; Bendixen, Mette; Lund, Magnus; Sigsgaard, Charlotte; Skov, Kirstine; Hansen, Birger Ulf

2017-02-01

A wide range of delta morphologies occurs along the fringes of the Young Sound in Northeast Greenland due to spatial heterogeneity of delta regimes. In general, the delta regime is related to catchment and basin characteristics (geology, topography, drainage pattern, sediment availability, and bathymetry), fluvial discharges and associated sediment load, and processes by waves and currents. Main factors steering the Arctic fluvial discharges into the Young Sound are the snow and ice melt and precipitation in the catchment, and extreme events like glacier lake outburst floods (GLOFs). Waves are subordinate and only rework fringes of the delta plain forming sandy bars if the exposure and fetch are optimal. Spatial gradients and variability in driving forces (snow and precipitation) and catchment characteristics (amount of glacier coverage, sediment characteristics) as well as the strong and local influence of GLOFs in a specific catchment impede a simple upscaling of sediment fluxes from individual catchments toward a total sediment flux into the Young Sound.

Valley s'Asymmetric Characteristics of the Loess Plateau in Northwestern Shanxi Based on DEM

NASA Astrophysics Data System (ADS)

Duan, J.

2016-12-01

The valleys of the Loess Plateau in northwestern Shanxi show great asymmetry. This study using multi-scale DEMs, high-resolution satellite images and digital terrain analysis method, put forward a quantitative index to describe the asymmetric morphology. Several typical areas are selected to test and verify the spatial variability. Results show: (1) Considering the difference of spatial distribution, Pianguanhe basin, Xianchuanhe basin and Yangjiachuan basin are the areas where show most significant asymmetric characteristics . (2) Considering the difference of scale, the shape of large-scale valleys represents three characteristics: randomness, equilibrium and relative symmetry, while small-scale valleys show directionality and asymmetry. (3) Asymmetric morphology performs orientation, and the east-west valleys extremely obvious. Combined with field survey, its formation mechanism can be interpreted as follows :(1)Loess uneven distribution in the valleys. (2) The distribution diversities of vegetation, water , heat conditions and other factors, make a difference in water erosion capability which leads to asymmetric characteristics.

Northern hemisphere jet stream positions indices as diagnostic tools for climate and ecosystem dynamics

USGS Publications Warehouse

Belmecheri, Soumaya; Babst, Flurin; Hudson, Amy R.; Betancourt, Julio L.; Trouet, Valerie

2017-01-01

The latitudinal position of the Northern Hemisphere jet stream (NHJ) modulates the occurrence and frequency of extreme weather events. Precipitation anomalies in particular are associated with NHJ variability; the resulting floods and droughts can have considerable societal and economic impacts. This study develops a new climatology of the 300-hPa NHJ using a bottom-up approach based on seasonally explicit latitudinal NHJ positions. Four seasons with coherent NHJ patterns were identified (January–February, April–May, July–August, and October–November), along with 32 longitudinal sectors where the seasonal NHJ shows strong spatial coherence. These 32 longitudinal sectors were then used as NHJ position indices to examine the influence of seasonal NHJ position on the geographical distribution of NH precipitation and temperature variability and their link to atmospheric circulation pattern. The analyses show that the NHJ indices are related to broad-scale patterns in temperature and precipitation variability, in terrestrial vegetation productivity and spring phenology, and can be used as diagnostic/prognostic tools to link ecosystem and socioeconomic dynamics to upper-level atmospheric patterns.

Modeling demand for public transit services in rural areas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Attaluri, P.; Seneviratne, P.N.; Javid, M.

1997-05-01

Accurate estimates of demand are critical for planning, designing, and operating public transit systems. Previous research has demonstrated that the expected demand in rural areas is a function of both demographic and transit system variables. Numerous models have been proposed to describe the relationship between the aforementioned variables. However, most of them are site specific and their validity over time and space is not reported or perhaps has not been tested. Moreover, input variables in some cases are extremely difficult to quantify. In this article, the estimation of demand using the generalized linear modeling technique is discussed. Two separate models,more » one for fixed-route and another for demand-responsive services, are presented. These models, calibrated with data from systems in nine different states, are used to demonstrate the appropriateness and validity of generalized linear models compared to the regression models. They explain over 70% of the variation in expected demand for fixed-route services and 60% of the variation in expected demand for demand-responsive services. It was found that the models are spatially transferable and that data for calibration are easily obtainable.« less

Variation in soil carbon dioxide efflux at two spatial scales in a topographically complex boreal forest

USGS Publications Warehouse

Kelsey, Katharine C.; Wickland, Kimberly P.; Striegl, Robert G.; Neff, Jason C.

2012-01-01

Carbon dynamics of high-latitude regions are an important and highly uncertain component of global carbon budgets, and efforts to constrain estimates of soil-atmosphere carbon exchange in these regions are contingent on accurate representations of spatial and temporal variability in carbon fluxes. This study explores spatial and temporal variability in soilatmosphere carbon dynamics at both fine and coarse spatial scales in a high-elevation, permafrost-dominated boreal black spruce forest. We evaluate the importance of landscape-level investigations of soil-atmosphere carbon dynamics by characterizing seasonal trends in soil-atmosphere carbon exchange, describing soil temperature-moisture-respiration relations, and quantifying temporal and spatial variability at two spatial scales: the plot scale (0–5 m) and the landscape scale (500–1000 m). Plot-scale spatial variability (average variation on a given measurement day) in soil CO2 efflux ranged from a coefficient of variation (CV) of 0.25 to 0.69, and plot-scale temporal variability (average variation of plots across measurement days) in efflux ranged from a CV of 0.19 to 0.36. Landscape-scale spatial and temporal variability in efflux was represented by a CV of 0.40 and 0.31, respectively, indicating that plot-scale spatial variability in soil respiration is as great as landscape-scale spatial variability at this site. While soil respiration was related to soil temperature at both the plot- and landscape scale, landscape-level descriptions of soil moisture were necessary to define soil respiration-moisture relations. Soil moisture variability was also integral to explaining temporal variability in soil respiration. Our results have important implications for research efforts in high-latitude regions where remote study sites make landscape-scale field campaigns challenging.

Characterizing Temperature Variability and Associated Large Scale Meteorological Patterns Across South America

NASA Astrophysics Data System (ADS)

Detzer, J.; Loikith, P. C.; Mechoso, C. R.; Barkhordarian, A.; Lee, H.

2017-12-01

South America's climate varies considerably owing to its large geographic range and diverse topographical features. Spanning the tropics to the mid-latitudes and from high peaks to tropical rainforest, the continent experiences an array of climate and weather patterns. Due to this considerable spatial extent, assessing temperature variability at the continent scale is particularly challenging. It is well documented in the literature that temperatures have been increasing across portions of South America in recent decades, and while there have been many studies that have focused on precipitation variability and change, temperature has received less scientific attention. Therefore, a more thorough understanding of the drivers of temperature variability is critical for interpreting future change. First, k-means cluster analysis is used to identify four primary modes of temperature variability across the continent, stratified by season. Next, composites of large scale meteorological patterns (LSMPs) are calculated for months assigned to each cluster. Initial results suggest that LSMPs, defined using meteorological variables such as sea level pressure (SLP), geopotential height, and wind, are able to identify synoptic scale mechanisms important for driving temperature variability at the monthly scale. Some LSMPs indicate a relationship with known recurrent modes of climate variability. For example, composites of geopotential height suggest that the Southern Annular Mode is an important, but not necessarily dominant, component of temperature variability over southern South America. This work will be extended to assess the drivers of temperature extremes across South America.

Hydrological extremes and their agricultural impacts under a changing climate in Texas

NASA Astrophysics Data System (ADS)

Lee, K.; Gao, H.; Huang, M.; Sheffield, J.

2015-12-01

With the changing climate, hydrologic extremes (such as floods, droughts, and heat waves) are becoming more frequent and intensified. Such changes in extreme events are expected to affect agricultural production and food supplies. This study focuses on the State of Texas, which has the largest farm area and the highest value of livestock production in the U.S. The objectives are two-fold: First, to investigate the climatic impact on the occurrence of future hydrologic extreme events; and second, to evaluate the effects of the future extremes on agricultural production. The Variable Infiltration Capacity (VIC) model, which is calibrated and validated over Texas river basins during the historical period, is employed for this study. The VIC model is forced by the statistically downscaled climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) model ensembles at a spatial resolution of 1/8°. The CMIP5 projections contain four different scenarios in terms of Representative Concentration Pathway (RCP) (i.e. 2.6, 4.5, 6.0 and 8.5 w/m2). To carry out the analysis, VIC outputs forced by the CMIP5 model scenarios over three 30-year periods (1970-1999, 2020-2049 and 2070-2099) are first evaluated to identify how the frequency and the extent of the extreme events will be altered in the ten Texas major river basins. The results suggest that a significant increase in the number of extreme events will occur starting in the first half of the 21st century in Texas. Then, the effects of the predicted hydrologic extreme events on the irrigation water demand are investigated. It is found that future changes in water demand vary by crop type and location, with an east-to-west gradient. The results are expected to contribute to future water management and planning in Texas.

Increased in Variability in Climatological Means and Extremes in the Great Plains

NASA Astrophysics Data System (ADS)

Basara, J. B.; Flanagan, P. X.; Christian, J.; Christian, K.

2016-12-01

The Great Plains (GP) of North America is characterized by orthogonal gradients of temperature and precipitation extending from the Gulf of Mexico in the south to the coniferous forests of Canada to the north and are bordered on the west by the Rocky Mountains and then spread east approximately 1000 km into the interior regions of North America. As a result, significant biodiversity exists across relatively short distances within the region. However, because the gradient of precipitation is large across the GP, multiple environmental factors can lead to significant variability in temperature and precipitation at periods spanning seasonal, to interannual, to decadal scales. In addition, the GP region has shown significant coupling between the surface and the atmosphere, especially during the warm season. As a result, the GP often experiences significant hydrometeorological and hydroclimatological extremes across varying spatial and temporal scales including long-term drought, flash drought, flash flooding, and long-term pluvial periods with significant impacts to ecosystem function. Results into analyses of drought to pluvial dipole events in the GP noted that on average, over twice as many dipoles existed in the latter half of the dataset (1955-2013) relative to the first half (1896-1954). In addition an Asynchronous Difference Index (ADI) computed by determining the difference between the dates of precipitation and temperature maxima revealed two physically distinct regimes of ADI (positive and negative), with comparable shifts in the timing of both the maximum of precipitation and temperature within the GP. Time series analysis of decadal average ADI yielded moderate shifts in ADI with increased variability occurring over much of the GP region.

Contamination risk and drinking water protection for a large-scale managed aquifer recharge site in a semi-arid karst region, Jordan

NASA Astrophysics Data System (ADS)

Xanke, Julian; Liesch, Tanja; Goeppert, Nadine; Klinger, Jochen; Gassen, Niklas; Goldscheider, Nico

2017-09-01

Karst aquifers in semi-arid regions are particularly threatened by surface contamination, especially during winter seasons when extremely variable rainfall of high intensities prevails. An additional challenge is posed when managed recharge of storm water is applied, since karst aquifers display a high spatial variability of hydraulic properties. In these cases, adapted protection concepts are required to address the interaction of surface water and groundwater. In this study a combined protection approach for the surface catchment of the managed aquifer recharge site at the Wala reservoir in Jordan and the downstream Hidan wellfield, which are both subject to frequent bacteriological contamination, is developed. The variability of groundwater quality was evaluated by correlating contamination events to rainfall, and to recharge from the reservoir. Both trigger increased wadi flow downstream of the reservoir by surface runoff generation and groundwater seepage, respectively. A tracer test verified the major pathway of the surface flow into the underground by infiltrating from pools along Wadi Wala. An intrinsic karst vulnerability and risk map was adapted to the regional characteristics and developed to account for the catchment separation by the Wala Dam and the interaction of surface water and groundwater. Implementation of the proposed protection zones for the wellfield and the reservoir is highly recommended, since the results suggest an extreme contamination risk resulting from livestock farming, arable agriculture and human occupation along the wadi. The applied methods can be transferred to other managed aquifer recharge sites in similar karstic environments of semi-arid regions.

Living on the edge: Flood risks to societies Balázs M. Fekete, Shahabeddin Afshari Tork and Charles J. Vörösmarty

NASA Astrophysics Data System (ADS)

Fekete, B. M.; Afshari Tork, S.; Vorosmarty, C. J.

2015-12-01

Characterizing hydrological extreme events and assessing their societal impacts is perpetual challenge for hydrologists. Climate models predict that anticipated temperature rise leads to an intensification of the hydrological cycle and to a corresponding increase in the reoccurrence and the severity of extreme events. The societal impact of the hydrological extremes are interlinked with anthropogenic activities therefore the damages to manmade infrastructures are rarely a good measure of the extreme events' magnitudes. Extreme events are rare by definition therefore detecting change in their distributions requires long-term observational records. Currently, only in-situ monitoring time series has the temporal extent necessary for assessing the reoccurrence probabilities of extreme events, but they frequently lack the spatial coverage. Satellite remote sensing is often advocated to provide the required spatial coverage, but satellites have to compromise between spatial and temporal resolutions. Furthermore, the retrieval algorithms are often as complex as comparable hydrological models with similar degree of uncertainties in their parameterization and the validity of the final data products. In addition, anticipated changes over time in the reoccurrence frequencies of extreme events invalidates the stationarity assumption, which is the basis for using past observations to predict the probabilities future extreme events. Probably the best approach to provide more robust predictions of extreme events is the integration of the available data (in-situ and remote sensing) in a comprehensive data assimilation frameworks built on top of adequate hydrological modeling platforms. Our presentation will provide an overview of the current state of hydrological models to support data assimilations and the viable pathways to integrate in-situ and remote sensing observations for flood predictions. We will demonstrate the use of socio-economic data in combination with hydrological data assimilation to assess the resiliency to extreme flood events.

Wave spectral energy variability in the northeast Pacific

USGS Publications Warehouse

Bromirski, P.D.; Cayan, D.R.; Flick, R.E.

2005-01-01

The dominant characteristics of wave energy variability in the eastern North Pacific are described from NOAA National Data Buoy Center (NDBC) buoy data collected from 1981 to 2003. Ten buoys at distributed locations were selected for comparison based on record duration and data continuity. Long-period (LP) [T > 12] s, intermediate-period [6 ??? T ??? 12] s, and short-period [T < 6] s wave spectral energy components are considered separately. Empirical orthogonal function (EOF) analyses of monthly wave energy anomalies reveal that all three wave energy components exhibit similar patterns of spatial variability. The dominant mode represents coherent heightened (or diminished) wave energy along the West Coast from Alaska to southern California, as indicated by composites of the 700 hPa height field. The second EOF mode reveals a distinct El Nin??o-Southern Oscillation (ENSO)-associated spatial distribution of wave energy, which occurs when the North Pacific storm track is extended unusually far south or has receded to the north. Monthly means and principal components (PCs) of wave energy levels indicate that the 1997-1998 El Nin??o winter had the highest basin-wide wave energy within this record, substantially higher than the 1982-1983 El Nin??o. An increasing trend in the dominant PC of LP wave energy suggests that storminess has increased in the northeast Pacific since 1980. This trend is emphasized at central eastern North Pacific locations. Patterns of storminess variability are consistent with increasing activity in the central North Pacific as well as the tendency for more extreme waves in the south during El Nin??o episodes and in the north during La Nin??a. Copyright 2005 by the American Geophysical Union.

Actor groups, related needs, and challenges at the climate downscaling interface

NASA Astrophysics Data System (ADS)

Rössler, Ole; Benestad, Rasmus; Diamando, Vlachogannis; Heike, Hübener; Kanamaru, Hideki; Pagé, Christian; Margarida Cardoso, Rita; Soares, Pedro; Maraun, Douglas; Kreienkamp, Frank; Christodoulides, Paul; Fischer, Andreas; Szabo, Peter

2016-04-01

At the climate downscaling interface, numerous downscaling techniques and different philosophies compete on being the best method in their specific terms. Thereby, it remains unclear to what extent and for which purpose these downscaling techniques are valid or even the most appropriate choice. A common validation framework that compares all the different available methods was missing so far. The initiative VALUE closes this gap with such a common validation framework. An essential part of a validation framework for downscaling techniques is the definition of appropriate validation measures. The selection of validation measures should consider the needs of the stakeholder: some might need a temporal or spatial average of a certain variable, others might need temporal or spatial distributions of some variables, still others might need extremes for the variables of interest or even inter-variable dependencies. Hence, a close interaction of climate data providers and climate data users is necessary. Thus, the challenge in formulating a common validation framework mirrors also the challenges between the climate data providers and the impact assessment community. This poster elaborates the issues and challenges at the downscaling interface as it is seen within the VALUE community. It suggests three different actor groups: one group consisting of the climate data providers, the other two groups being climate data users (impact modellers and societal users). Hence, the downscaling interface faces classical transdisciplinary challenges. We depict a graphical illustration of actors involved and their interactions. In addition, we identified four different types of issues that need to be considered: i.e. data based, knowledge based, communication based, and structural issues. They all may, individually or jointly, hinder an optimal exchange of data and information between the actor groups at the downscaling interface. Finally, some possible ways to tackle these issues are discussed.

Spatial heterogeneity of water quality in a highly degraded tropical freshwater ecosystem.

PubMed

Zambrano, Luis; Contreras, Victoria; Mazari-Hiriart, Marisa; Zarco-Arista, Alba E

2009-02-01

Awareness of environmental heterogeneity in ecosystems is critical for management and conservation. We used the Xochimilco freshwater system to describe the relationship between heterogeneity and human activities. This tropical aquatic ecosystem south of Mexico City is comprised of a network of interconnected canals and lakes that are influenced by agricultural and urban activities. Environmental heterogeneity was characterized by spatially extensive surveys within four regions of Xochimilco during rainy and dry seasons over 2 years. These surveys revealed a heterogeneous system that was shallow (1.1 m, SD=0.4 ), warm (17 degrees C, SD=2.9), well oxygenated (5.0 mg l(-1), SD=3), turbid (45.7 NTU SD = 26.96), and extremely nutrient-rich (NO(3)-N=15.9 mg l(-1), SD=13.7; NH(4)-N=2.88 mg l(-1), SD=4.24; and PO(4)-P=8.3 mg l(-1), SD=2.4). Most of the variables were not significantly different between years, but did differ between seasons, suggesting a dynamic system within a span of a year but with a high resilience over longer periods of time. Maps were produced using interpolations to describe distributions of all variables. There was no correlation between individual variables and land use. Consequently, we searched for relationships using all variables together by generating a combined water quality index. Significant differences in the index were apparent among the four regions. Index values also differed within individual region and individual water bodies (e.g., within canals), indicating that Xochimilco has high local heterogeneity. Using this index on a map helped to relate water quality to human activities and provides a simple and clear tool for managers and policymakers.

Spatial Heterogeneity of Water Quality in a Highly Degraded Tropical Freshwater Ecosystem

NASA Astrophysics Data System (ADS)

Zambrano, Luis; Contreras, Victoria; Mazari-Hiriart, Marisa; Zarco-Arista, Alba E.

2009-02-01

Awareness of environmental heterogeneity in ecosystems is critical for management and conservation. We used the Xochimilco freshwater system to describe the relationship between heterogeneity and human activities. This tropical aquatic ecosystem south of Mexico City is comprised of a network of interconnected canals and lakes that are influenced by agricultural and urban activities. Environmental heterogeneity was characterized by spatially extensive surveys within four regions of Xochimilco during rainy and dry seasons over 2 years. These surveys revealed a heterogeneous system that was shallow (1.1 m, SD = 0.4 ), warm (17°C, SD = 2.9), well oxygenated (5.0 mg l-1, SD = 3), turbid (45.7 NTU SD = 26.96), and extremely nutrient-rich (NO3-N = 15.9 mg l-1, SD=13.7; NH4-N = 2.88 mg l-1, SD = 4.24; and PO4-P = 8.3 mg l-1, SD = 2.4). Most of the variables were not significantly different between years, but did differ between seasons, suggesting a dynamic system within a span of a year but with a high resilience over longer periods of time. Maps were produced using interpolations to describe distributions of all variables. There was no correlation between individual variables and land use. Consequently, we searched for relationships using all variables together by generating a combined water quality index. Significant differences in the index were apparent among the four regions. Index values also differed within individual region and individual water bodies (e.g., within canals), indicating that Xochimilco has high local heterogeneity. Using this index on a map helped to relate water quality to human activities and provides a simple and clear tool for managers and policymakers.

Adaptive data-driven models for estimating carbon fluxes in the Northern Great Plains

USGS Publications Warehouse

Wylie, B.K.; Fosnight, E.A.; Gilmanov, T.G.; Frank, A.B.; Morgan, J.A.; Haferkamp, Marshall R.; Meyers, T.P.

2007-01-01

Rangeland carbon fluxes are highly variable in both space and time. Given the expansive areas of rangelands, how rangelands respond to climatic variation, management, and soil potential is important to understanding carbon dynamics. Rangeland carbon fluxes associated with Net Ecosystem Exchange (NEE) were measured from multiple year data sets at five flux tower locations in the Northern Great Plains. These flux tower measurements were combined with 1-km2 spatial data sets of Photosynthetically Active Radiation (PAR), Normalized Difference Vegetation Index (NDVI), temperature, precipitation, seasonal NDVI metrics, and soil characteristics. Flux tower measurements were used to train and select variables for a rule-based piece-wise regression model. The accuracy and stability of the model were assessed through random cross-validation and cross-validation by site and year. Estimates of NEE were produced for each 10-day period during each growing season from 1998 to 2001. Growing season carbon flux estimates were combined with winter flux estimates to derive and map annual estimates of NEE. The rule-based piece-wise regression model is a dynamic, adaptive model that captures the relationships of the spatial data to NEE as conditions evolve throughout the growing season. The carbon dynamics in the Northern Great Plains proved to be in near equilibrium, serving as a small carbon sink in 1999 and as a small carbon source in 1998, 2000, and 2001. Patterns of carbon sinks and sources are very complex, with the carbon dynamics tilting toward sources in the drier west and toward sinks in the east and near the mountains in the extreme west. Significant local variability exists, which initial investigations suggest are likely related to local climate variability, soil properties, and management.

Coastal Vulnerability and risk assessment of infrastructures, natural and cultural heritage sites in Greece.

NASA Astrophysics Data System (ADS)

Alexandrakis, George; Kampanis, Nikolaos

2016-04-01

The majority of human activities are concentrated around coastal areas, making coastline retreat, a significant threat to coastal infrastructure, thus increasing protection cost and investment revenue losses. In this study the management of coastal areas in terms of protecting coastal infrastructures, cultural and environmental heritage sites, through risk assessment analysis is been made. The scope is to provide data for spatial planning for future developments in the coastal zone and the protection of existing ones. Also to determine the impact of coastal changes related to the loss of natural resources, agricultural land and beaches. The analysis is based on a multidisciplinary approach, combining environmental, spatial and economic data. This can be implemented by integrating the assessment of vulnerability of coasts, the spatial distribution and structural elements of coastal infrastructure (transport, tourism, and energy) and financial data by region, in a spatial database. The approach is based on coastal vulnerability estimations, considering sea level rise, land loss, extreme events, safety, adaptability and resilience of infrastructure and natural sites. It is based on coupling of environmental indicators and econometric models to determine the socio-economic impact in coastal infrastructure, cultural and environmental heritage sites. The indicators include variables like the coastal geomorphology; coastal slope; relative sea-level rise rate; shoreline erosion/accretion rate; mean tidal range and mean wave height. The anthropogenic factors include variables like settlements, sites of cultural heritage, transport networks, land uses, significance of infrastructure (e.g. military, power plans) and economic activities. The analysis in performed by a GIS application. The forcing variables are determined with the use of sub-indices related to coastal geomorphology, climate and wave variables and the socioeconomics of the coastal zone. The Greek coastline in considered as a case study, where the majority of the coastline appears to be undergoing erosion, with approximately 25% of the Aegean coastline, consisting mainly of beach zones and low-lying coastal (including deltaic) plains. In terms of economic activates coastal tourism is most effected, as beach zones are very high vulnerable to erosion. Also, small ports in remote islands are also found to be highly vulnerable. Acknowledgments This work was implemented within the framework of "Post-Doctoral Excellence Scholarship. State Scholarships Foundation, Greece IKY- Siemens Action"

Extreme Events in China under Climate Change: Uncertainty and related impacts (CSSP-FOREX)

NASA Astrophysics Data System (ADS)

Leckebusch, Gregor C.; Befort, Daniel J.; Hodges, Kevin I.

2016-04-01

Suitable adaptation strategies or the timely initiation of related mitigation efforts in East Asia will strongly depend on robust and comprehensive information about future near-term as well as long-term potential changes in the climate system. Therefore, understanding the driving mechanisms associated with the East Asian climate is of major importance. The FOREX project (Fostering Regional Decision Making by the Assessment of Uncertainties of Future Regional Extremes and their Linkage to Global Climate System Variability for China and East Asia) focuses on the investigation of extreme wind and rainfall related events over Eastern Asia and their possible future changes. Here, analyses focus on the link between local extreme events and their driving weather systems. This includes the coupling between local rainfall extremes and tropical cyclones, the Meiyu frontal system, extra-tropical teleconnections and monsoonal activity. Furthermore, the relation between these driving weather systems and large-scale variability modes, e.g. NAO, PDO, ENSO is analysed. Thus, beside analysing future changes of local extreme events, the temporal variability of their driving weather systems and related large-scale variability modes will be assessed in current CMIP5 global model simulations to obtain more robust results. Beyond an overview of FOREX itself, first results regarding the link between local extremes and their steering weather systems based on observational and reanalysis data are shown. Special focus is laid on the contribution of monsoonal activity, tropical cyclones and the Meiyu frontal system on the inter-annual variability of the East Asian summer rainfall.

Application of a fully integrated surface-subsurface physically based flow model for evaluating groundwater recharge from a flash flood event

NASA Astrophysics Data System (ADS)

Pino, Cristian; Herrera, Paulo; Therrien, René

2017-04-01

In many arid regions around the world groundwater recharge occurs during flash floods. This transient spatially and temporally concentrated flood-recharge process takes place through the variably saturated zone between surface and usually the deep groundwater table. These flood events are characterized by rapid and extreme changes in surface flow depth and velocity and soil moisture conditions. Infiltration rates change over time controlled by the hydraulic gradients and the unsaturated hydraulic conductivity at the surface-subsurface interface. Today is a challenge to assess the spatial and temporal distribution of groundwater recharge from flash flood events under real field conditions at different scales in arid areas. We apply an integrated surface-subsurface variably saturated physically-based flow model at the watershed scale to assess the recharge process during and after a flash flood event registered in an arid fluvial valley in Northern Chile. We are able to reproduce reasonably well observed groundwater levels and surface flow discharges during and after the flood with a calibrated model. We also investigate the magnitude and spatio-temporal distribution of recharge and the response of the system to variations of different surface and subsurface parameters, initial soil moisture content and groundwater table depths and surface flow conditions. We demonstrate how an integrated physically based model allows the exploration of different spatial and temporal system states, and that the analysis of the results of the simulations help us to improve our understanding of the recharge processes in similar type of systems that are common to many arid areas around the world.

Trends analysis of rainfall and rainfall extremes in Sarawak, Malaysia using modified Mann-Kendall test

NASA Astrophysics Data System (ADS)

Sa'adi, Zulfaqar; Shahid, Shamsuddin; Ismail, Tarmizi; Chung, Eun-Sung; Wang, Xiao-Jun

2017-11-01

This study assesses the spatial pattern of changes in rainfall extremes of Sarawak in recent years (1980-2014). The Mann-Kendall (MK) test along with modified Mann-Kendall (m-MK) test, which can discriminate multi-scale variability of unidirectional trend, was used to analyze the changes at 31 stations. Taking account of the scaling effect through eliminating the effect of autocorrelation, m-MK was employed to discriminate multi-scale variability of the unidirectional trends of the annual rainfall in Sarawak. It can confirm the significance of the MK test. The annual rainfall trend from MK test showed significant changes at 95% confidence level at five stations. The seasonal trends from MK test indicate an increasing rate of rainfall during the Northeast monsoon and a decreasing trend during the Southwest monsoon in some region of Sarawak. However, the m-MK test detected an increasing trend in annual rainfall only at one station and no significant trend in seasonal rainfall at any stations. The significant increasing trends of the 1-h maximum rainfall from the MK test are detected mainly at the stations located in the urban area giving concern to the occurrence of the flash flood. On the other hand, the m-MK test detected no significant trend in 1- and 3-h maximum rainfalls at any location. On the contrary, it detected significant trends in 6- and 72-h maximum rainfalls at a station located in the Lower Rajang basin area which is an extensive low-lying agricultural area and prone to stagnant flood. These results indicate that the trends in rainfall and rainfall extremes reported in Malaysia and surrounding region should be verified with m-MK test as most of the trends may result from scaling effect.

Dissecting the multi-scale spatial relationship of earthworm assemblages with soil environmental variability.

PubMed

Jiménez, Juan J; Decaëns, Thibaud; Lavelle, Patrick; Rossi, Jean-Pierre

2014-12-05

Studying the drivers and determinants of species, population and community spatial patterns is central to ecology. The observed structure of community assemblages is the result of deterministic abiotic (environmental constraints) and biotic factors (positive and negative species interactions), as well as stochastic colonization events (historical contingency). We analyzed the role of multi-scale spatial component of soil environmental variability in structuring earthworm assemblages in a gallery forest from the Colombian "Llanos". We aimed to disentangle the spatial scales at which species assemblages are structured and determine whether these scales matched those expressed by soil environmental variables. We also tested the hypothesis of the "single tree effect" by exploring the spatial relationships between root-related variables and soil nutrient and physical variables in structuring earthworm assemblages. Multivariate ordination techniques and spatially explicit tools were used, namely cross-correlograms, Principal Coordinates of Neighbor Matrices (PCNM) and variation partitioning analyses. The relationship between the spatial organization of earthworm assemblages and soil environmental parameters revealed explicitly multi-scale responses. The soil environmental variables that explained nested population structures across the multi-spatial scale gradient differed for earthworms and assemblages at the very-fine- (<10 m) to medium-scale (10-20 m). The root traits were correlated with areas of high soil nutrient contents at a depth of 0-5 cm. Information on the scales of PCNM variables was obtained using variogram modeling. Based on the size of the plot, the PCNM variables were arbitrarily allocated to medium (>30 m), fine (10-20 m) and very fine scales (<10 m). Variation partitioning analysis revealed that the soil environmental variability explained from less than 1% to as much as 48% of the observed earthworm spatial variation. A large proportion of the spatial variation did not depend on the soil environmental variability for certain species. This finding could indicate the influence of contagious biotic interactions, stochastic factors, or unmeasured relevant soil environmental variables.

Biocrust spatial distribution at landscape scale is strongly controlled by terrain attributes: Topographic thresholds for colonization

NASA Astrophysics Data System (ADS)

Raúl Román Fernández, José; Rodríguez-Caballero, Emilio; Chamizo de la Piedra, Sonia; Roncero Ramos, Bea; Cantón Castilla, Yolanda

2017-04-01

Biological soil crusts (biocrusts) are spatially variable components of soil. Whereas biogeographic, climatic or soil properties drive biocrust distribution from regional to global scales, biocrust spatial distribution within the landscape is controlled by topographic forces that create specific microhabitats that promote or difficult biocrust growth. By knowing which are the variables that control biocrust distribution and their individual effect we can establish the abiotic thresholds that limit natural biocrust colonization on different environments, which may be very useful for designing soil restoration programmes. The objective of this study was to analyse the influence of topographic-related variables in the distribution of different types of biocrust within a semiarid catchment where cyanobacteria and lichen dominated biocrust represent the most important surface components, El Cautivo experimental area (SE Spain). To do this, natural coverage of i) bare soil, ii) vegetation, iii) cyanobacteria-dominated soil crust and iv) lichen-dominated soil crust were measured on 70 experimental plots distributed across 23 transect (three 4.5 x 4.5 m plots per transect). Following that, we used a 1m x 1m DEM (Digital Elevation Model) of the study site obtained from a LiDAR point cloud to calculate different topographic variables such as slope gradient, length slope (LS) factor (potential sediment transport index), potential incoming solar radiation, topographic wetness index (WI) and maximum flow accumulation. Canonical Correspondence Analysis was performed to infer the influence of each variable in the coverage of each class and thresholds of biocrust colonization were identified mathematically by means of linear regression analysis describing the relationship between each factor and biocrust cover. Our results show that the spatial distribution of cyanobacteria-dominated biocrust, which showed physiological and morphological adaptation to cope with drought and UVA radiation, was mostly controlled by incoming solar radiation, being mostly located on areas with high incoming solar radiation and low slope, showing a threshold at 48 degrees from which they are not found. Lichen-dominated biocrust, on the other hand, colonize the uppermost and steepest part of north aspect hillslopes where incoming solar radiation and ETP are low, as consequence of their lower capacity to survive under extreme temperatures and drought conditions. With higher capacity of the soil to retain run-on (WI), surface is mostly cover by plants instead of lichens. Bare soil distribution is controlled by the combination of two factors, slope and solar radiation, covering the south aspect hillslopes, where slope gradient is high and there is high incoming solar radiation and ETP for lichen colonization.

Variability In Long-Wave Runup as a Function of Nearshore Bathymetric Features

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dunkin, Lauren McNeill

Beaches and barrier islands are vulnerable to extreme storm events, such as hurricanes, that can cause severe erosion and overwash to the system. Having dunes and a wide beach in front of coastal infrastructure can provide protection during a storm, but the influence that nearshore bathymetric features have in protecting the beach and barrier island system is not completely understood. The spatial variation in nearshore features, such as sand bars and beach cusps, can alter nearshore hydrodynamics, including wave setup and runup. The influence of bathymetric features on long-wave runup can be used in evaluating the vulnerability of coastal regionsmore » to erosion and dune overtopping, evaluating the changing morphology, and implementing plans to protect infrastructure. In this thesis, long-wave runup variation due to changing bathymetric features as determined with the numerical model XBeach is quantified (eXtreme Beach behavior model). Wave heights are analyzed to determine the energy through the surfzone. XBeach assumes that coastal erosion at the land-sea interface is dominated by bound long-wave processes. Several hydrodynamic conditions are used to force the numerical model. The XBeach simulation results suggest that bathymetric irregularity induces significant changes in the extreme long-wave runup at the beach and the energy indicator through the surfzone.« less

Temporal and Spatial Variation of Chemical Water Quality in a Contour Canal.

NASA Astrophysics Data System (ADS)

Swanson, L. A.; Lunn, R. J.

2004-12-01

Chemical water quality is a highly variable aspect of any water body. Historically numerous researchers have investigated the chemical variability of rivers, streams and wetlands, artificial water bodies such as canals have been largely neglected. Canals are typically hydraulically characterised by low flows and a lack of mixing processes. This can potentially lead to significant spatial variability in water chemistry, and as a result many canals in the UK regularly fail water quality targets at specific locations. Recent changes to UK legislation, following the European Water Framework Directive (2000/60/EC), have resulted in canals being subject to achieving `good ecological status'. In the case of canals, what constitutes `good ecological status' is largely unknown and little expertise is available since historically canal management has not been driven by chemical and ecological quality targets. Consequently, there is an urgent need for new research to determine the main factors influencing canal water quality and their ecological status. This research presents results from a study based on a UK contour canal, the Union Canal in central Scotland. The Union Canal typically demonstrates spatially and temporally variable levels of dissolved oxygen (DO) and orthophosphate (PO4-P): simultaneously, seasonal and diel fluctuations of DO and PO4-P are pronounced at a small number of locations. During 1995, minimum levels of DO along the canal length ranged from 9mgl-1 in Edinburgh to as low as 2mgl-1 approximately 20kms away, this then rose again to 8mgl-1 after a further distance of 2km. These acutely low levels of DO are coupled with events of excessive PO4-P up to 0.235mgl-1:10 times greater than those normally found in rivers, causing localised eutrophication and extensive fish kills. To determine the cause of the `hot spots' of poor water quality found on the Union Canal, simultaneous investigations of the hydraulic regime, spatial and temporal water quality variation and the canal's biological status were carried out. Velocity metering in the canal identified extremely low flow rates ~0.15m3s-1. A tracer testing procedure for the canal's low flow conditions was designed and implemented which identified a lack of rapid dispersion processes with D~0.133m3s-1. Water quality sampling consisted of a year-long programme of high frequency temporal and spatial sampling along the canal length. Observations demonstrate significant variability, with widely differing measurements of DO as little as 5m apart. In addition, spot samples of water quality taken from individual incoming field drains showed PO4-P concentrations up to 2mgl-1, with a predominance of nutrient bound clay and silt sediments that ultimately settle on the canal bed. Due to low dispersion rates, residence times for pollutants are long and field drains, in combination with navigational activity, may well be one of the primary causes of raised nutrient levels at some locations. This research has shown that canal water quality is highly spatially and temporally variable; far in excess of the variability normally found in river systems. This is mainly determined by a lack of hydraulic mixing and the presence of small quantities of incoming runoff water of very low quality. Whilst low in volume, incoming sediment from the drains appears to strongly influence the nearby canal water quality. These results have important consequences both for future monitoring strategies of canals and management of their gradual ecological improvement.

Identification of tipping elements of the Indian Summer Monsoon using climate network approach

NASA Astrophysics Data System (ADS)

Stolbova, Veronika; Surovyatkina, Elena; Kurths, Jurgen

2015-04-01

Spatial and temporal variability of the rainfall is a vital question for more than one billion of people inhabiting the Indian subcontinent. Indian Summer Monsoon (ISM) rainfall is crucial for India's economy, social welfare, and environment and large efforts are being put into predicting the Indian Summer Monsoon. For predictability of the ISM, it is crucial to identify tipping elements - regions over the Indian subcontinent which play a key role in the spatial organization of the Indian monsoon system. Here, we use climate network approach for identification of such tipping elements of the ISM. First, we build climate networks of the extreme rainfall, surface air temperature and pressure over the Indian subcontinent for pre-monsoon, monsoon and post-monsoon seasons. We construct network of extreme rainfall event using observational satellite data from 1998 to 2012 from the Tropical Rainfall Measuring Mission (TRMM 3B42V7) and reanalysis gridded daily rainfall data for a time period of 57 years (1951-2007) (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE). For the network of surface air temperature and pressure fields, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). Second, we filter out data by coarse-graining the network through network measures, and identify tipping regions of the ISM. Finally, we compare obtained results of the network analysis with surface wind fields and show that occurrence of the tipping elements is mostly caused by monsoonal wind circulation, migration of the Intertropical Convergence Zone (ITCZ) and Westerlies. We conclude that climate network approach enables to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to identify tipping regions of the ISM. Obtained tipping elements deserve a special attention for the meteorologists and can be used as markers of the ISM variability.

Desert potholes: Ephemeral aquatic microsystems

USGS Publications Warehouse

Chan, M.A.; Moser, K.; Davis, J.M.; Southam, G.; Hughes, K.; Graham, T.

2005-01-01

An enigma of the Colorado Plateau high desert is the "pothole", which ranges from shallow ephemeral puddles to deeply carved pools. The existence of prokaryotic to eukaryotic organisms within these pools is largely controlled by the presence of collected rainwater. Multivariate statistical analysis of physical and chemical limnologic data variables measured from potholes indicates spatial and temporal variations, particularly in water depth, manganese, iron, nitrate and sulfate concentrations and salinity. Variation in water depth and salinity are likely related to the amount of time since the last precipitation, whereas the other variables may be related to redox potential. The spatial and temporal variations in water chemistry affect the distribution of organisms, which must adapt to daily and seasonal extremes of fluctuating temperature (0-60 ??C), pH changes of as much as 5 units over 12 days, and desiccation. For example, many species become dormant when potholes dry, in order to endure intense heat, UV radiation, desiccation and freezing, only to flourish again upon rehydration. But the pothole organisms also have a profound impact on the potholes. Through photosynthesis and respiration, pothole organisms affect redox potential, and indirectly alter the water chemistry. Laboratory examination of dried biofilm from the potholes revealed that within 2 weeks of hydration, the surface of the desiccated, black biofilm became green from cyanobacterial growth, which supported significant growth in heterotrophic bacterial populations. This complex biofilm is persumably responsible for dissolving the cement between the sandstone grains, allowing the potholes to enlarge, and for sealing the potholes, enabling them to retain water longer than the surrounding sandstone. Despite the remarkable ability of life in potholes to persist, desert potholes may be extremely sensitive to anthropogenic effects. The unique limnology and ecology of Utah potholes holds great scientific value for understanding water-rock-biological interactions with possible applications to life on other planetary bodies. ?? Springer 2005.

(When and where) Do extreme climate events trigger extreme ecosystem responses? - Development and initial results of a holistic analysis framework

NASA Astrophysics Data System (ADS)

Hauber, Eva K.; Donner, Reik V.

2015-04-01

In the context of ongoing climate change, extremes are likely to increase in magnitude and frequency. One of the most important consequences of these changes is that the associated ecological risks and impacts are potentially rising as well. In order to better anticipate and understand these impacts, it therefore becomes more and more crucial to understand the general connection between climate extremes and the response and functionality of ecosystems. Among other region of the world, Europe presents an excellent test case for studies concerning the interaction between climate and biosphere, since it lies in the transition region between cold polar and warm tropical air masses and thus covers a great variety of different climatic zones and associated terrestrial ecosystems. The large temperature differences across the continent make this region particularly interesting for investigating the effects of climate change on biosphere-climate interactions. However, previously used methods for defining an extreme event typically disregard the necessity of taking seasonality as well as seasonal variance appropriately into account. Furthermore, most studies have focused on the impacts of individual extreme events instead of considering a whole inventory of extremes with their respective spatio-temporal extents. In order to overcome the aforementioned research gaps, this work introduces a new approach to studying climate-biosphere interactions associated with extreme events, which comprises three consecutive steps: (1) Since Europe exhibits climatic conditions characterized by marked seasonality, a novel method is developed to define extreme events taking into account the seasonality in all quantiles of the probability distribution of the respective variable of interest. This is achieved by considering kernel density estimates individually for each observation date during the year, including the properly weighted information from adjacent dates. By this procedure, we obtain a seasonal cycle for each quantile of the distribution, which can be used for a fully data-adaptive definition of extremes as exceedances above this time-dependent quantile function. (2) Having thus identified the extreme events, their distribution is analyzed in both space and time. Following a procedure recently proposed by Lloyd-Hughes (2012) and further exploited by Zscheischler et al. (2013), extremes observed at neighboring points in space and time are considered to form connected sets. Investigating the size distribution of these sets provides novel insights into the development and dynamical characteristics of spatio-temporally extended climate and ecosystem extremes. (3) Finally, the timing of such spatio-temporally extended extremes in different climatic as well as ecological variables is tested pairwise to rule out that co-occurrences of extremes have emerged solely due to chance. For this purpose, the recently developed framework of coincidence analysis (Donges et al., 2011; Rammig et al. 2014) is applied. The corresponding analysis allows identifying potential causal linkages between climatic extremes and extreme ecosystem responses and, thus, to study their mechanisms and spatial as well as seasonal distribution in great detail. In this work, the described method is exemplified by using different climate data from the ERA-Interim reanalysis as well as remote sensing-based land surface temperature data. References: Donges et al., PNAS, 108, 20422, 2011 Lloyd-Hughes, Int. J. Climatol., 32, 406, 2012 Rammig et al., Biogeosc. Disc., 11, 2537, 2014 Zscheischler et al., Ecol. Inform., 15, 66, 2013

Spatial clustering and meteorological drivers of summer ozone in Europe

NASA Astrophysics Data System (ADS)

Carro-Calvo, Leopoldo; Ordóñez, Carlos; García-Herrera, Ricardo; Schnell, Jordan L.

2017-04-01

We present a regionalization of summer near-surface ozone (O3) in Europe. For this purpose we apply a K-means algorithm on a gridded MDA8 O3 (maximum daily average 8-h ozone) dataset covering a European domain [15° W - 30° E, 35°-70° N] at 1° x 1° horizontal resolution for the 1998-2012 period. This dataset was compiled by merging observations from the European Monitoring and Evaluation Programme (EMEP) and the European Environment Agency's air quality database (AirBase). The K-means method allows identifying sets of different regions where the O3 concentrations present coherent spatiotemporal patterns and are thus expected to be driven by similar meteorological factors. After some testing, 9 regions were selected: the British Isles, North-Central Europe, Northern Scandinavia, the Baltic countries, the Iberian Peninsula, Western Europe, South-Central Europe, Eastern Europe and the Balkans. For each region we examine the synoptic situations associated with elevated ozone extremes (days exceeding the 95th percentile of the summer MDA8 O3 distribution). Our analyses reveal that there are basically two different kinds of regions in Europe: (a) those in the centre and south of the continent where ozone extremes are associated with elevated temperature within the same region and (b) those in northern Europe where ozone extremes are driven by southerly advection of air masses from warmer, more polluted areas. Even when the observed patterns were initially identified only for days registering high O3 extremes, all summer days can be projected on such patterns to identify the main modes of meteorological variability of O3. We have found that such modes are partly responsible for the day-to-day variability in the O3 concentrations and can explain a relatively large fraction (from 44 to 88 %, depending on the region) of the interannual variability of summer mean MDA8 O3 during the period of analysis. On the other hand, some major teleconnection patterns have been tested but do not seem to exert a large impact on the variability of surface O3 over most regions. The identification of these independent regions where surface ozone presents a coherent behaviour and responds similarly to specific meteorological modes of variability has multiple applications. For instance, the performance of chemical transport models (CTMs) and chemistry-climate models (CCMs) can be separately assessed over such regions to identify areas where they present large biases that need to be corrected. Our results can also be used to test the models' sensitivity to the day-to-day changing meteorology and to climate change over specific regions.

EFFECT OF INTENSE FUNCTIONAL TASK TRAINING UPON TEMPORAL STRUCTURE OF VARIABILITY OF UPPER EXTREMITY POST STROKE

PubMed Central

Sethi, Amit; Davis, Sandra; McGuirk, Theresa; Patterson, Tara S.; Richards, Lorie G.

2012-01-01

Study Design Quasi-experimental design Introduction Although the effectiveness of constraint induced movement therapy (CIMT) in upper extremity (UE) rehabilitation post stroke is well known, the efficacy of CIMT to enhance the temporal structure of variability in upper extremity movement is not known. Purpose The purpose of this study was to investigate whether CIMT could enhance temporal structure of variability in upper extremity movement in individuals with chronic stroke. Methods Six participants with chronic stroke underwent CIMT for 4 hours/day for 2 weeks. Participants performed three trials of functional reach-to-grasp before and after CIMT. Temporal structure of variability was determined by calculating approximate entropy (ApEn) in shoulder, elbow and wrist flexion/extension joint angles. Results ApEn increased post CIMT, however, statistical significance was not achieved (p > 0.0167). Conclusion Future studies with larger sample size are warranted to investigate the effect of CIMT upon temporal structure of variability in UE movement. PMID:23084461

Integrating time-series and spatial surveys to assess annual, lake-wide emissions of carbon dioxide and methane from a eutrophic lake

NASA Astrophysics Data System (ADS)

Loken, L. C.; Crawford, J.; Schramm, P.; Stadler, P.; Stanley, E. H.

2017-12-01

Lakes are important regulators of global carbon cycling and conduits of greenhouse gases to the atmosphere; however, most efflux estimates for individual lakes are based on extrapolations from a limited number of locations. Within-lake variability in carbon dioxide (CO2) and methane (CH4) arises from differences in water sources, physical mixing, and biogeochemical transformations; all of which can vary at multiple temporal and spatial scales. We mapped surface water concentrations of CO2 and CH4 weekly across Lake Mendota (a 39.9 km2 eutrophic lake in Wisconsin, USA) spanning the majority of the 2016 ice-free season (249 days). Combining these maps with a spatially explicit gas transfer velocity (k) model, we estimated the diffusive exchange of both gases with the atmosphere taking into account both spatial and temporal heterogeneity. The cumulative efflux of CO2 (85.3 Mmol) and CH4 (9.47 Mmol) was positive, indicating that on the annual scale Lake Mendota was a net-source of both gases to the atmosphere. Although our model included variability in k, flux patterns reflected the patterns in gas concentrations. During the stratified period, CO2 was generally undersaturated throughout the pelagic zone due to high primary production and differed near river inlets and shorelines. The lake was routinely extremely supersaturated with CH4 with elevated concentrations in expansive littoral areas. During fall mixis, concentrations of both gases increased and became more variable across the lake surface, and their spatial arrangement changed reflecting hypolimentic mixing. In this system, samples collected from the lake center reasonably well-represented the lake-wide mean CO2 concentration, but they poorly represented CH4. While metabolic processes driving CO2 varied across the lake surface, pelagic phytoplankton contributed extensively to overall primary production, which acted at the lake-wide scale. Additionally Lake Mendota's high alkalinity may have masked the metabolic imprint on CO2 patterns. In contrast, heterogeneous CH4 transformations and transport lead to remarkable variation in CH4 across the lake surface that was dynamic through time. Thus, extrapolations from a limited number of locations or timepoints may not adequately describe lake-wide CH4 dynamics.

Reconstruction of climate in China during 17th-19th centuries using Chinese chronological records

NASA Astrophysics Data System (ADS)

Wang, Pao; Lin, Kuan-Hui; Liao, Yi-Chun; Lee, Shih-Yu; Liao, Hsiung-Ming; Pai, Pi-Ling; Fan, I.-Chun

2017-04-01

Chinese historical documents are an extremely useful source from which much climate information can be retrieved if treated carefully. This is especially relevant to the reconstruction of climate in East Asia in the last 2000 years as the Chinese has kept official chronicles since 500BC and China also represents a large portion of East Asia's land. In addition, there are also local records in many cities and counties. When available, such documentary sources are often superior to environmental proxy data, especially in the time resolution as they usually provide at least annual resolution and even as high as daily records in some cases. This research will report on our recent advances on using a new REACHS dataset that collects primarily documented meteorological records from thousands of imperial and local chronicles in the Chinese history for more than 2000 years. The meteorological records were digitized and coded in the relational database management system in which accurate time (from yearly to daily), space (from province to city/county) and event (from meteorological to phonological and social) information is carefully reserved for analysis. We then formed digital climate series and performed time series and spatial analysis on them to obtain their temporal and spatial characteristics. Our present research results on the annual and seasonal temperature reconstruction during 17th-19th indicates lower temperature in the 17th century. There were also strangely high occurrence frequency of summer snowfall records in the lower reaches of Yangtze River during the Maunder Minimum. Reconstructed precipitation series fluctuated with strong regional character in the Northeast, Central-east and Southeast China. Spectral analysis shows that precipitation series have significant periodicity of 3-5 and 8-12 years during the period, suggesting strong interannual variability and different regional signatures. Flood happened frequently but long lasting drought was more frequently occurred in the 17th than in the following century. Furthermore drought is highly correlated with locust records, especially in the 17th century. The temporal and spatial variability of the climate reconstruction implies hierarchical and multi-scaled climate variability and a likely changing regime of monsoon: its spatial distribution, pattern and intensity. More detailed spatial-temporal analysis will be applied to analyze the dynamism.

China's Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model.

PubMed

Cao, Qilong; Liang, Ying; Niu, Xueting

2017-09-18

Background : Air pollution has become an important factor restricting China's economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods : Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM 2.5 . Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results : It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM 2.5 pollutions in the control of other variables. Conclusions : Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables.

Climatological assessment of spatiotemporal trends in observational monthly snowfall totals and extremes over the Canadian Great Lakes Basin

NASA Astrophysics Data System (ADS)

Baijnath, Janine; Duguay, Claude; Sushama, Laxmi; Huziy, Oleksandr

2017-04-01

The Laurentian Great Lakes Basin (GLB) is susceptible to snowfall events that derive from extratropical cyclones and heavy lake effect snowfall (HLES). The former is generated by quasigeostropic forcing from positive temperature or vorticity advection associated with low-pressure centres. HLES is produced by planetary boundary layer (PBL) convection that is initiated as a result of cold and dry continental air mass advecting over relatively warm lakes and generating turbulent moisture and heat fluxes into the PBL. HLES events can have disastrous impacts on local communities such as the November 2014 Buffalo storm that caused 13 fatalities. Albeit the many HLES studies, most are focused on specific case study events with a discernible under examination of climatological HLES trend analyses for the Canadian GLB. The research objectives are to first determine the historical, climatological trends in monthly snowfall totals and to examine potential surface and atmospheric variables driving the resultant changes in HLES. The second aims to analyze the historical extremes in snowfall by assessing the intensity, frequency, and duration of snowfall within the domain of interest. Spatiotemporal snowfall and precipitation trends are computed for the 1982 to 2015 period using Daymet (Version 3) monthly gridded observational datasets from the Oak Ridge National Laboratory. The North American Regional Reanalysis (NARR), NOAA Optimum Interpolation Sea Surface Temperature (OISST), and the Canadian Ice Service (CIS) datasets are also used for evaluating trends in HLES driving variables such as air temperature, lake surface temperature (LST), ice cover concentration, omega, and vertical temperature gradient (VTGlst-850). Climatological trends in monthly snowfall totals show a significant decrease along the Ontario snowbelt of Lake Superior, Lake Huron and Georgian Bay at the 90 percent confidence level. These results are attributed to significant warming in LST, significant decrease in ice cover fraction, and an increase in VTGlst-850, which enhances evaporation into the lower PBL. It is suggested that inefficient moisture recycling and increase moisture storage in warmer air masses inhibits the development of HLES. The 99th percentile of snowfall events within the GLB suggests an extreme snowfall value equal to or exceeding 15 cm per day. Spatiotemporal snowfall patterns indicate that mostly lake effect processes and not extratropical cyclones drive the high intensity, frequency, and duration of these extreme events over the GLB. Furthermore, the Canadian snowbelt region of Lake Huron and Lake Superior exhibit different spatiotemporal trends in snowfall extremes but, even within a particular snowbelt region, trends in extreme snowfall are not spatially coherent. It is suggested that geographic location of the lakes, topography, lake bathymetry, and lake orientation can influence local and large scale surface-atmosphere variables.

Spatial patterns of frequent floods in Switzerland

NASA Astrophysics Data System (ADS)

Schneeberger, Klaus; Rössler, Ole; Weingartner, Rolf

2017-04-01

Information about the spatial characteristics of high and extreme streamflow is often needed for an accurate analysis of flood risk and effective co-ordination of flood related activities, such as flood defence planning. In this study we analyse the spatial dependence of frequent floods in Switzerland across different scales. Firstly, we determine the average length of high and extreme flow events for 56 runoff time series of Swiss rivers. Secondly, a dependence measure expressing the probability that streamflow peaks are as high as peaks at a conditional site is used to describe and map the spatial extend of joint occurrence of frequent floods across Switzerland. Thirdly, we apply a cluster analysis to identify groups of sites that are likely to react similarly in terms of joint occurrence of high flow events. The results indicate that a time interval with a length of 3 days seems to be most appropriate to characterise the average length of high streamflow events across spatial scales. In the main Swiss basins, high and extreme streamflows were found to be asymptotically independent. In contrast, at the meso-scale distinct flood regions, which react similarly in terms of occurrence of frequent flood, were found. The knowledge about these regions can help to optimise flood defence planning or to estimate regional flood risk properly.

Long-term Radiation Budget Variability in the Northern Eurasian Region: Assessing the Interaction with Fire

NASA Astrophysics Data System (ADS)

Stackhouse, P. W.; Soja, A. J.; Zhang, T.; Mikovitz, J. C.

2013-12-01

In terms of global change, boreal regions are particularly important, because significant warming and change are already evident and significant future warming is predicted. Mean global air temperature has increased by 0.74°C in the last century, and temperatures are predicted to increase by 1.8°C to 4°C by 2090, depending on the Inter-governmental Panel on Climate Change (IPCC) scenario. Some of the greatest temperature increases are currently found in the Northern Eurasian winter and spring, which has led to longer growing seasons, increased potential evapotranspiration and extreme fire weather [Groisman et al., 2007]. In the Siberian Sayan, winter temperatures have already exceeded a 2090 Hadley Centre scenario (HadCM3GGa1) [Soja et al., 2007]. There is evidence of climate-induced change across the circumboreal in terms of increased infestations, alterations in vegetation and increased fire regimes (area burned, fire frequency, severity and number of extreme fire seasons). In this paper, we analyzed long-term surface radiation data sets from the NASA/GEWEX (Global Energy and Water Exchanges) Surface Radiation Budget data products, CERES Surface EBAF and SYN data products and also the available surface radiation measurements in the region. First, we show that during overlap years SRB and CERES data products agree very well in terms of anomalies and we'll use this fact to evaluate 30 years of satellite based estimates of the variability of downwelling SW parameters first corresponding to locations of surface measurements and then for the region as a whole. We also show the observed variability of other SW components such as the net SW and the albedo. Next we assess the variability of the downward and LW fluxes over time and compare these to variability observed in the surface temperature and other meteorological measurements. We assess anomalies on various spatial scales. Finally, we assess the correlation of this variability in specific locations to known fire events. Extreme fires burned in Sakha and Tuva in 2002 and 2004, respectively, and in contrast, a normal fire season burned in Sakha and Tuva in 1999 and 2002, respectively. For this reason, we focus on the fire season (April - September) for 1999, 2002, and 2004. We assess these data sets for evidence of relationships between the net radiative fluxes and fire onset as well as evidence for residual influence of the fires upon the radiative budgets.

Spatio-temporal variability of droughts and terrestrial water storage over Lake Chad Basin using independent component analysis

NASA Astrophysics Data System (ADS)

Ndehedehe, Christopher E.; Agutu, Nathan O.; Okwuashi, Onuwa; Ferreira, Vagner G.

2016-09-01

Lake Chad has recently been perceived to be completely desiccated and almost extinct due to insufficient published ground observations. Given the high spatial variability of rainfall in the region, and the fact that extreme climatic conditions (for example, droughts) could be intensifying in the Lake Chad basin (LCB) due to human activities, a spatio-temporal approach to drought analysis becomes essential. This study employed independent component analysis (ICA), a fourth-order cumulant statistics, to decompose standardised precipitation index (SPI), standardised soil moisture index (SSI), and terrestrial water storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) into spatial and temporal patterns over the LCB. In addition, this study uses satellite altimetry data to estimate variations in the Lake Chad water levels, and further employs relevant climate teleconnection indices (El-Niño Southern Oscillation-ENSO, Atlantic Multi-decadal Oscillation-AMO, and Atlantic Meridional Mode-AMM) to examine their links to the observed drought temporal patterns over the basin. From the spatio-temporal drought analysis, temporal evolutions of SPI at 12 month aggregation show relatively wet conditions in the last two decades (although with marked alterations) with the 2012-2014 period being the wettest. In addition to the improved rainfall conditions during this period, there was a statistically significant increase of 0.04 m/yr in altimetry water levels observed over Lake Chad between 2008 and 2014, which confirms a shift in the hydrological conditions of the basin. Observed trend in TWS changes during the 2002-2014 period shows a statistically insignificant increase of 3.0 mm/yr at the centre of the basin, coinciding with soil moisture deficit indicated by the temporal evolutions of SSI at all monthly accumulations during the 2002-2003 and 2009-2012 periods. Further, SPI at 3 and 6 month scales indicated fluctuating drought conditions at the extreme south of the basin, coinciding with a statistically insignificant decline in TWS of about 4.5 mm/yr at the southern catchment of the basin. Finally, correlation analyses indicate that ENSO, AMO, and AMM are associated with extreme rainfall conditions in the basin, with AMO showing the strongest association (statistically significant correlation of 0.55) with SPI 12 month aggregation. Therefore, this study provides a framework that will support drought monitoring in the LCB.

Evaluation of GCMs in the context of regional predictive climate impact studies.

NASA Astrophysics Data System (ADS)

Kokorev, Vasily; Anisimov, Oleg

2016-04-01

Significant improvements in the structure, complexity, and general performance of earth system models (ESMs) have been made in the recent decade. Despite these efforts, the range of uncertainty in predicting regional climate impacts remains large. The problem is two-fold. Firstly, there is an intrinsic conflict between the local and regional scales of climate impacts and adaptation strategies, on one hand, and larger scales, at which ESMs demonstrate better performance, on the other. Secondly, there is a growing understanding that majority of the impacts involve thresholds, and are thus driven by extreme climate events, whereas accent in climate projections is conventionally made on gradual changes in means. In this study we assess the uncertainty in projecting extreme climatic events within a region-specific and process-oriented context by examining the skills and ranking of ESMs. We developed a synthetic regionalization of Northern Eurasia that accounts for the spatial features of modern climatic changes and major environmental and socio-economical impacts. Elements of such fragmentation could be considered as natural focus regions that bridge the gap between the spatial scales adopted in climate-impacts studies and patterns of climate change simulated by ESMs. In each focus region we selected several target meteorological variables that govern the key regional impacts, and examined the ability of the models to replicate their seasonal and annual means and trends by testing them against observations. We performed a similar evaluation with regard to extremes and statistics of the target variables. And lastly, we used the results of these analyses to select sets of models that demonstrate the best performance at selected focus regions with regard to selected sets of target meteorological parameters. Ultimately, we ranked the models according to their skills, identified top-end models that "better than average" reproduce the behavior of climatic parameters, and eliminated the outliers. Since the criteria of selecting the "best" models are somewhat loose, we constructed several regional ensembles consisting of different number of high-ranked models and compared results from these optimized ensembles with observations and with the ensemble of all models. We tested our approach in specific regional application of the terrestrial Russian Arctic, considering permafrost and Artic biomes as key regional climate-dependent systems, and temperature and precipitation characteristics governing their state as target meteorological parameters. Results of this case study are deposited on the web portal www.permafrost.su/gcms

Spatial patterns of trends and teleconnections in climate indices relevant for Mexican maize

NASA Astrophysics Data System (ADS)

Dewes, C. F.

2013-05-01

This study contributes to the discussion of climate trends in Mexico and the influence of hemispheric-scale variability patterns over the period 1950-2008. Its uniqueness is three-fold. First, the choice of climate indices under scrutiny aims to represent an agro-climatic perspective, geared towards maize in particular because of the major role this crop plays in Mexico's culture, diet, and economy. Second, the spatial resolution and coverage of these findings can be useful for interpretations at the local level (i.e. district or state), yet keeping the broad national picture in perspective. This should be particularly useful to agro-climate forecasting, assessment of impacts, and/or policy development. Third, this study uncovers a dominance of the North Atlantic over the Pacific Ocean in respect to remote influences on trend patterns in Mexico. Trends in precipitation show that east of the central highlands, the rainy season is starting later and becoming drier. The same is occurring along the Pacific coastal plain, but there an increase in extreme events is also observed. For south-central Mexico and the Yucatán, rains not only are starting earlier but intensity and frequency of extreme events are also increasing. In some of these areas dry days are becoming more frequent. Trends in temperature suggest that highlands are warming at faster rates than lowlands, which in some places are actually cooling. Warming in the fall-winter growing season is more pronounced than in the spring-summer growing season. On the other hand, cold spells during mid-summer are becoming more frequent over the highlands. Connections were found between these trends and large-scale variability patterns, namely El Niño Southern Oscillation (ENSO), Pacific North America (PNA), the North Atlantic Oscillation (NAO), and Caribbean sea surface temperatures (SSTs). Interannual variability related to ENSO and the PNA, and trends towards more Niño-like conditions, are associated with increasing precipitation over the Yucatán and a few areas along the southern Pacific coast, and with more dry days - though accompanied by stronger extreme precipitation events - over southwestern Mexico. They are also associated with a delaying start of the rainy season in a narrow area just east of the south-central highlands and higher risk of frost during late-winter up in the highlands. Increasing Caribbean SSTs are associated with increasing temperatures over the mainland and stronger precipitation over the Yucatán. Variability in the NAO and its trend towards more positive phases is associated the drying trend in central Mexico, intensification of precipitation over the northwest, and also increasing temperatures over the entire mainland in both the warm and cold growing seasons. The atmospheric circulation over the Atlantic Ocean and its increasing tropical SSTs, have a larger and broader participation in Mexican trends than is noted with the Pacific counterparts.

Characterizing groundwater quality ranks for drinking purposes in Sylhet district, Bangladesh, using entropy method, spatial autocorrelation index, and geostatistics.

PubMed

Islam, Abu Reza Md Towfiqul; Ahmed, Nasir; Bodrud-Doza, Md; Chu, Ronghao

2017-12-01

Drinking water is susceptible to the poor quality of contaminated water affecting the health of humans. Thus, it is an essential study to investigate factors affecting groundwater quality and its suitability for drinking uses. In this paper, the entropy theory, multivariate statistics, spatial autocorrelation index, and geostatistics are applied to characterize groundwater quality and its spatial variability in the Sylhet district of Bangladesh. A total of 91samples have been collected from wells (e.g., shallow, intermediate, and deep tube wells at 15-300-m depth) from the study area. The results show that NO 3 - , then SO 4 2- , and As are the most contributed parameters influencing the groundwater quality according to the entropy theory. The principal component analysis (PCA) and correlation coefficient also confirm the results of the entropy theory. However, Na + has the highest spatial autocorrelation and the most entropy, thus affecting the groundwater quality. Based on the entropy-weighted water quality index (EWQI) and groundwater quality index (GWQI) classifications, it is observed that 60.45 and 53.86% of water samples are classified as having an excellent to good qualities, while the remaining samples vary from medium to extremely poor quality domains for drinking purposes. Furthermore, the EWQI classification provides the more reasonable results than GWQIs due to its simplicity, accuracy, and ignoring of artificial weight. A Gaussian semivariogram model has been chosen to the best fit model, and groundwater quality indices have a weak spatial dependence, suggesting that both geogenic and anthropogenic factors play a pivotal role in spatial heterogeneity of groundwater quality oscillations.

Characterization of extreme flood and drought events in Singapore and investigation of their relationships with ENSO

NASA Astrophysics Data System (ADS)

Li, Xin; Babovic, Vladan

2016-04-01

Flood and drought are hydrologic extreme events that have significant impact on human and natural systems. Characterization of flood and drought in terms of their start, duration and strength, and investigation of the impact of natural climate variability (i.e., ENSO) and anthropogenic climate change on them can help decision makers to facilitate adaptions to mitigate potential enormous economic costs. To date, numerous studies in this area have been conducted, however, they are primarily focused on extra-tropical regions. Therefore, this study presented a detailed framework to characterize flood and drought events in a tropical urban city-state (i.e., Singapore), based on daily data from 26 precipitation stations. Flood and drought events are extracted from standardized precipitation anomalies from monthly to seasonal time scales. Frequency, duration and magnitude of flood and drought at all the stations are analyzed based on crossing theory. In addition, spatial variation of flood and drought characteristics in Singapore is investigated using ordinary kriging method. Lastly, the impact of ENSO condition on flood and drought characteristics is analyzed using regional regression method. The results show that Singapore can be prone to extreme flood and drought events at both monthly and seasonal time scales. ENSO has significant influence on flood and drought characteristics in Singapore, but mainly during the South West Monsoon season. During the El Niño phase, drought can become more extreme. The results have implications for water management practices in Singapore.

Future intensification of hydro-meteorological extremes: downscaling using the weather research and forecasting model

NASA Astrophysics Data System (ADS)

El-Samra, R.; Bou-Zeid, E.; Bangalath, H. K.; Stenchikov, G.; El-Fadel, M.

2017-12-01

A set of ten downscaling simulations at high spatial resolution (3 km horizontally) were performed using the Weather Research and Forecasting (WRF) model to generate future climate projections of annual and seasonal temperature and precipitation changes over the Eastern Mediterranean (with a focus on Lebanon). The model was driven with the High Resolution Atmospheric Model (HiRAM), running over the whole globe at a resolution of 25 km, under the conditions of two Representative Concentration Pathways (RCP) (4.5 and 8.5). Each downscaling simulation spanned one year. Two past years (2003 and 2008), also forced by HiRAM without data assimilation, were simulated to evaluate the model's ability to capture the cold and wet (2003) and hot and dry (2008) extremes. The downscaled data were in the range of recent observed climatic variability, and therefore corrected for the cold bias of HiRAM. Eight future years were then selected based on an anomaly score that relies on the mean annual temperature and accumulated precipitation to identify the worst year per decade from a water resources perspective. One hot and dry year per decade, from 2011 to 2050, and per scenario was simulated and compared to the historic 2008 reference. The results indicate that hot and dry future extreme years will be exacerbated and the study area might be exposed to a significant decrease in annual precipitation (rain and snow), reaching up to 30% relative to the current extreme conditions.

Interactions of Mean Climate Change and Climate Variability on Food Security Extremes

NASA Technical Reports Server (NTRS)

Ruane, Alexander C.; McDermid, Sonali; Mavromatis, Theodoros; Hudson, Nicholas; Morales, Monica; Simmons, John; Prabodha, Agalawatte; Ahmad, Ashfaq; Ahmad, Shakeel; Ahuja, Laj R.

2015-01-01

Recognizing that climate change will affect agricultural systems both through mean changes and through shifts in climate variability and associated extreme events, we present preliminary analyses of climate impacts from a network of 1137 crop modeling sites contributed to the AgMIP Coordinated Climate-Crop Modeling Project (C3MP). At each site sensitivity tests were run according to a common protocol, which enables the fitting of crop model emulators across a range of carbon dioxide, temperature, and water (CTW) changes. C3MP can elucidate several aspects of these changes and quantify crop responses across a wide diversity of farming systems. Here we test the hypothesis that climate change and variability interact in three main ways. First, mean climate changes can affect yields across an entire time period. Second, extreme events (when they do occur) may be more sensitive to climate changes than a year with normal climate. Third, mean climate changes can alter the likelihood of climate extremes, leading to more frequent seasons with anomalies outside of the expected conditions for which management was designed. In this way, shifts in climate variability can result in an increase or reduction of mean yield, as extreme climate events tend to have lower yield than years with normal climate.C3MP maize simulations across 126 farms reveal a clear indication and quantification (as response functions) of mean climate impacts on mean yield and clearly show that mean climate changes will directly affect the variability of yield. Yield reductions from increased climate variability are not as clear as crop models tend to be less sensitive to dangers on the cool and wet extremes of climate variability, likely underestimating losses from water-logging, floods, and frosts.

Effect of transmission intensity on hotspots and micro-epidemiology of malaria in sub-Saharan Africa.

PubMed

Mogeni, Polycarp; Omedo, Irene; Nyundo, Christopher; Kamau, Alice; Noor, Abdisalan; Bejon, Philip

2017-06-30

Malaria transmission intensity is heterogeneous, complicating the implementation of malaria control interventions. We provide a description of the spatial micro-epidemiology of symptomatic malaria and asymptomatic parasitaemia in multiple sites. We assembled data from 19 studies conducted between 1996 and 2015 in seven countries of sub-Saharan Africa with homestead-level geospatial data. Data from each site were used to quantify spatial autocorrelation and examine the temporal stability of hotspots. Parameters from these analyses were examined to identify trends over varying transmission intensity. Significant hotspots of malaria transmission were observed in most years and sites. The risk ratios of malaria within hotspots were highest at low malaria positive fractions (MPFs) and decreased with increasing MPF (p < 0.001). However, statistical significance of hotspots was lowest at extremely low and extremely high MPFs, with a peak in statistical significance at an MPF of ~0.3. In four sites with longitudinal data we noted temporal instability and variable negative correlations between MPF and average age of symptomatic malaria across all sites, suggesting varying degrees of temporal stability. We observed geographical micro-variation in malaria transmission at sites with a variety of transmission intensities across sub-Saharan Africa. Hotspots are marked at lower transmission intensity, but it becomes difficult to show statistical significance when cases are sparse at very low transmission intensity. Given the predictability with which hotspots occur as transmission intensity falls, malaria control programmes should have a low threshold for responding to apparent clustering of cases.

Changes in precipitation isotope-climate relationships from temporal grouping and aggregation of weekly-resolved USNIP data: impacts on paleoclimate and environmental applications

NASA Astrophysics Data System (ADS)

Akers, P. D.; Welker, J. M.

2015-12-01

Spatial variations in precipitation isotopes have been the focus of much recent research, but relatively less work has explored changes at various temporal scales. This is partly because most spatially-diverse and long-term isotope databases are offered at a monthly resolution, while daily or event-level records are spatially and temporally limited by cost and logistics. A subset of 25 United States Network for Isotopes in Precipitation (USNIP) sites with weekly-resolution in the east-central United States was analyzed for site-specific relationships between δ18O and δD (the local meteoric water line/LMWL), δ18O and surface temperature, and δ18O and precipitation amount. Weekly data were then aggregated into monthly and seasonal data to examine the effect of aggregation on correlation and slope values for each of the relationships. Generally, increasing aggregation improved correlations (>25% for some sites) due to a reduced effect of extreme values, but estimates on regression variable error increased (>100%) because of reduced sample sizes. Aggregation resulted in small, but significant drops (5-25%) in relationship slope values for some sites. Weekly data were also grouped by month and season to explore changes in relationships throughout the year. Significant subannual variability exists in slope values and correlations even for sites with very strong overall correlations. LMWL slopes are highest in winter and lowest in summer, while the δ18O-surface temperature relationship is strongest in spring. Despite these overall trends, a high level of month-to-month and season-to-season variability is the norm for these sites. Researchers blindly applying overall relationships drawn from monthly-resolved databases to paleoclimate or environmental research risk assuming these relationships apply at all temporal resolutions. When possible, researchers should match the temporal resolution used to calculate an isotopic relationship with the temporal resolution of their applied proxy.

Simulating Water Flow in Variably Saturated Soils - Exploring the Advantage of Three-dimensional Models

NASA Astrophysics Data System (ADS)

Hopp, L.; Ivanov, V. Y.

2010-12-01

There is still a debate in rainfall-runoff modeling over the advantage of using three-dimensional models based on partial differential equations describing variably saturated flow vs. models with simpler infiltration and flow routing algorithms. Fully explicit 3D models are computationally demanding but allow the representation of spatially complex domains, heterogeneous soils, conditions of ponded infiltration, and solute transport, among others. Models with simpler infiltration and flow routing algorithms provide faster run times and are likely to be more versatile in the treatment of extreme conditions such as soil drying but suffer from underlying assumptions and ad-hoc parameterizations. In this numerical study, we explore the question of whether these two model strategies are competing approaches or if they complement each other. As a 3D physics-based model we use HYDRUS-3D, a finite element model that numerically solves the Richards equation for variably-saturated water flow. As an example of a simpler model, we use tRIBS+VEGGIE that solves the 1D Richards equation for vertical flow and applies Dupuit-Forchheimer approximation for saturated lateral exchange and gravity-driven flow for unsaturated lateral exchange. The flow can be routed using either the D-8 (steepest descent) or D-infinity flow routing algorithms. We study lateral subsurface stormflow and moisture dynamics at the hillslope-scale, using a zero-order basin topography, as a function of storm size, antecedent moisture conditions and slope angle. The domain and soil characteristics are representative of a forested hillslope with conductive soils in a humid environment, where the major runoff generating process is lateral subsurface stormflow. We compare spatially integrated lateral subsurface flow at the downslope boundary as well as spatial patterns of soil moisture. We illustrate situations where both model approaches perform equally well and identify conditions under which the application of a fully-explicit 3D model may be required for a realistic description of the hydrologic response.

Demographic consequences of climate change and land cover help explain a history of extirpations and range contraction in a declining snake species.

PubMed

Pomara, Lars Y; LeDee, Olivia E; Martin, Karl J; Zuckerberg, Benjamin

2014-07-01

Developing conservation strategies for threatened species increasingly requires understanding vulnerabilities to climate change, in terms of both demographic sensitivities to climatic and other environmental factors, and exposure to variability in those factors over time and space. We conducted a range-wide, spatially explicit climate change vulnerability assessment for Eastern Massasauga (Sistrurus catenatus), a declining endemic species in a region showing strong environmental change. Using active season and winter adult survival estimates derived from 17 data sets throughout the species' range, we identified demographic sensitivities to winter drought, maximum precipitation during the summer, and the proportion of the surrounding landscape dominated by agricultural and urban land cover. Each of these factors was negatively associated with active season adult survival rates in binomial generalized linear models. We then used these relationships to back-cast adult survival with dynamic climate variables from 1950 to 2008 using spatially explicit demographic models. Demographic models for 189 population locations predicted known extant and extirpated populations well (AUC = 0.75), and models based on climate and land cover variables were superior to models incorporating either of those effects independently. These results suggest that increasing frequencies and severities of extreme events, including drought and flooding, have been important drivers of the long-term spatiotemporal variation in a demographic rate. We provide evidence that this variation reflects nonadaptive sensitivity to climatic stressors, which are contributing to long-term demographic decline and range contraction for a species of high-conservation concern. Range-wide demographic modeling facilitated an understanding of spatial shifts in climatic suitability and exposure, allowing the identification of important climate refugia for a dispersal-limited species. Climate change vulnerability assessment provides a framework for linking demographic and distributional dynamics to environmental change, and can thereby provide unique information for conservation planning and management. © 2013 John Wiley & Sons Ltd.

Impacts of Climate Change On The Occurrence of Extreme Events: The Mice Project

NASA Astrophysics Data System (ADS)

Palutikof, J. P.; Mice Team

It is widely accepted that climate change due to global warming will have substan- tial impacts on the natural environment, and on human activities. Furthermore, it is increasingly recognized that changes in the severity and frequency of extreme events, such as windstorm and flood, are likely to be more important than changes in the average climate. The EU-funded project MICE (Modelling the Impacts of Climate Extremes) commenced in January 2002. It seeks to identify the likely changes in the occurrence of extremes of rainfall, temperature and windstorm due to global warm- ing, using information from climate models as a basis, and to study the impacts of these changes in selected European environments. The objectives are: a) to evaluate, by comparison with gridded and station observations, the ability of climate models to successfully reproduce the occurrence of extremes at the required spatial and temporal scales. b) to analyse model output with respect to future changes in the occurrence of extremes. Statistical analyses will determine changes in (i) the return periods of ex- tremes, (ii) the joint probability of extremes (combinations of damaging events such as windstorm followed by heavy rain), (iii) the sequential behaviour of extremes (whether events are well-separated or clustered) and (iv) the spatial patterns of extreme event occurrence across Europe. The range of uncertainty in model predictions will be ex- plored by analysing changes in model experiments with different spatial resolutions and forcing scenarios. c) to determine the impacts of the predicted changes in extremes occurrence on selected activity sectors: agriculture (Mediterranean drought), commer- cial forestry and natural forest ecosystems (windstorm and flood in northern Europe, fire in the Mediterranean), energy use (temperature extremes), tourism (heat stress and Mediterranean beach holidays, changes in the snow pack and winter sports ) and civil protection/insurance (windstorm and flood). Impacts will be evaluated through a combination of techniques ranging from quantitative analyses through to expert judge- ment. Throughout the project, a continuing dialogue with stakeholders and end-users will be maintained.

The Microphysical Structure of Extreme Precipitation as Inferred from Ground-Based Raindrop Spectra.

NASA Astrophysics Data System (ADS)

Uijlenhoet, Remko; Smith, James A.; Steiner, Matthias

2003-05-01

The controls on the variability of raindrop size distributions in extreme rainfall and the associated radar reflectivity-rain rate relationships are studied using a scaling-law formalism for the description of raindrop size distributions and their properties. This scaling-law formalism enables a separation of the effects of changes in the scale of the raindrop size distribution from those in its shape. Parameters controlling the scale and shape of the scaled raindrop size distribution may be related to the microphysical processes generating extreme rainfall. A global scaling analysis of raindrop size distributions corresponding to rain rates exceeding 100 mm h1, collected during the 1950s with the Illinois State Water Survey raindrop camera in Miami, Florida, reveals that extreme rain rates tend to be associated with conditions in which the variability of the raindrop size distribution is strongly number controlled (i.e., characteristic drop sizes are roughly constant). This means that changes in properties of raindrop size distributions in extreme rainfall are largely produced by varying raindrop concentrations. As a result, rainfall integral variables (such as radar reflectivity and rain rate) are roughly proportional to each other, which is consistent with the concept of the so-called equilibrium raindrop size distribution and has profound implications for radar measurement of extreme rainfall. A time series analysis for two contrasting extreme rainfall events supports the hypothesis that the variability of raindrop size distributions for extreme rain rates is strongly number controlled. However, this analysis also reveals that the actual shapes of the (measured and scaled) spectra may differ significantly from storm to storm. This implies that the exponents of power-law radar reflectivity-rain rate relationships may be similar, and close to unity, for different extreme rainfall events, but their prefactors may differ substantially. Consequently, there is no unique radar reflectivity-rain rate relationship for extreme rain rates, but the variability is essentially reduced to one free parameter (i.e., the prefactor). It is suggested that this free parameter may be estimated on the basis of differential reflectivity measurements in extreme rainfall.

How does spatial variability of climate affect catchment streamflow predictions?

EPA Science Inventory

Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distribute...

Progress with lossy compression of data from the Community Earth System Model

NASA Astrophysics Data System (ADS)

Xu, H.; Baker, A.; Hammerling, D.; Li, S.; Clyne, J.

2017-12-01

Climate models, such as the Community Earth System Model (CESM), generate massive quantities of data, particularly when run at high spatial and temporal resolutions. The burden of storage is further exacerbated by creating large ensembles, generating large numbers of variables, outputting at high frequencies, and duplicating data archives (to protect against disk failures). Applying lossy compression methods to CESM datasets is an attractive means of reducing data storage requirements, but ensuring that the loss of information does not negatively impact science objectives is critical. In particular, test methods are needed to evaluate whether critical features (e.g., extreme values and spatial and temporal gradients) have been preserved and to boost scientists' confidence in the lossy compression process. We will provide an overview on our progress in applying lossy compression to CESM output and describe our unique suite of metric tests that evaluate the impact of information loss. Further, we will describe our processes how to choose an appropriate compression algorithm (and its associated parameters) given the diversity of CESM data (e.g., variables may be constant, smooth, change abruptly, contain missing values, or have large ranges). Traditional compression algorithms, such as those used for images, are not necessarily ideally suited for floating-point climate simulation data, and different methods may have different strengths and be more effective for certain types of variables than others. We will discuss our progress towards our ultimate goal of developing an automated multi-method parallel approach for compression of climate data that both maximizes data reduction and minimizes the impact of data loss on science results.

Real Time Land-Surface Hydrologic Modeling Over Continental US

NASA Technical Reports Server (NTRS)

Houser, Paul R.

1998-01-01

The land surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. Spatially and temporally variable rainfall and available energy, combined with land surface heterogeneity cause complex variations in all processes related to surface hydrology. The characterization of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of land surface-atmosphere interaction and the impact of land surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP to impose ad hoc corrections to the land surface states to prevent this drift. A proposed methodology is to develop Land Data Assimilation schemes (LDAS), which are uncoupled models forced with observations, and not affected by NWP forcing biases. The proposed research is being implemented as a real time operation using an existing Surface Vegetation Atmosphere Transfer Scheme (SVATS) model at a 40 km degree resolution across the United States to evaluate these critical science questions. The model will be forced with real time output from numerical prediction models, satellite data, and radar precipitation measurements. Model parameters will be derived from the existing GIS vegetation and soil coverages. The model results will be aggregated to various scales to assess water and energy balances and these will be validated with various in-situ observations.

Dynamic hypoxic zones in Lake Erie compress fish habitat, altering vulnerability to fishing gears

USGS Publications Warehouse

Kraus, Richard T.; Knight, Carey T.; Farmer, Troy M.; Gorman, Ann Marie; Collingsworth, Paris D.; Warren, Glenn J.; Kocovsky, Patrick M.; Conroy, Joseph D.

2015-01-01

Seasonal degradation of aquatic habitats from hypoxia occurs in numerous freshwater and coastal marine systems and can result in direct mortality or displacement of fish. Yet, fishery landings from these systems are frequently unresponsive to changes in the severity and extent of hypoxia, and population-scale effects have been difficult to measure except in extreme hypoxic conditions with hypoxia-sensitive species. We investigated fine-scale temporal and spatial variability in dissolved oxygen in Lake Erie as it related to fish distribution and catch efficiencies of both active (bottom trawls) and passive (trap nets) fishing gears. Temperature and dissolved oxygen loggers placed near the edge of the hypolimnion exhibited much higher than expected variability. Hypoxic episodes of variable durations were frequently punctuated by periods of normoxia, consistent with high-frequency internal waves. High-resolution interpolations of water quality and hydroacoustic surveys suggest that fish habitat is compressed during hypoxic episodes, resulting in higher fish densities near the edges of hypoxia. At fixed locations with passive commercial fishing gear, catches with the highest values occurred when bottom waters were hypoxic for intermediate proportions of time. Proximity to hypoxia explained significant variation in bottom trawl catches, with higher catch rates near the edge of hypoxia. These results emphasize how hypoxia may elevate catch rates in various types of fishing gears, leading to a lack of association between indices of hypoxia and fishery landings. Increased catch rates of fish at the edges of hypoxia have important implications for stock assessment models that assume catchability is spatially homogeneous.

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas - a review

NASA Astrophysics Data System (ADS)

Cristiano, Elena; ten Veldhuis, Marie-claire; van de Giesen, Nick

2017-07-01

In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

Changes in Extremely Hot Summers over the Global Land Area under Various Warming Targets.

PubMed

Wang, Lei; Huang, Jianbin; Luo, Yong; Yao, Yao; Zhao, Zongci

2015-01-01

Summer temperature extremes over the global land area were investigated by comparing 26 models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with observations from the Goddard Institute for Space Studies (GISS) and the Climate Research Unit (CRU). Monthly data of the observations and models were averaged for each season, and statistics were calculated for individual models before averaging them to obtain ensemble means. The summers with temperature anomalies (relative to 1951-1980) exceeding 3σ (σ is based on the local internal variability) are defined as "extremely hot". The models well reproduced the statistical characteristics evolution, and partly captured the spatial distributions of historical summer temperature extremes. If the global mean temperature increases 2°C relative to the pre-industrial level, "extremely hot" summers are projected to occur over nearly 40% of the land area (multi-model ensemble mean projection). Summers that exceed 5σ warming are projected to occur over approximately 10% of the global land area, which were rarely observed during the reference period. Scenarios reaching warming levels of 3°C to 5°C were also analyzed. After exceeding the 5°C warming target, "extremely hot" summers are projected to occur throughout the entire global land area, and summers that exceed 5σ warming would become common over 70% of the land area. In addition, the areas affected by "extremely hot" summers are expected to rapidly expand by more than 25%/°C as the global mean temperature increases by up to 3°C before slowing to less than 16%/°C as the temperature continues to increase by more than 3°C. The area that experiences summers with warming of 5σ or more above the warming target of 2°C is likely to maintain rapid expansion of greater than 17%/°C. To reduce the impacts and damage from severely hot summers, the global mean temperature increase should remain low.

The role of climate variability in extreme floods in Europe

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Aerts, Jeroen C. J. H.; Jongman, Brenden; Ward, Philip J.

2017-04-01

Between 1980 and 2015, Europe experienced 18% of worldwide weather-related loss events, which accounted for over US500 billion in damage. Consequently, it is urgent to further develop adaptation strategies to mitigate the consequences of weather-related disasters, such as floods. Europe's capability to prepare for such disasters is challenged by a large range of uncertainties and a limited understanding of the driving forces of hydrometeorological hazards. One of the major sources of uncertainty is the relationship between climate variability and weather-related losses. Previous studies show that climate variability drives temporal changes in hydrometereological variables in Europe. However, their influence on flood risk has received little attention. We investigated the influence of the positive and negative phases of El Niño Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO), on the seasonal frequency and intensity of extreme rainfall, and anomalies in flood occurrence and damage compared to the neutral phases of the indices of climate variability. Using statistical methods to analyze relationships between the indices of climate variability and four indicators of flooding, we found that positive and negative phases of NAO and AO are associated with more (or less) frequent and intense seasonal extreme rainfall over large areas of Europe. The relationship between ENSO and both the occurrence of extreme rainfall and intensity of extreme rainfall in Europe is much smaller than the relationship with NAO or AO, but still significant in some regions. We observe that flood damage and flood occurrence have strong links with climate variability, especially in southern and eastern Europe. Therefore, when investigating flooding across Europe, all three indices of climate variability should be considered. Seasonal forecasting of flooding could be enhanced by the inclusion of climate variability indicators .

Confined Floodplain Dynamics on Semi-Arid Systems

NASA Astrophysics Data System (ADS)

Entwistle, N. S.

2017-12-01

Many watercourses across southern Africa are characterised by a bedrock influenced `macro-channel' created as a result of geologically recent fluvial incision into ancient planation surfaces. The rivers of the Kruger National Park in Mpumalanga Province, South Africa are no exception, displaying a varied set of channel types within a bedrock template. Contemporary flows are largely contained within the confines of this `macro-channel' and a diverse valley bottom morphology and ecology has developed in response to this flow regime coupled with intermittent fine sediment delivery from the catchment. Aerial imagery and field monitoring of the impact of two cyclone driven extreme flows and subsequent recovery phases suggests that flood impact is spatially variable with bedrock exposure greatest along watercourses already severely impacted by previous events. Subsequent system development has been characterised by the redistribution and vegetative colonisation of unconsolidated sandy sediment over bedrock. On less impacted systems vegetative induced recovery has, in contrast, been rapid with many of the species present displaying significant resilience to extreme flows forming residual pockets which are subsequently developing alongside embryonic morphologic recovery. Using these observations a model of valley bottom recovery is presented.

Linking storm surge activity and circulation variability along the Spanish coast through a synoptic pattern classification

NASA Astrophysics Data System (ADS)

Rasilla Álvarez, Domingo; Garcia Codrón, Juan Carlos

2010-05-01

The potentially negative consequences resulting from the estimations of global sea level rising along the current century are a matter of serious concern in many coastal areas worldwide. Most of the negative consequences of the sea level variability, such as flooding or erosion, are linked to episodic events of strong atmospheric forcing represented by deep atmospheric disturbances, especially if they combine with extreme astronomical high tides. Moreover, the interaction between the prevailing flows during such events and the actual orientation of the coast line might accelerate or mitigate such impacts. This contribution analyses sea surge variations measured at five tide-gauge stations located around the Iberian Peninsula and their relationships with regional scale circulation patterns with local-scale winds. Its aim is to improve the knowledge of surge related-coastal-risks by analysing the relationship between surges and their atmospheric forcing factors at different spatial scales. The oceanographic data set consists of hourly data from 5 tide gauge stations (Santander, Vigo, Bonanza, Málaga, Valencia and Barcelona) disseminated along the Spanish coastline, provided by Puertos del Estado. To explore the atmospheric mechanisms responsible for the sign and magnitude of sea surges, a regional Eulerian approach (a synoptic typing) were combined with a larger-scale Lagrangian method, based on the analysis of storm-tracks over the Atlantic and local information (synop reports) obtained from the closest meteorological stations to the tide gauges. The synoptic catalogue was obtained following a procedure that combines Principal Component Analysis (PCA) for reduction purposes and clustering (Ward plus K-means) to define the circulation types. Sea level pressure, surface 10m U and V wind components gridded data were obtained from NCEP Reanalysis, while storm tracks and cyclone statistics were extracted from the CDC Map Room Climate Products Storm Track Data (http://www.cdc.noaa.gov/map/clim/st_data.html). The second task was to evaluate the performance of each circulation type on the spatial patterns of a daily fire danger risk index (Canadian Fire Weather Index, FWI). Finally, anomaly maps of several surface and low level climate variables, corresponding to the dates of ignition of the very large forest fires within each synoptic pattern, were calculated to provide insight of the specific conditions associated to those extreme events. A principal component analysis upon 6 hourly residuals highlighted the homogeneous behaviour of the tide gauges and provided a regional quantitative index to identify the largest storm surges. The leading PCA displayed a homogeneous spatial pattern, describing the low frequency variability along the entire coast, in spite of different orientations of the coast, accounting for more than 80% of the total variability. The second PCA displayed opposite phases between the Atlantic and the Mediterranean. Furthermore, the results suggest that surges are a regional rather than local phenomenon, probably related to the same single physical forcing. The comparison between extreme surge events and circulation patterns highlighted that single physical mechanism is represented by extratropical cyclonic disturbances located at the north-western corner of the Iberian Peninsula, responsible for an environment characterized by low pressure readings and westerly-southwesterly winds. That wind pattern acquires an onshore component in the Atlantic coast, but becomes offshore in the Mediterranean. So, the main mechanism responsible for those storm surges is the inverse barometer effect, being the wind dragging secondary. The main physical forcing of the storm surges, the extratropical cyclones, have experience a reduction of this frequency and a marked reduction in their strength since 1950, replaced by stable circulations. Both conditions suggest a long term reduction of the frequency and the magnitude of storm surges.

The Ionospheric Connection Explorer (ICON) : Mission Design and Planning

NASA Astrophysics Data System (ADS)

Immel, T. J.; England, S.; Mende, S. B.; Heelis, R. A.; Englert, C. R.; Edelstein, J.; Frey, H. U.; Taylor, E.; Craig, W.; Bust, G. S.; Crowley, G.; Forbes, J. M.; Gerard, J. C. M. C.; Harlander, J.; Huba, J.; Hubert, B. A.; Kamalabadi, F.; Makela, J. J.; Maute, A. I.; Meier, R. J.; Raftery, C. L.; Hauck, K.; Rochus, P.; Siegmund, O.; Stephan, A. W.; Swenson, G. R.; Frey, S.; Hysell, D. L.; Saito, A.

2016-12-01

The Ionospheric Connection Explorer is NASA's next Explorer mission, with a primary scientific goal of understanding the source of the extreme variability in Earth's ionosphere. The observatory is scheduled to be delivered to the Pegasus launch vehicle in early 2017 for a June launch. ICON carries unprecedented capability to orbit in a broader national and international effort to understand changes in our space environment occurring on a wide range of spatial and temporal scales. Here, we will discuss plans for the observatory checkout and early operations, and discuss the observing conditions expected in the atmosphere and ionosphere at that time. The status of the science data pipeline and the predicted performance of the observatory for scientific measurements will be discussed.

High resolution geochemical proxy record of the last 600yr in a speleothem from the northwest Iberian Peninsula

NASA Astrophysics Data System (ADS)

Iglesias González, Miguel; Pisonero, Jorge; Cheng, Hai; Edwards, R. Lawrence; Stoll, Heather

2017-04-01

In meteorology and climatology, the instrumental period is the period where we have measured directly by instrumentation, different meteorological data along the surface which allow us to determinate the evolution of the climate during the last 150 years over the world. At the beginning, the density of this data were very low, so we have to wait until the last 75-100 years to have a good network in most of the parts of the surface. This time period is very small if we want to analyze the relationship between geochemical and instrumental variability in any speleothem. So a very high resolution data is needed to determinate the connection between both of them in the instrumental period, to try to determinate de evolution of climate in the last 600 years. Here we present a high resolution speleothem record from a cave located in the middle of the Cantabrian Mountains without any anthropologic influence and with no CO2 seasonal variability. This 600yr stalagmite, dated with U/Th method with a growth rate from 100 to 200 micrometers/yr calculated with Bchron model, provide us accurate information of the climate conditions near the cave. Trace elements are analyzed at 8 micrometers intervals by Laser Ablation ICP-MS which resolves even monthly resolution during the last 600 years with special attention with Sr, Mg, Al and Si. This data, without seasonal variability and with the presence of a river inside the cave, give us very valuable information about the extreme flood events inside the cave during the whole period, which is related with the precipitations and the snow fusion events outside the cave. We identify more extremely flood events during the Little Ice Age than in the last 100yr. As well, we have trace elements data with spatial resolution of 0.2mm analyzed with ICP-AES which allow us to compare the geochemical variability with both technics. We also analyze stable isotope d13C and d18O with a spatial resolution of 0.2mm, so we are able to identify variations and all possible correlations between them, trace elements and instrumental records from the different weather stations located near the cave. We use instrumental data, and the statistical correlation between our proxy and them, to calibrate and analyze the variability along the 600yr which provide us a lot of information about the climate variability. In spite of the significate global warming during the last 25 years, we have less variability during this period than along the transition between the Medieval Warm Period and the Little Ice Age. We also analyze this variability along the 600 years with wavelet analysis, with special attention in the instrumental period. With this mathematical method, we can identify several cycles both in trace elements and stable isotopes at special scales compatible with the decadal and multidecadal variability with a value similar to very important climate index like AMO.

High Resolution Regional Climate Modeling for Lebanon, Eastern Mediterranean Coast

NASA Astrophysics Data System (ADS)

Katurji, Marwan; Soltanzadeh, Iman; Kuhnlein, Meike; Zawar-Reza, Peyman

2013-04-01

The Eastern Mediterranean coast consists of Lebanon, Palestine, Syria, Israel and a small part of southern Turkey. The region lies between latitudes 30 degrees S and 40 degrees N, which makes its climate affected by westerly propagating wintertime cyclones spinning off mid-latitude troughs (December, January and February), while during summer (June, July and August) the area is strongly affected by the sub-tropical anti-cyclonic belt as a result of the descending air of the Hadley cell circulation system. The area is considered to be in a transitional zone between tropical to mid-latitude climate regimes, and having a coastal topography up to 3000 m in elevation (like in the Western Ranges of Lebanon), which emphasizes the complexity of climate variability in this area under future predictions of climate change. This research incorporates both regional climate numerical simulations, Tropical Rainfall Measuring Mission (TRMM) satellite derived and surface rain gauge rainfall data to evaluate the Regional Climate Model (RegCM) version 4 ability to represent both the mean and variance of observed precipitation in the Eastern Mediterranean Region, with emphasis on the Lebanese coastal terrain and mountain ranges. The adopted methodology involves dynamically down scaling climate data from reanalysis synoptic files through a double nesting procedure. The retrospective analysis of 13 years with both 50 and 10 km spatial resolution allows for the assessment of the model results on both a climate scale and specific high intensity precipitating events. The spatial averaged mean bias error in precipitation rate for the rainy season predicted by RegCM 50 and 10 km resolution grids was 0.13 and 0.004 mm hr-1 respectively. When correlating RegCM and TRMM precipitation rate for the domain covering Lebanon's coastal mountains, the root mean square error (RMSE) for the mean quantities over the 13-year period was only 0.03, while the RMSE for the standard deviation was higher by one order of magnitude. Initial results showed good spatial variability agreement for precipitation with the satellite-derived data with improved results for the 10 km grid resolution setup. Also, results show a larger uncertainty within RegCM for predicting extreme precipitation events. Future work will investigate the ability of RegCM to simulate these extreme deviations in precipitation. The results from this research can be helpful for the better design of future regional climate down scaling predictions under climate change scenarios.

Using Four Downscaling Techniques to Characterize Uncertainty in Updating Intensity-Duration-Frequency Curves Under Climate Change

NASA Astrophysics Data System (ADS)

Cook, L. M.; Samaras, C.; McGinnis, S. A.

2017-12-01

Intensity-duration-frequency (IDF) curves are a common input to urban drainage design, and are used to represent extreme rainfall in a region. As rainfall patterns shift into a non-stationary regime as a result of climate change, these curves will need to be updated with future projections of extreme precipitation. Many regions have begun to update these curves to reflect the trends from downscaled climate models; however, few studies have compared the methods for doing so, as well as the uncertainty that results from the selection of the native grid scale and temporal resolution of the climate model. This study examines the variability in updated IDF curves for Pittsburgh using four different methods for adjusting gridded regional climate model (RCM) outputs into station scale precipitation extremes: (1) a simple change factor applied to observed return levels, (2) a naïve adjustment of stationary and non-stationary Generalized Extreme Value (GEV) distribution parameters, (3) a transfer function of the GEV parameters from the annual maximum series, and (4) kernel density distribution mapping bias correction of the RCM time series. Return level estimates (rainfall intensities) and confidence intervals from these methods for the 1-hour to 48-hour duration are tested for sensitivity to the underlying spatial and temporal resolution of the climate ensemble from the NA-CORDEX project, as well as, the future time period for updating. The first goal is to determine if uncertainty is highest for: (i) the downscaling method, (ii) the climate model resolution, (iii) the climate model simulation, (iv) the GEV parameters, or (v) the future time period examined. Initial results of the 6-hour, 10-year return level adjusted with the simple change factor method using four climate model simulations of two different spatial resolutions show that uncertainty is highest in the estimation of the GEV parameters. The second goal is to determine if complex downscaling methods and high-resolution climate models are necessary for updating, or if simpler methods and lower resolution climate models will suffice. The final results can be used to inform the most appropriate method and climate model resolutions to use for updating IDF curves for urban drainage design.

Spatio-Temporal Variability of Groundwater Storage in India

NASA Technical Reports Server (NTRS)

Bhanja, Soumendra; Rodell, Matthew; Li, Bailing; Mukherjee, Abhijit

2016-01-01

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Ground water storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent).In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.

Spatio-temporal variability of groundwater storage in India.

PubMed

Bhanja, Soumendra N; Rodell, Matthew; Li, Bailing; Mukherjee, Abhijit

2017-01-01

Groundwater level measurements from 3907 monitoring wells, distributed within 22 major river basins of India, are assessed to characterize their spatial and temporal variability. Groundwater storage (GWS) anomalies (relative to the long-term mean) exhibit strong seasonality, with annual maxima observed during the monsoon season and minima during pre-monsoon season. Spatial variability of GWS anomalies increases with the extent of measurements, following the power law relationship, i.e., log-(spatial variability) is linearly dependent on log-(spatial extent). In addition, the impact of well spacing on spatial variability and the power law relationship is investigated. We found that the mean GWS anomaly sampled at a 0.25 degree grid scale closes to unweighted average over all wells. The absolute error corresponding to each basin grows with increasing scale, i.e., from 0.25 degree to 1 degree. It was observed that small changes in extent could create very large changes in spatial variability at large grid scales. Spatial variability of GWS anomaly has been found to vary with climatic conditions. To our knowledge, this is the first study of the effects of well spacing on groundwater spatial variability. The results may be useful for interpreting large scale groundwater variations from unevenly spaced or sparse groundwater well observations or for siting and prioritizing wells in a network for groundwater management. The output of this study could be used to maintain a cost effective groundwater monitoring network in the study region and the approach can also be used in other parts of the globe.

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

PubMed Central

Gopal, Shruti; Miller, Robyn L.; Michael, Andrew; Adali, Tulay; Cetin, Mustafa; Rachakonda, Srinivas; Bustillo, Juan R.; Cahill, Nathan; Baum, Stefi A.; Calhoun, Vince D.

2016-01-01

Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects. PMID:26106217

Temporal and spatial characterization of zenith total delay (ZTD) in North Europe

NASA Astrophysics Data System (ADS)

Stoew, B.; Elgered, G.

2003-04-01

The estimates of ZTD are often treated as realizations of random walk stochastic processes. We derive the corresponding process parameters for 34 different locations in North Europe using two measurement techniques - Global Positioning System (GPS) and Water Vapor Radiometer (WVR). GPS-estimated ZTD is an excellent candidate for data assimilation in numerical weather prediction (NWP) models in terms of both spatial and temporal resolution. We characterize the long term behavior of the ZTD as a function of site latitude and height. The spatial characteristics of the ZTD are studied as a function of site separation and season. We investigate the influence of the time-interpolated atmospheric pressure data used for the estimation of zenith wet delay (ZWD) from ZTD. Characterization of extreme atmospheric events can aid the development of an early warning system. We consider two types of extreme meteorological phenomena with regard to their spatial scales. The first type concerns larger regions (including several GPS sites); the extreme weather is characterized by intense precipitation which may result in a flood. The second type is related to local variations in the ZWD/ZTD and can be used for detection/monitoring of passing atmospheric fronts.

China’s Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model

PubMed Central

Cao, Qilong; Liang, Ying; Niu, Xueting

2017-01-01

Background: Air pollution has become an important factor restricting China’s economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods: Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM2.5. Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results: It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM2.5 pollutions in the control of other variables. Conclusions: Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables. PMID:28927016

Metacommunity composition of web-spiders in a fragmented neotropical forest: relative importance of environmental and spatial effects.

PubMed

Baldissera, Ronei; Rodrigues, Everton N L; Hartz, Sandra M

2012-01-01

The distribution of beta diversity is shaped by factors linked to environmental and spatial control. The relative importance of both processes in structuring spider metacommunities has not yet been investigated in the Atlantic Forest. The variance explained by purely environmental, spatially structured environmental, and purely spatial components was compared for a metacommunity of web spiders. The study was carried out in 16 patches of Atlantic Forest in southern Brazil. Field work was done in one landscape mosaic representing a slight gradient of urbanization. Environmental variables encompassed plot- and patch-level measurements and a climatic matrix, while principal coordinates of neighbor matrices (PCNMs) acted as spatial variables. A forward selection procedure was carried out to select environmental and spatial variables influencing web-spider beta diversity. Variation partitioning was used to estimate the contribution of pure environmental and pure spatial effects and their shared influence on beta-diversity patterns, and to estimate the relative importance of selected environmental variables. Three environmental variables (bush density, land use in the surroundings of patches, and shape of patches) and two spatial variables were selected by forward selection procedures. Variation partitioning revealed that 15% of the variation of beta diversity was explained by a combination of environmental and PCNM variables. Most of this variation (12%) corresponded to pure environmental and spatially environmental structure. The data indicated that (1) spatial legacy was not important in explaining the web-spider beta diversity; (2) environmental predictors explained a significant portion of the variation in web-spider composition; (3) one-third of environmental variation was due to a spatial structure that jointly explains variation in species distributions. We were able to detect important factors related to matrix management influencing the web-spider beta-diversity patterns, which are probably linked to historical deforestation events.

Final Technical Report for DE-SC0005467

DOE Office of Scientific and Technical Information (OSTI.GOV)

Broccoli, Anthony J.

2014-09-14

The objective of this project is to gain a comprehensive understanding of the key atmospheric mechanisms and physical processes associated with temperature extremes in order to better interpret and constrain uncertainty in climate model simulations of future extreme temperatures. To achieve this objective, we first used climate observations and a reanalysis product to identify the key atmospheric circulation patterns associated with extreme temperature days over North America during the late twentieth century. We found that temperature extremes were associated with distinctive signatures in near-surface and mid-tropospheric circulation. The orientations and spatial scales of these circulation anomalies vary with latitude, season,more » and proximity to important geographic features such as mountains and coastlines. We next examined the associations between daily and monthly temperature extremes and large-scale, recurrent modes of climate variability, including the Pacific-North American (PNA) pattern, the northern annular mode (NAM), and the El Niño-Southern Oscillation (ENSO). The strength of the associations are strongest with the PNA and NAM and weaker for ENSO, and also depend upon season, time scale, and location. The associations are stronger in winter than summer, stronger for monthly than daily extremes, and stronger in the vicinity of the centers of action of the PNA and NAM patterns. In the final stage of this project, we compared climate model simulations of the circulation patterns associated with extreme temperature days over North America with those obtained from observations. Using a variety of metrics and self-organizing maps, we found the multi-model ensemble and the majority of individual models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) generally capture the observed patterns well, including their strength and as well as variations with latitude and season. The results from this project indicate that current models are capable of simulating the large-scale meteorological patterns associated with daily temperature extremes and they suggest that such models can be used to evaluate the extent to which changes in atmospheric circulation will influence future changes in temperature extremes.« less

Spatial variability in the trends in extreme storm surges and weekly-scale high water levels in the eastern Baltic Sea

NASA Astrophysics Data System (ADS)

Soomere, Tarmo; Pindsoo, Katri

2016-03-01

We address the possibilities of a separation of the overall increasing trend in maximum water levels of semi-enclosed water bodies into associated trends in the heights of local storm surges and basin-scale components of the water level based on recorded and modelled local water level time series. The test area is the Baltic Sea. Sequences of strong storms may substantially increase its water volume and raise the average sea level by almost 1 m for a few weeks. Such events are singled out from the water level time series using a weekly-scale average. The trends in the annual maxima of the weekly average have an almost constant value along the entire eastern Baltic Sea coast for averaging intervals longer than 4 days. Their slopes are ~4 cm/decade for 8-day running average and decrease with an increase of the averaging interval. The trends for maxima of local storm surge heights represent almost the entire spatial variability in the water level maxima. Their slopes vary from almost zero for the open Baltic Proper coast up to 5-7 cm/decade in the eastern Gulf of Finland and Gulf of Riga. This pattern suggests that an increase in wind speed in strong storms is unlikely in this area but storm duration may have increased and wind direction may have rotated.

Impacts of climate change on mangrove ecosystems: A region by region overview

USGS Publications Warehouse

Ward, Raymond D.; Friess, Daniel A.; Day, Richard H.; MacKenzie, Richard A.

2016-01-01

Inter-related and spatially variable climate change factors including sea level rise, increased storminess, altered precipitation regime and increasing temperature are impacting mangroves at regional scales. This review highlights extreme regional variation in climate change threats and impacts, and how these factors impact the structure of mangrove communities, their biodiversity and geomorphological setting. All these factors interplay to determine spatially variable resiliency to climate change impacts, and because mangroves are varied in type and geographical location, these systems are good models for understanding such interactions at different scales. Sea level rise is likely to influence mangroves in all regions although local impacts are likely to be more varied. Changes in the frequency and intensity of storminess are likely to have a greater impact on N and Central America, Asia, Australia, and East Africa than West Africa and S. America. This review also highlights the numerous geographical knowledge gaps of climate change impacts, with some regions particularly understudied (e.g., Africa and the Middle East). While there has been a recent drive to address these knowledge gaps especially in South America and Asia, further research is required to allow researchers to tease apart the processes that influence both vulnerability and resilience to climate change. A more globally representative view of mangroves would allow us to better understand the importance of mangrove type and landscape setting in determining system resiliency to future climate change.

Shifts of environmental and phytoplankton variables in a regulated river: A spatial-driven analysis.

PubMed

Sabater-Liesa, Laia; Ginebreda, Antoni; Barceló, Damià

2018-06-18

The longitudinal structure of the environmental and phytoplankton variables was investigated in the Ebro River (NE Spain), which is heavily affected by water abstraction and regulation. A first exploration indicated that the phytoplankton community did not resist the impact of reservoirs and barely recovered downstream of them. The spatial analysis showed that the responses of the phytoplankton and environmental variables were not uniform. The two set of variables revealed spatial variability discontinuities and river fragmentation upstream and downstream from the reservoirs. Reservoirs caused the replacement of spatially heterogeneous habitats by homogeneous spatially distributed water bodies, these new environmental conditions downstream benefiting the opportunist and cosmopolitan algal taxa. The application of a spatial auto-regression model to algal biomass (chlorophyll-a) permitted to capture the relevance and contribution of extra-local influences in the river ecosystem. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

The analysis of dependence between extreme rainfall and storm surge in the coastal zone

NASA Astrophysics Data System (ADS)

Zheng, F.; Westra, S.

2012-12-01

Flooding in coastal catchments can be caused by runoff generated by an extreme rainfall event, elevated sea levels due to an extreme storm surge event, or the combination of both processes occurring simultaneously or in close succession. Dependence in extreme rainfall and storm surge arises because common meteorological forcings often drive both variables; for example, cyclonic systems may produce extreme rainfall, strong onshore winds and an inverse barometric effect simultaneously, which the former factor influencing catchment discharge and the latter two factors influencing storm surge. Nevertheless there is also the possibility that only one of the variables is extreme at any given time, so that the dependence between rainfall and storm surge is not perfect. Quantification of the strength of dependence between these processes is critical in evaluating the magnitude of flood risk in the coastal zone. This may become more important in the future as the majority of the coastal areas are threatened by the sea level rise due to the climate change. This research uses the most comprehensive record of rainfall and storm surge along the coastline of Australia collected to-date to investigate the strength of dependence between the extreme rainfall and storm surge along the Australia coastline. A bivariate logistic threshold-excess model was employed to this end to carry out the dependence analysis. The strength of the estimated dependence is then evaluated as a function of several factors including: the distance between the tidal gauge and the rain gauge; the lag between the extreme precipitation event and extreme surge event; and the duration of the maximum storm burst. The results show that the dependence between the extreme rainfall and storm surge along the Australia coastline is statistically significant, although some locations clearly exhibit stronger dependence than others. We hypothesize that this is due to a combination of large-scale meteorological effects as well as local scale bathymetry. Additionally, significant dependence can be observed over spatial distances of up to several hundred kilometers, implying that meso-scale meteorological forcings may play an important role in driving the dependence. This is also consistent with the result which shows that significant dependence often remaining for lags of up to one or two days between extremal rainfall and storm surge events. The influence of storm burst duration can also be observed, with rainfall extremes lasting more than several hours typically being more closely associated with storm surge compared with sub-hourly rainfall extremes. These results will have profound implications for how flood risk is evaluated along the coastal zone in Australia, with the strength of dependence varying depending on: (1) the dominant meteorological conditions; (2) the local estuary configuration, influencing the strength of the surge; and (3) the catchment attributes, influencing the duration of the storm burst that will deliver the peak flood events. Although a strong random component remains, we show that the probability of an extreme storm surge during an extreme rainfall event (or vice versa) can be up to ten times greater than under the situation under which there is no dependence, suggesting that failure to account for these interactions can result in a substantial underestimation of flood risk.

SPATIAL AND TEMPORAL VARIABILITY AND DRIVERS OF NET ECOSYSTEM METABOLISM IN WESTERN GULF OF MEXICO ESTUARIES

EPA Science Inventory

Net ecosystem metabolism (NEM) is becoming a commonly used ecological indicator of estuarine ecosystem metabolic rates. Estuarine ecosystem processes are spatially and temporally variable, but the corresponding variability in NEM has not been properly assessed. Spatial and temp...

Decadal variability of extreme wave height representing storm severity in the northeast Atlantic and North Sea since the foundation of the Royal Society.

PubMed

Santo, H; Taylor, P H; Gibson, R

2016-09-01

Long-term estimation of extreme wave height remains a key challenge because of the short duration of available wave data, and also because of the possible impact of climate variability on ocean waves. Here, we analyse storm-based statistics to obtain estimates of extreme wave height at locations in the northeast Atlantic and North Sea using the NORA10 wave hindcast (1958-2011), and use a 5 year sliding window to examine temporal variability. The decadal variability is correlated to the North Atlantic oscillation and other atmospheric modes, using a six-term predictor model incorporating the climate indices and their Hilbert transforms. This allows reconstruction of the historic extreme climate back to 1661, using a combination of known and proxy climate indices. Significant decadal variability primarily driven by the North Atlantic oscillation is observed, and this should be considered for the long-term survivability of offshore structures and marine renewable energy devices. The analysis on wave climate reconstruction reveals that the variation of the mean, 99th percentile and extreme wave climates over decadal time scales for locations close to the dominant storm tracks in the open North Atlantic are comparable, whereas the wave climates for the rest of the locations including the North Sea are rather different.

Decadal variability of extreme wave height representing storm severity in the northeast Atlantic and North Sea since the foundation of the Royal Society

NASA Astrophysics Data System (ADS)

Santo, H.; Taylor, P. H.; Gibson, R.

2016-09-01

Long-term estimation of extreme wave height remains a key challenge because of the short duration of available wave data, and also because of the possible impact of climate variability on ocean waves. Here, we analyse storm-based statistics to obtain estimates of extreme wave height at locations in the northeast Atlantic and North Sea using the NORA10 wave hindcast (1958-2011), and use a 5 year sliding window to examine temporal variability. The decadal variability is correlated to the North Atlantic oscillation and other atmospheric modes, using a six-term predictor model incorporating the climate indices and their Hilbert transforms. This allows reconstruction of the historic extreme climate back to 1661, using a combination of known and proxy climate indices. Significant decadal variability primarily driven by the North Atlantic oscillation is observed, and this should be considered for the long-term survivability of offshore structures and marine renewable energy devices. The analysis on wave climate reconstruction reveals that the variation of the mean, 99th percentile and extreme wave climates over decadal time scales for locations close to the dominant storm tracks in the open North Atlantic are comparable, whereas the wave climates for the rest of the locations including the North Sea are rather different.

Structural Variability of Tropospheric Growth Factors Transforming Mid-latitude Cyclones to Severe Storms over the North Atlantic

NASA Astrophysics Data System (ADS)

Wild, Simon; Befort, Daniel J.; Leckebusch, Gregor C.

2015-04-01

The development of European surface wind storms out of normal mid-latitude cyclones is substantially influenced by upstream tropospheric growth factors over the Northern Atlantic. The main factors include divergence and vorticity advection in the upper troposphere, latent heat release and the presence of instabilities of short baroclinic waves of suitable wave lengths. In this study we examine a subset of these potential growth factors and their related influences on the transformation of extra-tropical cyclones into severe damage prone surface storm systems. Previous studies have shown links between specific growth factors and surface wind storms related to extreme cyclones. In our study we investigate in further detail spatial and temporal variability patterns of these upstream processes at different vertical levels of the troposphere. The analyses will comprise of the three growth factors baroclinicity, latent heat release and upper tropospheric divergence. Our definition of surface wind storms is based on the Storm Severity Index (SSI) alongside a wind tracking algorithm identifying areas of exceedances of the local 98th percentile of the 10m wind speed. We also make use of a well-established extra-tropical cyclone identification and tracking algorithm. These cyclone tracks form the base for a composite analysis of the aforementioned growth factors using ERA-Interim Reanalysis from 1979 - 2014 for the extended winter season (ONDJFM). Our composite analysis corroborates previous similar studies but extends them by using an impact based algorithm for the identification of strong wind systems. Based on this composite analysis we further identify variability patterns for each growth factor most important for the transformation of a cyclone into a surface wind storm. We thus also address the question whether the link between storm intensity and related growth factor anomaly taking into account its spatial variability is stable and can be quantified. While the robustness of our preliminary results is generally dependent on the growth factor investigated, some examples include i) the overall availability of latent heat seems to be less important than its spatial structure around the cyclone core and ii) the variability of upper-tropospheric baroclinicity appears to be highest north of the surface position of the cyclone, especially for those that transform into a surface storm.

Inter-annual Variability of Temperature and Extreme Heat Events during the Nairobi Warm Season

NASA Astrophysics Data System (ADS)

Scott, A.; Misiani, H. O.; Zaitchik, B. F.; Ouma, G. O.; Anyah, R. O.; Jordan, A.

2016-12-01

Extreme heat events significantly stress all organisms in the ecosystem, and are likely to be amplified in peri-urban and urban areas. Understanding the variability and drivers behind these events is key to generating early warnings, yet in Equatorial East Africa, this information is currently unavailable. This study uses daily maximum and minimum temperature records from weather stations within Nairobi and its surroundings to characterize variability in daily minimum temperatures and the number of extreme heat events. ERA-Interim reanalysis is applied to assess the drivers of these events at event and seasonal time scales. At seasonal time scales, high temperatures in Nairobi are a function of large scale climate variability associated with the Atlantic Multi-decadal Oscillation (AMO) and Global Mean Sea Surface Temperature (GMSST). Extreme heat events, however, are more strongly associated with the El Nino Southern Oscillation (ENSO). For instance, the persistence of AMO and ENSO, in particular, provide a basis for seasonal prediction of extreme heat events/days in Nairobi. It is also apparent that the temporal signal from extreme heat events in tropics differs from classic heat wave definitions developed in the mid-latitudes, which suggests that a new approach for defining these events is necessary for tropical regions.

Analysis of walking variability through simultaneous evaluation of the head, lumbar, and lower-extremity acceleration in healthy youth

PubMed Central

Toda, Haruki; Nagano, Akinori; Luo, Zhiwei

2016-01-01

[Purpose] The purpose of this study was to clarify whether walking speed affects acceleration variability of the head, lumbar, and lower extremity by simultaneously evaluating of acceleration. [Subjects and Methods] Twenty young individuals recruited from among the staff at Kurashiki Heisei Hospital participated in this study. Eight accelerometers were used to measure the head, lumbar and lower extremity accelerations. The participants were instructed to walk at five walking speeds prescribed by a metronome. Acceleration variability was assessed by a cross-correlation analysis normalized using z-transform in order to evaluate stride-to-stride variability. [Results] Vertical acceleration variability was the smallest in all body parts, and walking speed effect had laterality. Antero-posterior acceleration variability was significantly associated with walking speed at sites other than the head. Medio-lateral acceleration variability of the bilateral hip alone was smaller than the antero-posterior variability. [Conclusion] The findings of this study suggest that the effect of walking speed changes on the stride-to-stride acceleration variability was individual for each body parts, and differs among directions. PMID:27390419

Spatial pattern analysis of Cu, Zn and Ni and their interpretation in the Campania region (Italy)

NASA Astrophysics Data System (ADS)

Petrik, Attila; Albanese, Stefano; Jordan, Gyozo; Rolandi, Roberto; De Vivo, Benedetto

2017-04-01

The uniquely abundant Campanian topsoil dataset enabled us to perform a spatial pattern analysis on 3 potentially toxic elements of Cu, Zn and Ni. This study is focusing on revealing the spatial texture and distribution of these elements by spatial point pattern and image processing analysis such as lineament density and spatial variability index calculation. The application of these methods on geochemical data provides a new and efficient tool to understand the spatial variation of concentrations and their background/baseline values. The determination and quantification of spatial variability is crucial to understand how fast the change in concentration is in a certain area and what processes might govern the variation. The spatial variability index calculation and image processing analysis including lineament density enables us to delineate homogenous areas and analyse them with respect to lithology and land use. Identification of spatial outliers and their patterns were also investigated by local spatial autocorrelation and image processing analysis including the determination of local minima and maxima points and singularity index analysis. The spatial variability of Cu and Zn reveals the highest zone (Cu: 0.5 MAD, Zn: 0.8-0.9 MAD, Median Deviation Index) along the coast between Campi Flegrei and the Sorrento Peninsula with the vast majority of statistically identified outliers and high-high spatial clustered points. The background/baseline maps of Cu and Zn reveals a moderate to high variability (Cu: 0.3 MAD, Zn: 0.4-0.5 MAD) NW-SE oriented zone including disrupted patches from Bisaccia to Mignano following the alluvial plains of Appenine's rivers. This zone has high abundance of anomaly concentrations identified using singularity analysis and it also has a high density of lineaments. The spatial variability of Ni shows the highest variability zone (0.6-0.7 MAD) around Campi Flegrei where the majority of low outliers are concentrated. The variability of background/baseline map of Ni reveals a shift to the east in case of highest variability zones coinciding with limestone outcrops. The high segmented area between Mignano and Bisaccia partially follows the alluvial plains of Appenine's rivers which seem to be playing a crucial role in the distribution and redistribution pattern of Cu, Zn and Ni in Campania. The high spatial variability zones of the later elements are located in topsoils on volcanoclastic rocks and are mostly related to cultivation and urbanised areas.

Investigation of the relationship between hurricane waves and extreme runup

NASA Astrophysics Data System (ADS)

Thompson, D. M.; Stockdon, H. F.

2006-12-01

In addition to storm surge, the elevation of wave-induced runup plays a significant role in forcing geomorphic change during extreme storms. Empirical formulations for extreme runup, defined as the 2% exceedence level, are dependent on some measure of significant offshore wave height. Accurate prediction of extreme runup, particularly during hurricanes when wave heights are large, depends on selecting the most appropriate measure of wave height that provides energy to the nearshore system. Using measurements from deep-water wave buoys results in an overprediction of runup elevation. Under storm forcing these large waves dissipate across the shelf through friction, whitecapping and depth-limited breaking before reaching the beach and forcing swash processes. The use of a local, shallow water wave height has been shown to provide a more accurate estimate of extreme runup elevation (Stockdon, et. al. 2006); however, a specific definition of this local wave height has yet to be defined. Using observations of nearshore waves from the U.S. Army Corps of Engineers' Field Research Facility (FRF) in Duck, NC during Hurricane Isabel, the most relevant measure of wave height for use in empirical runup parameterizations was examined. Spatial and temporal variability of the hurricane wave field, which made landfall on September 18, 2003, were modeled using SWAN. Comparisons with wave data from FRF gages and deep-water buoys operated by NOAA's National Data Buoy Center were used for model calibration. Various measures of local wave height (breaking, dissipation-based, etc.) were extracted from the model domain and used as input to the runup parameterizations. Video based observations of runup collected at the FRF during the storm were used to ground truth modeled values. Assessment of the most appropriate measure of wave height can be extended over a large area through comparisons to observations of storm- induced geomorphic change.

The Effects of Spectral Nudging on Arctic Temperature and Precipitation Extremes as Produced by the Pan-Arctic WRF

NASA Astrophysics Data System (ADS)

Glisan, J. M.; Gutowski, W. J.; Higgins, M.; Cassano, J. J.

2011-12-01

Pan-Arctic WRF (PAW) simulations produced using the 50-km wr50a domain developed for the fully-coupled Regional Arctic Climate Model (RACM) were found to produce deep atmospheric circulation biases over the northern Pacific Ocean, manifested in pressure, geopotential height, and temperature fields. Various remedies were unsuccessfully tested to correct these large biases, such as modifying the physical domain or using different initial/boundary conditions. Spectral (interior) nudging was introduced as a way of constraining the model to be more consistent with observed behavior. However, such control over numerical model behavior raises concerns over how much nudging may affect unforced variability and extremes. Strong nudging may reduce or filter out extreme events, since the nudging pushes the model toward a relatively smooth, large-scale state. The question then becomes - what is the minimum spectral nudging needed to correct the biases occurring on the RACM domain while not limiting PAW simulation of extreme events? To determine this, case studies were devised, using a six-member PAW ensemble on the RACM grid with varying spectral nudging strength. Two simulations were run, one in the cold season (January 2007) and one in a warm season (July 2007). Precipitation and 2-m temperature fields were extracted from the output and analyzed to determine how changing spectral nudging strength impacts both temporal and spatial temperature and precipitation extremes. The maximum and minimum temperatures at each point from among the ensemble members were examined, on the 95th confidence interval. The maximum and minimums over the simulation period will also be considered. Results suggest that there is a marked lack of sensitivity to the degrees of nudging. Moreover, it appears nudging strength can be considerably smaller than the standard strength and still produce reliably good simulations.

Weather extremes in very large, high-resolution ensembles: the weatherathome experiment

NASA Astrophysics Data System (ADS)

Allen, M. R.; Rosier, S.; Massey, N.; Rye, C.; Bowery, A.; Miller, J.; Otto, F.; Jones, R.; Wilson, S.; Mote, P.; Stone, D. A.; Yamazaki, Y. H.; Carrington, D.

2011-12-01

Resolution and ensemble size are often seen as alternatives in climate modelling. Models with sufficient resolution to simulate many classes of extreme weather cannot normally be run often enough to assess the statistics of rare events, still less how these statistics may be changing. As a result, assessments of the impact of external forcing on regional climate extremes must be based either on statistical downscaling from relatively coarse-resolution models, or statistical extrapolation from 10-year to 100-year events. Under the weatherathome experiment, part of the climateprediction.net initiative, we have compiled the Met Office Regional Climate Model HadRM3P to run on personal computer volunteered by the general public at 25 and 50km resolution, embedded within the HadAM3P global atmosphere model. With a global network of about 50,000 volunteers, this allows us to run time-slice ensembles of essentially unlimited size, exploring the statistics of extreme weather under a range of scenarios for surface forcing and atmospheric composition, allowing for uncertainty in both boundary conditions and model parameters. Current experiments, developed with the support of Microsoft Research, focus on three regions, the Western USA, Europe and Southern Africa. We initially simulate the period 1959-2010 to establish which variables are realistically simulated by the model and on what scales. Our next experiments are focussing on the Event Attribution problem, exploring how the probability of various types of extreme weather would have been different over the recent past in a world unaffected by human influence, following the design of Pall et al (2011), but extended to a longer period and higher spatial resolution. We will present the first results of the unique, global, participatory experiment and discuss the implications for the attribution of recent weather events to anthropogenic influence on climate.

Simulating maize yield and biomass with spatial variability of soil field capacity

USDA-ARS?s Scientific Manuscript database

Spatial variability in field soil water and other properties is a challenge for system modelers who use only representative values for model inputs, rather than their distributions. In this study, we compared simulation results from a calibrated model with spatial variability of soil field capacity ...

Extreme Value Analysis of hydro meteorological extremes in the ClimEx Large-Ensemble

NASA Astrophysics Data System (ADS)

Wood, R. R.; Martel, J. L.; Willkofer, F.; von Trentini, F.; Schmid, F. J.; Leduc, M.; Frigon, A.; Ludwig, R.

2017-12-01

Many studies show an increase in the magnitude and frequency of hydrological extreme events in the course of climate change. However the contribution of natural variability to the magnitude and frequency of hydrological extreme events is not yet settled. A reliable estimate of extreme events is from great interest for water management and public safety. In the course of the ClimEx Project (www.climex-project.org) a new single-model large-ensemble was created by dynamically downscaling the CanESM2 large-ensemble with the Canadian Regional Climate Model version 5 (CRCM5) for an European Domain and a Northeastern North-American domain. By utilizing the ClimEx 50-Member Large-Ensemble (CRCM5 driven by CanESM2 Large-Ensemble) a thorough analysis of natural variability in extreme events is possible. Are the current extreme value statistical methods able to account for natural variability? How large is the natural variability for e.g. a 1/100 year return period derived from a 50-Member Large-Ensemble for Europe and Northeastern North-America? These questions should be answered by applying various generalized extreme value distributions (GEV) to the ClimEx Large-Ensemble. Hereby various return levels (5-, 10-, 20-, 30-, 60- and 100-years) based on various lengths of time series (20-, 30-, 50-, 100- and 1500-years) should be analyzed for the maximum one day precipitation (RX1d), the maximum three hourly precipitation (RX3h) and the streamflow for selected catchments in Europe. The long time series of the ClimEx Ensemble (7500 years) allows us to give a first reliable estimate of the magnitude and frequency of certain extreme events.

Variable magnification variable dispersion glancing incidence imaging x-ray spectroscopic telescope

NASA Technical Reports Server (NTRS)

Hoover, Richard B. (Inventor)

1991-01-01

A variable magnification variable dispersion glancing incidence x-ray spectroscopic telescope capable of multiple high spatial revolution imaging at precise spectral lines of solar and stellar x-ray and extreme ultraviolet radiation sources includes a pirmary optical system which focuses the incoming radiation to a primary focus. Two or more rotatable carries each providing a different magnification are positioned behind the primary focus at an inclination to the optical axis, each carrier carrying a series of ellipsoidal diffraction grating mirrors each having a concave surface on which the gratings are ruled and coated with a mutlilayer coating to reflect by diffraction a different desired wavelength. The diffraction grating mirrors of both carriers are segments of ellipsoids having a common first focus coincident with the primary focus. A contoured detector such as an x-ray sensitive photogrpahic film is positioned at the second respective focus of each diffraction grating so that each grating may reflect the image at the first focus to the detector at the second focus. The carriers are selectively rotated to position a selected mirror for receiving radiation from the primary optical system, and at least the first carrier may be withdrawn from the path of the radiation to permit a selected grating on the second carrier to receive radiation.

Variable magnification variable dispersion glancing incidence imaging x ray spectroscopic telescope

NASA Technical Reports Server (NTRS)

Hoover, Richard (Inventor)

1990-01-01

A variable magnification variable dispersion glancing incidence x ray spectroscopic telescope capable of multiple high spatial revolution imaging at precise spectral lines of solar and stellar x ray and extreme ultraviolet radiation sources includes a primary optical system which focuses the incoming radiation to a primary focus. Two or more rotatable carriers each providing a different magnification are positioned behind the primary focus at an inclination to the optical axis, each carrier carrying a series of ellipsoidal diffraction grating mirrors each having a concave surface on which the gratings are ruled and coated with a multilayer coating to reflect by diffraction a different desired wavelength. The diffraction grating mirrors of both carriers are segments of ellipsoids having a common first focus coincident with the primary focus. A contoured detector such as an x ray sensitive photographic film is positioned at the second respective focus of each diffraction grating so that each grating may reflect the image at the first focus to the detector at the second focus. The carriers are selectively rotated to position a selected mirror for receiving radiation from the primary optical system, and at least the first carrier may be withdrawn from the path of the radiation to permit a selected grating on the second carrier to receive radiation.

Extreme Fire Severity Patterns in Topographic, Convective and Wind-Driven Historical Wildfires of Mediterranean Pine Forests

PubMed Central

Lecina-Diaz, Judit; Alvarez, Albert; Retana, Javier

2014-01-01

Crown fires associated with extreme fire severity are extremely difficult to control. We have assessed fire severity using differenced Normalized Burn Ratio (dNBR) from Landsat imagery in 15 historical wildfires of Pinus halepensis Mill. We have considered a wide range of innovative topographic, fuel and fire behavior variables with the purposes of (1) determining the variables that influence fire severity patterns among fires (considering the 15 wildfires together) and (2) ascertaining whether different variables affect extreme fire severity within the three fire types (topographic, convective and wind-driven fires). The among-fires analysis showed that fires in less arid climates and with steeper slopes had more extreme severity. In less arid conditions there was more crown fuel accumulation and closer forest structures, promoting high vertical and horizontal fuel continuity and extreme fire severity. The analyses carried out for each fire separately (within fires) showed more extreme fire severity in areas in northern aspects, with steeper slopes, with high crown biomass and in climates with more water availability. In northern aspects solar radiation was lower and fuels had less water limitation to growth which, combined with steeper slopes, produced more extreme severity. In topographic fires there was more extreme severity in northern aspects with steeper slopes and in areas with more water availability and high crown biomass; in convection-dominated fires there was also more extreme fire severity in northern aspects with high biomass; while in wind-driven fires there was only a slight interaction between biomass and water availability. This latter pattern could be related to the fact that wind-driven fires spread with high wind speed, which could have minimized the effect of other variables. In the future, and as a consequence of climate change, new zones with high crown biomass accumulated in non-common drought areas will be available to burn as extreme severity wildfires. PMID:24465492

Extreme fire severity patterns in topographic, convective and wind-driven historical wildfires of Mediterranean pine forests.

PubMed

Lecina-Diaz, Judit; Alvarez, Albert; Retana, Javier

2014-01-01

Crown fires associated with extreme fire severity are extremely difficult to control. We have assessed fire severity using differenced Normalized Burn Ratio (dNBR) from Landsat imagery in 15 historical wildfires of Pinus halepensis Mill. We have considered a wide range of innovative topographic, fuel and fire behavior variables with the purposes of (1) determining the variables that influence fire severity patterns among fires (considering the 15 wildfires together) and (2) ascertaining whether different variables affect extreme fire severity within the three fire types (topographic, convective and wind-driven fires). The among-fires analysis showed that fires in less arid climates and with steeper slopes had more extreme severity. In less arid conditions there was more crown fuel accumulation and closer forest structures, promoting high vertical and horizontal fuel continuity and extreme fire severity. The analyses carried out for each fire separately (within fires) showed more extreme fire severity in areas in northern aspects, with steeper slopes, with high crown biomass and in climates with more water availability. In northern aspects solar radiation was lower and fuels had less water limitation to growth which, combined with steeper slopes, produced more extreme severity. In topographic fires there was more extreme severity in northern aspects with steeper slopes and in areas with more water availability and high crown biomass; in convection-dominated fires there was also more extreme fire severity in northern aspects with high biomass; while in wind-driven fires there was only a slight interaction between biomass and water availability. This latter pattern could be related to the fact that wind-driven fires spread with high wind speed, which could have minimized the effect of other variables. In the future, and as a consequence of climate change, new zones with high crown biomass accumulated in non-common drought areas will be available to burn as extreme severity wildfires.