Accurate and efficient calculation of response times for groundwater flow

NASA Astrophysics Data System (ADS)

Carr, Elliot J.; Simpson, Matthew J.

2018-03-01

We study measures of the amount of time required for transient flow in heterogeneous porous media to effectively reach steady state, also known as the response time. Here, we develop a new approach that extends the concept of mean action time. Previous applications of the theory of mean action time to estimate the response time use the first two central moments of the probability density function associated with the transition from the initial condition, at t = 0, to the steady state condition that arises in the long time limit, as t → ∞ . This previous approach leads to a computationally convenient estimation of the response time, but the accuracy can be poor. Here, we outline a powerful extension using the first k raw moments, showing how to produce an extremely accurate estimate by making use of asymptotic properties of the cumulative distribution function. Results are validated using an existing laboratory-scale data set describing flow in a homogeneous porous medium. In addition, we demonstrate how the results also apply to flow in heterogeneous porous media. Overall, the new method is: (i) extremely accurate; and (ii) computationally inexpensive. In fact, the computational cost of the new method is orders of magnitude less than the computational effort required to study the response time by solving the transient flow equation. Furthermore, the approach provides a rigorous mathematical connection with the heuristic argument that the response time for flow in a homogeneous porous medium is proportional to L2 / D , where L is a relevant length scale, and D is the aquifer diffusivity. Here, we extend such heuristic arguments by providing a clear mathematical definition of the proportionality constant.

Determining molecular responses to environmental change in soybeans

USDA-ARS?s Scientific Manuscript database

As the global climate changes, plants will be challenged by environmental stresses that are more extreme and more frequent. The average yield loss due to environmental stresses is currently estimated to be more than 50% for major crop species and is the major limitation to world food production. The...

Regional Frequency and Uncertainty Analysis of Extreme Precipitation in Bangladesh

NASA Astrophysics Data System (ADS)

Mortuza, M. R.; Demissie, Y.; Li, H. Y.

2014-12-01

Increased frequency of extreme precipitations, especially those with multiday durations, are responsible for recent urban floods and associated significant losses of lives and infrastructures in Bangladesh. Reliable and routinely updated estimation of the frequency of occurrence of such extreme precipitation events are thus important for developing up-to-date hydraulic structures and stormwater drainage system that can effectively minimize future risk from similar events. In this study, we have updated the intensity-duration-frequency (IDF) curves for Bangladesh using daily precipitation data from 1961 to 2010 and quantified associated uncertainties. Regional frequency analysis based on L-moments is applied on 1-day, 2-day and 5-day annual maximum precipitation series due to its advantages over at-site estimation. The regional frequency approach pools the information from climatologically similar sites to make reliable estimates of quantiles given that the pooling group is homogeneous and of reasonable size. We have used Region of influence (ROI) approach along with homogeneity measure based on L-moments to identify the homogenous pooling groups for each site. Five 3-parameter distributions (i.e., Generalized Logistic, Generalized Extreme value, Generalized Normal, Pearson Type Three, and Generalized Pareto) are used for a thorough selection of appropriate models that fit the sample data. Uncertainties related to the selection of the distributions and historical data are quantified using the Bayesian Model Averaging and Balanced Bootstrap approaches respectively. The results from this study can be used to update the current design and management of hydraulic structures as well as in exploring spatio-temporal variations of extreme precipitation and associated risk.

Can new passenger cars reduce pedestrian lower extremity injury? A review of geometrical changes of front-end design before and after regulatory efforts.

PubMed

Nie, Bingbing; Zhou, Qing

2016-10-02

Pedestrian lower extremity represents the most frequently injured body region in car-to-pedestrian accidents. The European Directive concerning pedestrian safety was established in 2003 for evaluating pedestrian protection performance of car models. However, design changes have not been quantified since then. The goal of this study was to investigate front-end profiles of representative passenger car models and the potential influence on pedestrian lower extremity injury risk. The front-end styling of sedans and sport utility vehicles (SUV) released from 2008 to 2011 was characterized by the geometrical parameters related to pedestrian safety and compared to representative car models before 2003. The influence of geometrical design change on the resultant risk of injury to pedestrian lower extremity-that is, knee ligament rupture and long bone fracture-was estimated by a previously developed assessment tool assuming identical structural stiffness. Based on response surface generated from simulation results of a human body model (HBM), the tool provided kinematic and kinetic responses of pedestrian lower extremity resulted from a given car's front-end design. Newer passenger cars exhibited a "flatter" front-end design. The median value of the sedan models provided 87.5 mm less bottom depth, and the SUV models exhibited 94.7 mm less bottom depth. In the lateral impact configuration similar to that in the regulatory test methods, these geometrical changes tend to reduce the injury risk of human knee ligament rupture by 36.6 and 39.6% based on computational approximation. The geometrical changes did not significantly influence the long bone fracture risk. The present study reviewed the geometrical changes in car front-ends along with regulatory concerns regarding pedestrian safety. A preliminary quantitative benefit of the lower extremity injury reduction was estimated based on these geometrical features. Further investigation is recommended on the structural changes and inclusion of more accident scenarios.

A New Model for Acquiescence at the Interface of Psychometrics and Cognitive Psychology.

PubMed

Plieninger, Hansjörg; Heck, Daniel W

2018-05-29

When measuring psychological traits, one has to consider that respondents often show content-unrelated response behavior in answering questionnaires. To disentangle the target trait and two such response styles, extreme responding and midpoint responding, Böckenholt ( 2012a ) developed an item response model based on a latent processing tree structure. We propose a theoretically motivated extension of this model to also measure acquiescence, the tendency to agree with both regular and reversed items. Substantively, our approach builds on multinomial processing tree (MPT) models that are used in cognitive psychology to disentangle qualitatively distinct processes. Accordingly, the new model for response styles assumes a mixture distribution of affirmative responses, which are either determined by the underlying target trait or by acquiescence. In order to estimate the model parameters, we rely on Bayesian hierarchical estimation of MPT models. In simulations, we show that the model provides unbiased estimates of response styles and the target trait, and we compare the new model and Böckenholt's model in a recovery study. An empirical example from personality psychology is used for illustrative purposes.

Extreme Mean and Its Applications

NASA Technical Reports Server (NTRS)

Swaroop, R.; Brownlow, J. D.

1979-01-01

Extreme value statistics obtained from normally distributed data are considered. An extreme mean is defined as the mean of p-th probability truncated normal distribution. An unbiased estimate of this extreme mean and its large sample distribution are derived. The distribution of this estimate even for very large samples is found to be nonnormal. Further, as the sample size increases, the variance of the unbiased estimate converges to the Cramer-Rao lower bound. The computer program used to obtain the density and distribution functions of the standardized unbiased estimate, and the confidence intervals of the extreme mean for any data are included for ready application. An example is included to demonstrate the usefulness of extreme mean application.

A randomized trial of mailed questionnaires versus telephone interviews: Response patterns in a survey

PubMed Central

Feveile, Helene; Olsen, Ole; Hogh, Annie

2007-01-01

Background Data for health surveys are often collected using either mailed questionnaires, telephone interviews or a combination. Mode of data collection can affect the propensity to refuse to respond and result in different patterns of responses. The objective of this paper is to examine and quantify effects of mode of data collection in health surveys. Methods A stratified sample of 4,000 adults residing in Denmark was randomised to mailed questionnaires or computer-assisted telephone interviews. 45 health-related items were analyzed; four concerning behaviour and 41 concerning self assessment. Odds ratios for more positive answers and more frequent use of extreme response categories (both positive and negative) among telephone respondents compared to questionnaire respondents were estimated. Tests were Bonferroni corrected. Results For the four health behaviour items there were no significant differences in the response patterns. For 32 of the 41 health self assessment items the response pattern was statistically significantly different and extreme response categories were used more frequently among telephone respondents (Median estimated odds ratio: 1.67). For a majority of these mode sensitive items (26/32), a more positive reporting was observed among telephone respondents (Median estimated odds ratio: 1.73). The overall response rate was similar among persons randomly assigned to questionnaires (58.1%) and to telephone interviews (56.2%). A differential nonresponse bias for age and gender was observed. The rate of missing responses was higher for questionnaires (0.73 – 6.00%) than for telephone interviews (0 – 0.51%). The "don't know" option was used more often by mail respondents (10 – 24%) than by telephone respondents (2 – 4%). Conclusion The mode of data collection affects the reporting of self assessed health items substantially. In epidemiological studies, the method effect may be as large as the effects under investigation. Caution is needed when comparing prevalences across surveys or when studying time trends. PMID:17592653

Simulation of Theoretical Most-Extreme Geomagnetic Sudden Commencements

NASA Astrophysics Data System (ADS)

Welling, D. T.; Love, J. J.; Wiltberger, M. J.; Rigler, E. J.

2016-12-01

We report results from a numerical simulation of geomagnetic sudden commencements driven by solar wind conditions given by theoretical-limit extreme coronal-mass ejections (CMEs) estimated by Tsurutani and Lakhina [2014]. The CME characteristics at Earth are a step function that jumps from typical quiet values to 2700 km/s flow speed and a magnetic field magnitude of 127 nT. These values are used to drive three coupled models: a global magnetohydrodynamic (MHD) magnetospheric model (BATS-R-US), a ring current model (the Rice Convection Model, RCM), and a height-integrated ionospheric electrodynamics model (the Ridley Ionosphere Model, RIM), all coupled together using the Space Weather Modeling Framework (SWMF). Additionally, simulations from the Lyon-Fedder-Mobarry MHD model are performed for comparison. The commencement is simulated with both purely northward and southward IMF orientations. Low-latitude ground-level geomagnetic variations, both B and dB/dt, are estimated in response to the storm sudden commencement. For a northward interplanetary magnetic field (IMF) storm, the combined models predict a maximum sudden commencement response, Dst-equivalent of +200 nT and a maximum local dB/dt of 200nT/s. While this positive Dst response is driven mainly by magnetopause currents, complicated and dynamic Birkeland current patterns also develop, which drive the strong dB/dt responses at high latitude. For southward IMF conditions, erosion of dayside magnetic flux allows magnetopause currents to approach much closer to the Earth, leading to a stronger terrestrial response (Dst-equivalent of +250 nT). Further, high latitude signals from Region 1 Birkeland currents move to lower latitudes during the southward IMF case, increasing the risk to populated areas around the globe. Results inform fundamental understanding of solar-terrestrial interaction and benchmark estimates for induction hazards of interest to the electric-power grid industry.

Extremity movements help occupational therapists identify stress responses in preterm infants in the neonatal intensive care unit: a systematic review.

PubMed

Holsti, Liisa; Grunau, Ruth E

2007-06-01

Accurate assessment and treatment of pain and stress in preterm infants in neonatal intensive care units (NICU) is vital because pain and stress responses have been linked to long-term alterations in development in this population. To review the evidence of specific extremity movements in preterm infants as observed during stressful procedures. Five on-line databases were searched for relevant studies. For each study, levels of evidence were determined and effect size estimates were calculated. Each study was also evaluated for specific factors that presented potential threats to its validity. Eighteen studies were identified and seven comprised the review. The combined sample included 359 preterm infants. Six specific movements were associated with painful and intrusive procedures. A set of specific extremity movements, when combined with other reliable biobehavioural measures of pain and stress, can form the basis for future research and development of a clinical stress scale for preterm infants.

An analytic data analysis method for oscillatory slug tests.

PubMed

Chen, Chia-Shyun

2006-01-01

An analytical data analysis method is developed for slug tests in partially penetrating wells in confined or unconfined aquifers of high hydraulic conductivity. As adapted from the van der Kamp method, the determination of the hydraulic conductivity is based on the occurrence times and the displacements of the extreme points measured from the oscillatory data and their theoretical counterparts available in the literature. This method is applied to two sets of slug test response data presented by Butler et al.: one set shows slow damping with seven discernable extremities, and the other shows rapid damping with three extreme points. The estimates of the hydraulic conductivity obtained by the analytic method are in good agreement with those determined by an available curve-matching technique.

Estimating suppression expenditures for individual large wildland fires

Treesearch

Krista M. Gebert; David E. Calkin; Jonathan Yoder

2007-01-01

The extreme cost of fighting wildland fires has brought fire suppression expenditures to the forefront of budgetary and policy debate in the United States. Inasmuch as large fires are responsible for the bulk of fire suppression expenditures, understanding fire characteristics that influence expenditures is important for both strategic fire planning and onsite fire...

Responses of gross primary production of grasslands and croplands under drought, pluvial, and irrigation conditions during 2010-2016, Oklahoma, USA

USDA-ARS?s Scientific Manuscript database

To accurately estimate carbon cycling and food production, it is essential to understand how gross primary production (GPP) of irrigated and non-irrigated grasslands and croplands respond to drought and pluvial events. Oklahoma experienced extreme drought in 2011 and record-breaking precipitation in...

Censored rainfall modelling for estimation of fine-scale extremes

NASA Astrophysics Data System (ADS)

Cross, David; Onof, Christian; Winter, Hugo; Bernardara, Pietro

2018-01-01

Reliable estimation of rainfall extremes is essential for drainage system design, flood mitigation, and risk quantification. However, traditional techniques lack physical realism and extrapolation can be highly uncertain. In this study, we improve the physical basis for short-duration extreme rainfall estimation by simulating the heavy portion of the rainfall record mechanistically using the Bartlett-Lewis rectangular pulse (BLRP) model. Mechanistic rainfall models have had a tendency to underestimate rainfall extremes at fine temporal scales. Despite this, the simple process representation of rectangular pulse models is appealing in the context of extreme rainfall estimation because it emulates the known phenomenology of rainfall generation. A censored approach to Bartlett-Lewis model calibration is proposed and performed for single-site rainfall from two gauges in the UK and Germany. Extreme rainfall estimation is performed for each gauge at the 5, 15, and 60 min resolutions, and considerations for censor selection discussed.

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.

New Insights into the Estimation of Extreme Geomagnetic Storm Occurrences

NASA Astrophysics Data System (ADS)

Ruffenach, Alexis; Winter, Hugo; Lavraud, Benoit; Bernardara, Pietro

2017-04-01

Space weather events such as intense geomagnetic storms are major disturbances of the near-Earth environment that may lead to serious impacts on our modern society. As such, it is of great importance to estimate their probability, and in particular that of extreme events. One approach largely used in statistical sciences for extreme events probability estimates is Extreme Value Analysis (EVA). Using this rigorous statistical framework, estimations of the occurrence of extreme geomagnetic storms are performed here based on the most relevant global parameters related to geomagnetic storms, such as ground parameters (e.g. geomagnetic Dst and aa indexes), and space parameters related to the characteristics of Coronal Mass Ejections (CME) (velocity, southward magnetic field component, electric field). Using our fitted model, we estimate the annual probability of a Carrington-type event (Dst = -850nT) to be on the order of 10-3, with a lower limit of the uncertainties on the return period of ˜500 years. Our estimate is significantly higher than that of most past studies, which typically had a return period of a few 100 years at maximum. Thus precautions are required when extrapolating intense values. Currently, the complexity of the processes and the length of available data inevitably leads to significant uncertainties in return period estimates for the occurrence of extreme geomagnetic storms. However, our application of extreme value models for extrapolating into the tail of the distribution provides a mathematically justified framework for the estimation of extreme return periods, thereby enabling the determination of more accurate estimates and reduced associated uncertainties.

Extremes in ecology: Avoiding the misleading effects of sampling variation in summary analyses

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.

1996-01-01

Surveys such as the North American Breeding Bird Survey (BBS) produce large collections of parameter estimates. One's natural inclination when confronted with lists of parameter estimates is to look for the extreme values: in the BBS, these correspond to the species that appear to have the greatest changes in population size through time. Unfortunately, extreme estimates are liable to correspond to the most poorly estimated parameters. Consequently, the most extreme parameters may not match up with the most extreme parameter estimates. The ranking of parameter values on the basis of their estimates are a difficult statistical problem. We use data from the BBS and simulations to illustrate the potential misleading effects of sampling variation in rankings of parameters. We describe empirical Bayes and constrained empirical Bayes procedures which provide partial solutions to the problem of ranking in the presence of sampling variation.

Responses of gross primary production of grasslands and croplands to drought and pluvial events and irrigation during 2010-2016, Oklahoma, USA

USDA-ARS?s Scientific Manuscript database

To accurately estimate carbon cycling and food production, it is essential to understand how gross primary production (GPP) of irrigated and non-irrigated grasslands and croplands respond to drought and pluvial events. Oklahoma experienced extreme drought in 2011 and record-breaking precipitation in...

Variation in vulnerability to extreme-temperature-related mortality in Japan: A 40-year time-series analysis.

PubMed

Onozuka, Daisuke; Hagihara, Akihito

2015-07-01

Although the impact of extreme heat and cold on mortality has been documented in recent years, few studies have investigated whether variation in susceptibility to extreme temperatures has changed in Japan. We used data on daily total mortality and mean temperatures in Fukuoka, Japan, for 1973-2012. We used time-series analysis to assess the effects of extreme hot and low temperatures on all-cause mortality, stratified by decade, gender, and age, adjusting for time trends. We used a multivariate meta-analysis with a distributed lag non-linear model to estimate pooled non-linear lag-response relationships associated with extreme temperatures on mortality. The relative risk of mortality increased during heat extremes in all decades, with a declining trend over time. The mortality risk was higher during cold extremes for the entire study period, with a dispersed pattern across decades. Meta-analysis showed that both heat and cold extremes increased the risk of mortality. Cold effects were delayed and lasted for several days, whereas heat effects appeared quickly and did not last long. Our study provides quantitative evidence that extreme heat and low temperatures were significantly and non-linearly associated with the increased risk of mortality with substantial variation. Our results suggest that timely preventative measures are important for extreme high temperatures, whereas several days' protection should be provided for extreme low temperatures. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Response of shoal grass, Halodule wrightii, to extreme winter conditions in the Lower Laguna Madre, Texas

USGS Publications Warehouse

Hicks, D.W.; Onuf, C.P.; Tunnell, J.W.

1998-01-01

Effects of a severe freeze on the shoal grass, Halodule wrightii, were documented through analysis of temporal and spatial trends in below-ground biomass. The coincidence of the second lowest temperature (-10.6??C) in 107 years of record, 56 consecutive hours below freezing, high winds and extremely low water levels exposed the Laguna Madre, TX, to the most severe cold stress in over a century. H. wrightii tolerated this extreme freeze event. Annual pre- and post-freeze surveys indicated that below-ground biomass estimated from volume was Unaffected by the freeze event. Nor was there any post-freeze change in biomass among intertidal sites directly exposed to freezing air temperatures relative to subtidal sites which remained submerged during the freezing period.

Regional estimation of extreme suspended sediment concentrations using watershed characteristics

NASA Astrophysics Data System (ADS)

Tramblay, Yves; Ouarda, Taha B. M. J.; St-Hilaire, André; Poulin, Jimmy

2010-01-01

SummaryThe number of stations monitoring daily suspended sediment concentration (SSC) has been decreasing since the 1980s in North America while suspended sediment is considered as a key variable for water quality. The objective of this study is to test the feasibility of regionalising extreme SSC, i.e. estimating SSC extremes values for ungauged basins. Annual maximum SSC for 72 rivers in Canada and USA were modelled with probability distributions in order to estimate quantiles corresponding to different return periods. Regionalisation techniques, originally developed for flood prediction in ungauged basins, were tested using the climatic, topographic, land cover and soils attributes of the watersheds. Two approaches were compared, using either physiographic characteristics or seasonality of extreme SSC to delineate the regions. Multiple regression models to estimate SSC quantiles as a function of watershed characteristics were built in each region, and compared to a global model including all sites. Regional estimates of SSC quantiles were compared with the local values. Results show that regional estimation of extreme SSC is more efficient than a global regression model including all sites. Groups/regions of stations have been identified, using either the watershed characteristics or the seasonality of occurrence for extreme SSC values providing a method to better describe the extreme events of SSC. The most important variables for predicting extreme SSC are the percentage of clay in the soils, precipitation intensity and forest cover.

Impacts of climate extremes on gross primary productivity of terrestrial ecosystems in conterminous USA

NASA Astrophysics Data System (ADS)

Wu, X.; Xiao, X.; Zhang, Y.; Zhang, G.

2016-12-01

By offsetting one-third of anthropogenic carbon emissions, terrestrial carbon uptake mitigates atmospheric CO2 concentration and consequent global warming. However, the current global warming trend is inducing more climate extremes, which in turn cause large changes in terrestrial carbon uptake. Here we report the seasonal and regional anomalies of gross primary productivity (GPP) across the conterminous USA (CONUS) in response to two contrasting climate extremes: the cool and wet 2009 versus the warm and dry 2012. We used the Vegetation Photosynthesis Model (VPM, Xiao et al., 2006), MODIS images and NCEP/NARR climate data to estimate GPP from 2009-2014, and evaluated the VPM-predicted GPP with the estimated GPP from the CO2 eddy flux tower sites (24 sites). We analyze the correlation between the anomalies of the continental GPP and the anomalies of temperature and precipitation. The results show a substantial, negative GPP anomaly in 2009, in addition to the positive GPP anomaly in 2012, which was already reported in a previous study (Wolf et al., 2016). We also found that GPP anomalies of different climate regions in four seasons are controlled by either temperature or precipitation. Our study shows the robustness of the VPM to simulate GPP under the condition of climate extremes, and highlights the need of investigating the impacts of cooling events on the terrestrial carbon cycle. Our finding also suggests that there is no uniform pattern for terrestrial ecosystems responding to climate extremes, and that climate extremes should be studied in a case-by-case, location-based approach.

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

Individual and culture-level components of survey response styles: A multi-level analysis using cultural models of selfhood.

PubMed

Smith, Peter B; Vignoles, Vivian L; Becker, Maja; Owe, Ellinor; Easterbrook, Matthew J; Brown, Rupert; Bourguignon, David; Garðarsdóttir, Ragna B; Kreuzbauer, Robert; Cendales Ayala, Boris; Yuki, Masaki; Zhang, Jianxin; Lv, Shaobo; Chobthamkit, Phatthanakit; Jaafar, Jas Laile; Fischer, Ronald; Milfont, Taciano L; Gavreliuc, Alin; Baguma, Peter; Bond, Michael Harris; Martin, Mariana; Gausel, Nicolay; Schwartz, Seth J; Des Rosiers, Sabrina E; Tatarko, Alexander; González, Roberto; Didier, Nicolas; Carrasco, Diego; Lay, Siugmin; Nizharadze, George; Torres, Ana; Camino, Leoncio; Abuhamdeh, Sami; Macapagal, Ma Elizabeth J; Koller, Silvia H; Herman, Ginette; Courtois, Marie; Fritsche, Immo; Espinosa, Agustín; Villamar, Juan A; Regalia, Camillo; Manzi, Claudia; Brambilla, Maria; Zinkeng, Martina; Jalal, Baland; Kusdil, Ersin; Amponsah, Benjamin; Çağlar, Selinay; Mekonnen, Kassahun Habtamu; Möller, Bettina; Zhang, Xiao; Schweiger Gallo, Inge; Prieto Gil, Paula; Lorente Clemares, Raquel; Campara, Gabriella; Aldhafri, Said; Fülöp, Márta; Pyszczynski, Tom; Kesebir, Pelin; Harb, Charles

2016-12-01

Variations in acquiescence and extremity pose substantial threats to the validity of cross-cultural research that relies on survey methods. Individual and cultural correlates of response styles when using 2 contrasting types of response mode were investigated, drawing on data from 55 cultural groups across 33 nations. Using 7 dimensions of self-other relatedness that have often been confounded within the broader distinction between independence and interdependence, our analysis yields more specific understandings of both individual- and culture-level variations in response style. When using a Likert-scale response format, acquiescence is strongest among individuals seeing themselves as similar to others, and where cultural models of selfhood favour harmony, similarity with others and receptiveness to influence. However, when using Schwartz's (2007) portrait-comparison response procedure, acquiescence is strongest among individuals seeing themselves as self-reliant but also connected to others, and where cultural models of selfhood favour self-reliance and self-consistency. Extreme responding varies less between the two types of response modes, and is most prevalent among individuals seeing themselves as self-reliant, and in cultures favouring self-reliance. As both types of response mode elicit distinctive styles of response, it remains important to estimate and control for style effects to ensure valid comparisons. © 2016 International Union of Psychological Science.

Changes in extremes due to half a degree warming in observations and models

NASA Astrophysics Data System (ADS)

Fischer, E. M.; Schleussner, C. F.; Pfleiderer, P.

2017-12-01

Assessing the climate impacts of half-a-degree warming increments is high on the post-Paris science agenda. Discriminating those effects is particularly challenging for climate extremes such as heavy precipitation and heat extremes for which model uncertainties are generally large, and for which internal variability is so important that it can easily offset or strongly amplify the forced local changes induced by half a degree warming. Despite these challenges we provide evidence for large-scale changes in the intensity and frequency of climate extremes due to half a degree warming. We first assess the difference in extreme climate indicators in observational data for the 1960s and 1970s versus the recent past, two periods differ by half a degree. We identify distinct differences for the global and continental-scale occurrence of heat and heavy precipitation extremes. We show that those observed changes in heavy precipitation and heat extremes broadly agree with simulated historical differences and are informative for the projected differences between 1.5 and 2°C warming despite different radiative forcings. We therefore argue that evidence from the observational record can inform the debate about discernible climate impacts in the light of model uncertainty by providing a conservative estimate of the implications of 0.5°C warming. A limitation of using the observational record arises from potential non-linearities in the response of climate extremes to a certain level of warming. We test for potential non-linearities in the response of heat and heavy precipitation extremes in a large ensemble of transient climate simulations. We further quantify differences between a time-window approach in a coupled model large ensemble vs. time-slice experiments using prescribed SST experiments performed in the context of the HAPPI-MIP project. Thereby we provide different lines of evidence that half a degree warming leads to substantial changes in the expected occurrence of heat and heavy precipitation extremes.

Cycles till failure of silver-zinc cells with completing failures modes: Preliminary data analysis

NASA Technical Reports Server (NTRS)

Sidik, S. M.; Leibecki, H. F.; Bozek, J. M.

1980-01-01

One hundred and twenty nine cells were run through charge-discharge cycles until failure. The experiment design was a variant of a central composite factorial in five factors. Preliminary data analysis consisted of response surface estimation of life. Batteries fail under two basic modes; a low voltage condition and an internal shorting condition. A competing failure modes analysis using maximum likelihood estimation for the extreme value life distribution was performed. Extensive diagnostics such as residual plotting and probability plotting were employed to verify data quality and choice of model.

Vector autoregressive models: A Gini approach

NASA Astrophysics Data System (ADS)

Mussard, Stéphane; Ndiaye, Oumar Hamady

2018-02-01

In this paper, it is proven that the usual VAR models may be performed in the Gini sense, that is, on a ℓ1 metric space. The Gini regression is robust to outliers. As a consequence, when data are contaminated by extreme values, we show that semi-parametric VAR-Gini regressions may be used to obtain robust estimators. The inference about the estimators is made with the ℓ1 norm. Also, impulse response functions and Gini decompositions for prevision errors are introduced. Finally, Granger's causality tests are properly derived based on U-statistics.

Cycles till failure of silver-zinc cells with competing failure modes - Preliminary data analysis

NASA Technical Reports Server (NTRS)

Sidik, S. M.; Leibecki, H. F.; Bozek, J. M.

1980-01-01

The data analysis of cycles to failure of silver-zinc electrochemical cells with competing failure modes is presented. The test ran 129 cells through charge-discharge cycles until failure; preliminary data analysis consisted of response surface estimate of life. Batteries fail through low voltage condition and an internal shorting condition; a competing failure modes analysis was made using maximum likelihood estimation for the extreme value life distribution. Extensive residual plotting and probability plotting were used to verify data quality and selection of model.

Continental-Scale Estimates of Runoff Using Future Climate ...

EPA Pesticide Factsheets

Recent runoff events have had serious repercussions to both natural ecosystems and human infrastructure. Understanding how shifts in storm event intensities are expected to change runoff responses are valuable for local, regional, and landscape planning. To address this challenge, relative changes in runoff using predicted future climate conditions were estimated over different biophysical areas for the CONterminous U.S. (CONUS). Runoff was estimated using the Curve Number (CN) developed by the USDA Soil Conservation Service (USDA, 1986). A seamless gridded dataset representing a CN for existing land use/land cover (LULC) across the CONUS was used along with two different storm event grids created specifically for this effort. The two storm event grids represent a 2- and a 100-year, 24-hour storm event under current climate conditions. The storm event grids were generated using a compilation of county-scale Texas USGS Intensity-Duration-Frequency (IDF) data (provided by William Asquith, USGS, Lubbock, Texas), and NOAA Atlas-2 and NOAA Atlas-14 gridded data sets. Future CN runoff was predicted using extreme storm events grids created using a method based on Kao and Ganguly (2011) where precipitation extremes reflect changes in saturated water vapor pressure of the atmosphere in response to temperature changes. The Clausius-Clapeyron relationship establishes that the total water vapor mass of fully saturated air increases with increasing temperature, leading to

Hierarchy and extremes in selections from pools of randomized proteins

PubMed Central

Boyer, Sébastien; Biswas, Dipanwita; Kumar Soshee, Ananda; Scaramozzino, Natale; Nizak, Clément; Rivoire, Olivier

2016-01-01

Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display, and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings. First, libraries with the same sequence diversity but built around different “frameworks” typically have vastly different responses; second, the distribution of responses of the best binders in a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes the latter finding. Our results have implications for designing synthetic protein libraries, estimating the density of functional biomolecules in sequence space, characterizing diversity in natural populations, and experimentally investigating evolvability (i.e., the potential for future evolution). PMID:26969726

Hierarchy and extremes in selections from pools of randomized proteins.

PubMed

Boyer, Sébastien; Biswas, Dipanwita; Kumar Soshee, Ananda; Scaramozzino, Natale; Nizak, Clément; Rivoire, Olivier

2016-03-29

Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display, and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings. First, libraries with the same sequence diversity but built around different "frameworks" typically have vastly different responses; second, the distribution of responses of the best binders in a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes the latter finding. Our results have implications for designing synthetic protein libraries, estimating the density of functional biomolecules in sequence space, characterizing diversity in natural populations, and experimentally investigating evolvability (i.e., the potential for future evolution).

Robust ridge regression estimators for nonlinear models with applications to high throughput screening assay data.

PubMed

Lim, Changwon

2015-03-30

Nonlinear regression is often used to evaluate the toxicity of a chemical or a drug by fitting data from a dose-response study. Toxicologists and pharmacologists may draw a conclusion about whether a chemical is toxic by testing the significance of the estimated parameters. However, sometimes the null hypothesis cannot be rejected even though the fit is quite good. One possible reason for such cases is that the estimated standard errors of the parameter estimates are extremely large. In this paper, we propose robust ridge regression estimation procedures for nonlinear models to solve this problem. The asymptotic properties of the proposed estimators are investigated; in particular, their mean squared errors are derived. The performances of the proposed estimators are compared with several standard estimators using simulation studies. The proposed methodology is also illustrated using high throughput screening assay data obtained from the National Toxicology Program. Copyright © 2014 John Wiley & Sons, Ltd.

Evidence of population resistance to extreme low flows in a fluvial-dependent fish species

USGS Publications Warehouse

Katz, Rachel A.; Freeman, Mary C.

2015-01-01

Extreme low streamflows are natural disturbances to aquatic populations. Species in naturally intermittent streams display adaptations that enhance persistence during extreme events; however, the fate of populations in perennial streams during unprecedented low-flow periods is not well-understood. Biota requiring swift-flowing habitats may be especially vulnerable to flow reductions. We estimated the abundance and local survival of a native fluvial-dependent fish species (Etheostoma inscriptum) across 5 years encompassing historic low flows in a sixth-order southeastern USA perennial river. Based on capturemark-recapture data, the study shoal may have acted as a refuge during severe drought, with increased young-of-the-year (YOY) recruitment and occasionally high adult immigration. Contrary to expectations, summer and autumn survival rates (30 days) were not strongly depressed during low-flow periods, despite 25%-80% reductions in monthly discharge. Instead, YOY survival increased with lower minimum discharge and in response to small rain events that increased low-flow variability. Age-1+ fish showed the opposite pattern, with survival decreasing in response to increasing low-flow variability. Results from this population dynamics study of a small fish in a perennial river suggest that fluvial-dependent species can be resistant to extreme flow reductions through enhanced YOY recruitment and high survival

Extreme precipitation depths for Texas, excluding the Trans-Pecos region

USGS Publications Warehouse

Lanning-Rush, Jennifer; Asquith, William H.; Slade, Raymond M.

1998-01-01

Storm durations of 1, 2, 3, 4, 5, and 6 days were investigated for this report. The extreme precipitation depth for a particular area is estimated from an “extreme precipitation curve” (an upper limit or envelope curve developed from graphs of extreme precipitation depths for each climatic region). The extreme precipitation curves were determined using precipitation depth-duration information from a subset (24 “extreme” storms) of 213 “notable” storms documented throughout Texas. The extreme precipitation curves can be used to estimate extreme precipitation depth for a particular area. The extreme precipitation depth represents a limiting depth, which can provide useful comparative information for more quantitative analyses.

The dichotomous response of flood and storm extremes to rising global temperatures

NASA Astrophysics Data System (ADS)

Sharma, A.; Wasko, C.

2017-12-01

Rising temperature have resulted in increases in short-duration rainfall extremes across the world. Additionally it has been shown (doi:10.1038/ngeo2456) that storms will intensify, causing derived flood peaks to rise even more. This leads us to speculate that flood peaks will increase as a result, complying with the storyline presented in past IPCC reports. This talk, however, shows that changes in flood extremes are much more complex. Using global data on extreme flow events, the study conclusively shows that while the very extreme floods may be rising as a result of storm intensification, the more frequent flood events are decreasing in magnitude. The study argues that changes in the magnitude of floods are a function of changes in storm patterns and as well as pre-storm or antecedent conditions. It goes on to show that while changes in storms dominate for the most extreme events and over smaller, more urbanised catchments, changes in pre-storm conditions are the driving factor in modulating flood peaks in large rural catchments. The study concludes by providing recommendations on how future flood design should proceed, arguing that current practices (or using a design storm to estimate floods) are flawed and need changing.

Detecting Changes in Functional Traits of Forest after Extreme Climate Episode using Model Data Fusion

NASA Astrophysics Data System (ADS)

Yokozawa, M.; Kawai, Y.; Toda, M.

2016-12-01

The increase in extreme climate episodes associated with ongoing climate change may induce extensive damage to terrestrial ecosystems, changing plant functional traits that regulate ecosystem carbon budget. Over the last two decades, an advanced observational operation of tower-based eddy covariance has enhanced our ability to understand spatial and temporal features of ecosystem carbon exchange worldwide. In contrast, there remain several unresolved issues regarding plant function responses to extreme climate episodes and the resulting effects on the terrestrial carbon balance. In this work, we examined the effects of an extreme climatic event (typhoon) on plant functional traits of a cool-temperate forest in Japan using a model data fusion technique. We used a semi-process model to describes the time changes in net ecosystem exchange (NEE) of CO2 between atmosphere and ecosystem based on the distributions of foliage and size of an individual in a plant population, assuming the diameter profile and the pipe model theory (Shinozaki et al., 1964). The canopy photosynthesis model (Yokozawa et al., 1996) provides us the vertical distribution of gross photosynthetic rates within stand. It can allow us to examine the differences in photosynthetic rate with plant functional traits changed by climate disturbance. The DREAM(ZS) algorithm (ter Braak & Vrugt, 2008) was used to estimate the model parameters. To reduce the effects of heteroscedastic error, a generalized likelihood function was adopted (Schoup & Vrugt, 2010). The estimated annual parameter which represents the initial slope of light-photosynthetic rate curve, significantly changed after typhoon disturbance in 2004. Time changes in the profile of the maximum photosynthetic rate also shows the intensive response to the disturbance. After the disturbance, the values at upper foliage layer are higher than at lower foliage layer in contrast to that before disturbance. Specifically, just after disturbance in 2004b-5a, the value at uppermost foliage layer was estimated to be the highest value. It implies that the plant population recovered the damage by changing the distribution of leaves having different functional traits, i.e. resilient behavior.

Bayesian hierarchical modeling for subject-level response classification in peptide microarray immunoassays

PubMed Central

Imholte, Gregory; Gottardo, Raphael

2017-01-01

Summary The peptide microarray immunoassay simultaneously screens sample serum against thousands of peptides, determining the presence of antibodies bound to array probes. Peptide microarrays tiling immunogenic regions of pathogens (e.g. envelope proteins of a virus) are an important high throughput tool for querying and mapping antibody binding. Because of the assay’s many steps, from probe synthesis to incubation, peptide microarray data can be noisy with extreme outliers. In addition, subjects may produce different antibody profiles in response to an identical vaccine stimulus or infection, due to variability among subjects’ immune systems. We present a robust Bayesian hierarchical model for peptide microarray experiments, pepBayes, to estimate the probability of antibody response for each subject/peptide combination. Heavy-tailed error distributions accommodate outliers and extreme responses, and tailored random effect terms automatically incorporate technical effects prevalent in the assay. We apply our model to two vaccine trial datasets to demonstrate model performance. Our approach enjoys high sensitivity and specificity when detecting vaccine induced antibody responses. A simulation study shows an adaptive thresholding classification method has appropriate false discovery rate control with high sensitivity, and receiver operating characteristics generated on vaccine trial data suggest that pepBayes clearly separates responses from non-responses. PMID:27061097

Anatomy of extraordinary rainfall and flash flood in a Dutch lowland catchment

NASA Astrophysics Data System (ADS)

Brauer, C. C.; Teuling, A. J.; Overeem, A.; van der Velde, Y.; Hazenberg, P.; Warmerdam, P. M. M.; Uijlenhoet, R.

2011-06-01

On 26 August 2010 the eastern part of The Netherlands and the bordering part of Germany were struck by a series of rainfall events lasting for more than a day. Over an area of 740 km2 more than 120 mm of rainfall were observed in 24 h. This extreme event resulted in local flooding of city centres, highways and agricultural fields, and considerable financial loss. In this paper we report on the unprecedented flash flood triggered by this exceptionally heavy rainfall event in the 6.5 km2 Hupsel Brook catchment, which has been the experimental watershed employed by Wageningen University since the 1960s. This study aims to improve our understanding of the dynamics of such lowland flash floods. We present a detailed hydrometeorological analysis of this extreme event, focusing on its synoptic meteorological characteristics, its space-time rainfall dynamics as observed with rain gauges, weather radar and a microwave link, as well as the measured soil moisture, groundwater and discharge response of the catchment. At the Hupsel Brook catchment 160 mm of rainfall was observed in 24 h, corresponding to an estimated return period of well over 1000 years. As a result, discharge at the catchment outlet increased from 4.4 × 10-3 to nearly 5 m3 s-1. Within 7 h discharge rose from 5 × 10-2 to 4.5 m3 s-1. The catchment response can be divided into four phases: (1) soil moisture reservoir filling, (2) groundwater response, (3) surface depression filling and surface runoff and (4) backwater feedback. The first 35 mm of rainfall were stored in the soil without a significant increase in discharge. Relatively dry initial conditions (in comparison to those for past discharge extremes) prevented an even faster and more extreme hydrological response.

Estimating extreme losses for the Florida Public Hurricane Model—part II

NASA Astrophysics Data System (ADS)

Gulati, Sneh; George, Florence; Hamid, Shahid

2018-02-01

Rising global temperatures are leading to an increase in the number of extreme events and losses (http://www.epa.gov/climatechange/science/indicators/). Accurate estimation of these extreme losses with the intention of protecting themselves against them is critical to insurance companies. In a previous paper, Gulati et al. (2014) discussed probable maximum loss (PML) estimation for the Florida Public Hurricane Loss Model (FPHLM) using parametric and nonparametric methods. In this paper, we investigate the use of semi-parametric methods to do the same. Detailed analysis of the data shows that the annual losses from FPHLM do not tend to be very heavy tailed, and therefore, neither the popular Hill's method nor the moment's estimator work well. However, Pickand's estimator with threshold around the 84th percentile provides a good fit for the extreme quantiles for the losses.

Evaluating the extreme precipitation events using a mesoscale atmopshere model

NASA Astrophysics Data System (ADS)

Yucel, I.; Onen, A.

2012-04-01

Evidence is showing that global warming or climate change has a direct influence on changes in precipitation and the hydrological cycle. Extreme weather events such as heavy rainfall and flooding are projected to become much more frequent as climate warms. Mesoscale atmospheric models coupled with land surface models provide efficient forecasts for meteorological events in high lead time and therefore they should be used for flood forecasting and warning issues as they provide more continuous monitoring of precipitation over large areas. This study examines the performance of the Weather Research and Forecasting (WRF) model in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in West Black Sea Region of Turkey. Extreme precipitation events usually resulted in flood conditions as an associated hydrologic response of the basin. The performance of the WRF system is further investigated by using the three dimensional variational (3D-VAR) data assimilation scheme within WRF. WRF performance with and without data assimilation at high spatial resolution (4 km) is evaluated by making comparison with gauge precipitation and satellite-estimated rainfall data from Multi Precipitation Estimates (MPE). WRF-derived precipitation showed capabilities in capturing the timing of the precipitation extremes and in some extent spatial distribution and magnitude of the heavy rainfall events. These precipitation characteristics are enhanced with the use of 3D-VAR scheme in WRF system. Data assimilation improved area-averaged precipitation forecasts by 9 percent and at some points there exists quantitative match in precipitation events, which are critical for hydrologic forecast application.

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.

Extreme Precipitation, Public Health Emergencies, and Safe Drinking Water in the USA.

PubMed

Exum, Natalie G; Betanzo, Elin; Schwab, Kellogg J; Chen, Thomas Y J; Guikema, Seth; Harvey, David E

2018-06-01

This review examines the effectiveness of drinking water regulations to inform public health during extreme precipitation events. This paper estimates the vulnerability of specific populations to flooding in their public water system, reviews the literature linking precipitation to waterborne outbreaks, examines the role that Safe Drinking Water Act and Public Notification (PN) Rule have in public health emergencies, and reviews the effectiveness of the PN Rule during the 2017 Hurricane Maria in Puerto Rico. Public water systems in large metropolitan areas have substantial portions of their customer base at risk for a waterborne outbreak during a flooding event. The PN Rule are ambiguous for who is responsible for declaring a "waterborne emergency" following a natural disaster like Hurricane Maria. Revisions to the current PN Rule that mandate public notification and water quality sampling during extreme precipitation events are necessary to ensure the public is aware of their drinking water quality following these events.

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis.

PubMed

Quintela-del-Río, Alejandro; Francisco-Fernández, Mario

2011-02-01

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

Research progress of extreme climate and its vegetation response

NASA Astrophysics Data System (ADS)

Cui, Xiaolin; Wei, Xiaoqing; Wang, Tao

2017-08-01

The IPCC’s fifth assessment report indicates that climate warming is unquestionable, the frequency and intensity of extreme weather events may increase, and extreme weather events can destroy the growth conditions of vegetation that is otherwise in a stable condition. Therefore, it is essential to research the formation of extreme weather events and its ecological response, both in terms scientific development and the needs of societal development. This paper mainly examines these issues from the following aspects: (1) the definition of extreme climate events and the methods of studying the associated response of vegetation; (2) the research progress on extreme climate events and their vegetation response; and (3) the future direction of research on extreme climate and its vegetation response.

Evaluation of the National Weather Service Extreme Cold Warning Experiment in North Dakota

PubMed Central

Chiu, Cindy H.; Vagi, Sara J.; Wolkin, Amy F.; Martin, John Paul; Noe, Rebecca S.

2016-01-01

Dangerously cold weather threatens life and property. During periods of extreme cold due to wind chill, the National Weather Service (NWS) issues wind chill warnings to prompt the public to take action to mitigate risks. Wind chill warnings are based on ambient temperatures and wind speeds. Since 2010, NWS has piloted a new extreme cold warning issued for cold temperatures in wind and nonwind conditions. The North Dakota Department of Health, NWS, and the Centers for Disease Control and Prevention collaborated in conducting household surveys in Burleigh County, North Dakota, to evaluate this new warning. The objectives of the evaluation were to assess whether residents heard the new warning and to determine if protective behaviors were prompted by the warning. This was a cross-sectional survey design using the Community Assessment for Public Health Emergency Response (CASPER) methodology to select a statistically representative sample of households from Burleigh County. From 10 to 11 April 2012, 188 door-to-door household interviews were completed. The CASPER methodology uses probability sampling with weighted analysis to estimate the number and percentage of households with a specific response within Burleigh County. The majority of households reported having heard both the extreme cold and wind chill warnings, and both warnings prompted protective behaviors. These results suggest this community heard the new warning and took protective actions after hearing the warning. PMID:27239260

Comparative Statistical Analysis of Auroral Models

DTIC Science & Technology

2012-03-22

was willing to add this project to her extremely busy schedule. Lastly, I must also express my sincere appreciation for the rest of the faculty and...models have been extensively used for estimating GPS and other communication satellite disturbances ( Newell et al., 2010a). The auroral oval...models predict changes in the auroral oval in response to various geomagnetic conditions. In 2010, Newell et al. conducted a comparative study of

A comparison of two global datasets of extreme sea levels and resulting flood exposure

NASA Astrophysics Data System (ADS)

Muis, Sanne; Verlaan, Martin; Nicholls, Robert J.; Brown, Sally; Hinkel, Jochen; Lincke, Daniel; Vafeidis, Athanasios T.; Scussolini, Paolo; Winsemius, Hessel C.; Ward, Philip J.

2017-04-01

Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of -0.2 m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39-59% higher estimate of population exposure.

A Metastatistical Approach to Satellite Estimates of Extreme Rainfall Events

NASA Astrophysics Data System (ADS)

Zorzetto, E.; Marani, M.

2017-12-01

The estimation of the average recurrence interval of intense rainfall events is a central issue for both hydrologic modeling and engineering design. These estimates require the inference of the properties of the right tail of the statistical distribution of precipitation, a task often performed using the Generalized Extreme Value (GEV) distribution, estimated either from a samples of annual maxima (AM) or with a peaks over threshold (POT) approach. However, these approaches require long and homogeneous rainfall records, which often are not available, especially in the case of remote-sensed rainfall datasets. We use here, and tailor it to remotely-sensed rainfall estimates, an alternative approach, based on the metastatistical extreme value distribution (MEVD), which produces estimates of rainfall extreme values based on the probability distribution function (pdf) of all measured `ordinary' rainfall event. This methodology also accounts for the interannual variations observed in the pdf of daily rainfall by integrating over the sample space of its random parameters. We illustrate the application of this framework to the TRMM Multi-satellite Precipitation Analysis rainfall dataset, where MEVD optimally exploits the relatively short datasets of satellite-sensed rainfall, while taking full advantage of its high spatial resolution and quasi-global coverage. Accuracy of TRMM precipitation estimates and scale issues are here investigated for a case study located in the Little Washita watershed, Oklahoma, using a dense network of rain gauges for independent ground validation. The methodology contributes to our understanding of the risk of extreme rainfall events, as it allows i) an optimal use of the TRMM datasets in estimating the tail of the probability distribution of daily rainfall, and ii) a global mapping of daily rainfall extremes and distributional tail properties, bridging the existing gaps in rain gauges networks.

Impacts of droughts and extreme-temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones

NASA Astrophysics Data System (ADS)

von Buttlar, Jannis; Zscheischler, Jakob; Rammig, Anja; Sippel, Sebastian; Reichstein, Markus; Knohl, Alexander; Jung, Martin; Menzer, Olaf; Altaf Arain, M.; Buchmann, Nina; Cescatti, Alessandro; Gianelle, Damiano; Kiely, Gerard; Law, Beverly E.; Magliulo, Vincenzo; Margolis, Hank; McCaughey, Harry; Merbold, Lutz; Migliavacca, Mirco; Montagnani, Leonardo; Oechel, Walter; Pavelka, Marian; Peichl, Matthias; Rambal, Serge; Raschi, Antonio; Scott, Russell L.; Vaccari, Francesco P.; van Gorsel, Eva; Varlagin, Andrej; Wohlfahrt, Georg; Mahecha, Miguel D.

2018-03-01

Extreme climatic events, such as droughts and heat stress, induce anomalies in ecosystem-atmosphere CO2 fluxes, such as gross primary production (GPP) and ecosystem respiration (Reco), and, hence, can change the net ecosystem carbon balance. However, despite our increasing understanding of the underlying mechanisms, the magnitudes of the impacts of different types of extremes on GPP and Reco within and between ecosystems remain poorly predicted. Here we aim to identify the major factors controlling the amplitude of extreme-event impacts on GPP, Reco, and the resulting net ecosystem production (NEP). We focus on the impacts of heat and drought and their combination. We identified hydrometeorological extreme events in consistently downscaled water availability and temperature measurements over a 30-year time period. We then used FLUXNET eddy covariance flux measurements to estimate the CO2 flux anomalies during these extreme events across dominant vegetation types and climate zones. Overall, our results indicate that short-term heat extremes increased respiration more strongly than they downregulated GPP, resulting in a moderate reduction in the ecosystem's carbon sink potential. In the absence of heat stress, droughts tended to have smaller and similarly dampening effects on both GPP and Reco and, hence, often resulted in neutral NEP responses. The combination of drought and heat typically led to a strong decrease in GPP, whereas heat and drought impacts on respiration partially offset each other. Taken together, compound heat and drought events led to the strongest C sink reduction compared to any single-factor extreme. A key insight of this paper, however, is that duration matters most: for heat stress during droughts, the magnitude of impacts systematically increased with duration, whereas under heat stress without drought, the response of Reco over time turned from an initial increase to a downregulation after about 2 weeks. This confirms earlier theories that not only the magnitude but also the duration of an extreme event determines its impact. Our study corroborates the results of several local site-level case studies but as a novelty generalizes these findings on the global scale. Specifically, we find that the different response functions of the two antipodal land-atmosphere fluxes GPP and Reco can also result in increasing NEP during certain extreme conditions. Apparently counterintuitive findings of this kind bear great potential for scrutinizing the mechanisms implemented in state-of-the-art terrestrial biosphere models and provide a benchmark for future model development and testing.

Climate Change Extreme Events: Meeting the Information Needs of Water Resource Managers

NASA Astrophysics Data System (ADS)

Quay, R.; Garfin, G. M.; Dominguez, F.; Hirschboeck, K. K.; Woodhouse, C. A.; Guido, Z.; White, D. D.

2013-12-01

Information about climate has long been used by water managers to develop short term and long term plans and strategies for regional and local water resources. Inherent within longer term forecasts is an element of uncertainty, which is particularly evident in Global Climate model results for precipitation. For example in the southwest estimates in the flow of the Colorado River based on GCM results indicate changes from 120% or current flow to 60%. Many water resource managers are now using global climate model down scaled estimates results as indications of potential climate change as part of that planning. They are addressing the uncertainty within these estimates by using an anticipatory planning approach looking at a range of possible futures. One aspect of climate that is important for such planning are estimates of future extreme storm (short term) and drought (long term) events. However, the climate science of future possible changes in extreme events is less mature than general climate change science. At a recent workshop among climate scientists and water managers in the southwest, it was concluded the science of climate change extreme events is at least a decade away from being robust enough to be useful for water managers in their water resource management activities. However, it was proposed that there are existing estimates and records of past flooding and drought events that could be combined with general climate change science to create possible future events. These derived events could be of sufficient detail to be used by water resource managers until such time that the science of extreme events is able to provide more detailed estimates. Based on the results of this workshop and other work being done by the Decision Center for a Desert City at Arizona State University and the Climate Assessment for the Southwest center at University of Arizona., this article will 1) review what are the extreme event data needs of Water Resource Managers in the southwest, 2) review of the current state of extreme event climate science, 3) review what information is available about past extreme events in the southwest, 4) report the results of the 2012 workshop on climate change and extreme events, and 5) propose a method for combining this past information with current climate science information to produce estimates of possible future extreme events in sufficient detail to be useful to water resource managers.

Rainfall extremes from TRMM data and the Metastatistical Extreme Value Distribution

NASA Astrophysics Data System (ADS)

Zorzetto, Enrico; Marani, Marco

2017-04-01

A reliable quantification of the probability of weather extremes occurrence is essential for designing resilient water infrastructures and hazard mitigation measures. However, it is increasingly clear that the presence of inter-annual climatic fluctuations determines a substantial long-term variability in the frequency of occurrence of extreme events. This circumstance questions the foundation of the traditional extreme value theory, hinged on stationary Poisson processes or on asymptotic assumptions to derive the Generalized Extreme Value (GEV) distribution. We illustrate here, with application to daily rainfall, a new approach to extreme value analysis, the Metastatistical Extreme Value Distribution (MEVD). The MEVD relaxes the above assumptions and is based on the whole distribution of daily rainfall events, thus allowing optimal use of all available observations. Using a global dataset of rain gauge observations, we show that the MEVD significantly outperforms the Generalized Extreme Value distribution, particularly for long average recurrence intervals and when small samples are available. The latter property suggests MEVD to be particularly suited for applications to satellite rainfall estimates, which only cover two decades, thus making extreme value estimation extremely challenging. Here we apply MEVD to the TRMM TMPA 3B42 product, an 18-year dataset of remotely-sensed daily rainfall providing a quasi-global coverage. Our analyses yield a global scale mapping of daily rainfall extremes and of their distributional tail properties, bridging the existing large gaps in ground-based networks. Finally, we illustrate how our global-scale analysis can provide insight into how properties of local rainfall regimes affect tail estimation uncertainty when using the GEV or MEVD approach. We find a dependence of the estimation uncertainty, for both the GEV- and MEV-based approaches, on the average annual number and on the inter-annual variability of rainy days. In particular, estimation uncertainty decreases 1) as the mean annual number of wet days increases, and 2) as the variability in the number of rainy days, expressed by its coefficient of variation, decreases. We tentatively explain this behavior in terms of the assumptions underlying the two approaches.

Advances in the Assessment of Wind Turbine Operating Extreme Loads via More Efficient Calculation Approaches

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

Graf, Peter; Damiani, Rick R.; Dykes, Katherine

2017-01-09

A new adaptive stratified importance sampling (ASIS) method is proposed as an alternative approach for the calculation of the 50 year extreme load under operational conditions, as in design load case 1.1 of the the International Electrotechnical Commission design standard. ASIS combines elements of the binning and extrapolation technique, currently described by the standard, and of the importance sampling (IS) method to estimate load probability of exceedances (POEs). Whereas a Monte Carlo (MC) approach would lead to the sought level of POE with a daunting number of simulations, IS-based techniques are promising as they target the sampling of the inputmore » parameters on the parts of the distributions that are most responsible for the extreme loads, thus reducing the number of runs required. We compared the various methods on select load channels as output from FAST, an aero-hydro-servo-elastic tool for the design and analysis of wind turbines developed by the National Renewable Energy Laboratory (NREL). Our newly devised method, although still in its infancy in terms of tuning of the subparameters, is comparable to the others in terms of load estimation and its variance versus computational cost, and offers great promise going forward due to the incorporation of adaptivity into the already powerful importance sampling concept.« less

Linear models: permutation methods

USGS Publications Warehouse

Cade, B.S.; Everitt, B.S.; Howell, D.C.

2005-01-01

Permutation tests (see Permutation Based Inference) for the linear model have applications in behavioral studies when traditional parametric assumptions about the error term in a linear model are not tenable. Improved validity of Type I error rates can be achieved with properly constructed permutation tests. Perhaps more importantly, increased statistical power, improved robustness to effects of outliers, and detection of alternative distributional differences can be achieved by coupling permutation inference with alternative linear model estimators. For example, it is well-known that estimates of the mean in linear model are extremely sensitive to even a single outlying value of the dependent variable compared to estimates of the median [7, 19]. Traditionally, linear modeling focused on estimating changes in the center of distributions (means or medians). However, quantile regression allows distributional changes to be estimated in all or any selected part of a distribution or responses, providing a more complete statistical picture that has relevance to many biological questions [6]...

Nonstationary Extreme Value Analysis in a Changing Climate: A Software Package

NASA Astrophysics Data System (ADS)

Cheng, L.; AghaKouchak, A.; Gilleland, E.

2013-12-01

Numerous studies show that climatic extremes have increased substantially in the second half of the 20th century. For this reason, analysis of extremes under a nonstationary assumption has received a great deal of attention. This paper presents a software package developed for estimation of return levels, return periods, and risks of climatic extremes in a changing climate. This MATLAB software package offers tools for analysis of climate extremes under both stationary and non-stationary assumptions. The Nonstationary Extreme Value Analysis (hereafter, NEVA) provides an efficient and generalized framework for analyzing extremes using Bayesian inference. NEVA estimates the extreme value parameters using a Differential Evolution Markov Chain (DE-MC) which utilizes the genetic algorithm Differential Evolution (DE) for global optimization over the real parameter space with the Markov Chain Monte Carlo (MCMC) approach and has the advantage of simplicity, speed of calculation and convergence over conventional MCMC. NEVA also offers the confidence interval and uncertainty bounds of estimated return levels based on the sampled parameters. NEVA integrates extreme value design concepts, data analysis tools, optimization and visualization, explicitly designed to facilitate analysis extremes in geosciences. The generalized input and output files of this software package make it attractive for users from across different fields. Both stationary and nonstationary components of the package are validated for a number of case studies using empirical return levels. The results show that NEVA reliably describes extremes and their return levels.

Improving multisensor estimation of heavy-to-extreme precipitation via conditional bias-penalized optimal estimation

NASA Astrophysics Data System (ADS)

Kim, Beomgeun; Seo, Dong-Jun; Noh, Seong Jin; Prat, Olivier P.; Nelson, Brian R.

2018-01-01

A new technique for merging radar precipitation estimates and rain gauge data is developed and evaluated to improve multisensor quantitative precipitation estimation (QPE), in particular, of heavy-to-extreme precipitation. Unlike the conventional cokriging methods which are susceptible to conditional bias (CB), the proposed technique, referred to herein as conditional bias-penalized cokriging (CBPCK), explicitly minimizes Type-II CB for improved quantitative estimation of heavy-to-extreme precipitation. CBPCK is a bivariate version of extended conditional bias-penalized kriging (ECBPK) developed for gauge-only analysis. To evaluate CBPCK, cross validation and visual examination are carried out using multi-year hourly radar and gauge data in the North Central Texas region in which CBPCK is compared with the variant of the ordinary cokriging (OCK) algorithm used operationally in the National Weather Service Multisensor Precipitation Estimator. The results show that CBPCK significantly reduces Type-II CB for estimation of heavy-to-extreme precipitation, and that the margin of improvement over OCK is larger in areas of higher fractional coverage (FC) of precipitation. When FC > 0.9 and hourly gauge precipitation is > 60 mm, the reduction in root mean squared error (RMSE) by CBPCK over radar-only (RO) is about 12 mm while the reduction in RMSE by OCK over RO is about 7 mm. CBPCK may be used in real-time analysis or in reanalysis of multisensor precipitation for which accurate estimation of heavy-to-extreme precipitation is of particular importance.

"On-off-on" switchable sensor: a fluorescent spiropyran responds to extreme pH conditions and its bioimaging applications.

PubMed

Wan, Shulin; Zheng, Yang; Shen, Jie; Yang, Wantai; Yin, Meizhen

2014-11-26

A novel spiropyran that responds to both extreme acid and extreme alkali and has an "on-off-on" switch is reported. Benzoic acid at the indole N-position and carboxyl group at the indole 6-position contribute to the extreme acid response. The ionizations of carboxyl and phenolic hydroxyl groups cause the extreme alkali response. Moreover, the fluorescent imaging in bacterial cells under extreme pH conditions supports the mechanism of pH response.

The use of historical information for regional frequency analysis of extreme skew surge

NASA Astrophysics Data System (ADS)

Frau, Roberto; Andreewsky, Marc; Bernardara, Pietro

2018-03-01

The design of effective coastal protections requires an adequate estimation of the annual occurrence probability of rare events associated with a return period up to 103 years. Regional frequency analysis (RFA) has been proven to be an applicable way to estimate extreme events by sorting regional data into large and spatially distributed datasets. Nowadays, historical data are available to provide new insight on past event estimation. The utilisation of historical information would increase the precision and the reliability of regional extreme's quantile estimation. However, historical data are from significant extreme events that are not recorded by tide gauge. They usually look like isolated data and they are different from continuous data from systematic measurements of tide gauges. This makes the definition of the duration of our observations period complicated. However, the duration of the observation period is crucial for the frequency estimation of extreme occurrences. For this reason, we introduced here the concept of credible duration. The proposed RFA method (hereinafter referenced as FAB, from the name of the authors) allows the use of historical data together with systematic data, which is a result of the use of the credible duration concept.

Use of Magnetic Resonance Imaging to Monitor Iron Overload

PubMed Central

Wood, John C.

2014-01-01

SYNOPSIS Treatment of iron overload requires robust estimates of total body iron burden and its response to iron chelation therapy. Compliance with chelation therapy varies considerably among patients and individual reporting is notoriously unreliable. Even with perfect compliance, intersubject variability in chelator effectiveness is extremely high, necessitating reliable iron estimates to guide dose titration. In addition, each chelator has a unique profile with respect to clearing iron stores from different organs. This chapter will present the tools available to clinicians monitoring their patients, focusing on non-invasive magnetic resonance imaging methods because they have become the de-facto standard of care. PMID:25064711

A review of statistical methods to analyze extreme precipitation and temperature events in the Mediterranean region

NASA Astrophysics Data System (ADS)

Lazoglou, Georgia; Anagnostopoulou, Christina; Tolika, Konstantia; Kolyva-Machera, Fotini

2018-04-01

The increasing trend of the intensity and frequency of temperature and precipitation extremes during the past decades has substantial environmental and socioeconomic impacts. Thus, the objective of the present study is the comparison of several statistical methods of the extreme value theory (EVT) in order to identify which is the most appropriate to analyze the behavior of the extreme precipitation, and high and low temperature events, in the Mediterranean region. The extremes choice was made using both the block maxima and the peaks over threshold (POT) technique and as a consequence both the generalized extreme value (GEV) and generalized Pareto distributions (GPDs) were used to fit them. The results were compared, in order to select the most appropriate distribution for extremes characterization. Moreover, this study evaluates the maximum likelihood estimation, the L-moments and the Bayesian method, based on both graphical and statistical goodness-of-fit tests. It was revealed that the GPD can characterize accurately both precipitation and temperature extreme events. Additionally, GEV distribution with the Bayesian method is proven to be appropriate especially for the greatest values of extremes. Another important objective of this investigation was the estimation of the precipitation and temperature return levels for three return periods (50, 100, and 150 years) classifying the data into groups with similar characteristics. Finally, the return level values were estimated with both GEV and GPD and with the three different estimation methods, revealing that the selected method can affect the return level values for both the parameter of precipitation and temperature.

Influence of Forest Disturbance on Hydrologic Extremes in the Colorado River Basin

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Middleton, R. S.; McDowell, N. G.; Xu, C.; Wilson, C. J.

2015-12-01

The Colorado River is one of the most important freshwater rivers in the United States: it provides water supply to more than 30 million people, irrigation to 5.7 million acres of cropland, and produces over 8 billion kilowatt hours of hydroelectric power each year. Our study focuses on changes to hydrological extremes and threshold responses across the Colorado River basin due to forest fires, infestations, and stress-induced tree mortality using a scenario-based approach to estimate forest cover disturbance. Scenarios include static vegetation reductions and dynamic reductions in forest compositions based on three CMIP5 global climate models and one emission scenario (1950-2099). For headwater systems, large intra-year variability exists, indicating the influence of climate on these snowmelt driven basins. Strong seasonality in flow responses are also noted; in the Piedra River higher runoff occurs during freshet under a no-forest condition, with the greatest changes observed for maximum streamflow. Conversely, during the recessional period, flows are lower in scenarios with reduced forest compositions. Low-flows appear to be affected in some basins but not others; for example small headwater systems demonstrate higher low-flows with increased disturbance. Global Climate Model scenarios indicate a range of responses in these basins, characterized by lower peak streamflow but with higher winter flows. This response is influenced by shifts in water, and energy balances associated with a combined response of changing climate and forest cover compositions. Results also clearly show how changes in extreme events are forced by shifts in major water balance parameters (runoff, evapotranspiration, snow water equivalent, and soil moisture) from headwater basins spanning a range of hydrological regimes and ecological environments across the Colorado.

Correlation dimension and phase space contraction via extreme value theory

NASA Astrophysics Data System (ADS)

Faranda, Davide; Vaienti, Sandro

2018-04-01

We show how to obtain theoretical and numerical estimates of correlation dimension and phase space contraction by using the extreme value theory. The maxima of suitable observables sampled along the trajectory of a chaotic dynamical system converge asymptotically to classical extreme value laws where: (i) the inverse of the scale parameter gives the correlation dimension and (ii) the extremal index is associated with the rate of phase space contraction for backward iteration, which in dimension 1 and 2, is closely related to the positive Lyapunov exponent and in higher dimensions is related to the metric entropy. We call it the Dynamical Extremal Index. Numerical estimates are straightforward to obtain as they imply just a simple fit to a univariate distribution. Numerical tests range from low dimensional maps, to generalized Henon maps and climate data. The estimates of the indicators are particularly robust even with relatively short time series.

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.

Estimating the Extreme Behaviors of Students Performance Using Quantile Regression--Evidences from Taiwan

ERIC Educational Resources Information Center

Chen, Sheng-Tung; Kuo, Hsiao-I.; Chen, Chi-Chung

2012-01-01

The two-stage least squares approach together with quantile regression analysis is adopted here to estimate the educational production function. Such a methodology is able to capture the extreme behaviors of the two tails of students' performance and the estimation outcomes have important policy implications. Our empirical study is applied to the…

A diversity index for model space selection in the estimation of benchmark and infectious doses via model averaging.

PubMed

Kim, Steven B; Kodell, Ralph L; Moon, Hojin

2014-03-01

In chemical and microbial risk assessments, risk assessors fit dose-response models to high-dose data and extrapolate downward to risk levels in the range of 1-10%. Although multiple dose-response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose-response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA. © 2013 Society for Risk Analysis.

Estimating the extreme low-temperature event using nonparametric methods

NASA Astrophysics Data System (ADS)

D'Silva, Anisha

This thesis presents a new method of estimating the one-in-N low temperature threshold using a non-parametric statistical method called kernel density estimation applied to daily average wind-adjusted temperatures. We apply our One-in-N Algorithm to local gas distribution companies (LDCs), as they have to forecast the daily natural gas needs of their consumers. In winter, demand for natural gas is high. Extreme low temperature events are not directly related to an LDCs gas demand forecasting, but knowledge of extreme low temperatures is important to ensure that an LDC has enough capacity to meet customer demands when extreme low temperatures are experienced. We present a detailed explanation of our One-in-N Algorithm and compare it to the methods using the generalized extreme value distribution, the normal distribution, and the variance-weighted composite distribution. We show that our One-in-N Algorithm estimates the one-in- N low temperature threshold more accurately than the methods using the generalized extreme value distribution, the normal distribution, and the variance-weighted composite distribution according to root mean square error (RMSE) measure at a 5% level of significance. The One-in- N Algorithm is tested by counting the number of times the daily average wind-adjusted temperature is less than or equal to the one-in- N low temperature threshold.

Flood frequency estimates and documented and potential extreme peak discharges in Oklahoma

USGS Publications Warehouse

Tortorelli, Robert L.; McCabe, Lan P.

2001-01-01

Knowledge of the magnitude and frequency of floods is required for the safe and economical design of highway bridges, culverts, dams, levees, and other structures on or near streams; and for flood plain management programs. Flood frequency estimates for gaged streamflow sites were updated, documented extreme peak discharges for gaged and miscellaneous measurement sites were tabulated, and potential extreme peak discharges for Oklahoma streamflow sites were estimated. Potential extreme peak discharges, derived from the relation between documented extreme peak discharges and contributing drainage areas, can provide valuable information concerning the maximum peak discharge that could be expected at a stream site. Potential extreme peak discharge is useful in conjunction with flood frequency analysis to give the best evaluation of flood risk at a site. Peak discharge and flood frequency for selected recurrence intervals from 2 to 500 years were estimated for 352 gaged streamflow sites. Data through 1999 water year were used from streamflow-gaging stations with at least 8 years of record within Oklahoma or about 25 kilometers into the bordering states of Arkansas, Kansas, Missouri, New Mexico, and Texas. These sites were in unregulated basins, and basins affected by regulation, urbanization, and irrigation. Documented extreme peak discharges and associated data were compiled for 514 sites in and near Oklahoma, 352 with streamflow-gaging stations and 162 at miscellaneous measurements sites or streamflow-gaging stations with short record, with a total of 671 measurements.The sites are fairly well distributed statewide, however many streams, large and small, have never been monitored. Potential extreme peak-discharge curves were developed for streamflow sites in hydrologic regions of the state based on documented extreme peak discharges and the contributing drainage areas. Two hydrologic regions, east and west, were defined using 98 degrees 15 minutes longitude as the dividing line.

Estimating maximum instantaneous distortion from inlet total pressure rms and PSD measurements. [Root Mean Square and Power Spectral Density methods

NASA Technical Reports Server (NTRS)

Melick, H. C., Jr.; Ybarra, A. H.; Bencze, D. P.

1975-01-01

An inexpensive method is developed to determine the extreme values of instantaneous inlet distortion. This method also provides insight into the basic mechanics of unsteady inlet flow and the associated engine reaction. The analysis is based on fundamental fluid dynamics and statistical methods to provide an understanding of the turbulent inlet flow and quantitatively relate the rms level and power spectral density (PSD) function of the measured time variant total pressure fluctuations to the strength and size of the low pressure regions. The most probable extreme value of the instantaneous distortion is then synthesized from this information in conjunction with the steady state distortion. Results of the analysis show the extreme values to be dependent upon the steady state distortion, the measured turbulence rms level and PSD function, the time on point, and the engine response characteristics. Analytical projections of instantaneous distortion are presented and compared with data obtained by a conventional, highly time correlated, 40 probe instantaneous pressure measurement system.

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.

Estimating the Effect of Climate Change on Crop Yields and Farmland Values: The Importance of Extreme Temperatures

EPA Pesticide Factsheets

This is a presentation titled Estimating the Effect of Climate Change on Crop Yields and Farmland Values: The Importance of Extreme Temperatures that was given for the National Center for Environmental Economics

Bayesian estimation of extreme flood quantiles using a rainfall-runoff model and a stochastic daily rainfall generator

NASA Astrophysics Data System (ADS)

Costa, Veber; Fernandes, Wilson

2017-11-01

Extreme flood estimation has been a key research topic in hydrological sciences. Reliable estimates of such events are necessary as structures for flood conveyance are continuously evolving in size and complexity and, as a result, their failure-associated hazards become more and more pronounced. Due to this fact, several estimation techniques intended to improve flood frequency analysis and reducing uncertainty in extreme quantile estimation have been addressed in the literature in the last decades. In this paper, we develop a Bayesian framework for the indirect estimation of extreme flood quantiles from rainfall-runoff models. In the proposed approach, an ensemble of long daily rainfall series is simulated with a stochastic generator, which models extreme rainfall amounts with an upper-bounded distribution function, namely, the 4-parameter lognormal model. The rationale behind the generation model is that physical limits for rainfall amounts, and consequently for floods, exist and, by imposing an appropriate upper bound for the probabilistic model, more plausible estimates can be obtained for those rainfall quantiles with very low exceedance probabilities. Daily rainfall time series are converted into streamflows by routing each realization of the synthetic ensemble through a conceptual hydrologic model, the Rio Grande rainfall-runoff model. Calibration of parameters is performed through a nonlinear regression model, by means of the specification of a statistical model for the residuals that is able to accommodate autocorrelation, heteroscedasticity and nonnormality. By combining the outlined steps in a Bayesian structure of analysis, one is able to properly summarize the resulting uncertainty and estimating more accurate credible intervals for a set of flood quantiles of interest. The method for extreme flood indirect estimation was applied to the American river catchment, at the Folsom dam, in the state of California, USA. Results show that most floods, including exceptionally large non-systematic events, were reasonably estimated with the proposed approach. In addition, by accounting for uncertainties in each modeling step, one is able to obtain a better understanding of the influential factors in large flood formation dynamics.

Grassland responses to precipitation extremes

USDA-ARS?s Scientific Manuscript database

Grassland ecosystems are naturally subjected to periods of prolonged drought and sequences of wet years. Climate change is expected to enhance the magnitude and frequency of extreme events at the intraannual and multiyear scales. Are grassland responses to extreme precipitation simply a response to ...

Measurement Properties of the Lower Extremity Functional Scale: A Systematic Review.

PubMed

Mehta, Saurabh P; Fulton, Allison; Quach, Cedric; Thistle, Megan; Toledo, Cesar; Evans, Neil A

2016-03-01

Systematic review of measurement properties. Many primary studies have examined the measurement properties, such as reliability, validity, and sensitivity to change, of the Lower Extremity Functional Scale (LEFS) in different clinical populations. A systematic review summarizing these properties for the LEFS may provide an important resource. To locate and synthesize evidence on the measurement properties of the LEFS and to discuss the clinical implications of the evidence. A literature search was conducted in 4 databases (PubMed, MEDLINE, Embase, and CINAHL), using predefined search terms. Two reviewers performed a critical appraisal of the included studies using a standardized assessment form. A total of 27 studies were included in the review, of which 18 achieved a very good to excellent methodological quality level. The LEFS scores demonstrated excellent test-retest reliability (intraclass correlation coefficients ranging between 0.85 and 0.99) and demonstrated the expected relationships with measures assessing similar constructs (Pearson correlation coefficient values of greater than 0.7). The responsiveness of the LEFS scores was excellent, as suggested by consistently high effect sizes (greater than 0.8) in patients with different lower extremity conditions. Minimal detectable change at the 90% confidence level (MDC90) for the LEFS scores varied between 8.1 and 15.3 across different reassessment intervals in a wide range of patient populations. The pooled estimate of the MDC90 was 6 points and the minimal clinically important difference was 9 points in patients with lower extremity musculoskeletal conditions, which are indicative of true change and clinically meaningful change, respectively. The results of this review support the reliability, validity, and responsiveness of the LEFS scores for assessing functional impairment in a wide array of patient groups with lower extremity musculoskeletal conditions.

The Science of Climate Responsibility

NASA Astrophysics Data System (ADS)

Mitchell, D.; Frumhoff, P. C.; Sparrow, S.; Allen, M. R.

2015-12-01

Extreme events linked with human induced climate change have now been reported around the globe. Among the most troublesome impacts are increased wild fires, failed crop yields, extreme flooding and increase human mortality (Hansen and Cramer, 2015). Many of these impacts are predicted to increase into the future. Non-industrialised communities around the world will be the least capable of adapting, while the industrial communities, who are often responsible for historical carbon emissions, will find adaptation easier. Such a situation lends itself to the issue of responsibility. In order to assess responsibility, it must first be established where the major carbon and methane emissions are originating. It must then be estimated how these emissions project onto localised climate, which is often the primary indicator behind impacts on society. In this study, we address this question using a 25 km regional climate model capable of simulating climate thousands of times under the Weather@home citizen science project. The use of this framework allows us to generate huge data sample sizes, which can be put in the context of very low sample sizes of observational data. We concentrate on the 2003 heat wave over Europe, but show how this method could be applied to less data rich regions, including the Middle East and the Horn of Africa.

(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

Post-flight Analysis of Mars Science Laboratory Entry Aerothermal Environment and Thermal Protection System Response

NASA Technical Reports Server (NTRS)

White, Todd Richard; Mahazari, Milad; Bose, Deepak; Santos, Jose Antonio

2013-01-01

The Mars Science Laboratory successfully landed on the Martian surface on August 5th, 2012. The rover was protected from the extreme heating environments of atmospheric entry by an ablative heatshield. This Phenolic Impregnated Carbon Ablator heatshield was instrumented with a suite of embedded thermocouples, isotherm sensors, and pressure transducers. The sensors monitored the in-depth ablator response, as well as the surface pressure at discrete locations throughout the hypersonic deceleration. This paper presents a comparison of the flight data with post-entry estimates. An assessment of the aerothermal environments, as well as the in-depth response of the heatshield material is made, and conclusions regarding the overall performance of the ablator at the suite locations are presented.

The end of trend-estimation for extreme floods under climate change?

NASA Astrophysics Data System (ADS)

Schulz, Karsten; Bernhardt, Matthias

2016-04-01

An increased risk of flood events is one of the major threats under future climate change conditions. Therefore, many recent studies have investigated trends in flood extreme occurences using historic long-term river discharge data as well as simulations from combined global/regional climate and hydrological models. Severe floods are relatively rare events and the robust estimation of their probability of occurrence requires long time series of data (6). Following a method outlined by the IPCC research community, trends in extreme floods are calculated based on the difference of discharge values exceeding e.g. a 100-year level (Q100) between two 30-year windows, which represents prevailing conditions in a reference and a future time period, respectively. Following this approach, we analysed multiple, synthetically derived 2,000-year trend-free, yearly maximum runoff data generated using three different extreme value distributions (EDV). The parameters were estimated from long term runoff data of four large European watersheds (Danube, Elbe, Rhine, Thames). Both, Q100-values estimated from 30-year moving windows, as well as the subsequently derived trends showed enormous variations with time: for example, estimating the Extreme Value (Gumbel) - distribution for the Danube data, trends of Q100 in the synthetic time-series range from -4,480 to 4,028 m³/s per 100 years (Q100 =10,071m³/s, for reference). Similar results were found when applying other extreme value distributions (Weibull, and log-Normal) to all of the watersheds considered. This variability or "background noise" of estimating trends in flood extremes makes it almost impossible to significantly distinguish any real trend in observed as well as modelled data when such an approach is applied. These uncertainties, even though known in principle are hardly addressed and discussed by the climate change impact community. Any decision making and flood risk management, including the dimensioning of flood protection measures, that is based on such studies might therefore be fundamentally flawed.

General circulation model response to production-limited fossil fuel emission estimates.

NASA Astrophysics Data System (ADS)

Bowman, K. W.; Rutledge, D.; Miller, C.

2008-12-01

The differences in emissions scenarios used to drive IPCC climate projections are the largest sources of uncertainty in future temperature predictions. These estimates are critically dependent on oil, gas, and coal production where the extremal variations in fossil fuel production used in these scenarios is roughly 10:1 after 2100. The development of emission scenarios based on production-limited fossil fuel estimates, i.e., total fossil fuel reserves can be reliably predicted from cumulative production, offers the opportunity to significantly reduce this uncertainty. We present preliminary results of the response of the NASA GISS atmospheric general circulation model to input forcings constrained by production-limited cumulative future fossil-fuel CO2 emissions estimates that reach roughly 500 GtC by 2100, which is significantly lower than any of the IPCC emission scenarios. For climate projections performed from 1958 through 2400 and a climate sensitivity of 5C/2xCO2, the change in globally averaged annual mean temperature relative to fixed CO2 does not exceed 3C with most changes occurring at high latitudes. We find that from 2100-2400 other input forcings such as increased in N2O play an important role in maintaining increase surface temperatures.

Climate change and drought effects on rural income distribution in the Mediterranean: a case study for Spain

NASA Astrophysics Data System (ADS)

Quiroga, Sonia; Suárez, Cristina

2016-06-01

This paper examines the effects of climate change and drought on agricultural incomes in Spanish rural areas. Present research has focused on the effects of these extreme climatological events through response functions, considering effects on crop productivity and average incomes. Among the impacts of droughts, we focused on potential effects on income distribution. The study of the effects on abnormally dry periods is therefore needed in order to perform an analysis of diverse social aspects in the long term. We estimate crop production functions for a range of Mediterranean crops in Spain and we use a measure of the decomposition of inequality to estimate the impact of climate change and drought on yield disparities. Certain adaptation measures may require a better understanding of risks by the public to achieve general acceptance. We provide empirical estimations for the marginal effects of the two impacts considered: farms' average income and income distribution. Our estimates consider crop production response to both biophysical and socio-economic aspects to analyse long-term implications on competitiveness and disparities. As for the results, we find disparities in the adaptation priorities depending on the crop and the region analysed.

A non-stationary cost-benefit based bivariate extreme flood estimation approach

NASA Astrophysics Data System (ADS)

Qi, Wei; Liu, Junguo

2018-02-01

Cost-benefit analysis and flood frequency analysis have been integrated into a comprehensive framework to estimate cost effective design values. However, previous cost-benefit based extreme flood estimation is based on stationary assumptions and analyze dependent flood variables separately. A Non-Stationary Cost-Benefit based bivariate design flood estimation (NSCOBE) approach is developed in this study to investigate influence of non-stationarities in both the dependence of flood variables and the marginal distributions on extreme flood estimation. The dependence is modeled utilizing copula functions. Previous design flood selection criteria are not suitable for NSCOBE since they ignore time changing dependence of flood variables. Therefore, a risk calculation approach is proposed based on non-stationarities in both marginal probability distributions and copula functions. A case study with 54-year observed data is utilized to illustrate the application of NSCOBE. Results show NSCOBE can effectively integrate non-stationarities in both copula functions and marginal distributions into cost-benefit based design flood estimation. It is also found that there is a trade-off between maximum probability of exceedance calculated from copula functions and marginal distributions. This study for the first time provides a new approach towards a better understanding of influence of non-stationarities in both copula functions and marginal distributions on extreme flood estimation, and could be beneficial to cost-benefit based non-stationary bivariate design flood estimation across the world.

A non-stationary cost-benefit analysis approach for extreme flood estimation to explore the nexus of 'Risk, Cost and Non-stationarity'

NASA Astrophysics Data System (ADS)

Qi, Wei

2017-11-01

Cost-benefit analysis is commonly used for engineering planning and design problems in practice. However, previous cost-benefit based design flood estimation is based on stationary assumption. This study develops a non-stationary cost-benefit based design flood estimation approach. This approach integrates a non-stationary probability distribution function into cost-benefit analysis, and influence of non-stationarity on expected total cost (including flood damage and construction costs) and design flood estimation can be quantified. To facilitate design flood selections, a 'Risk-Cost' analysis approach is developed, which reveals the nexus of extreme flood risk, expected total cost and design life periods. Two basins, with 54-year and 104-year flood data respectively, are utilized to illustrate the application. It is found that the developed approach can effectively reveal changes of expected total cost and extreme floods in different design life periods. In addition, trade-offs are found between extreme flood risk and expected total cost, which reflect increases in cost to mitigate risk. Comparing with stationary approaches which generate only one expected total cost curve and therefore only one design flood estimation, the proposed new approach generate design flood estimation intervals and the 'Risk-Cost' approach selects a design flood value from the intervals based on the trade-offs between extreme flood risk and expected total cost. This study provides a new approach towards a better understanding of the influence of non-stationarity on expected total cost and design floods, and could be beneficial to cost-benefit based non-stationary design flood estimation across the world.

Using Kriging with a heterogeneous measurement error to improve the accuracy of extreme precipitation return level estimation

NASA Astrophysics Data System (ADS)

Yin, Shui-qing; Wang, Zhonglei; Zhu, Zhengyuan; Zou, Xu-kai; Wang, Wen-ting

2018-07-01

Extreme precipitation can cause flooding and may result in great economic losses and deaths. The return level is a commonly used measure of extreme precipitation events and is required for hydrological engineer designs, including those of sewerage systems, dams, reservoirs and bridges. In this paper, we propose a two-step method to estimate the return level and its uncertainty for a study region. In the first step, we use the generalized extreme value distribution, the L-moment method and the stationary bootstrap to estimate the return level and its uncertainty at the site with observations. In the second step, a spatial model incorporating the heterogeneous measurement errors and covariates is trained to estimate return levels at sites with no observations and to improve the estimates at sites with limited information. The proposed method is applied to the daily rainfall data from 273 weather stations in the Haihe river basin of North China. We compare the proposed method with two alternatives: the first one is based on the ordinary Kriging method without measurement error, and the second one smooths the estimated location and scale parameters of the generalized extreme value distribution by the universal Kriging method. Results show that the proposed method outperforms its counterparts. We also propose a novel approach to assess the two-step method by comparing it with the at-site estimation method with a series of reduced length of observations. Estimates of the 2-, 5-, 10-, 20-, 50- and 100-year return level maps and the corresponding uncertainties are provided for the Haihe river basin, and a comparison with those released by the Hydrology Bureau of Ministry of Water Resources of China is made.

The role of ensemble post-processing for modeling the ensemble tail

NASA Astrophysics Data System (ADS)

Van De Vyver, Hans; Van Schaeybroeck, Bert; Vannitsem, Stéphane

2016-04-01

The past decades the numerical weather prediction community has witnessed a paradigm shift from deterministic to probabilistic forecast and state estimation (Buizza and Leutbecher, 2015; Buizza et al., 2008), in an attempt to quantify the uncertainties associated with initial-condition and model errors. An important benefit of a probabilistic framework is the improved prediction of extreme events. However, one may ask to what extent such model estimates contain information on the occurrence probability of extreme events and how this information can be optimally extracted. Different approaches have been proposed and applied on real-world systems which, based on extreme value theory, allow the estimation of extreme-event probabilities conditional on forecasts and state estimates (Ferro, 2007; Friederichs, 2010). Using ensemble predictions generated with a model of low dimensionality, a thorough investigation is presented quantifying the change of predictability of extreme events associated with ensemble post-processing and other influencing factors including the finite ensemble size, lead time and model assumption and the use of different covariates (ensemble mean, maximum, spread...) for modeling the tail distribution. Tail modeling is performed by deriving extreme-quantile estimates using peak-over-threshold representation (generalized Pareto distribution) or quantile regression. Common ensemble post-processing methods aim to improve mostly the ensemble mean and spread of a raw forecast (Van Schaeybroeck and Vannitsem, 2015). Conditional tail modeling, on the other hand, is a post-processing in itself, focusing on the tails only. Therefore, it is unclear how applying ensemble post-processing prior to conditional tail modeling impacts the skill of extreme-event predictions. This work is investigating this question in details. Buizza, Leutbecher, and Isaksen, 2008: Potential use of an ensemble of analyses in the ECMWF Ensemble Prediction System, Q. J. R. Meteorol. Soc. 134: 2051-2066.Buizza and Leutbecher, 2015: The forecast skill horizon, Q. J. R. Meteorol. Soc. 141: 3366-3382.Ferro, 2007: A probability model for verifying deterministic forecasts of extreme events. Weather and Forecasting 22 (5), 1089-1100.Friederichs, 2010: Statistical downscaling of extreme precipitation events using extreme value theory. Extremes 13, 109-132.Van Schaeybroeck and Vannitsem, 2015: Ensemble post-processing using member-by-member approaches: theoretical aspects. Q.J.R. Meteorol. Soc., 141: 807-818.

Extreme geomagnetic storms: Probabilistic forecasts and their uncertainties

USGS Publications Warehouse

Riley, Pete; Love, Jeffrey J.

2017-01-01

Extreme space weather events are low-frequency, high-risk phenomena. Estimating their rates of occurrence, as well as their associated uncertainties, is difficult. In this study, we derive statistical estimates and uncertainties for the occurrence rate of an extreme geomagnetic storm on the scale of the Carrington event (or worse) occurring within the next decade. We model the distribution of events as either a power law or lognormal distribution and use (1) Kolmogorov-Smirnov statistic to estimate goodness of fit, (2) bootstrapping to quantify the uncertainty in the estimates, and (3) likelihood ratio tests to assess whether one distribution is preferred over another. Our best estimate for the probability of another extreme geomagnetic event comparable to the Carrington event occurring within the next 10 years is 10.3% 95%  confidence interval (CI) [0.9,18.7] for a power law distribution but only 3.0% 95% CI [0.6,9.0] for a lognormal distribution. However, our results depend crucially on (1) how we define an extreme event, (2) the statistical model used to describe how the events are distributed in intensity, (3) the techniques used to infer the model parameters, and (4) the data and duration used for the analysis. We test a major assumption that the data represent time stationary processes and discuss the implications. If the current trends persist, suggesting that we are entering a period of lower activity, our forecasts may represent upper limits rather than best estimates.

Mapping Dependence Between Extreme Rainfall and Storm Surge

NASA Astrophysics Data System (ADS)

Wu, Wenyan; McInnes, Kathleen; O'Grady, Julian; Hoeke, Ron; Leonard, Michael; Westra, Seth

2018-04-01

Dependence between extreme storm surge and rainfall can have significant implications for flood risk in coastal and estuarine regions. To supplement limited observational records, we use reanalysis surge data from a hydrodynamic model as the basis for dependence mapping, providing information at a resolution of approximately 30 km along the Australian coastline. We evaluated this approach by comparing the dependence estimates from modeled surge to that calculated using historical surge records from 79 tide gauges around Australia. The results show reasonable agreement between the two sets of dependence values, with the exception of lower seasonal variation in the modeled dependence values compared to the observed data, especially at locations where there are multiple processes driving extreme storm surge. This is due to the combined impact of local bathymetry as well as the resolution of the hydrodynamic model and its meteorological inputs. Meteorological drivers were also investigated for different combinations of extreme rainfall and surge—namely rain-only, surge-only, and coincident extremes—finding that different synoptic patterns are responsible for each combination. The ability to supplement observational records with high-resolution modeled surge data enables a much more precise quantification of dependence along the coastline, strengthening the physical basis for assessments of flood risk in coastal regions.

Estimates of peak flood discharge for 21 sites in the Front Range in Colorado in response to extreme rainfall in September 2013

USGS Publications Warehouse

Moody, John A.

2016-03-21

Extreme rainfall in September 2013 caused destructive floods in part of the Front Range in Boulder County, Colorado. Erosion from these floods cut roads and isolated mountain communities for several weeks, and large volumes of eroded sediment were deposited downstream, which caused further damage of property and infrastructures. Estimates of peak discharge for these floods and the associated rainfall characteristics will aid land and emergency managers in the future. Several methods (an ensemble) were used to estimate peak discharge at 21 measurement sites, and the ensemble average and standard deviation provided a final estimate of peak discharge and its uncertainty. Because of the substantial erosion and deposition of sediment, an additional estimate of peak discharge was made based on the flow resistance caused by sediment transport effects.Although the synoptic-scale rainfall was extreme (annual exceedance probability greater than 1,000 years, about 450 millimeters in 7 days) for these mountains, the resulting peak discharges were not. Ensemble average peak discharges per unit drainage area (unit peak discharge, [Qu]) for the floods were 1–2 orders of magnitude less than those for the maximum worldwide floods with similar drainage areas and had a wide range of values (0.21–16.2 cubic meters per second per square kilometer [m3 s-1 km-2]). One possible explanation for these differences was that the band of high-accumulation, high-intensity rainfall was narrow (about 50 kilometers wide), oriented nearly perpendicular to the predominant drainage pattern of the mountains, and therefore entire drainage areas were not subjected to the same range of extreme rainfall. A linear relation (coefficient of determination [R2]=0.69) between Qu and the rainfall intensity (ITc, computed for a time interval equal to the time-of-concentration for the drainage area upstream from each site), had the form: Qu=0.26(ITc-8.6), where the coefficient 0.26 can be considered to be an area-averaged peak runoff coefficient for the September 2013 rain storms in Boulder County, and the 8.6 millimeters per hour to be the rainfall intensity corresponding to a soil moisture threshold that controls the soil infiltration rate. Peak discharge estimates based on the sediment transport effects were generally less than the ensemble average and indicated that sediment transport may be a mechanism that limits velocities in these types of mountain streams such that the Froude number fluctuates about 1 suggesting that this type of floodflow can be approximated as critical flow.

An inverse method to estimate emission rates based on nonlinear least-squares-based ensemble four-dimensional variational data assimilation with local air concentration measurements.

PubMed

Geng, Xiaobing; Xie, Zhenghui; Zhang, Lijun; Xu, Mei; Jia, Binghao

2018-03-01

An inverse source estimation method is proposed to reconstruct emission rates using local air concentration sampling data. It involves the nonlinear least squares-based ensemble four-dimensional variational data assimilation (NLS-4DVar) algorithm and a transfer coefficient matrix (TCM) created using FLEXPART, a Lagrangian atmospheric dispersion model. The method was tested by twin experiments and experiments with actual Cs-137 concentrations measured around the Fukushima Daiichi Nuclear Power Plant (FDNPP). Emission rates can be reconstructed sequentially with the progression of a nuclear accident, which is important in the response to a nuclear emergency. With pseudo observations generated continuously, most of the emission rates were estimated accurately, except under conditions when the wind blew off land toward the sea and at extremely slow wind speeds near the FDNPP. Because of the long duration of accidents and variability in meteorological fields, monitoring networks composed of land stations only in a local area are unable to provide enough information to support an emergency response. The errors in the estimation compared to the real observations from the FDNPP nuclear accident stemmed from a shortage of observations, lack of data control, and an inadequate atmospheric dispersion model without improvement and appropriate meteorological data. The proposed method should be developed further to meet the requirements of a nuclear emergency response. Copyright © 2017 Elsevier Ltd. All rights reserved.

Representing Extremes in Agricultural Models

NASA Technical Reports Server (NTRS)

Ruane, Alex

2015-01-01

AgMIP and related projects are conducting several activities to understand and improve crop model response to extreme events. This involves crop model studies as well as the generation of climate datasets and scenarios more capable of capturing extremes. Models are typically less responsive to extreme events than we observe, and miss several forms of extreme events. Models also can capture interactive effects between climate change and climate extremes. Additional work is needed to understand response of markets and economic systems to food shocks. AgMIP is planning a Coordinated Global and Regional Assessment of Climate Change Impacts on Agricultural Production and Food Security with an aim to inform the IPCC Sixth Assessment Report.

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.

Climate Change and Health Risks from Extreme Heat and Air Pollution in the Eastern United States

NASA Astrophysics Data System (ADS)

Limaye, V.; Vargo, J.; Harkey, M.; Holloway, T.; Meier, P.; Patz, J.

2013-12-01

Climate change is expected to exacerbate health risks from exposure to extreme heat and air pollution through both direct and indirect mechanisms. Directly, warmer ambient temperatures promote biogenic emissions of ozone precursors and favor the formation of ground-level ozone, while an anticipated increase in the frequency of stagnant air masses will allow fine particulates to accumulate. Indirectly, warmer summertime temperatures stimulate energy demand and exacerbate polluting emissions from the electricity sector. Thus, while technological adaptations such as air conditioning can reduce risks from exposures to extreme heat, they can trigger downstream damage to air quality and public health. Through an interdisciplinary modeling effort, we quantify the impacts of climate change on ambient temperatures, summer energy demand, air quality, and public health. The first phase of this work explores how climate change will directly impact the burden of heat-related mortality. Climatic patterns, demographic trends, and epidemiologic risk models suggest that populations in the eastern United States are likely to experience an increasing heat stress mortality burden in response to rising summertime air temperatures. We use North American Regional Climate Change Assessment Program modeling data to estimate mid-century 2-meter air temperatures and humidity across the eastern US from June-August, and quantify how long-term changes in actual and apparent temperatures from present-day will affect the annual burden of heat-related mortality across this region. With the US Environmental Protection Agency's Environmental Benefits Mapping and Analysis Program, we estimate health risks using concentration-response functions, which relate temperature increases to changes in annual mortality rates. We compare mid-century summertime temperature data, downscaled using the Weather Research and Forecasting model, to 2007 baseline temperatures at a 12 km resolution in order to estimate the number of annual excess deaths attributable to increased summer temperatures. Warmer average temperatures are expected to cause 173 additional deaths due to cardiovascular stress, while higher minimum temperatures will cause 67 additional deaths. This work particularly improves on the spatial resolution of published analyses of heat-related mortality in the US.

An assessment of temporal effect on extreme rainfall estimates

NASA Astrophysics Data System (ADS)

Das, Samiran; Zhu, Dehua; Chi-Han, Cheng

2018-06-01

This study assesses the temporal behaviour in terms of inter-decadal variability of extreme daily rainfall of stated return period relevant for hydrologic risk analysis using a novel regional parametric approach. The assessment is carried out based on annual maximum daily rainfall series of 180 meteorological stations of Yangtze River Basin over a 50-year period (1961-2010). The outcomes of the analysis reveal that while there were effects present indicating higher quantile values when estimated from data of the 1990s, it is found not to be noteworthy to exclude the data of any decade from the extreme rainfall estimation process for hydrologic risk analysis.

The Effect of Low Survey Response Rates on Estimates of Alcohol Consumption in a General Population Survey

PubMed Central

Meiklejohn, Jessica; Connor, Jennie; Kypri, Kypros

2012-01-01

Background Response rates for surveys of alcohol use are declining for all modes of administration (postal, telephone, face-to-face). Low response rates may result in estimates that are biased by selective non-response. We examined non-response bias in the NZ GENACIS survey, a postal survey of a random electoral roll sample, with a response rate of 49.5% (n = 1924). Our aim was to estimate the magnitude of non-response bias in estimating the prevalence of current drinking and heavy episodic (binge) drinking. Methods We used the “continuum of resistance” model to guide the investigation. In this model the likelihood of response by sample members is related to the amount of effort required from the researchers to elicit a response. First, the demographic characteristics of respondents and non-respondents were compared. Second, respondents who returned their questionnaire before the first reminder (early), before the second reminder (intermediate) or after the second reminder (late) were compared by demographic characteristics, 12-month prevalence of drinking and prevalence of binge drinking. Results Demographic characteristics and prevalence of binge drinking were significantly different between late respondents and early/intermediate respondents, with the demographics of early and intermediate respondents being similar to people who refused to participate while late respondents were similar to all other non-respondents. Assuming non-respondents who did not actively refuse to participate had the same drinking patterns as late respondents, the prevalence of binge drinking amongst current drinkers was underestimated. Adjusting the prevalence of binge drinkers amongst current drinkers using population weights showed that this method of adjustment still resulted in an underestimate of the prevalence. Conclusions The findings suggest non-respondents who did not actively refuse to participate are likely to have similar or more extreme drinking behaviours than late respondents, and that surveys of health compromising behaviours such as alcohol use are likely to underestimate the prevalence of these behaviours. PMID:22532858

Comparison of different statistical methods for estimation of extreme sea levels with wave set-up contribution

NASA Astrophysics Data System (ADS)

Kergadallan, Xavier; Bernardara, Pietro; Benoit, Michel; Andreewsky, Marc; Weiss, Jérôme

2013-04-01

Estimating the probability of occurrence of extreme sea levels is a central issue for the protection of the coast. Return periods of sea level with wave set-up contribution are estimated here in one site : Cherbourg in France in the English Channel. The methodology follows two steps : the first one is computation of joint probability of simultaneous wave height and still sea level, the second one is interpretation of that joint probabilities to assess a sea level for a given return period. Two different approaches were evaluated to compute joint probability of simultaneous wave height and still sea level : the first one is multivariate extreme values distributions of logistic type in which all components of the variables become large simultaneously, the second one is conditional approach for multivariate extreme values in which only one component of the variables have to be large. Two different methods were applied to estimate sea level with wave set-up contribution for a given return period : Monte-Carlo simulation in which estimation is more accurate but needs higher calculation time and classical ocean engineering design contours of type inverse-FORM in which the method is simpler and allows more complex estimation of wave setup part (wave propagation to the coast for example). We compare results from the two different approaches with the two different methods. To be able to use both Monte-Carlo simulation and design contours methods, wave setup is estimated with an simple empirical formula. We show advantages of the conditional approach compared to the multivariate extreme values approach when extreme sea-level occurs when either surge or wave height is large. We discuss the validity of the ocean engineering design contours method which is an alternative when computation of sea levels is too complex to use Monte-Carlo simulation method.

Slice sampling technique in Bayesian extreme of gold price modelling

NASA Astrophysics Data System (ADS)

Rostami, Mohammad; Adam, Mohd Bakri; Ibrahim, Noor Akma; Yahya, Mohamed Hisham

2013-09-01

In this paper, a simulation study of Bayesian extreme values by using Markov Chain Monte Carlo via slice sampling algorithm is implemented. We compared the accuracy of slice sampling with other methods for a Gumbel model. This study revealed that slice sampling algorithm offers more accurate and closer estimates with less RMSE than other methods . Finally we successfully employed this procedure to estimate the parameters of Malaysia extreme gold price from 2000 to 2011.

Generalized IRT Models for Extreme Response Style

ERIC Educational Resources Information Center

Jin, Kuan-Yu; Wang, Wen-Chung

2014-01-01

Extreme response style (ERS) is a systematic tendency for a person to endorse extreme options (e.g., strongly disagree, strongly agree) on Likert-type or rating-scale items. In this study, we develop a new class of item response theory (IRT) models to account for ERS so that the target latent trait is free from the response style and the tendency…

Modelling of extreme rainfall events in Peninsular Malaysia based on annual maximum and partial duration series

NASA Astrophysics Data System (ADS)

Zin, Wan Zawiah Wan; Shinyie, Wendy Ling; Jemain, Abdul Aziz

2015-02-01

In this study, two series of data for extreme rainfall events are generated based on Annual Maximum and Partial Duration Methods, derived from 102 rain-gauge stations in Peninsular from 1982-2012. To determine the optimal threshold for each station, several requirements must be satisfied and Adapted Hill estimator is employed for this purpose. A semi-parametric bootstrap is then used to estimate the mean square error (MSE) of the estimator at each threshold and the optimal threshold is selected based on the smallest MSE. The mean annual frequency is also checked to ensure that it lies in the range of one to five and the resulting data is also de-clustered to ensure independence. The two data series are then fitted to Generalized Extreme Value and Generalized Pareto distributions for annual maximum and partial duration series, respectively. The parameter estimation methods used are the Maximum Likelihood and the L-moment methods. Two goodness of fit tests are then used to evaluate the best-fitted distribution. The results showed that the Partial Duration series with Generalized Pareto distribution and Maximum Likelihood parameter estimation provides the best representation for extreme rainfall events in Peninsular Malaysia for majority of the stations studied. Based on these findings, several return values are also derived and spatial mapping are constructed to identify the distribution characteristic of extreme rainfall in Peninsular Malaysia.

Analyzing phenological extreme events over the past five decades in Germany

NASA Astrophysics Data System (ADS)

Schleip, Christoph; Menzel, Annette; Estrella, Nicole; Graeser, Philipp

2010-05-01

As climate change may alter the frequency and intensity of extreme temperatures, we analysed whether warming of the last 5 decades has already changed the statistics of phenological extreme events. In this context, two extreme value statistical concepts are discussed and applied to existing phenological datasets of German Weather Service (DWD) in order to derive probabilities of occurrence for extreme early or late phenological events. We analyse four phenological groups; "begin of flowering, "leaf foliation", "fruit ripening" and "leaf colouring" as well as DWD indicator phases of the "phenological year". Additionally we put an emphasis on a between-species analysis; a comparison of differences in extreme onsets between three common northern conifers. Furthermore we conducted a within-species analysis with different phases of horse chestnut throughout a year. The first statistical approach fits data to a Gaussian model using traditional statistical techniques, and then analyses the extreme quantile. The key point of this approach is the adoption of an appropriate probability density function (PDF) to the observed data and the assessment of the PDF parameters change in time. The full analytical description in terms of the estimated PDF for defined time steps of the observation period allows probability assessments of extreme values for e.g. annual or decadal time steps. Related with this approach is the possibility of counting out the onsets which fall in our defined extreme percentiles. The estimation of the probability of extreme events on the basis of the whole data set is in contrast to analyses with the generalized extreme value distribution (GEV). The second approach deals with the extreme PDFs itself and fits the GEV distribution to annual minima of phenological series to provide useful estimates about return levels. For flowering and leaf unfolding phases exceptionally early extremes are seen since the mid 1980s and especially for the single years 1961, 1990 and 2007 whereas exceptionally extreme late events are seen in the year 1970. Summer phases such as fruit ripening exhibit stronger shifts to early extremes than spring phases. Leaf colouring phases reveal increasing probability for late extremes. The with GEV estimated 100-year event of Picea, Pinus and Larix amount to extreme early events of about -27, -31.48 and -32.79 days, respectively. If we assume non-stationary minimum data we get a more extreme 100-year event of about -35.40 for Picea but associated with wider confidence intervals. The GEV is simply another probability distribution but for purposes of extreme analysis in phenology it should be considered as equally important as (if not more important than) the Gaussian PDF approach.

Asymmetrical Responses of Ecosystem Processes to Positive Versus Negative Precipitation Extremes: a Replicated Regression Experimental Approach

NASA Astrophysics Data System (ADS)

Felton, A. J.; Smith, M. D.

2016-12-01

Heightened climatic variability due to atmospheric warming is forecast to increase the frequency and severity of climate extremes. In particular, changes to interannual variability in precipitation, characterized by increases in extreme wet and dry years, are likely to impact virtually all terrestrial ecosystem processes. However, to date experimental approaches have yet to explicitly test how ecosystem processes respond to multiple levels of climatic extremity, limiting our understanding of how ecosystems will respond to forecast increases in the magnitude of climate extremes. Here we report the results of a replicated regression experimental approach, in which we imposed 9 and 11 levels of growing season precipitation amount and extremity in mesic grassland during 2015 and 2016, respectively. Each level corresponded to a specific percentile of the long-term record, which produced a large gradient of soil moisture conditions that ranged from extreme wet to extreme dry. In both 2015 and 2016, asymptotic responses to water availability were observed for soil respiration. This asymmetry was driven in part by transitions between soil moisture versus temperature constraints on respiration as conditions became increasingly dry versus increasingly wet. In 2015, aboveground net primary production (ANPP) exhibited asymmetric responses to precipitation that largely mirrored those of soil respiration. In total, our results suggest that in this mesic ecosystem, these two carbon cycle processes were more sensitive to extreme drought than to extreme wet years. Future work will assess ANPP responses for 2016, soil nutrient supply and physiological responses of the dominant plant species. Future efforts are needed to compare our findings across a diverse array of ecosystem types, and in particular how the timing and magnitude of precipitation events may modify the response of ecosystem processes to increasing magnitudes of precipitation extremes.

Multi-element stochastic spectral projection for high quantile estimation

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

Ko, Jordan, E-mail: jordan.ko@mac.com; Garnier, Josselin

2013-06-15

We investigate quantile estimation by multi-element generalized Polynomial Chaos (gPC) metamodel where the exact numerical model is approximated by complementary metamodels in overlapping domains that mimic the model’s exact response. The gPC metamodel is constructed by the non-intrusive stochastic spectral projection approach and function evaluation on the gPC metamodel can be considered as essentially free. Thus, large number of Monte Carlo samples from the metamodel can be used to estimate α-quantile, for moderate values of α. As the gPC metamodel is an expansion about the means of the inputs, its accuracy may worsen away from these mean values where themore » extreme events may occur. By increasing the approximation accuracy of the metamodel, we may eventually improve accuracy of quantile estimation but it is very expensive. A multi-element approach is therefore proposed by combining a global metamodel in the standard normal space with supplementary local metamodels constructed in bounded domains about the design points corresponding to the extreme events. To improve the accuracy and to minimize the sampling cost, sparse-tensor and anisotropic-tensor quadratures are tested in addition to the full-tensor Gauss quadrature in the construction of local metamodels; different bounds of the gPC expansion are also examined. The global and local metamodels are combined in the multi-element gPC (MEgPC) approach and it is shown that MEgPC can be more accurate than Monte Carlo or importance sampling methods for high quantile estimations for input dimensions roughly below N=8, a limit that is very much case- and α-dependent.« less

Determining return water levels at ungauged coastal sites: a case study for northern Germany

NASA Astrophysics Data System (ADS)

Arns, Arne; Wahl, Thomas; Haigh, Ivan D.; Jensen, Jürgen

2015-04-01

We estimate return periods and levels of extreme still water levels for the highly vulnerable and historically and culturally important small marsh islands known as the Halligen, located in the Wadden Sea offshore of the coast of northern Germany. This is a challenging task as only few water level records are available for this region, and they are currently too short to apply traditional extreme value analysis methods. Therefore, we use the Regional Frequency Analysis (RFA) approach. This originates from hydrology but has been used before in several coastal studies and is also currently applied by the local federal administration responsible for coastal protection in the study area. The RFA enables us to indirectly estimate return levels by transferring hydrological information from gauged to related ungauged sites. Our analyses highlight that this methodology has some drawbacks and may over- or underestimate return levels compared to direct analyses using station data. To overcome these issues, we present an alternative approach, combining numerical and statistical models. First, we produced a numerical multidecadal model hindcast of water levels for the entire North Sea. Predicted water levels from the hindcast are bias corrected using the information from the available tide gauge records. Hence, the simulated water levels agree well with the measured water levels at gauged sites. The bias correction is then interpolated spatially to obtain correction functions for the simulated water levels at each coastal and island model grid point in the study area. Using a recommended procedure to conduct extreme value analyses from a companion study, return water levels suitable for coastal infrastructure design are estimated continuously along the entire coastline of the study area, including the offshore islands. A similar methodology can be applied in other regions of the world where tide gauge observations are sparse.

Global mortality consequences of climate change accounting for adaptation costs and benefits

NASA Astrophysics Data System (ADS)

Rising, J. A.; Jina, A.; Carleton, T.; Hsiang, S. M.; Greenstone, M.

2017-12-01

Empirically-based and plausibly causal estimates of the damages of climate change are greatly needed to inform rapidly developing global and local climate policies. To accurately reflect the costs of climate change, it is essential to estimate how much populations will adapt to a changing climate, yet adaptation remains one of the least understood aspects of social responses to climate. In this paper, we develop and implement a novel methodology to estimate climate impacts on mortality rates. We assemble comprehensive sub-national panel data in 41 countries that account for 56% of the world's population, and combine them with high resolution daily climate data to flexibly estimate the causal effect of temperature on mortality. We find the impacts of temperature on mortality have a U-shaped response; both hot days and cold days cause excess mortality. However, this average response obscures substantial heterogeneity, as populations are differentially adapted to extreme temperatures. Our empirical model allows us to extrapolate response functions across the entire globe, as well as across time, using a range of economic, population, and climate change scenarios. We also develop a methodology to capture not only the benefits of adaptation, but also its costs. We combine these innovations to produce the first causal, micro-founded, global, empirically-derived climate damage function for human health. We project that by 2100, business-as-usual climate change is likely to incur mortality-only costs that amount to approximately 5% of global GDP for 5°C degrees of warming above pre-industrial levels. On average across model runs, we estimate that the upper bound on adaptation costs amounts to 55% of the total damages.

Quantile-based bias correction and uncertainty quantification of extreme event attribution statements

DOE PAGES

Jeon, Soyoung; Paciorek, Christopher J.; Wehner, Michael F.

2016-02-16

Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. However, such climate models may have biases in their representation of extreme events. To account for discrepancies in the probabilities of extreme events between observational datasets and model datasets, we demonstrate an appropriate rescaling of the model output basedmore » on the quantiles of the datasets to estimate an adjusted risk ratio. Our methodology accounts for various components of uncertainty in estimation of the risk ratio. In particular, we present an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model. In this example, we find that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event.« less

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.

Towards a General Theory of Extremes for Observables of Chaotic Dynamical Systems.

PubMed

Lucarini, Valerio; Faranda, Davide; Wouters, Jeroen; Kuna, Tobias

2014-01-01

In this paper we provide a connection between the geometrical properties of the attractor of a chaotic dynamical system and the distribution of extreme values. We show that the extremes of so-called physical observables are distributed according to the classical generalised Pareto distribution and derive explicit expressions for the scaling and the shape parameter. In particular, we derive that the shape parameter does not depend on the chosen observables, but only on the partial dimensions of the invariant measure on the stable, unstable, and neutral manifolds. The shape parameter is negative and is close to zero when high-dimensional systems are considered. This result agrees with what was derived recently using the generalized extreme value approach. Combining the results obtained using such physical observables and the properties of the extremes of distance observables, it is possible to derive estimates of the partial dimensions of the attractor along the stable and the unstable directions of the flow. Moreover, by writing the shape parameter in terms of moments of the extremes of the considered observable and by using linear response theory, we relate the sensitivity to perturbations of the shape parameter to the sensitivity of the moments, of the partial dimensions, and of the Kaplan-Yorke dimension of the attractor. Preliminary numerical investigations provide encouraging results on the applicability of the theory presented here. The results presented here do not apply for all combinations of Axiom A systems and observables, but the breakdown seems to be related to very special geometrical configurations.

Raising Awareness on Heat Related Mortality in Bangladesh

NASA Astrophysics Data System (ADS)

Arrighi, J.; Burkart, K.; Nissan, H.

2017-12-01

Extreme heat is the leading cause of weather-related deaths in the United States and Europe, and was responsible for four of the ten deadliest natural disasters worldwide in 2015. Near the tropics, where hot weather is considered the norm, perceived heat risk is often low, but recent heat waves in South Asia have caught the attention of the health community, policy-makers and the public. In a recent collaboration between the Red Cross Red Crescent Climate Centre, Columbia University and BBC Media Action the effects of extreme heat in Bangladesh were analyzed and the findings were subsequently used as a basis to raise awareness about the impacts of extreme heat on the most vulnerable, to the general public. Analysis of excess heat in Bangladesh between 2003 and 2007 showed that heatwaves occur between April and June with most extreme heat events occurring in May. Between 2003 and 2007 it is estimated that an average of 1500 people died per year due to heatwaves lasting three days or longer, with an eight-day heatwave in 2005 resulting in a minimum of 3,800 excess deaths. Utilizing these findings BBC Media Action launched an online communications campaign in May 2017 ultimately reaching approximately 3.9 million people with information on reducing the impacts of extreme heat. This presentation will highlight key findings from the study of heat related mortality in Bangladesh as well as highlight the benefit of collaboration between scientists and communicators for increasing awareness about the effects of extreme heat on the most vulnerable.

Towards a General Theory of Extremes for Observables of Chaotic Dynamical Systems

NASA Astrophysics Data System (ADS)

Lucarini, Valerio; Faranda, Davide; Wouters, Jeroen; Kuna, Tobias

2014-02-01

In this paper we provide a connection between the geometrical properties of the attractor of a chaotic dynamical system and the distribution of extreme values. We show that the extremes of so-called physical observables are distributed according to the classical generalised Pareto distribution and derive explicit expressions for the scaling and the shape parameter. In particular, we derive that the shape parameter does not depend on the chosen observables, but only on the partial dimensions of the invariant measure on the stable, unstable, and neutral manifolds. The shape parameter is negative and is close to zero when high-dimensional systems are considered. This result agrees with what was derived recently using the generalized extreme value approach. Combining the results obtained using such physical observables and the properties of the extremes of distance observables, it is possible to derive estimates of the partial dimensions of the attractor along the stable and the unstable directions of the flow. Moreover, by writing the shape parameter in terms of moments of the extremes of the considered observable and by using linear response theory, we relate the sensitivity to perturbations of the shape parameter to the sensitivity of the moments, of the partial dimensions, and of the Kaplan-Yorke dimension of the attractor. Preliminary numerical investigations provide encouraging results on the applicability of the theory presented here. The results presented here do not apply for all combinations of Axiom A systems and observables, but the breakdown seems to be related to very special geometrical configurations.

Tropical precipitation extremes: Response to SST-induced warming in aquaplanet simulations

NASA Astrophysics Data System (ADS)

Bhattacharya, Ritthik; Bordoni, Simona; Teixeira, João.

2017-04-01

Scaling of tropical precipitation extremes in response to warming is studied in aquaplanet experiments using the global Weather Research and Forecasting (WRF) model. We show how the scaling of precipitation extremes is highly sensitive to spatial and temporal averaging: while instantaneous grid point extreme precipitation scales more strongly than the percentage increase (˜7% K-1) predicted by the Clausius-Clapeyron (CC) relationship, extremes for zonally and temporally averaged precipitation follow a slight sub-CC scaling, in agreement with results from Climate Model Intercomparison Project (CMIP) models. The scaling depends crucially on the employed convection parameterization. This is particularly true when grid point instantaneous extremes are considered. These results highlight how understanding the response of precipitation extremes to warming requires consideration of dynamic changes in addition to the thermodynamic response. Changes in grid-scale precipitation, unlike those in convective-scale precipitation, scale linearly with the resolved flow. Hence, dynamic changes include changes in both large-scale and convective-scale motions.

On the dimension of complex responses in nonlinear structural vibrations

NASA Astrophysics Data System (ADS)

Wiebe, R.; Spottswood, S. M.

2016-07-01

The ability to accurately model engineering systems under extreme dynamic loads would prove a major breakthrough in many aspects of aerospace, mechanical, and civil engineering. Extreme loads frequently induce both nonlinearities and coupling which increase the complexity of the response and the computational cost of finite element models. Dimension reduction has recently gained traction and promises the ability to distill dynamic responses down to a minimal dimension without sacrificing accuracy. In this context, the dimensionality of a response is related to the number of modes needed in a reduced order model to accurately simulate the response. Thus, an important step is characterizing the dimensionality of complex nonlinear responses of structures. In this work, the dimensionality of the nonlinear response of a post-buckled beam is investigated. Significant detail is dedicated to carefully introducing the experiment, the verification of a finite element model, and the dimensionality estimation algorithm as it is hoped that this system may help serve as a benchmark test case. It is shown that with minor modifications, the method of false nearest neighbors can quantitatively distinguish between the response dimension of various snap-through, non-snap-through, random, and deterministic loads. The state-space dimension of the nonlinear system in question increased from 2-to-10 as the system response moved from simple, low-level harmonic to chaotic snap-through. Beyond the problem studied herein, the techniques developed will serve as a prescriptive guide in developing fast and accurate dimensionally reduced models of nonlinear systems, and eventually as a tool for adaptive dimension-reduction in numerical modeling. The results are especially relevant in the aerospace industry for the design of thin structures such as beams, panels, and shells, which are all capable of spatio-temporally complex dynamic responses that are difficult and computationally expensive to model.

Integrating plant ecological responses to climate extremes from individual to ecosystem levels.

PubMed

Felton, Andrew J; Smith, Melinda D

2017-06-19

Climate extremes will elicit responses from the individual to the ecosystem level. However, only recently have ecologists begun to synthetically assess responses to climate extremes across multiple levels of ecological organization. We review the literature to examine how plant responses vary and interact across levels of organization, focusing on how individual, population and community responses may inform ecosystem-level responses in herbaceous and forest plant communities. We report a high degree of variability at the individual level, and a consequential inconsistency in the translation of individual or population responses to directional changes in community- or ecosystem-level processes. The scaling of individual or population responses to community or ecosystem responses is often predicated upon the functional identity of the species in the community, in particular, the dominant species. Furthermore, the reported stability in plant community composition and functioning with respect to extremes is often driven by processes that operate at the community level, such as species niche partitioning and compensatory responses during or after the event. Future research efforts would benefit from assessing ecological responses across multiple levels of organization, as this will provide both a holistic and mechanistic understanding of ecosystem responses to increasing climatic variability.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'. © 2017 The Author(s).

Bismuth molybdate thick films as ethanol sensor

NASA Astrophysics Data System (ADS)

Jain, Kiran; Kumar, Vipin; Gupta, H. P.; Rastogi, A. C.

2003-10-01

Ethanol sensitivity of bismuth molybdate thick films and sintered pellets were investigated. Sintered pellets were prepared by traditional ceramic processing. Thick films were prepared by metallorganic decomposition process. Ethanol gas sensitivity was measured at various temperatures and concentrations. Thick films of alpha phase bismuth molybdate prepared by spray pyrolysis showed a very fast response to ethanol detection. The response time for the bulk samples is about 40 sec which decreased to about 6 sec for thick films at an operating temperature of 300°C. An extremely low level approximately 10 ppm detection and fast response makes this technique ideal for sensor element fabrication for detection and estimation of alcohol in breath-analyzer. Unlike SnO2, the resistance of these sensors is not affected by humidity at the operating temperature.

A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence

PubMed Central

Spain, S L; Pedroso, I; Kadeva, N; Miller, M B; Iacono, W G; McGue, M; Stergiakouli, E; Smith, G D; Putallaz, M; Lubinski, D; Meaburn, E L; Plomin, R; Simpson, M A

2016-01-01

Although individual differences in intelligence (general cognitive ability) are highly heritable, molecular genetic analyses to date have had limited success in identifying specific loci responsible for its heritability. This study is the first to investigate exome variation in individuals of extremely high intelligence. Under the quantitative genetic model, sampling from the high extreme of the distribution should provide increased power to detect associations. We therefore performed a case–control association analysis with 1409 individuals drawn from the top 0.0003 (IQ >170) of the population distribution of intelligence and 3253 unselected population-based controls. Our analysis focused on putative functional exonic variants assayed on the Illumina HumanExome BeadChip. We did not observe any individual protein-altering variants that are reproducibly associated with extremely high intelligence and within the entire distribution of intelligence. Moreover, no significant associations were found for multiple rare alleles within individual genes. However, analyses using genome-wide similarity between unrelated individuals (genome-wide complex trait analysis) indicate that the genotyped functional protein-altering variation yields a heritability estimate of 17.4% (s.e. 1.7%) based on a liability model. In addition, investigation of nominally significant associations revealed fewer rare alleles associated with extremely high intelligence than would be expected under the null hypothesis. This observation is consistent with the hypothesis that rare functional alleles are more frequently detrimental than beneficial to intelligence. PMID:26239293

A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence.

PubMed

Spain, S L; Pedroso, I; Kadeva, N; Miller, M B; Iacono, W G; McGue, M; Stergiakouli, E; Davey Smith, G; Putallaz, M; Lubinski, D; Meaburn, E L; Plomin, R; Simpson, M A

2016-08-01

Although individual differences in intelligence (general cognitive ability) are highly heritable, molecular genetic analyses to date have had limited success in identifying specific loci responsible for its heritability. This study is the first to investigate exome variation in individuals of extremely high intelligence. Under the quantitative genetic model, sampling from the high extreme of the distribution should provide increased power to detect associations. We therefore performed a case-control association analysis with 1409 individuals drawn from the top 0.0003 (IQ >170) of the population distribution of intelligence and 3253 unselected population-based controls. Our analysis focused on putative functional exonic variants assayed on the Illumina HumanExome BeadChip. We did not observe any individual protein-altering variants that are reproducibly associated with extremely high intelligence and within the entire distribution of intelligence. Moreover, no significant associations were found for multiple rare alleles within individual genes. However, analyses using genome-wide similarity between unrelated individuals (genome-wide complex trait analysis) indicate that the genotyped functional protein-altering variation yields a heritability estimate of 17.4% (s.e. 1.7%) based on a liability model. In addition, investigation of nominally significant associations revealed fewer rare alleles associated with extremely high intelligence than would be expected under the null hypothesis. This observation is consistent with the hypothesis that rare functional alleles are more frequently detrimental than beneficial to intelligence.

Genetic selection for temperament traits in dairy and beef cattle.

PubMed

Haskell, Marie J; Simm, Geoff; Turner, Simon P

2014-01-01

Animal temperament can be defined as a response to environmental or social stimuli. There are a number of temperament traits in cattle that contribute to their welfare, including their response to handling or milking, response to challenge such as human approach or intervention at calving, and response to conspecifics. In a number of these areas, the genetic basis of the trait has been studied. Heritabilities have been estimated and in some cases quantitative trait loci (QTL) have been identified. The variation is sometimes considerable and moderate heritabilities have been found for the major handling temperament traits, making them amenable to selection. Studies have also investigated the correlations between temperament and other traits, such as productivity and meat quality. Despite this, there are relatively few examples of temperament traits being used in selection programmes. Most often, animals are screened for aggression or excessive fear during handling or milking, with extreme animals being culled, or EBVs for temperament are estimated, but these traits are not commonly included routinely in selection indices, despite there being economic, welfare and human safety drivers for their. There may be a number of constraints and barriers. For some traits and breeds, there may be difficulties in collecting behavioral data on sufficiently large populations of animals to estimate genetic parameters. Most selection indices require estimates of economic values, and it is often difficult to assign an economic value to a temperament trait. The effects of selection primarily for productivity traits on temperament and welfare are discussed. Future opportunities include automated data collection methods and the wider use of genomic information in selection.

Genetic selection for temperament traits in dairy and beef cattle

PubMed Central

Haskell, Marie J.; Simm, Geoff; Turner, Simon P.

2014-01-01

Animal temperament can be defined as a response to environmental or social stimuli. There are a number of temperament traits in cattle that contribute to their welfare, including their response to handling or milking, response to challenge such as human approach or intervention at calving, and response to conspecifics. In a number of these areas, the genetic basis of the trait has been studied. Heritabilities have been estimated and in some cases quantitative trait loci (QTL) have been identified. The variation is sometimes considerable and moderate heritabilities have been found for the major handling temperament traits, making them amenable to selection. Studies have also investigated the correlations between temperament and other traits, such as productivity and meat quality. Despite this, there are relatively few examples of temperament traits being used in selection programmes. Most often, animals are screened for aggression or excessive fear during handling or milking, with extreme animals being culled, or EBVs for temperament are estimated, but these traits are not commonly included routinely in selection indices, despite there being economic, welfare and human safety drivers for their. There may be a number of constraints and barriers. For some traits and breeds, there may be difficulties in collecting behavioral data on sufficiently large populations of animals to estimate genetic parameters. Most selection indices require estimates of economic values, and it is often difficult to assign an economic value to a temperament trait. The effects of selection primarily for productivity traits on temperament and welfare are discussed. Future opportunities include automated data collection methods and the wider use of genomic information in selection. PMID:25374582

Recent variations in Amazon carbon balance driven by climate anomalies

NASA Astrophysics Data System (ADS)

Miller, J. B.

2015-12-01

Understanding tropical rainforest response to heat and drought is critical for quantifying the effects of climate change on tropical ecosystems, including global climate-carbon feedbacks. Of particular importance for the global carbon budget is net ecosystem exchange of CO2 with the atmosphere (NEE), a metric that represents the total integrated signal of carbon fluxes into and out of ecosystems. Sub-annual and sub-basin NEE estimates have previously been derived from process-based biosphere models, despite often disagreeing with plot-scale observations. Our analysis of airborne CO2 and CO measurements reveals monthly, sub-Basin scale (~106 km2) NEE variations in a framework that is largely independent of bottom-up estimates. As such, our approach provides new insights about tropical forest response to climate. We find acute sensitivity of NEE to daily and monthly climate extremes. In particular, increased central-Amazon NEE was associated with wet-season heat and dry-season drought in 2010. We analyze satellite proxies for photosynthesis and find that suppression of photosynthesis may have contributed to increased carbon loss in the 2010 drought, consistent with recent analysis of plot-scale measurements. In the eastern Amazon, pulses of increased NEE (i.e. net respiration) persisted through 2011, suggesting legacy effects of the drought that occurred in 2010. Regional differences in post-drought recovery in 2011 and 2012 appear related to long-term water availability. These results provide novel evidence of the vulnerability of Amazon carbon stocks to short-term temperature and moisture extremes.

Beyond Traditional Extreme Value Theory Through a Metastatistical Approach: Lessons Learned from Precipitation, Hurricanes, and Storm Surges

NASA Astrophysics Data System (ADS)

Marani, M.; Zorzetto, E.; Hosseini, S. R.; Miniussi, A.; Scaioni, M.

2017-12-01

The Generalized Extreme Value (GEV) distribution is widely adopted irrespective of the properties of the stochastic process generating the extreme events. However, GEV presents several limitations, both theoretical (asymptotic validity for a large number of events/year or hypothesis of Poisson occurrences of Generalized Pareto events), and practical (fitting uses just yearly maxima or a few values above a high threshold). Here we describe the Metastatistical Extreme Value Distribution (MEVD, Marani & Ignaccolo, 2015), which relaxes asymptotic or Poisson/GPD assumptions and makes use of all available observations. We then illustrate the flexibility of the MEVD by applying it to daily precipitation, hurricane intensity, and storm surge magnitude. Application to daily rainfall from a global raingauge network shows that MEVD estimates are 50% more accurate than those from GEV when the recurrence interval of interest is much greater than the observational period. This makes MEVD suited for application to satellite rainfall observations ( 20 yrs length). Use of MEVD on TRMM data yields extreme event patterns that are in better agreement with surface observations than corresponding GEV estimates.Applied to the HURDAT2 Atlantic hurricane intensity dataset, MEVD significantly outperforms GEV estimates of extreme hurricanes. Interestingly, the Generalized Pareto distribution used for "ordinary" hurricane intensity points to the existence of a maximum limit wind speed that is significantly smaller than corresponding physically-based estimates. Finally, we applied the MEVD approach to water levels generated by tidal fluctuations and storm surges at a set of coastal sites spanning different storm-surge regimes. MEVD yields accurate estimates of large quantiles and inferences on tail thickness (fat vs. thin) of the underlying distribution of "ordinary" surges. In summary, the MEVD approach presents a number of theoretical and practical advantages, and outperforms traditional approaches in several applications. We conclude that the MEVD is a significant contribution to further generalize extreme value theory, with implications for a broad range of Earth Sciences.

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.

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

Estimation of genetic parameters and breeding values across challenged environments to select for robust pigs.

PubMed

Herrero-Medrano, J M; Mathur, P K; ten Napel, J; Rashidi, H; Alexandri, P; Knol, E F; Mulder, H A

2015-04-01

Robustness is an important issue in the pig production industry. Since pigs from international breeding organizations have to withstand a variety of environmental challenges, selection of pigs with the inherent ability to sustain their productivity in diverse environments may be an economically feasible approach in the livestock industry. The objective of this study was to estimate genetic parameters and breeding values across different levels of environmental challenge load. The challenge load (CL) was estimated as the reduction in reproductive performance during different weeks of a year using 925,711 farrowing records from farms distributed worldwide. A wide range of levels of challenge, from favorable to unfavorable environments, was observed among farms with high CL values being associated with confirmed situations of unfavorable environment. Genetic parameters and breeding values were estimated in high- and low-challenge environments using a bivariate analysis, as well as across increasing levels of challenge with a random regression model using Legendre polynomials. Although heritability estimates of number of pigs born alive were slightly higher in environments with extreme CL than in those with intermediate levels of CL, the heritabilities of number of piglet losses increased progressively as CL increased. Genetic correlations among environments with different levels of CL suggest that selection in environments with extremes of low or high CL would result in low response to selection. Therefore, selection programs of breeding organizations that are commonly conducted under favorable environments could have low response to selection in commercial farms that have unfavorable environmental conditions. Sows that had experienced high levels of challenge at least once during their productive life were ranked according to their EBV. The selection of pigs using EBV ignoring environmental challenges or on the basis of records from only favorable environments resulted in a sharp decline in productivity as the level of challenge increased. In contrast, selection using the random regression approach resulted in limited change in productivity with increasing levels of challenge. Hence, we demonstrate that the use of a quantitative measure of environmental CL and a random regression approach can be comprehensively combined for genetic selection of pigs with enhanced ability to maintain high productivity in harsh environments.

Diabetic Driving Studies-Part 1: Brake Response Time in Diabetic Drivers With Lower Extremity Neuropathy.

PubMed

Meyr, Andrew J; Spiess, Kerianne E

Although the effect of lower extremity pathology and surgical intervention on automobile driving function has been a topic of contemporary interest, we are unaware of any analysis of the effect of lower extremity diabetic sensorimotor neuropathy on driving performance. The objective of the present case-control investigation was to assess the mean brake response time in diabetic drivers with lower extremity neuropathy compared with that of a control group and a brake response safety threshold. The driving performances of participants were evaluated using a computerized driving simulator with specific measurement of the mean brake response time and frequency of abnormally delayed brake responses. We analyzed a control group of 25 active drivers with neither diabetes nor lower extremity neuropathy and an experimental group of 25 active drivers with type 2 diabetes and lower extremity neuropathy. The experimental group demonstrated a 37.89% slower mean brake response time (0.757 ± 0.180 versus 0.549 ± 0.076 second; p < .001), with abnormally delayed responses occurring at a greater frequency (57.5% versus 3.5%; p < .001). Independent of a comparative statistical analysis, the observed mean brake response time in the experimental group was slower than the reported safety brake response threshold of 0.70 second. The results of the present investigation provide original data with respect to abnormally delayed brake responses in diabetic patients with lower extremity neuropathy and might raise the potential for impaired driving function in this population. Copyright © 2017 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

The effect of extreme cold temperatures on the risk of death in the two major Portuguese cities

NASA Astrophysics Data System (ADS)

Antunes, Liliana; Silva, Susana Pereira; Marques, Jorge; Nunes, Baltazar; Antunes, Sílvia

2017-01-01

It is well known that meteorological conditions influence the comfort and human health. Southern European countries, including Portugal, show the highest mortality rates during winter, but the effects of extreme cold temperatures in Portugal have never been estimated. The objective of this study was the estimation of the effect of extreme cold temperatures on the risk of death in Lisbon and Oporto, aiming the production of scientific evidence for the development of a real-time health warning system. Poisson regression models combined with distributed lag non-linear models were applied to assess the exposure-response relation and lag patterns of the association between minimum temperature and all-causes mortality and between minimum temperature and circulatory and respiratory system diseases mortality from 1992 to 2012, stratified by age, for the period from November to March. The analysis was adjusted for over dispersion and population size, for the confounding effect of influenza epidemics and controlled for long-term trend, seasonality and day of the week. Results showed that the effect of cold temperatures in mortality was not immediate, presenting a 1-2-day delay, reaching maximum increased risk of death after 6-7 days and lasting up to 20-28 days. The overall effect was generally higher and more persistent in Lisbon than in Oporto, particularly for circulatory and respiratory mortality and for the elderly. Exposure to cold temperatures is an important public health problem for a relevant part of the Portuguese population, in particular in Lisbon.

Accessibility assessment of Houston's roadway network during Harvey through integration of observed flood impacts and hydrologic modeling

NASA Astrophysics Data System (ADS)

Gidaris, I.; Gori, A.; Panakkal, P.; Padgett, J.; Bedient, P. B.

2017-12-01

The record-breaking rainfall produced over the Houston region by Hurricane Harvey resulted in catastrophic and unprecedented impacts on the region's infrastructure. Notably, Houston's transportation network was crippled, with almost every major highway flooded during the five-day event. Entire neighborhoods and subdivisions were inundated, rendering them completely inaccessible to rescue crews and emergency services. Harvey has tragically highlighted the vulnerability of major thoroughfares, as well as neighborhood roads, to severe inundation during extreme precipitation events. Furthermore, it has emphasized the need for detailed accessibility characterization of road networks under extreme event scenarios in order to determine which areas of the city are most vulnerable. This analysis assesses and tracks the accessibility of Houston's major highways during Harvey's evolution by utilizing road flood/closure data from the Texas DOT. In the absence of flooded/closure data for local roads, a hybrid approach is adopted that utilizes a physics-based hydrologic model to produce high-resolution inundation estimates for selected urban watersheds in the Houston area. In particular, hydrologic output in the form of inundation depths is used to estimate the operability of local roads. Ultimately, integration of hydrologic-based estimation of road conditions with observed data from DOT supports a network accessibility analysis of selected urban neighborhoods. This accessibility analysis can identify operable routes for emergency response (rescue crews, medical services, etc.) during the storm event.

Estimating missing daily temperature extremes in Jaffna, Sri Lanka

NASA Astrophysics Data System (ADS)

Thevakaran, A.; Sonnadara, D. U. J.

2018-04-01

The accuracy of reconstructing missing daily temperature extremes in the Jaffna climatological station, situated in the northern part of the dry zone of Sri Lanka, is presented. The adopted method utilizes standard departures of daily maximum and minimum temperature values at four neighbouring stations, Mannar, Anuradhapura, Puttalam and Trincomalee to estimate the standard departures of daily maximum and minimum temperatures at the target station, Jaffna. The daily maximum and minimum temperatures from 1966 to 1980 (15 years) were used to test the validity of the method. The accuracy of the estimation is higher for daily maximum temperature compared to daily minimum temperature. About 95% of the estimated daily maximum temperatures are within ±1.5 °C of the observed values. For daily minimum temperature, the percentage is about 92. By calculating the standard deviation of the difference in estimated and observed values, we have shown that the error in estimating the daily maximum and minimum temperatures is ±0.7 and ±0.9 °C, respectively. To obtain the best accuracy when estimating the missing daily temperature extremes, it is important to include Mannar which is the nearest station to the target station, Jaffna. We conclude from the analysis that the method can be applied successfully to reconstruct the missing daily temperature extremes in Jaffna where no data is available due to frequent disruptions caused by civil unrests and hostilities in the region during the period, 1984 to 2000.

Induction Hazard Assessment: The Variability of Geoelectric Responses During Geomagnetic Storms Within Common Hazard Zones

NASA Astrophysics Data System (ADS)

Cuttler, S. W.; Love, J. J.; Swidinsky, A.

2017-12-01

Geomagnetic field data obtained through the INTERMAGNET program are convolved with four validated EarthScope USArray impedances to estimate the geoelectric variations throughout the duration of a geomagnetic storm. A four day long geomagnetic storm began on June 22, 2016, and was recorded at the Brandon (BRD), Manitoba and Fredericksburg (FRD), Virginia magnetic observatories over four days. Two impedance tensors corresponding to each magnetic observatory produce extremely different responses, despite being within close geographical proximity. Estimated time series of the geoelectric field throughout the duration of the geomagnetic storm were calculated, providing an understanding of how the geoelectric field differs across small geographic distances within the same geomagnetic hazard zones derived from prior geomagnetic hazard assessment. We show that the geoelectric response of two sites within 200km of one another can differ by up to two orders of magnitude (4245 mV/km at one location and 38 mV/km at another location 125km away). In addition, we compare these results with estimations of the geoelectric field generated from synthetic 1-dimensional resistivity models commonly used to represent large geographic regions when assessing geomagnetically induced current (GIC) hazards. This comparison shows that estimations of the geomagnetic field from these models differ greatly from estimations produced from Earthscope USArray sites (1205 mV/km in the 1D and 4245 mV/km in the 3D case in one example). This study demonstrates that the application of uniform 1-dimensional resistivity models of the subsurface to wide geographic regions is insufficient to predict the geoelectric hazard at a given location. Furthermore an evaluation of the 3-dimensional resistivity distribution at a given location is necessary to produce a reliable estimation of how the geoelectric field evolves over the course of a geomagnetic storm.

Footbridge system identification using wireless inertial measurement units for force and response measurements

NASA Astrophysics Data System (ADS)

Brownjohn, James Mark William; Bocian, Mateusz; Hester, David; Quattrone, Antonino; Hudson, William; Moore, Daniel; Goh, Sushma; Lim, Meng Sun

2016-12-01

With the main focus on safety, design of structures for vibration serviceability is often overlooked or mismanaged, resulting in some high profile structures failing publicly to perform adequately under human dynamic loading due to walking, running or jumping. A standard tool to inform better design, prove fitness for purpose before entering service and design retrofits is modal testing, a procedure that typically involves acceleration measurements using an array of wired sensors and force generation using a mechanical shaker. A critical but often overlooked aspect is using input (force) to output (response) relationships to enable estimation of modal mass, which is a key parameter directly controlling vibration levels in service. This paper describes the use of wireless inertial measurement units (IMUs), designed for biomechanics motion capture applications, for the modal testing of a 109 m footbridge. IMUs were first used for an output-only vibration survey to identify mode frequencies, shapes and damping ratios, then for simultaneous measurement of body accelerations of a human subject jumping to excite specific vibrations modes and build up bridge deck accelerations at the jumping location. Using the mode shapes and the vertical acceleration data from a suitable body landmark scaled by body mass, thus providing jumping force data, it was possible to create frequency response functions and estimate modal masses. The modal mass estimates for this bridge were checked against estimates obtained using an instrumented hammer and known mass distributions, showing consistency among the experimental estimates. Finally, the method was used in an applied research application on a short span footbridge where the benefits of logistical and operational simplicity afforded by the highly portable and easy to use IMUs proved extremely useful for an efficient evaluation of vibration serviceability, including estimation of modal masses.

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.

Excessive heat and respiratory hospitalizations in New York State: estimating current and future public health burden related to climate change.

PubMed

Lin, Shao; Hsu, Wan-Hsiang; Van Zutphen, Alissa R; Saha, Shubhayu; Luber, George; Hwang, Syni-An

2012-11-01

Although many climate-sensitive environmental exposures are related to mortality and morbidity, there is a paucity of estimates of the public health burden attributable to climate change. We estimated the excess current and future public health impacts related to respiratory hospitalizations attributable to extreme heat in summer in New York State (NYS) overall, its geographic regions, and across different demographic strata. On the basis of threshold temperature and percent risk changes identified from our study in NYS, we estimated recent and future attributable risks related to extreme heat due to climate change using the global climate model with various climate scenarios. We estimated effects of extreme high apparent temperature in summer on respiratory admissions, days hospitalized, direct hospitalization costs, and lost productivity from days hospitalized after adjusting for inflation. The estimated respiratory disease burden attributable to extreme heat at baseline (1991-2004) in NYS was 100 hospital admissions, US$644,069 in direct hospitalization costs, and 616 days of hospitalization per year. Projections for 2080-2099 based on three different climate scenarios ranged from 206-607 excess hospital admissions, US$26-$76 million in hospitalization costs, and 1,299-3,744 days of hospitalization per year. Estimated impacts varied by geographic region and population demographics. We estimated that excess respiratory admissions in NYS due to excessive heat would be 2 to 6 times higher in 2080-2099 than in 1991-2004. When combined with other heat-associated diseases and mortality, the potential public health burden associated with global warming could be substantial.

Site restoration: Estimation of attributable costs from plutonium-dispersal accidents

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

Chanin, D.I.; Murfin, W.B.

1996-05-01

A nuclear weapons accident is an extremely unlikely event due to the extensive care taken in operations. However, under some hypothetical accident conditions, plutonium might be dispersed to the environment. This would result in costs being incurred by the government to remediate the site and compensate for losses. This study is a multi-disciplinary evaluation of the potential scope of the post-accident response that includes technical factors, current and proposed legal requirements and constraints, as well as social/political factors that could influence decision making. The study provides parameters that can be used to assess economic costs for accidents postulated to occurmore » in urban areas, Midwest farmland, Western rangeland, and forest. Per-area remediation costs have been estimated, using industry-standard methods, for both expedited and extended remediation. Expedited remediation costs have been evaluated for highways, airports, and urban areas. Extended remediation costs have been evaluated for all land uses except highways and airports. The inclusion of cost estimates in risk assessments, together with the conventional estimation of doses and health effects, allows a fuller understanding of the post-accident environment. The insights obtained can be used to minimize economic risks by evaluation of operational and design alternatives, and through development of improved capabilities for accident response.« less

Consistency of extreme flood estimation approaches

NASA Astrophysics Data System (ADS)

Felder, Guido; Paquet, Emmanuel; Penot, David; Zischg, Andreas; Weingartner, Rolf

2017-04-01

Estimations of low-probability flood events are frequently used for the planning of infrastructure as well as for determining the dimensions of flood protection measures. There are several well-established methodical procedures to estimate low-probability floods. However, a global assessment of the consistency of these methods is difficult to achieve, the "true value" of an extreme flood being not observable. Anyway, a detailed comparison performed on a given case study brings useful information about the statistical and hydrological processes involved in different methods. In this study, the following three different approaches for estimating low-probability floods are compared: a purely statistical approach (ordinary extreme value statistics), a statistical approach based on stochastic rainfall-runoff simulation (SCHADEX method), and a deterministic approach (physically based PMF estimation). These methods are tested for two different Swiss catchments. The results and some intermediate variables are used for assessing potential strengths and weaknesses of each method, as well as for evaluating the consistency of these methods.

Challenges estimating the return period of extreme floods for reinsurance applications

NASA Astrophysics Data System (ADS)

Raven, Emma; Busby, Kathryn; Liu, Ye

2013-04-01

Mapping and modelling extreme natural events is fundamental within the insurance and reinsurance industry for assessing risk. For example, insurers might use a 1 in 100-year flood hazard map to set the annual premium of a property, whilst a reinsurer might assess the national scale loss associated with the 1 in 200-year return period for capital and regulatory requirements. Using examples from a range of international flood projects, we focus on exploring how to define what the n-year flood looks like for predictive uses in re/insurance applications, whilst considering challenges posed by short historical flow records and the spatial and temporal complexities of flood. First, we shall explore the use of extreme value theory (EVT) statistics for extrapolating data beyond the range of observations in a marginal analysis. In particular, we discuss how to estimate the return period of historical flood events and explore the impact that a range of statistical decisions have on these estimates. Decisions include: (1) selecting which distribution type to apply (e.g. generalised Pareto distribution (GPD) vs. generalised extreme value distribution (GEV)); (2) if former, the choice of the threshold above which the GPD is fitted to the data; and (3) the necessity to perform a cluster analysis to group flow peaks to temporally represent individual flood events. Second, we summarise a specialised multivariate extreme value model, which combines the marginal analysis above with dependence modelling to generate industry standard event sets containing thousands of simulated, equi-probable floods across a region/country. These events represent the typical range of anticipated flooding across a region and can be used to estimate the largest or most widespread events that are expected to occur. Finally, we summarise how a reinsurance catastrophe model combines the event set with detailed flood hazard maps to estimate the financial cost of floods; both the full event set and also individual extreme events. Since the predicted loss estimates, typically in the form of a curve plotting return period against modelled loss, are used in the pricing of reinsurance, we demonstrate the importance of the estimated return period and understanding the uncertainties associated with it.

An "Ensemble Approach" to Modernizing Extreme Precipitation Estimation for Dam Safety Decision-Making

NASA Astrophysics Data System (ADS)

Cifelli, R.; Mahoney, K. M.; Webb, R. S.; McCormick, B.

2017-12-01

To ensure structural and operational safety of dams and other water management infrastructure, water resources managers and engineers require information about the potential for heavy precipitation. The methods and data used to estimate extreme rainfall amounts for managing risk are based on 40-year-old science and in need of improvement. The need to evaluate new approaches based on the best science available has led the states of Colorado and New Mexico to engage a body of scientists and engineers in an innovative "ensemble approach" to updating extreme precipitation estimates. NOAA is at the forefront of one of three technical approaches that make up the "ensemble study"; the three approaches are conducted concurrently and in collaboration with each other. One approach is the conventional deterministic, "storm-based" method, another is a risk-based regional precipitation frequency estimation tool, and the third is an experimental approach utilizing NOAA's state-of-the-art High Resolution Rapid Refresh (HRRR) physically-based dynamical weather prediction model. The goal of the overall project is to use the individual strengths of these different methods to define an updated and broadly acceptable state of the practice for evaluation and design of dam spillways. This talk will highlight the NOAA research and NOAA's role in the overarching goal to better understand and characterizing extreme precipitation estimation uncertainty. The research led by NOAA explores a novel high-resolution dataset and post-processing techniques using a super-ensemble of hourly forecasts from the HRRR model. We also investigate how this rich dataset may be combined with statistical methods to optimally cast the data in probabilistic frameworks. NOAA expertise in the physical processes that drive extreme precipitation is also employed to develop careful testing and improved understanding of the limitations of older estimation methods and assumptions. The process of decision making in the midst of uncertainty is a major part of this study. We will speak to how the ensemble approach may be used in concert with one another to manage risk and enhance resiliency in the midst of uncertainty. Finally, the presentation will also address the implications of including climate change in future extreme precipitation estimation studies.

A joint sparse representation-based method for double-trial evoked potentials estimation.

PubMed

Yu, Nannan; Liu, Haikuan; Wang, Xiaoyan; Lu, Hanbing

2013-12-01

In this paper, we present a novel approach to solving an evoked potentials estimating problem. Generally, the evoked potentials in two consecutive trials obtained by repeated identical stimuli of the nerves are extremely similar. In order to trace evoked potentials, we propose a joint sparse representation-based double-trial evoked potentials estimation method, taking full advantage of this similarity. The estimation process is performed in three stages: first, according to the similarity of evoked potentials and the randomness of a spontaneous electroencephalogram, the two consecutive observations of evoked potentials are considered as superpositions of the common component and the unique components; second, making use of their characteristics, the two sparse dictionaries are constructed; and finally, we apply the joint sparse representation method in order to extract the common component of double-trial observations, instead of the evoked potential in each trial. A series of experiments carried out on simulated and human test responses confirmed the superior performance of our method. © 2013 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.

Adjustment for survey non‐representativeness using record‐linkage: refined estimates of alcohol consumption by deprivation in Scotland

PubMed Central

Gorman, Emma; Leyland, Alastair H.; McCartney, Gerry; Katikireddi, Srinivasa Vittal; Rutherford, Lisa; Graham, Lesley; Robinson, Mark

2017-01-01

Abstract Background and aims Analytical approaches to addressing survey non‐participation bias typically use only demographic information to improve estimates. We applied a novel methodology which uses health information from data linkage to adjust for non‐representativeness. We illustrate the method by presenting adjusted alcohol consumption estimates for Scotland. Design Data on consenting respondents to the Scottish Health Surveys (SHeSs) 1995–2010 were linked confidentially to routinely collected hospital admission and mortality records. Synthetic observations representing non‐respondents were created using general population data. Multiple imputation was performed to compute adjusted alcohol estimates given a range of assumptions about the missing data. Adjusted estimates of mean weekly consumption were additionally calibrated to per‐capita alcohol sales data. Setting Scotland. Participants 13 936 male and 18 021 female respondents to the SHeSs 1995–2010, aged 20–64 years. Measurements Weekly alcohol consumption, non‐, binge‐ and problem‐drinking. Findings Initial adjustment for non‐response resulted in estimates of mean weekly consumption that were elevated by up to 17.8% [26.5 units (18.6–34.4)] compared with corrections based solely on socio‐demographic data [22.5 (17.7–27.3)]; other drinking behaviour estimates were little changed. Under more extreme assumptions the overall difference was up to 53%, and calibrating to sales estimates resulted in up to 88% difference. Increases were especially pronounced among males in deprived areas. Conclusions The use of routinely collected health data to reduce bias arising from survey non‐response resulted in higher alcohol consumption estimates among working‐age males in Scotland, with less impact for females. This new method of bias reduction can be generalized to other surveys to improve estimates of alternative harmful behaviours. PMID:28276110

Constraining the long-term climate reponse to stratospheric sulfate aerosols injection by the short-term volcanic climate response

NASA Astrophysics Data System (ADS)

Plazzotta, M.; Seferian, R.; Douville, H.; Kravitz, B.; Tilmes, S.; Tjiputra, J.

2016-12-01

Rising greenhouse gas emissions are leading to global warming and climate change, which will have multiple impacts on human society. Geoengineering methods like solar radiation management by stratospheric sulfate aerosols injection (SSA-SRM) aim at treating the symptoms of climate change by reducing the global temperature. Since a real-world testing cannot be implemented, Earth System Models (ESMs) are useful tools to assess the climate impacts of such geoengineering methods. However, coordinated simulations performed with the Geoengineering Model Intercomparison Project (GeoMIP) have shown that climate cooling in response to a continuous injection of 5Tg of SO2 per year under RCP45 future projection (the so-called G4 experiment) differs substantially between ESMs. Here, we employ a volcano analog approach to constrain the climate response in SSA-SRM geoengineering simulations across an ensemble of 10 ESMs. We identify an emergent relationship between the long-term cooling in responses to the mitigation of the clear-sky surface downwelling shortwave radiation (RSDSCS), and the short-term cooling related to the change in RSDSCS during the major tropical volcanic eruptions observed over the historical period (1850-2005). This relationship explains almost 80% of the multi-model spread. Combined with contemporary observations of the latest volcanic eruptions (satellite observations and model reanalyzes), this relationship provides a tight constraint on the climate impacts of SSA-SRM. We estimate that a continuous injection of SO2 aerosols into the stratosphere will reduce the global average temperature of continental land surface by 0.47 K per W m-2, impacting both hydrological and carbon cycles. Compared with the unconstrained ESMs ensemble (range from 0.32 to 0.92 K per W m-2 ), our estimate represents much higher confidence ways to assess the impacts of SSA-SRM on the climate while ruling the most extreme projections of the unconstrained ensemble extremely unlikely.

A Non-Stationary Approach for Estimating Future Hydroclimatic Extremes Using Monte-Carlo Simulation

NASA Astrophysics Data System (ADS)

Byun, K.; Hamlet, A. F.

2017-12-01

There is substantial evidence that observed hydrologic extremes (e.g. floods, extreme stormwater events, and low flows) are changing and that climate change will continue to alter the probability distributions of hydrologic extremes over time. These non-stationary risks imply that conventional approaches for designing hydrologic infrastructure (or making other climate-sensitive decisions) based on retrospective analysis and stationary statistics will become increasingly problematic through time. To develop a framework for assessing risks in a non-stationary environment our study develops a new approach using a super ensemble of simulated hydrologic extremes based on Monte Carlo (MC) methods. Specifically, using statistically downscaled future GCM projections from the CMIP5 archive (using the Hybrid Delta (HD) method), we extract daily precipitation (P) and temperature (T) at 1/16 degree resolution based on a group of moving 30-yr windows within a given design lifespan (e.g. 10, 25, 50-yr). Using these T and P scenarios we simulate daily streamflow using the Variable Infiltration Capacity (VIC) model for each year of the design lifespan and fit a Generalized Extreme Value (GEV) probability distribution to the simulated annual extremes. MC experiments are then used to construct a random series of 10,000 realizations of the design lifespan, estimating annual extremes using the estimated unique GEV parameters for each individual year of the design lifespan. Our preliminary results for two watersheds in Midwest show that there are considerable differences in the extreme values for a given percentile between conventional MC and non-stationary MC approach. Design standards based on our non-stationary approach are also directly dependent on the design lifespan of infrastructure, a sensitivity which is notably absent from conventional approaches based on retrospective analysis. The experimental approach can be applied to a wide range of hydroclimatic variables of interest.

Dynamic response signatures of a scaled model platform for floating wind turbines in an ocean wave basin

PubMed Central

Jaksic, V.; O'Shea, R.; Cahill, P.; Murphy, J.; Mandic, D. P.; Pakrashi, V.

2015-01-01

Understanding of dynamic behaviour of offshore wind floating substructures is extremely important in relation to design, operation, maintenance and management of floating wind farms. This paper presents assessment of nonlinear signatures of dynamic responses of a scaled tension-leg platform (TLP) in a wave tank exposed to different regular wave conditions and sea states characterized by the Bretschneider, the Pierson–Moskowitz and the JONSWAP spectra. Dynamic responses of the TLP were monitored at different locations using load cells, a camera-based motion recognition system and a laser Doppler vibrometer. The analysis of variability of the TLP responses and statistical quantification of their linearity or nonlinearity, as non-destructive means of structural monitoring from the output-only condition, remains a challenging problem. In this study, the delay vector variance (DVV) method is used to statistically study the degree of nonlinearity of measured response signals from a TLP. DVV is observed to create a marker estimating the degree to which a change in signal nonlinearity reflects real-time behaviour of the structure and also to establish the sensitivity of the instruments employed to these changes. The findings can be helpful in establishing monitoring strategies and control strategies for undesirable levels or types of dynamic response and can help to better estimate changes in system characteristics over the life cycle of the structure. PMID:25583866

Prospective EUV observations of hot DA white dwarfs with the EUV Explorer

NASA Technical Reports Server (NTRS)

Finley, David S.; Malina, Roger F.; Bowyer, Stuart

1987-01-01

The Extreme Ultraviolet Explorer (EUVE) will perform a high sensitivity EUV all-sky survey. A major category of sources which will be detected with the EUVE instruments consists of hot white dwarfs. Detailed preliminary studies of synthetic EUV observations of white dwarfs have been carried out using the predicted EUVE instrumental response functions. Using available information regarding space densities of white dwarfs and the distribution of neutral hydrogen in the interstellar medium, the numbers of DA white dwarfs which will be detectable in the different EUV bandpasses have been estimated.

Bias-correction of PERSIANN-CDR Extreme Precipitation Estimates Over the United States

NASA Astrophysics Data System (ADS)

Faridzad, M.; Yang, T.; Hsu, K. L.; Sorooshian, S.

2017-12-01

Ground-based precipitation measurements can be sparse or even nonexistent over remote regions which make it difficult for extreme event analysis. PERSIANN-CDR (CDR), with 30+ years of daily rainfall information, provides an opportunity to study precipitation for regions where ground measurements are limited. In this study, the use of CDR annual extreme precipitation for frequency analysis of extreme events over limited/ungauged basins is explored. The adjustment of CDR is implemented in two steps: (1) Calculated CDR bias correction factor at limited gauge locations based on the linear regression analysis of gauge and CDR annual maxima precipitation; and (2) Extend the bias correction factor to the locations where gauges are not available. The correction factors are estimated at gauge sites over various catchments, elevation zones, and climate regions and the results were generalized to ungauged sites based on regional and climatic similarity. Case studies were conducted on 20 basins with diverse climate and altitudes in the Eastern and Western US. Cross-validation reveals that the bias correction factors estimated on limited calibration data can be extended to regions with similar characteristics. The adjusted CDR estimates also outperform gauge interpolation on validation sites consistently. It is suggested that the CDR with bias adjustment has a potential for study frequency analysis of extreme events, especially for regions with limited gauge observations.

Assessing the impact of climate and land use changes on extreme floods in a large tropical catchment

NASA Astrophysics Data System (ADS)

Jothityangkoon, Chatchai; Hirunteeyakul, Chow; Boonrawd, Kowit; Sivapalan, Murugesu

2013-05-01

In the wake of the recent catastrophic floods in Thailand, there is considerable concern about the safety of large dams designed and built some 50 years ago. In this paper a distributed rainfall-runoff model appropriate for extreme flood conditions is used to generate revised estimates of the Probable Maximum Flood (PMF) for the Upper Ping River catchment (area 26,386 km2) in northern Thailand, upstream of location of the large Bhumipol Dam. The model has two components: a continuous water balance model based on a configuration of parameters estimated from climate, soil and vegetation data and a distributed flood routing model based on non-linear storage-discharge relationships of the river network under extreme flood conditions. The model is implemented under several alternative scenarios regarding the Probable Maximum Precipitation (PMP) estimates and is also used to estimate the potential effects of both climate change and land use and land cover changes on the extreme floods. These new estimates are compared against estimates using other hydrological models, including the application of the original prediction methods under current conditions. Model simulations and sensitivity analyses indicate that a reasonable Probable Maximum Flood (PMF) at the dam site is 6311 m3/s, which is only slightly higher than the original design flood of 6000 m3/s. As part of an uncertainty assessment, the estimated PMF is sensitive to the design method, input PMP, land use changes and the floodplain inundation effect. The increase of PMP depth by 5% can cause a 7.5% increase in PMF. Deforestation by 10%, 20%, 30% can result in PMF increases of 3.1%, 6.2%, 9.2%, respectively. The modest increase of the estimated PMF (to just 6311 m3/s) in spite of these changes is due to the factoring of the hydraulic effects of trees and buildings on the floodplain as the flood situation changes from normal floods to extreme floods, when over-bank flows may be the dominant flooding process, leading to a substantial reduction in the PMF estimates.

On the Simulation of Sea States with High Significant Wave Height for the Validation of Parameter Retrieval Algorithms for Future Altimetry Missions

NASA Astrophysics Data System (ADS)

Kuschenerus, Mieke; Cullen, Robert

2016-08-01

To ensure reliability and precision of wave height estimates for future satellite altimetry missions such as Sentinel 6, reliable parameter retrieval algorithms that can extract significant wave heights up to 20 m have to be established. The retrieved parameters, i.e. the retrieval methods need to be validated extensively on a wide range of possible significant wave heights. Although current missions require wave height retrievals up to 20 m, there is little evidence of systematic validation of parameter retrieval methods for sea states with wave heights above 10 m. This paper provides a definition of a set of simulated sea states with significant wave height up to 20 m, that allow simulation of radar altimeter response echoes for extreme sea states in SAR and low resolution mode. The simulated radar responses are used to derive significant wave height estimates, which can be compared with the initial models, allowing precision estimations of the applied parameter retrieval methods. Thus we establish a validation method for significant wave height retrieval for sea states causing high significant wave heights, to allow improved understanding and planning of future satellite altimetry mission validation.

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

Improving the Accuracy of Estimation of Climate Extremes

NASA Astrophysics Data System (ADS)

Zolina, Olga; Detemmerman, Valery; Trenberth, Kevin E.

2010-12-01

Workshop on Metrics and Methodologies of Estimation of Extreme Climate Events; Paris, France, 27-29 September 2010; Climate projections point toward more frequent and intense weather and climate extremes such as heat waves, droughts, and floods, in a warmer climate. These projections, together with recent extreme climate events, including flooding in Pakistan and the heat wave and wildfires in Russia, highlight the need for improved risk assessments to help decision makers and the public. But accurate analysis and prediction of risk of extreme climate events require new methodologies and information from diverse disciplines. A recent workshop sponsored by the World Climate Research Programme (WCRP) and hosted at United Nations Educational, Scientific and Cultural Organization (UNESCO) headquarters in France brought together, for the first time, a unique mix of climatologists, statisticians, meteorologists, oceanographers, social scientists, and risk managers (such as those from insurance companies) who sought ways to improve scientists' ability to characterize and predict climate extremes in a changing climate.

Airport full-body screening: what is the risk?

PubMed

Mehta, Pratik; Smith-Bindman, Rebecca

2011-06-27

In the past year, the Transportation Security Administration has deployed full-body scanners in airports across the United States in response to heightened security needs. Several groups have opposed the scans, citing privacy concerns and fear of the radiation emitted by the backscatter x-ray scanners, 1 of the 2 types of machines in use. The radiation doses emitted by the scans are extremely small; the scans deliver an amount of radiation equivalent to 3 to 9 minutes of the radiation received through normal daily living. Furthermore, since flying itself increases exposure to ionizing radiation, the scan will contribute less than 1% of the dose a flyer will receive from exposure to cosmic rays at elevated altitudes. The estimation of cancer risks associated with these scans is difficult, but using the only available models, the risk would be extremely small, even among frequent flyers. We conclude that there is no significant threat of radiation from the scans.

A Rasch-validated version of the upper extremity functional index for interval-level measurement of upper extremity function.

PubMed

Hamilton, Clayon B; Chesworth, Bert M

2013-11-01

The original 20-item Upper Extremity Functional Index (UEFI) has not undergone Rasch validation. The purpose of this study was to determine whether Rasch analysis supports the UEFI as a measure of a single construct (ie, upper extremity function) and whether a Rasch-validated UEFI has adequate reproducibility for individual-level patient evaluation. This was a secondary analysis of data from a repeated-measures study designed to evaluate the measurement properties of the UEFI over a 3-week period. Patients (n=239) with musculoskeletal upper extremity disorders were recruited from 17 physical therapy clinics across 4 Canadian provinces. Rasch analysis of the UEFI measurement properties was performed. If the UEFI did not fit the Rasch model, misfitting patients were deleted, items with poor response structure were corrected, and misfitting items and redundant items were deleted. The impact of differential item functioning on the ability estimate of patients was investigated. A 15-item modified UEFI was derived to achieve fit to the Rasch model where the total score was supported as a measure of upper extremity function only. The resultant UEFI-15 interval-level scale (0-100, worst to best state) demonstrated excellent internal consistency (person separation index=0.94) and test-retest reliability (intraclass correlation coefficient [2,1]=.95). The minimal detectable change at the 90% confidence interval was 8.1. Patients who were ambidextrous or bilaterally affected were excluded to allow for the analysis of differential item functioning due to limb involvement and arm dominance. Rasch analysis did not support the validity of the 20-item UEFI. However, the UEFI-15 was a valid and reliable interval-level measure of a single dimension: upper extremity function. Rasch analysis supports using the UEFI-15 in physical therapist practice to quantify upper extremity function in patients with musculoskeletal disorders of the upper extremity.

A Rasch-Validated Version of the Upper Extremity Functional Index for Interval-Level Measurement of Upper Extremity Function

PubMed Central

Chesworth, Bert M.

2013-01-01

Background The original 20-item Upper Extremity Functional Index (UEFI) has not undergone Rasch validation. Objective The purpose of this study was to determine whether Rasch analysis supports the UEFI as a measure of a single construct (ie, upper extremity function) and whether a Rasch-validated UEFI has adequate reproducibility for individual-level patient evaluation. Design This was a secondary analysis of data from a repeated-measures study designed to evaluate the measurement properties of the UEFI over a 3-week period. Methods Patients (n=239) with musculoskeletal upper extremity disorders were recruited from 17 physical therapy clinics across 4 Canadian provinces. Rasch analysis of the UEFI measurement properties was performed. If the UEFI did not fit the Rasch model, misfitting patients were deleted, items with poor response structure were corrected, and misfitting items and redundant items were deleted. The impact of differential item functioning on the ability estimate of patients was investigated. Results A 15-item modified UEFI was derived to achieve fit to the Rasch model where the total score was supported as a measure of upper extremity function only. The resultant UEFI-15 interval-level scale (0–100, worst to best state) demonstrated excellent internal consistency (person separation index=0.94) and test-retest reliability (intraclass correlation coefficient [2,1]=.95). The minimal detectable change at the 90% confidence interval was 8.1. Limitations Patients who were ambidextrous or bilaterally affected were excluded to allow for the analysis of differential item functioning due to limb involvement and arm dominance. Conclusion Rasch analysis did not support the validity of the 20-item UEFI. However, the UEFI-15 was a valid and reliable interval-level measure of a single dimension: upper extremity function. Rasch analysis supports using the UEFI-15 in physical therapist practice to quantify upper extremity function in patients with musculoskeletal disorders of the upper extremity. PMID:23813086

Public Health System Response to Extreme Weather Events.

PubMed

Hunter, Mark D; Hunter, Jennifer C; Yang, Jane E; Crawley, Adam W; Aragón, Tomás J

2016-01-01

Extreme weather events, unpredictable and often far-reaching, constitute a persistent challenge for public health preparedness. The goal of this research is to inform public health systems improvement through examination of extreme weather events, comparing across cases to identify recurring patterns in event and response characteristics. Structured telephone-based interviews were conducted with representatives from health departments to assess characteristics of recent extreme weather events and agencies' responses. Response activities were assessed using the Centers for Disease Control and Prevention Public Health Emergency Preparedness Capabilities framework. Challenges that are typical of this response environment are reported. Forty-five local health departments in 20 US states. Respondents described public health system responses to 45 events involving tornadoes, flooding, wildfires, winter weather, hurricanes, and other storms. Events of similar scale were infrequent for a majority (62%) of the communities involved; disruption to critical infrastructure was universal. Public Health Emergency Preparedness Capabilities considered most essential involved environmental health investigations, mass care and sheltering, surveillance and epidemiology, information sharing, and public information and warning. Unanticipated response activities or operational constraints were common. We characterize extreme weather events as a "quadruple threat" because (1) direct threats to population health are accompanied by damage to public health protective and community infrastructure, (2) event characteristics often impose novel and pervasive burdens on communities, (3) responses rely on critical infrastructures whose failure both creates new burdens and diminishes response capacity, and (4) their infrequency and scale further compromise response capacity. Given the challenges associated with extreme weather events, we suggest opportunities for organizational learning and preparedness improvements.

Probability modeling of high flow extremes in Yingluoxia watershed, the upper reaches of Heihe River basin

NASA Astrophysics Data System (ADS)

Li, Zhanling; Li, Zhanjie; Li, Chengcheng

2014-05-01

Probability modeling of hydrological extremes is one of the major research areas in hydrological science. Most basins in humid and semi-humid south and east of China are concerned for probability modeling analysis of high flow extremes. While, for the inland river basin which occupies about 35% of the country area, there is a limited presence of such studies partly due to the limited data availability and a relatively low mean annual flow. The objective of this study is to carry out probability modeling of high flow extremes in the upper reach of Heihe River basin, the second largest inland river basin in China, by using the peak over threshold (POT) method and Generalized Pareto Distribution (GPD), in which the selection of threshold and inherent assumptions for POT series are elaborated in details. For comparison, other widely used probability distributions including generalized extreme value (GEV), Lognormal, Log-logistic and Gamma are employed as well. Maximum likelihood estimate is used for parameter estimations. Daily flow data at Yingluoxia station from 1978 to 2008 are used. Results show that, synthesizing the approaches of mean excess plot, stability features of model parameters, return level plot and the inherent independence assumption of POT series, an optimum threshold of 340m3/s is finally determined for high flow extremes in Yingluoxia watershed. The resulting POT series is proved to be stationary and independent based on Mann-Kendall test, Pettitt test and autocorrelation test. In terms of Kolmogorov-Smirnov test, Anderson-Darling test and several graphical diagnostics such as quantile and cumulative density function plots, GPD provides the best fit to high flow extremes in the study area. The estimated high flows for long return periods demonstrate that, as the return period increasing, the return level estimates are probably more uncertain. The frequency of high flow extremes exhibits a very slight but not significant decreasing trend from 1978 to 2008, while the intensity of such flow extremes is comparatively increasing especially for the higher return levels.

Rapid exposure and loss estimates for the May 12, 2008 Mw 7.9 Wenchuan earthquake provided by the U.S. Geological Survey's PAGER system

USGS Publications Warehouse

Earle, P.S.; Wald, D.J.; Allen, T.I.; Jaiswal, K.S.; Porter, K.A.; Hearne, M.G.

2008-01-01

One half-hour after the May 12th Mw 7.9 Wenchuan, China earthquake, the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) system distributed an automatically generated alert stating that 1.2 million people were exposed to severe-to-extreme shaking (Modified Mercalli Intensity VIII or greater). It was immediately clear that a large-scale disaster had occurred. These alerts were widely distributed and referenced by the major media outlets and used by governments, scientific, and relief agencies to guide their responses. The PAGER alerts and Web pages included predictive ShakeMaps showing estimates of ground shaking, maps of population density, and a list of estimated intensities at impacted cities. Manual, revised alerts were issued in the following hours that included the dimensions of the fault rupture. Within a half-day, PAGER’s estimates of the population exposed to strong shaking levels stabilized at 5.2 million people. A coordinated research effort is underway to extend PAGER’s capability to include estimates of the number of casualties. We are pursuing loss models that will allow PAGER the flexibility to use detailed inventory and engineering results in regions where these data are available while also calculating loss estimates in regions where little is known about the type and strength of the built infrastructure. Prototype PAGER fatality estimates are currently implemented and can be manually triggered. In the hours following the Wenchuan earthquake, these models predicted fatalities in the tens of thousands.

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.

Excessive Heat and Respiratory Hospitalizations in New York State: Estimating Current and Future Public Health Burden Related to Climate Change

PubMed Central

Hsu, Wan-Hsiang; Van Zutphen, Alissa R.; Saha, Shubhayu; Luber, George; Hwang, Syni-An

2012-01-01

Background: Although many climate-sensitive environmental exposures are related to mortality and morbidity, there is a paucity of estimates of the public health burden attributable to climate change. Objective: We estimated the excess current and future public health impacts related to respiratory hospitalizations attributable to extreme heat in summer in New York State (NYS) overall, its geographic regions, and across different demographic strata. Methods: On the basis of threshold temperature and percent risk changes identified from our study in NYS, we estimated recent and future attributable risks related to extreme heat due to climate change using the global climate model with various climate scenarios. We estimated effects of extreme high apparent temperature in summer on respiratory admissions, days hospitalized, direct hospitalization costs, and lost productivity from days hospitalized after adjusting for inflation. Results: The estimated respiratory disease burden attributable to extreme heat at baseline (1991–2004) in NYS was 100 hospital admissions, US$644,069 in direct hospitalization costs, and 616 days of hospitalization per year. Projections for 2080–2099 based on three different climate scenarios ranged from 206–607 excess hospital admissions, US$26–$76 million in hospitalization costs, and 1,299–3,744 days of hospitalization per year. Estimated impacts varied by geographic region and population demographics. Conclusions: We estimated that excess respiratory admissions in NYS due to excessive heat would be 2 to 6 times higher in 2080–2099 than in 1991–2004. When combined with other heat-associated diseases and mortality, the potential public health burden associated with global warming could be substantial. PMID:22922791

The Individual Consistency of Acquiescence and Extreme Response Style in Self-Report Questionnaires

ERIC Educational Resources Information Center

Weijters, Bert; Geuens, Maggie; Schillewaert, Niels

2010-01-01

The severity of bias in respondents' self-reports due to acquiescence response style (ARS) and extreme response style (ERS) depends strongly on how consistent these response styles are over the course of a questionnaire. In the literature, different alternative hypotheses on response style (in)consistency circulate. Therefore, nine alternative…

Extreme Response Style: Which Model Is Best?

ERIC Educational Resources Information Center

Leventhal, Brian

2017-01-01

More robust and rigorous psychometric models, such as multidimensional Item Response Theory models, have been advocated for survey applications. However, item responses may be influenced by construct-irrelevant variance factors such as preferences for extreme response options. Through empirical and simulation methods, this study evaluates the use…

Climate change, variability and extreme events : risk assessment and management strategies in a Peach cultivated area in Italy.

NASA Astrophysics Data System (ADS)

Alfieri, Silvia Maria; De Lorenzi, Francesca; Basile, Angelo; Bonfante, Antonello; Missere, Daniele; Menenti, Massimo

2014-05-01

Climate change in Mediterranean area is likely to reduce precipitation amounts and to increase temperature thus affecting the timing of development stages and the productivity of crops. Further, extreme weather events are expected to increase in the future leading to significant increase in agricultural risk. Some strategies for effectively managing risks and adapting to climate change involve adjustments to irrigation management and use of different varieties. We quantified the risk on Peach production in an irrigated area of "Emilia Romagna" region ( Italy) taking into account the impact on crop yield due to climate change and variability and to extreme weather events as well as the ability of the agricultural system to modulate this impact (adaptive capacity) through changes in water and crop management. We have focused on climatic events causing insufficient water supply to crops, while taking into account the effect of climate on the duration and timing of phenological stages. Further, extreme maximum and minimum temperature events causing significant reduction of crop yield have been considered using phase-specific critical temperatures. In our study risk was assessed as the product of the probability of a damaging event (hazard), such as drought or extreme temperatures, and the estimated impact of such an event (vulnerability). To estimate vulnerability we took into account the possible options to reduce risk, by combining estimates of the sensitivity of the system (negative impact on crop yield) and its adaptive capacity. The latter was evaluated as the relative improvement due to alternate management options: the use of alternate varieties or the changes in irrigation management. Vulnerability was quantified using cultivar-specific thermal and hydrologic requirements of a set of cultivars determined by experimental data and from scientific literature. Critical temperatures determining a certain reduction of crop yield have been estimated and used to assess thermal hazard and vulnerability in sensitive phenological stages. Cultivar-specific yield response functions to water availability were used to assess the reduction of yield for a determinate management option. Downscaled climate scenarios have been used to calculate indicators of soil water availability and thermal times and to evaluate the variability of crop phenology in combination with critical temperatures. Two climate scenarios were considered: reference (1961-90) and future (2021-2050) climate, the former from climatic statistics on observed variables, and the latter from statistical downscaling of general circulation models (AOGCM). Management options were defined by combinations of irrigation strategies (optimal, rainfed and deficit) with use of alternate varieties. As regards hydrologic conditions, risk assessment has been done at landscape scale in all soil units within each study area. The mechanistic model SWAP (Soil-Water-Atmosphere-Plant model) of water flow in the soil-plant-atmosphere system was used to describe the hydrological conditions in response to climate and irrigation. Different farm management options were evaluated. In a moderate water shortage scenario, deficit irrigation was an effective strategy to cope with climate change risks. In a severe water shortage scenario, the study showed the potentiality of intra-specific biodiversity to reduce risk of yield losses, although costs should be evaluated against the benefits of each specific management option. The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008)

Spatio-temporal characteristics of the extreme precipitation by L-moment-based index-flood method in the Yangtze River Delta region, China

NASA Astrophysics Data System (ADS)

Yin, Yixing; Chen, Haishan; Xu, Chong-Yu; Xu, Wucheng; Chen, Changchun; Sun, Shanlei

2016-05-01

The regionalization methods, which "trade space for time" by pooling information from different locations in the frequency analysis, are efficient tools to enhance the reliability of extreme quantile estimates. This paper aims at improving the understanding of the regional frequency of extreme precipitation by using regionalization methods, and providing scientific background and practical assistance in formulating the regional development strategies for water resources management in one of the most developed and flood-prone regions in China, the Yangtze River Delta (YRD) region. To achieve the main goals, L-moment-based index-flood (LMIF) method, one of the most popular regionalization methods, is used in the regional frequency analysis of extreme precipitation with special attention paid to inter-site dependence and its influence on the accuracy of quantile estimates, which has not been considered by most of the studies using LMIF method. Extensive data screening of stationarity, serial dependence, and inter-site dependence was carried out first. The entire YRD region was then categorized into four homogeneous regions through cluster analysis and homogenous analysis. Based on goodness-of-fit statistic and L-moment ratio diagrams, generalized extreme-value (GEV) and generalized normal (GNO) distributions were identified as the best fitted distributions for most of the sub-regions, and estimated quantiles for each region were obtained. Monte Carlo simulation was used to evaluate the accuracy of the quantile estimates taking inter-site dependence into consideration. The results showed that the root-mean-square errors (RMSEs) were bigger and the 90 % error bounds were wider with inter-site dependence than those without inter-site dependence for both the regional growth curve and quantile curve. The spatial patterns of extreme precipitation with a return period of 100 years were finally obtained which indicated that there are two regions with highest precipitation extremes and a large region with low precipitation extremes. However, the regions with low precipitation extremes are the most developed and densely populated regions of the country, and floods will cause great loss of human life and property damage due to the high vulnerability. The study methods and procedure demonstrated in this paper will provide useful reference for frequency analysis of precipitation extremes in large regions, and the findings of the paper will be beneficial in flood control and management in the study area.

Regional trends in short-duration precipitation extremes: a flexible multivariate monotone quantile regression approach

NASA Astrophysics Data System (ADS)

Cannon, Alex

2017-04-01

Estimating historical trends in short-duration rainfall extremes at regional and local scales is challenging due to low signal-to-noise ratios and the limited availability of homogenized observational data. In addition to being of scientific interest, trends in rainfall extremes are of practical importance, as their presence calls into question the stationarity assumptions that underpin traditional engineering and infrastructure design practice. Even with these fundamental challenges, increasingly complex questions are being asked about time series of extremes. For instance, users may not only want to know whether or not rainfall extremes have changed over time, they may also want information on the modulation of trends by large-scale climate modes or on the nonstationarity of trends (e.g., identifying hiatus periods or periods of accelerating positive trends). Efforts have thus been devoted to the development and application of more robust and powerful statistical estimators for regional and local scale trends. While a standard nonparametric method like the regional Mann-Kendall test, which tests for the presence of monotonic trends (i.e., strictly non-decreasing or non-increasing changes), makes fewer assumptions than parametric methods and pools information from stations within a region, it is not designed to visualize detected trends, include information from covariates, or answer questions about the rate of change in trends. As a remedy, monotone quantile regression (MQR) has been developed as a nonparametric alternative that can be used to estimate a common monotonic trend in extremes at multiple stations. Quantile regression makes efficient use of data by directly estimating conditional quantiles based on information from all rainfall data in a region, i.e., without having to precompute the sample quantiles. The MQR method is also flexible and can be used to visualize and analyze the nonlinearity of the detected trend. However, it is fundamentally a univariate technique, and cannot incorporate information from additional covariates, for example ENSO state or physiographic controls on extreme rainfall within a region. Here, the univariate MQR model is extended to allow the use of multiple covariates. Multivariate monotone quantile regression (MMQR) is based on a single hidden-layer feedforward network with the quantile regression error function and partial monotonicity constraints. The MMQR model is demonstrated via Monte Carlo simulations and the estimation and visualization of regional trends in moderate rainfall extremes based on homogenized sub-daily precipitation data at stations in Canada.

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.

PROBABILITIES OF TEMPERATURE EXTREMES IN THE U.S.

EPA Science Inventory

The model Temperature Extremes Version 1.0 provides the capability to estimate the probability, for 332 locations in the 50 U.S. states, that an extreme temperature will occur for one or more consecutive days and/or for any number of days in a given month or season, based on stat...

Temporal development of extreme precipitation in Germany projected by EURO-CORDEX simulations

NASA Astrophysics Data System (ADS)

Brendel, Christoph; Deutschländer, Thomas

2017-04-01

A sustainable operation of transport infrastructure requires an enhanced resilience to the increasing impacts of climate change and related extreme meteorological events. To meet this challenge, the German Federal Ministry of Transport and Digital Infrastructure (BMVI) commenced a comprehensive national research program on safe and sustainable transport in Germany. A network of departmental research institutes addresses the "Adaptation of the German transport infrastructure towards climate change and extreme events". Various studies already have identified an increase in the average global precipitation for the 20th century. There is some indication that these increases are most visible in a rising frequency of precipitation extremes. However, the changes are highly variable between regions and seasons. With a further increase of atmospheric greenhouse gas concentrations in the 21st century, the likelihood of occurrence of such extreme events will continue to rise. A kernel estimator has been used in order to obtain a robust estimate of the temporal development of extreme precipitation events projected by an ensemble of EURO-CORDEX simulations. The kernel estimator measures the intensity of the poisson point process indicating temporal changes in the frequency of extreme events. Extreme precipitation events were selected using the peaks over threshold (POT) method with the 90th, 95th and 99th quantile of daily precipitation sums as thresholds. Application of this non-parametric approach with relative thresholds renders the use of a bias correction non-mandatory. In addition, in comparison to fitting an extreme value theory (EVT) distribution, the method is completely unsusceptible to outliers. First results show an overall increase of extreme precipitation events for Germany until the end of the 21st century. However, major differences between seasons, quantiles and the three different Representative Concentration Pathways (RCP 2.6, 4.5, and 8.5) have been identified. For instance, the frequency of extreme precipitation events more than triples in the most extreme scenario. Regional differences are rather small with the largest increase in northern Germany, particularly in coastal regions and the weakest increase in the most southern parts of Germany.

Evaluating the applicability of four recent satellite–gauge combined precipitation estimates for extreme precipitation and streamflow predictions over the upper Yellow river basin in China

USDA-ARS?s Scientific Manuscript database

This study aimed to statistically and hydrologically assess the performance of four latest and widely used satellite–gauge combined precipitation estimates (SGPEs), namely CRT, BLD, 3B42CDR, and 3B42 for the extreme precipitation and stream'ow scenarios over the upper Yellow river basin (UYRB) in ch...

Warmest extreme year in U.S. history alters thermal requirements for tree phenology.

PubMed

Carter, Jacob M; Orive, Maria E; Gerhart, Laci M; Stern, Jennifer H; Marchin, Renée M; Nagel, Joane; Ward, Joy K

2017-04-01

The frequency of extreme warm years is increasing across the majority of the planet. Shifts in plant phenology in response to extreme years can influence plant survival, productivity, and synchrony with pollinators/herbivores. Despite extensive work on plant phenological responses to climate change, little is known about responses to extreme warm years, particularly at the intraspecific level. Here we investigate 43 populations of white ash trees (Fraxinus americana) from throughout the species range that were all grown in a common garden. We compared the timing of leaf emergence during the warmest year in U.S. history (2012) with relatively non-extreme years. We show that (a) leaf emergence among white ash populations was accelerated by 21 days on average during the extreme warm year of 2012 relative to non-extreme years; (b) rank order for the timing of leaf emergence was maintained among populations across extreme and non-extreme years, with southern populations emerging earlier than northern populations; (c) greater amounts of warming units accumulated prior to leaf emergence during the extreme warm year relative to non-extreme years, and this constrained the potential for even earlier leaf emergence by an average of 9 days among populations; and (d) the extreme warm year reduced the reliability of a relevant phenological model for white ash by producing a consistent bias toward earlier predicted leaf emergence relative to observations. These results demonstrate a critical need to better understand how extreme warm years will impact tree phenology, particularly at the intraspecific level.

Diabetic Driving Studies-Part 2: A Comparison of Brake Response Time Between Drivers With Diabetes With and Without Lower Extremity Sensorimotor Neuropathy.

PubMed

Spiess, Kerianne E; Sansosti, Laura E; Meyr, Andrew J

We have previously demonstrated an abnormally delayed mean brake response time and an increased frequency of abnormally delayed brake responses in a group of neuropathic drivers with diabetes compared with a control group of drivers with neither diabetes nor lower extremity neuropathy. The objective of the present case-control study was to compare the mean brake response time between 2 groups of drivers with diabetes with and without lower extremity sensorimotor neuropathy. The braking performances of the participants were evaluated using a computerized driving simulator with specific measurement of the mean brake response time and the frequency of the abnormally delayed brake responses. We compared a control group of 25 active drivers with type 2 diabetes without lower extremity neuropathy and an experimental group of 25 active drivers with type 2 diabetes and lower extremity neuropathy from an urban U.S. podiatric medical clinic. The experimental group demonstrated an 11.49% slower mean brake response time (0.757 ± 0.180 versus 0.679 ± 0.120 second; p < .001), with abnormally delayed reactions occurring at a greater frequency (57.5% versus 35.0%; p < .001). Independent of a comparative statistical analysis, diabetic drivers with neuropathy demonstrated a mean brake response time slower than a suggested safety threshold of 0.70 second, and diabetic drivers without neuropathy demonstrated a mean brake response time faster than this threshold. The results of the present investigation provide evidence that the specific onset of lower extremity sensorimotor neuropathy associated with diabetes appears to impart a negative effect on automobile brake responses. Copyright © 2017 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Risk assessment of precipitation extremes in northern Xinjiang, China

NASA Astrophysics Data System (ADS)

Yang, Jun; Pei, Ying; Zhang, Yanwei; Ge, Quansheng

2018-05-01

This study was conducted using daily precipitation records gathered at 37 meteorological stations in northern Xinjiang, China, from 1961 to 2010. We used the extreme value theory model, generalized extreme value (GEV) and generalized Pareto distribution (GPD), statistical distribution function to fit outputs of precipitation extremes with different return periods to estimate risks of precipitation extremes and diagnose aridity-humidity environmental variation and corresponding spatial patterns in northern Xinjiang. Spatiotemporal patterns of daily maximum precipitation showed that aridity-humidity conditions of northern Xinjiang could be well represented by the return periods of the precipitation data. Indices of daily maximum precipitation were effective in the prediction of floods in the study area. By analyzing future projections of daily maximum precipitation (2, 5, 10, 30, 50, and 100 years), we conclude that the flood risk will gradually increase in northern Xinjiang. GEV extreme value modeling yielded the best results, proving to be extremely valuable. Through example analysis for extreme precipitation models, the GEV statistical model was superior in terms of favorable analog extreme precipitation. The GPD model calculation results reflect annual precipitation. For most of the estimated sites' 2 and 5-year T for precipitation levels, GPD results were slightly greater than GEV results. The study found that extreme precipitation reaching a certain limit value level will cause a flood disaster. Therefore, predicting future extreme precipitation may aid warnings of flood disaster. A suitable policy concerning effective water resource management is thus urgently required.

Evaluation of a new satellite-based precipitation dataset for climate studies in the Xiang River basin, Southern China

NASA Astrophysics Data System (ADS)

Zhu, Q.; Xu, Y. P.; Hsu, K. L.

2017-12-01

A new satellite-based precipitation dataset, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) with long-term time series dating back to 1983 can be one valuable dataset for climate studies. This study investigates the feasibility of using PERSIANN-CDR as a reference dataset for climate studies. Sixteen CMIP5 models are evaluated over the Xiang River basin, southern China, by comparing their performance on precipitation projection and streamflow simulation, particularly on extreme precipitation and streamflow events. The results show PERSIANN-CDR is a valuable dataset for climate studies, even on extreme precipitation events. The precipitation estimates and their extreme events from CMIP5 models are improved significantly compared with rain gauge observations after bias-correction by the PERSIANN-CDR precipitation estimates. Given streamflows simulated with raw and bias-corrected precipitation estimates from 16 CMIP5 models, 10 out of 16 are improved after bias-correction. The impact of bias-correction on extreme events for streamflow simulations are unstable, with eight out of 16 models can be clearly claimed they are improved after the bias-correction. Concerning the performance of raw CMIP5 models on precipitation, IPSL-CM5A-MR excels the other CMIP5 models, while MRI-CGCM3 outperforms on extreme events with its better performance on six extreme precipitation metrics. Case studies also show that raw CCSM4, CESM1-CAM5, and MRI-CGCM3 outperform other models on streamflow simulation, while MIROC5-ESM-CHEM, MIROC5-ESM and IPSL-CM5A-MR behaves better than the other models after bias-correction.

The application of the statistical theory of extreme values to gust-load problems

NASA Technical Reports Server (NTRS)

Press, Harry

1950-01-01

An analysis is presented which indicates that the statistical theory of extreme values is applicable to the problems of predicting the frequency of encountering the larger gust loads and gust velocities for both specific test conditions as well as commercial transport operations. The extreme-value theory provides an analytic form for the distributions of maximum values of gust load and velocity. Methods of fitting the distribution are given along with a method of estimating the reliability of the predictions. The theory of extreme values is applied to available load data from commercial transport operations. The results indicate that the estimates of the frequency of encountering the larger loads are more consistent with the data and more reliable than those obtained in previous analyses. (author)

TRMM rainfall estimative coupled with Bell (1969) methodology for extreme rainfall characterization

NASA Astrophysics Data System (ADS)

Schiavo Bernardi, E.; Allasia, D.; Basso, R.; Freitas Ferreira, P.; Tassi, R.

2015-06-01

The lack of rainfall data in Brazil, and, in particular, in Rio Grande do Sul State (RS), hinders the understanding of the spatial and temporal distribution of rainfall, especially in the case of the more complex extreme events. In this context, rainfall's estimation from remote sensors is seen as alternative to the scarcity of rainfall gauges. However, as they are indirect measures, such estimates needs validation. This paper aims to verify the applicability of the Tropical Rainfall Measuring Mission (TRMM) satellite information for extreme rainfall determination in RS. The analysis was accomplished at different temporal scales that ranged from 5 min to daily rainfall while spatial distribution of rainfall was investigated by means of regionalization. An initial test verified TRMM rainfall estimative against measured rainfall at gauges for 1998-2013 period considering different durations and return periods (RP). Results indicated that, for the RP of 2, 5, 10 and 15 years, TRMM overestimated on average 24.7% daily rainfall. As TRMM minimum time-steps is 3 h, in order to verify shorter duration rainfall, the TRMM data were adapted to fit Bell's (1969) generalized IDF formula (based on the existence of similarity between the mechanisms of extreme rainfall events as they are associated to convective cells). Bell`s equation error against measured precipitation was around 5-10%, which varied based on location, RP and duration while the coupled BELL+TRMM error was around 10-35%. However, errors were regionally distributed, allowing a correction to be implemented that reduced by half these values. These findings in turn permitted the use of TRMM+Bell estimates to improve the understanding of spatiotemporal distribution of extreme hydrological rainfall events.

Predicting the effects of muscle activation on knee, thigh, and hip injuries in frontal crashes using a finite-element model with muscle forces from subject testing and musculoskeletal modeling.

PubMed

Chang, Chia-Yuan; Rupp, Jonathan D; Reed, Matthew P; Hughes, Richard E; Schneider, Lawrence W

2009-11-01

In a previous study, the authors reported on the development of a finite-element model of the midsize male pelvis and lower extremities with lower-extremity musculature that was validated using PMHS knee-impact response data. Knee-impact simulations with this model were performed using forces from four muscles in the lower extremities associated with two-foot bracing reported in the literature to provide preliminary estimates of the effects of lower-extremity muscle activation on knee-thigh-hip injury potential in frontal impacts. The current study addresses a major limitation of these preliminary simulations by using the AnyBody three-dimensional musculoskeletal model to estimate muscle forces produced in 35 muscles in each lower extremity during emergency one-foot braking. To check the predictions of the AnyBody Model, activation levels of twelve major muscles in the hip and lower extremities were measured using surface EMG electrodes on 12 midsize-male subjects performing simulated maximum and 50% of maximum braking in a laboratory seating buck. Comparisons between test results and the predictions of the AnyBody Model when it was used to simulate these same braking tests suggest that the AnyBody model appropriately predicts agonistic muscle activations but under predicts antagonistic muscle activations. Simulations of knee-to-knee-bolster impacts were performed by impacting the knees of the lower-extremity finite element model with and without the muscle forces predicted by the validated AnyBody Model. Results of these simulations confirm previous findings that muscle tension increases knee-impact force by increasing the effective mass of the KTH complex due to tighter coupling of muscle mass to bone. They also indicate that muscle activation preferentially couples mass distal to the hip, thereby accentuating the decrease in femur force from the knee to the hip. However, the reduction in force transmitted from the knee to the hip is offset by the increased force at the knee and by increased compressive forces at the hip due to activation of lower-extremity muscles. As a result, approximately 45% to 60% and 50% to 65% of the force applied to the knee is applied to the hip in the simulations without and with muscle tension, respectively. The simulation results suggest that lower-extremity muscle tension has little effect on the risk of hip injuries, but it increases the bending moments in the femoral shaft, thereby increasing the risk of femoral shaft fractures by 20%-40%. However, these findings may be affected by the inability of the AnyBody Model to appropriately predict antagonistic muscle forces.

Extreme-event geoelectric hazard maps: Chapter 9

USGS Publications Warehouse

Love, Jeffrey J.; Bedrosian, Paul A.

2018-01-01

Maps of geoelectric amplitude covering about half the continental United States are presented that will be exceeded, on average, once per century in response to an extreme-intensity geomagnetic disturbance. These maps are constructed using an empirical parameterization of induction: convolving latitude-dependent statistical maps of extreme-value geomagnetic disturbances, obtained from decades of 1-minute magnetic observatory data, with local estimates of Earth-surface impedance obtained at discrete geographic sites from magnetotelluric surveys. Geoelectric amplitudes are estimated for geomagnetic waveforms having a 240-s (and 1200-s) sinusoidal period and amplitudes over 10 min (1 h) that exceed a once-per-century threshold. As a result of the combination of geographic differences in geomagnetic variation and Earth-surface impedance, once-per-century geoelectric amplitudes span more than two orders of magnitude and are a highly granular function of location. Specifically for north-south 240-s induction, once-per-century geoelectric amplitudes across large parts of the United States have a median value of 0.34 V/km; for east-west variation, they have a median value of 0.23 V/km. In Northern Minnesota, amplitudes exceed 14.00 V/km for north-south geomagnetic variation (23.34 V/km for east-west variation), while just over 100 km away, amplitudes are only 0.08 V/km (0.02 V/km). At some sites in the northern-central United States, once-per-century geoelectric amplitudes exceed the 2 V/km realized in Québec during the March 1989 storm.

Kriging and local polynomial methods for blending satellite-derived and gauge precipitation estimates to support hydrologic early warning systems

USGS Publications Warehouse

Verdin, Andrew; Funk, Christopher C.; Rajagopalan, Balaji; Kleiber, William

2016-01-01

Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.

Capturing spatial and temporal patterns of widespread, extreme flooding across Europe

NASA Astrophysics Data System (ADS)

Busby, Kathryn; Raven, Emma; Liu, Ye

2013-04-01

Statistical characterisation of physical hazards is an integral part of probabilistic catastrophe models used by the reinsurance industry to estimate losses from large scale events. Extreme flood events are not restricted by country boundaries which poses an issue for reinsurance companies as their exposures often extend beyond them. We discuss challenges and solutions that allow us to appropriately capture the spatial and temporal dependence of extreme hydrological events on a continental-scale, which in turn enables us to generate an industry-standard stochastic event set for estimating financial losses for widespread flooding. By presenting our event set methodology, we focus on explaining how extreme value theory (EVT) and dependence modelling are used to account for short, inconsistent hydrological data from different countries, and how to make appropriate statistical decisions that best characterise the nature of flooding across Europe. The consistency of input data is of vital importance when identifying historical flood patterns. Collating data from numerous sources inherently causes inconsistencies and we demonstrate our robust approach to assessing the data and refining it to compile a single consistent dataset. This dataset is then extrapolated using a parameterised EVT distribution to estimate extremes. Our method then captures the dependence of flood events across countries using an advanced multivariate extreme value model. Throughout, important statistical decisions are explored including: (1) distribution choice; (2) the threshold to apply for extracting extreme data points; (3) a regional analysis; (4) the definition of a flood event, which is often linked with reinsurance industry's hour's clause; and (5) handling of missing values. Finally, having modelled the historical patterns of flooding across Europe, we sample from this model to generate our stochastic event set comprising of thousands of events over thousands of years. We then briefly illustrate how this is applied within a probabilistic model to estimate catastrophic loss curves used by the reinsurance industry.

Effect of Extreme Cold Treatment on Morphology and Behavior of Hydrogels and Microgels (Poster Session)

DTIC Science & Technology

2017-08-20

UNCLASSIFIED Effect of Extreme Cold Treatment on Morphology and Behavior of Hydrogels and Microgels BACKGROUND • Stimuli responsive hydrogel systems...particularly for cold weather and Arctic uniforms, • The effect of extreme cold on gel responsiveness however is not well studied • This project seeks...to understand the effect of cold temperature ( down to -80 ° C) on hydrogel and microgel particles properties and response to thermal stimuli • We

Asymmetric responses of primary productivity to precipitation extremes: A synthesis of grassland precipitation manipulation experiments

DOE PAGES

Wilcox, Kevin R.; Shi, Zheng; Gherardi, Laureano A.; ...

2017-04-02

Climatic changes are altering Earth's hydrological cycle, resulting in altered precipitation amounts, increased interannual variability of precipitation, and more frequent extreme precipitation events. These trends will likely continue into the future, having substantial impacts on net primary productivity (NPP) and associated ecosystem services such as food production and carbon sequestration. Frequently, experimental manipulations of precipitation have linked altered precipitation regimes to changes in NPP. Yet, findings have been diverse and substantial uncertainty still surrounds generalities describing patterns of ecosystem sensitivity to altered precipitation. Additionally, we do not know whether previously observed correlations between NPP and precipitation remain accurate when precipitationmore » changes become extreme. We synthesized results from 83 case studies of experimental precipitation manipulations in grasslands worldwide. Here, we used meta-analytical techniques to search for generalities and asymmetries of aboveground NPP (ANPP) and belowground NPP (BNPP) responses to both the direction and magnitude of precipitation change. Sensitivity (i.e., productivity response standardized by the amount of precipitation change) of BNPP was similar under precipitation additions and reductions, but ANPP was more sensitive to precipitation additions than reductions; this was especially evident in drier ecosystems. Additionally, overall relationships between the magnitude of productivity responses and the magnitude of precipitation change were saturating in form. The saturating form of this relationship was likely driven by ANPP responses to very extreme precipitation increases, although there were limited studies imposing extreme precipitation change, and there was considerable variation among experiments. Finally, this highlights the importance of incorporating gradients of manipulations, ranging from extreme drought to extreme precipitation increases into future climate change experiments. Additionally, policy and land management decisions related to global change scenarios should consider how ANPP and BNPP responses may differ, and that ecosystem responses to extreme events might not be predicted from relationships found under moderate environmental changes.« less

Asymmetric responses of primary productivity to precipitation extremes: A synthesis of grassland precipitation manipulation experiments.

PubMed

Wilcox, Kevin R; Shi, Zheng; Gherardi, Laureano A; Lemoine, Nathan P; Koerner, Sally E; Hoover, David L; Bork, Edward; Byrne, Kerry M; Cahill, James; Collins, Scott L; Evans, Sarah; Gilgen, Anna K; Holub, Petr; Jiang, Lifen; Knapp, Alan K; LeCain, Daniel; Liang, Junyi; Garcia-Palacios, Pablo; Peñuelas, Josep; Pockman, William T; Smith, Melinda D; Sun, Shanghua; White, Shannon R; Yahdjian, Laura; Zhu, Kai; Luo, Yiqi

2017-10-01

Climatic changes are altering Earth's hydrological cycle, resulting in altered precipitation amounts, increased interannual variability of precipitation, and more frequent extreme precipitation events. These trends will likely continue into the future, having substantial impacts on net primary productivity (NPP) and associated ecosystem services such as food production and carbon sequestration. Frequently, experimental manipulations of precipitation have linked altered precipitation regimes to changes in NPP. Yet, findings have been diverse and substantial uncertainty still surrounds generalities describing patterns of ecosystem sensitivity to altered precipitation. Additionally, we do not know whether previously observed correlations between NPP and precipitation remain accurate when precipitation changes become extreme. We synthesized results from 83 case studies of experimental precipitation manipulations in grasslands worldwide. We used meta-analytical techniques to search for generalities and asymmetries of aboveground NPP (ANPP) and belowground NPP (BNPP) responses to both the direction and magnitude of precipitation change. Sensitivity (i.e., productivity response standardized by the amount of precipitation change) of BNPP was similar under precipitation additions and reductions, but ANPP was more sensitive to precipitation additions than reductions; this was especially evident in drier ecosystems. Additionally, overall relationships between the magnitude of productivity responses and the magnitude of precipitation change were saturating in form. The saturating form of this relationship was likely driven by ANPP responses to very extreme precipitation increases, although there were limited studies imposing extreme precipitation change, and there was considerable variation among experiments. This highlights the importance of incorporating gradients of manipulations, ranging from extreme drought to extreme precipitation increases into future climate change experiments. Additionally, policy and land management decisions related to global change scenarios should consider how ANPP and BNPP responses may differ, and that ecosystem responses to extreme events might not be predicted from relationships found under moderate environmental changes. © 2017 John Wiley & Sons Ltd.

Observed changes in extremes of daily rainfall and temperature in Jemma Sub-Basin, Upper Blue Nile Basin, Ethiopia

NASA Astrophysics Data System (ADS)

Worku, Gebrekidan; Teferi, Ermias; Bantider, Amare; Dile, Yihun T.

2018-02-01

Climate variability has been a threat to the socio-economic development of Ethiopia. This paper examined the changes in rainfall, minimum, and maximum temperature extremes of Jemma Sub-Basin of the Upper Blue Nile Basin for the period of 1981 to 2014. The nonparametric Mann-Kendall, seasonal Mann-Kendall, and Sen's slope estimator were used to estimate annual trends. Ten rainfall and 12 temperature indices were used to study changes in rainfall and temperature extremes. The results showed an increasing trend of annual and summer rainfall in more than 78% of the stations and a decreasing trend of spring rainfall in most of the stations. An increase in rainfall extreme events was detected in the majority of the stations. Several rainfall extreme indices showed wetting trends in the sub-basin, whereas limited indices indicated dryness in most of the stations. Annual maximum and minimum temperature and extreme temperature indices showed warming trend in the sub-basin. Presence of extreme rainfall and a warming trend of extreme temperature indices may suggest signs of climate change in the Jemma Sub-Basin. This study, therefore, recommended the need for exploring climate induced risks and implementing appropriate climate change adaptation and mitigation strategies.

Applications of Extreme Value Theory in Public Health.

PubMed

Thomas, Maud; Lemaitre, Magali; Wilson, Mark L; Viboud, Cécile; Yordanov, Youri; Wackernagel, Hans; Carrat, Fabrice

2016-01-01

We present how Extreme Value Theory (EVT) can be used in public health to predict future extreme events. We applied EVT to weekly rates of Pneumonia and Influenza (P&I) deaths over 1979-2011. We further explored the daily number of emergency department visits in a network of 37 hospitals over 2004-2014. Maxima of grouped consecutive observations were fitted to a generalized extreme value distribution. The distribution was used to estimate the probability of extreme values in specified time periods. An annual P&I death rate of 12 per 100,000 (the highest maximum observed) should be exceeded once over the next 30 years and each year, there should be a 3% risk that the P&I death rate will exceed this value. Over the past 10 years, the observed maximum increase in the daily number of visits from the same weekday between two consecutive weeks was 1133. We estimated at 0.37% the probability of exceeding a daily increase of 1000 on each month. The EVT method can be applied to various topics in epidemiology thus contributing to public health planning for extreme events.

Extreme Precipitation and Beach Closures in the Great Lakes Region: Evaluating Risk among the Elderly

PubMed Central

Bush, Kathleen F.; Fossani, Cheryl L.; Li, Shi; Mukherjee, Bhramar; Gronlund, Carina J.; O’Neill, Marie S.

2014-01-01

As a result of climate change, extreme precipitation events are expected to increase in frequency and intensity. Runoff from these extreme events poses threats to water quality and human health. We investigated the impact of extreme precipitation and beach closings on the risk of gastrointestinal illness (GI)-related hospital admissions among individuals 65 and older in 12 Great Lakes cities from 2000 to 2006. Poisson regression models were fit in each city, controlling for temperature and long-term time trends. City-specific estimates were combined to form an overall regional risk estimate. Approximately 40,000 GI-related hospital admissions and over 100 beach closure days were recorded from May through September during the study period. Extreme precipitation (≥90th percentile) occurring the previous day (lag 1) is significantly associated with beach closures in 8 of the 12 cities (p < 0.05). However, no association was observed between beach closures and GI-related hospital admissions. These results support previous work linking extreme precipitation to compromised recreational water quality. PMID:24534768

Systematic effects of foreground removal in 21-cm surveys of reionization

NASA Astrophysics Data System (ADS)

Petrovic, Nada; Oh, S. Peng

2011-05-01

21-cm observations have the potential to revolutionize our understanding of the high-redshift Universe. Whilst extremely bright radio continuum foregrounds exist at these frequencies, their spectral smoothness can be exploited to allow efficient foreground subtraction. It is well known that - regardless of other instrumental effects - this removes power on scales comparable to the survey bandwidth. We investigate associated systematic biases. We show that removing line-of-sight fluctuations on large scales aliases into suppression of the 3D power spectrum across a broad range of scales. This bias can be dealt with by correctly marginalizing over small wavenumbers in the 1D power spectrum; however, the unbiased estimator will have unavoidably larger variance. We also show that Gaussian realizations of the power spectrum permit accurate and extremely rapid Monte Carlo simulations for error analysis; repeated realizations of the fully non-Gaussian field are unnecessary. We perform Monte Carlo maximum likelihood simulations of foreground removal which yield unbiased, minimum variance estimates of the power spectrum in agreement with Fisher matrix estimates. Foreground removal also distorts the 21-cm probability distribution function (PDF), reducing the contrast between neutral and ionized regions, with potentially serious consequences for efforts to extract information from the PDF. We show that it is the subtraction of large-scale modes which is responsible for this distortion, and that it is less severe in the earlier stages of reionization. It can be reduced by using larger bandwidths. In the late stages of reionization, identification of the largest ionized regions (which consist of foreground emission only) provides calibration points which potentially allow recovery of large-scale modes. Finally, we also show that (i) the broad frequency response of synchrotron and free-free emission will smear out any features in the electron momentum distribution and ensure spectrally smooth foregrounds and (ii) extragalactic radio recombination lines should be negligible foregrounds.

XDGMM: eXtreme Deconvolution Gaussian Mixture Modeling

NASA Astrophysics Data System (ADS)

Holoien, Thomas W.-S.; Marshall, Philip J.; Wechsler, Risa H.

2017-08-01

XDGMM uses Gaussian mixtures to do density estimation of noisy, heterogenous, and incomplete data using extreme deconvolution (XD) algorithms which is compatible with the scikit-learn machine learning methods. It implements both the astroML and Bovy et al. (2011) algorithms, and extends the BaseEstimator class from scikit-learn so that cross-validation methods work. It allows the user to produce a conditioned model if values of some parameters are known.

Modeling And Economics Of Extreme Subduction Earthquakes: Two Case Studies

NASA Astrophysics Data System (ADS)

Chavez, M.; Cabrera, E.; Emerson, D.; Perea, N.; Moulinec, C.

2008-05-01

The destructive effects of large magnitude, thrust subduction superficial (TSS) earthquakes on Mexico City (MC) and Guadalajara (G) has been shown in the recent centuries. For example, the 7/04/1845 and the 19/09/1985, two TSS earthquakes occurred on the coast of the state of Guerrero and Michoacan, with Ms 7+ and 8.1. The economical losses for the later were of about 7 billion US dollars. Also, the largest Ms 8.2, instrumentally observed TSS earthquake in Mexico, occurred in the Colima-Jalisco region the 3/06/1932, and the 9/10/1995 another similar, Ms 7.4 event occurred in the same region, the later produced economical losses of hundreds of thousands US dollars.The frequency of occurrence of large TSS earthquakes in Mexico is poorly known, but it might vary from decades to centuries [1]. Therefore there is a lack of strong ground motions records for extreme TSS earthquakes in Mexico, which as mentioned above, recently had an important economical impact on MC and potentially could have it in G. In this work we obtained samples of broadband synthetics [2,3] expected in MC and G, associated to extreme (plausible) magnitude Mw 8.5, TSS scenario earthquakes, with epicenters in the so-called Guerrero gap and in the Colima-Jalisco zone, respectively. The economical impacts of the proposed extreme TSS earthquake scenarios for MC and G were considered as follows: For MC by using a risk acceptability criteria, the probabilities of exceedance of the maximum seismic responses of their construction stock under the assumed scenarios, and the estimated economical losses observed for the 19/09/1985 earthquake; and for G, by estimating the expected economical losses, based on the seismic vulnerability assessment of their construction stock under the extreme seismic scenario considered. ----------------------- [1] Nishenko S.P. and Singh SK, BSSA 77, 6, 1987 [2] Cabrera E., Chavez M., Madariaga R., Mai M, Frisenda M., Perea N., AGU, Fall Meeting, 2005 [3] Chavez M., Olsen K.B., Cabrera E., 13th World Conf. Earthq. Eng., 2004

Temperatures and the growth and development of maize and rice: a review.

PubMed

Sánchez, Berta; Rasmussen, Anton; Porter, John R

2014-02-01

Because of global land surface warming, extreme temperature events are expected to occur more often and more intensely, affecting the growth and development of the major cereal crops in several ways, thus affecting the production component of food security. In this study, we have identified rice and maize crop responses to temperature in different, but consistent, phenological phases and development stages. A literature review and data compilation of around 140 scientific articles have determined the key temperature thresholds and response to extreme temperature effects for rice and maize, complementing an earlier study on wheat. Lethal temperatures and cardinal temperatures, together with error estimates, have been identified for phenological phases and development stages. Following the methodology of previous work, we have collected and statistically analysed temperature thresholds of the three crops for the key physiological processes such as leaf initiation, shoot growth and root growth and for the most susceptible phenological phases such as sowing to emergence, anthesis and grain filling. Our summary shows that cardinal temperatures are conservative between studies and are seemingly well defined in all three crops. Anthesis and ripening are the most sensitive temperature stages in rice as well as in wheat and maize. We call for further experimental studies of the effects of transgressing threshold temperatures so such responses can be included into crop impact and adaptation models. © 2013 John Wiley & Sons Ltd.

Coupling hydrologic and hydraulic models to take into consideration retention effects on extreme peak discharges in Switzerland

NASA Astrophysics Data System (ADS)

Felder, Guido; Zischg, Andreas; Weingartner, Rolf

2015-04-01

Estimating peak discharges with very low probabilities is still accompanied by large uncertainties. Common estimation methods are usually based on extreme value statistics applied to observed time series or to hydrological model outputs. However, such methods assume the system to be stationary and do not specifically consider non-stationary effects. Observed time series may exclude events where peak discharge is damped by retention effects, as this process does not occur until specific thresholds, possibly beyond those of the highest measured event, are exceeded. Hydrological models can be complemented and parameterized with non-linear functions. However, in such cases calibration depends on observed data and non-stationary behaviour is not deterministically calculated. Our study discusses the option of considering retention effects on extreme peak discharges by coupling hydrological and hydraulic models. This possibility is tested by forcing the semi-distributed deterministic hydrological model PREVAH with randomly generated, physically plausible extreme precipitation patterns. The resulting hydrographs are then used to force the hydraulic model BASEMENT-ETH (riverbed in 1D, potential inundation areas in 2D). The procedure ensures that the estimated extreme peak discharge does not exceed the physical limit given by the riverbed capacity and that the dampening effect of inundation processes on peak discharge is considered.

The Hydrological Sensitivity to Global Warming and Solar Geoengineering Derived from Thermodynamic Constraints

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

Kleidon, Alex; Kravitz, Benjamin S.; Renner, Maik

2015-01-16

We derive analytic expressions of the transient response of the hydrological cycle to surface warming from an extremely simple energy balance model in which turbulent heat fluxes are constrained by the thermodynamic limit of maximum power. For a given magnitude of steady-state temperature change, this approach predicts the transient response as well as the steady-state change in surface energy partitioning and the hydrologic cycle. We show that the transient behavior of the simple model as well as the steady state hydrological sensitivities to greenhouse warming and solar geoengineering are comparable to results from simulations using highly complex models. Many ofmore » the global-scale hydrological cycle changes can be understood from a surface energy balance perspective, and our thermodynamically-constrained approach provides a physically robust way of estimating global hydrological changes in response to altered radiative forcing.« less

Changes in the probability of co-occurring extreme climate events

NASA Astrophysics Data System (ADS)

Diffenbaugh, N. S.

2017-12-01

Extreme climate events such as floods, droughts, heatwaves, and severe storms exert acute stresses on natural and human systems. When multiple extreme events co-occur, either in space or time, the impacts can be substantially compounded. A diverse set of human interests - including supply chains, agricultural commodities markets, reinsurance, and deployment of humanitarian aid - have historically relied on the rarity of extreme events to provide a geographic hedge against the compounded impacts of co-occuring extremes. However, changes in the frequency of extreme events in recent decades imply that the probability of co-occuring extremes is also changing, and is likely to continue to change in the future in response to additional global warming. This presentation will review the evidence for historical changes in extreme climate events and the response of extreme events to continued global warming, and will provide some perspective on methods for quantifying changes in the probability of co-occurring extremes in the past and future.

Modeling U-shaped dose-response curves for manganese using categorical regression.

PubMed

Milton, Brittany; Krewski, Daniel; Mattison, Donald R; Karyakina, Nataliya A; Ramoju, Siva; Shilnikova, Natalia; Birkett, Nicholas; Farrell, Patrick J; McGough, Doreen

2017-01-01

Manganese is an essential nutrient which can cause adverse effects if ingested to excess or in insufficient amounts, leading to a U-shaped exposure-response relationship. Methods have recently been developed to describe such relationships by simultaneously modeling the exposure-response curves for excess and deficiency. These methods incorporate information from studies with diverse adverse health outcomes within the same analysis by assigning severity scores to achieve a common response metric for exposure-response modeling. We aimed to provide an estimate of the optimal dietary intake of manganese to balance adverse effects from deficient or excess intake. We undertook a systematic review of the literature from 1930 to 2013 and extracted information on adverse effects from manganese deficiency and excess to create a database on manganese toxicity following oral exposure. Although data were available for seven different species, only the data from rats was sufficiently comprehensive to support analytical modelling. The toxicological outcomes were standardized on an 18-point severity scale, allowing for a common analysis of all available toxicological data. Logistic regression modelling was used to simultaneously estimate the exposure-response profile for dietary deficiency and excess for manganese and generate a U-shaped exposure-response curve for all outcomes. Data were available on the adverse effects of 6113 rats. The nadir of the U-shaped joint response curve occurred at a manganese intake of 2.70mg/kgbw/day with a 95% confidence interval of 2.51-3.02. The extremes of both deficient and excess intake were associated with a 90% probability of some measurable adverse event. The manganese database supports estimation of optimal intake based on combining information on adverse effects from systematic review of published experiments. There is a need for more studies on humans. Translation of our results from rats to humans will require adjustment for interspecies differences in sensitivity to manganese. Copyright © 2016 Elsevier B.V. All rights reserved.

Seismic isolation of nuclear power plants using elastomeric bearings

NASA Astrophysics Data System (ADS)

Kumar, Manish

Seismic isolation using low damping rubber (LDR) and lead-rubber (LR) bearings is a viable strategy for mitigating the effects of extreme earthquake shaking on safety-related nuclear structures. Although seismic isolation has been deployed in nuclear structures in France and South Africa, it has not seen widespread use because of limited new build nuclear construction in the past 30 years and a lack of guidelines, codes and standards for the analysis, design and construction of isolation systems specific to nuclear structures. The nuclear accident at Fukushima Daiichi in March 2011 has led the nuclear community to consider seismic isolation for new large light water and small modular reactors to withstand the effects of extreme earthquakes. The mechanical properties of LDR and LR bearings are not expected to change substantially in design basis shaking. However, under shaking more intense than design basis, the properties of the lead cores in lead-rubber bearings may degrade due to heating associated with energy dissipation, some bearings in an isolation system may experience net tension, and the compression and tension stiffness may be affected by the horizontal displacement of the isolation system. The effects of intra-earthquake changes in mechanical properties on the response of base-isolated nuclear power plants (NPPs) were investigated using an advanced numerical model of a lead-rubber bearing that has been verified and validated, and implemented in OpenSees and ABAQUS. A series of experiments were conducted at University at Buffalo to characterize the behavior of elastomeric bearings in tension. The test data was used to validate a phenomenological model of an elastomeric bearing in tension. The value of three times the shear modulus of rubber in elastomeric bearing was found to be a reasonable estimate of the cavitation stress of a bearing. The sequence of loading did not change the behavior of an elastomeric bearing under cyclic tension, and there was no significant change in the shear modulus, compressive stiffness, and buckling load of a bearing following cavitation. Response-history analysis of base-isolated NPPs was performed using a two-node macro model and a lumped-mass stick model. A comparison of responses obtained from analysis using simplified and advanced isolator models showed that the variation in buckling load due to horizontal displacement and strength degradation due to heating of lead cores affect the responses of a base-isolated NPP most significantly. The two-node macro model can be used to estimate the horizontal displacement response of a base-isolated NPP, but a three-dimensional model that explicitly considers all of the bearings in the isolation system will be required to estimate demands on individual bearings, and to investigate rocking and torsional responses. The use of the simplified LR bearing model underestimated the torsional and rocking response of the base-isolated NPP. Vertical spectral response at the top of containment building was very sensitive to how damping was defined for the response-history analysis.

Evolution caused by extreme events.

PubMed

Grant, Peter R; Grant, B Rosemary; Huey, Raymond B; Johnson, Marc T J; Knoll, Andrew H; Schmitt, Johanna

2017-06-19

Extreme events can be a major driver of evolutionary change over geological and contemporary timescales. Outstanding examples are evolutionary diversification following mass extinctions caused by extreme volcanism or asteroid impact. The evolution of organisms in contemporary time is typically viewed as a gradual and incremental process that results from genetic change, environmental perturbation or both. However, contemporary environments occasionally experience strong perturbations such as heat waves, floods, hurricanes, droughts and pest outbreaks. These extreme events set up strong selection pressures on organisms, and are small-scale analogues of the dramatic changes documented in the fossil record. Because extreme events are rare, almost by definition, they are difficult to study. So far most attention has been given to their ecological rather than to their evolutionary consequences. We review several case studies of contemporary evolution in response to two types of extreme environmental perturbations, episodic (pulse) or prolonged (press). Evolution is most likely to occur when extreme events alter community composition. We encourage investigators to be prepared for evolutionary change in response to rare events during long-term field studies.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'. © 2017 The Author(s).

Characterization of extreme air-sea turbulent fluxes

NASA Astrophysics Data System (ADS)

Gulev, Sergey; Belyaev, Konstantin

2017-04-01

Extreme ocean-atmosphere turbulent fluxes play a critical role in the convective processes in the mid and subpolar latitudes and may also affect a variety of atmospheric processes, such as generation and re-intensification of extreme cyclones in the areas of the mid latitude storm tracks. From the ocean dynamics perspective, specifically for quantifying extreme vertical mixing, characterization of the extreme fluxes requires, besides estimation of the extreme events, also consideration of the relative extremeness of surface fluxes and their timing, e.g. the duration of periods of high surface fluxes. In order to comprehensively characterize extreme turbulent fluxes at sea surface we propose a formalism based upon probability density distributions of surface turbulent fluxes and flux-related variables. Individual absolute flux extremes were derived using Modified Fisher-Tippett (MFT) distribution of turbulent fluxes. Then, we extend this distribution to the fractional distribution, characterizing the fraction of time-integrated turbulent heat flux provided by the fluxes exceeding a given percentile. Finally, we consider the time durations during which fluxes of a given intensity provide extreme accumulations of heat loss from the surface. For estimation of these characteristics of surface fluxes we use fluxes recomputed from the state variables available from modern era reanalyses (ERA-Interim, MERRA and CFSR) for the period from 1979 onwards. Applications of the formalism to the VOS (Voluntary Observing Ship) - based surface fluxes are also considered. We discuss application of the new metrics of mesoscale and synoptic variability of surface fluxes to the dynamics of mixed layer depth in the North Atlantic.

Time-varying Concurrent Risk of Extreme Droughts and Heatwaves in California

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Diffenbaugh, N. S.; Ausin, M. C.

2016-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena such as droughts and heatwaves. The concurrent of these nature-changing climatic extremes may result in intensifying undesirable consequences in terms of human health and destructive effects in water resources. The present study assesses the risk of concurrent extreme droughts and heatwaves under dynamic nonstationary conditions arising from climate change in California. For doing so, a generalized fully Bayesian time-varying multivariate risk framework is proposed evolving through time under dynamic human-induced environment. In this methodology, an extreme, Bayesian, dynamic copula (Gumbel) is developed to model the time-varying dependence structure between the two different climate extremes. The time-varying extreme marginals are previously modeled using a Generalized Extreme Value (GEV) distribution. Bayesian Markov Chain Monte Carlo (MCMC) inference is integrated to estimate parameters of the nonstationary marginals and copula using a Gibbs sampling method. Modelled marginals and copula are then used to develop a fully Bayesian, time-varying joint return period concept for the estimation of concurrent risk. Here we argue that climate change has increased the chance of concurrent droughts and heatwaves over decades in California. It is also demonstrated that a time-varying multivariate perspective should be incorporated to assess realistic concurrent risk of the extremes for water resources planning and management in a changing climate in this area. The proposed generalized methodology can be applied for other stochastic nature-changing compound climate extremes that are under the influence of climate change.

Flourish or flush: effects of simulated extreme rainfall events on Sphagnum-dwelling testate amoebae in a subarctic bog (Abisko, Sweden).

PubMed

Tsyganov, Andrey N; Keuper, Frida; Aerts, Rien; Beyens, Louis

2013-01-01

Extreme precipitation events are recognised as important drivers of ecosystem responses to climate change and can considerably affect high-latitude ombrotrophic bogs. Therefore, understanding the relationships between increased rainfall and the biotic components of these ecosystems is necessary for an estimation of climate change impacts. We studied overall effects of increased magnitude, intensity and frequency of rainfall on assemblages of Sphagnum-dwelling testate amoebae in a field climate manipulation experiment located in a relatively dry subarctic bog (Abisko, Sweden). The effects of the treatment were estimated using abundance, species diversity and structure of living and empty shell assemblages of testate amoebae in living and decaying layers of Sphagnum. Our results show that increased rainfall reduced the mean abundance and species richness of living testate amoebae. Besides, the treatment affected species structure of both living and empty shell assemblages, reducing proportions of hydrophilous species. The effects are counterintuitive as increased precipitation-related substrate moisture was expected to have opposite effects on testate amoeba assemblages in relatively dry biotopes. Therefore, we conclude that other rainfall-related factors such as increased infiltration rates and frequency of environmental disturbances can also affect testate amoeba assemblages in Sphagnum and that hydrophilous species are particularly sensitive to variation in these environmental variables.

Dynamic response signatures of a scaled model platform for floating wind turbines in an ocean wave basin.

PubMed

Jaksic, V; O'Shea, R; Cahill, P; Murphy, J; Mandic, D P; Pakrashi, V

2015-02-28

Understanding of dynamic behaviour of offshore wind floating substructures is extremely important in relation to design, operation, maintenance and management of floating wind farms. This paper presents assessment of nonlinear signatures of dynamic responses of a scaled tension-leg platform (TLP) in a wave tank exposed to different regular wave conditions and sea states characterized by the Bretschneider, the Pierson-Moskowitz and the JONSWAP spectra. Dynamic responses of the TLP were monitored at different locations using load cells, a camera-based motion recognition system and a laser Doppler vibrometer. The analysis of variability of the TLP responses and statistical quantification of their linearity or nonlinearity, as non-destructive means of structural monitoring from the output-only condition, remains a challenging problem. In this study, the delay vector variance (DVV) method is used to statistically study the degree of nonlinearity of measured response signals from a TLP. DVV is observed to create a marker estimating the degree to which a change in signal nonlinearity reflects real-time behaviour of the structure and also to establish the sensitivity of the instruments employed to these changes. The findings can be helpful in establishing monitoring strategies and control strategies for undesirable levels or types of dynamic response and can help to better estimate changes in system characteristics over the life cycle of the structure. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Does quality control matter? Surface urban heat island intensity variations estimated by satellite-derived land surface temperature products

NASA Astrophysics Data System (ADS)

Lai, Jiameng; Zhan, Wenfeng; Huang, Fan; Quan, Jinling; Hu, Leiqiu; Gao, Lun; Ju, Weimin

2018-05-01

The temporally regular and spatially comprehensive monitoring of surface urban heat islands (SUHIs) have been extremely difficult, until the advent of satellite-based land surface temperature (LST) products. However, these LST products have relatively higher errors compared to in situ measurements. This has resulted in comparatively inaccurate estimations of SUHI indicators and, consequently, may have distorted interpretations of SUHIs. Although reports have shown that LST qualities are important for SUHI interpretations, systematic investigations of the response of SUHI indicators to LST qualities across cities with dissimilar bioclimates are rare. To address this issue, we chose eighty-six major cities across mainland China and analyzed SUHI intensity (SUHII) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The LST-based SUHII differences due to inclusion or exclusion of MODIS quality control (QC) flags (i.e., ΔSUHII) were evaluated. Our major findings included, but are not limited to, the following four aspects: (1) SUHIIs can be significantly impacted by MODIS QC flags, and the associated QC-induced ΔSUHIIs generally accounted for 24.3% (29.9%) of the total SUHII value during the day (night); (2) the ΔSUHIIs differed between seasons, with considerable differences between transitional (spring and autumn) and extreme (summer and winter) seasons; (3) significant discrepancies also appeared among cities located in northern and southern regions, with northern cities often possessing higher annual mean ΔSUHIIs. The internal variations of ΔSUHIIs within individual cities also showed high heterogeneity, with ΔSUHII variations that generally exceeded 5.0 K (3.0 K) in northern (southern) cities; (4) ΔSUHIIs were negatively related to SUHIIs and cloud cover percentages (mostly in transitional seasons). No significant relationship was found in the extreme seasons. Our findings highlight the need to be extremely cautious when using LST product-based SUHIIs to interpret SUHIs.

The Sm-Nd history of KREEP. [in lunar rocks

NASA Technical Reports Server (NTRS)

Lugmair, G. W.; Carlson, R. W.

1978-01-01

Sm-Nd whole rock measurements on a variety of KREEP-rich samples from different landing sites are reported. Despite a variation of Nd and Sm concentrations of almost a factor of 3, the Sm-Nd ratios, as well as the Nd-143/Nd-144 values, show an extremely close grouping. No systematic differences between samples from different landing sites are resolved. These results are taken to be indicative of a moon-wide process having been responsible for the generation of the KREEP source reservoir, 4.36 plus or minus 0.06 AE ago, as estimated from model age calculation.

On alternative q-Weibull and q-extreme value distributions: Properties and applications

NASA Astrophysics Data System (ADS)

Zhang, Fode; Ng, Hon Keung Tony; Shi, Yimin

2018-01-01

Tsallis statistics and Tsallis distributions have been attracting a significant amount of research work in recent years. Importantly, the Tsallis statistics, q-distributions have been applied in different disciplines. Yet, a relationship between some existing q-Weibull distributions and q-extreme value distributions that is parallel to the well-established relationship between the conventional Weibull and extreme value distributions through a logarithmic transformation has not be established. In this paper, we proposed an alternative q-Weibull distribution that leads to a q-extreme value distribution via the q-logarithm transformation. Some important properties of the proposed q-Weibull and q-extreme value distributions are studied. Maximum likelihood and least squares estimation methods are used to estimate the parameters of q-Weibull distribution and their performances are investigated through a Monte Carlo simulation study. The methodologies and the usefulness of the proposed distributions are illustrated by fitting the 2014 traffic fatalities data from The National Highway Traffic Safety Administration.

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.

Future projection of design storms using a GCM-informed weather generator

NASA Astrophysics Data System (ADS)

KIm, T. W.; Wi, S.; Valdés-Pineda, R.; Valdés, J. B.

2017-12-01

The rainfall Intensity-Duration-Frequency (IDF) curves are one of the most common tools used to provide planners with a description of the frequency of extreme rainfall events of various intensities and durations. Therefore deriving appropriate IDF estimates is important to avoid malfunctions of water structures that cause huge damage. Evaluating IDF estimates in the context of climate change has become more important because projections from climate models suggest that the frequency of intense rainfall events will increase in the future due to the increase in greenhouse gas emissions. In this study, the Bartlett-Lewis (BL) stochastic rainfall model is employed to generate annual maximum series of various sub-daily durations for test basins of the Model Parameter Estimation Experiment (MOPEX) project, and to derive the IDF curves in the context of climate changes projected by the North American Regional Climate Change (NARCCAP) models. From our results, it has been found that the observed annual rainfall maximum series is reasonably represented by the synthetic annual maximum series generated by the BL model. The observed data is perturbed by change factors to incorporate the NARCCAP climate change scenarios into the IDF estimates. The future IDF curves show a significant difference from the historical IDF curves calculated for the period 1968-2000. Overall, the projected IDF curves show an increasing trend over time. The impacts of changes in extreme rainfall on the hydrologic response of the MOPEX basins are also explored. Acknowledgement: This research was supported by a grant [MPSS-NH-2015-79] through the Disaster and Safety Management Institute funded by Ministry of Public Safety and Security of Korean government.

Sediment Dynamics Within Buffer Zone and Sinkhole Splay Areas Under Extreme Soil Disturbance Conditions.

PubMed

Schoonover, Jon E; Crim, Jackie F; Williard, Karl W J; Groninger, John W; Zaczek, James J; Pattumma, Klairoong

2015-09-01

Sedimentation dynamics were assessed in sinkholes within training areas at Ft. Knox Military Installation, a karst landscape subjected to decades of tracked vehicle use and extreme soil disturbance. Sinkholes sampled were sediment-laden and behaved as intermittent ponds. Dendrogeomorphic analyses were conducted using willow trees (Salix spp.) located around the edge of 18 sinkholes to estimate historical sedimentation rates, and buried bottles were installed in 20 sinkholes at the center, outer edge, and at the midpoint between the center and edge to estimate annual sedimentation rates. Sedimentation data were coupled with vegetation characteristics of sinkhole buffers to determine relationships among these variables. The dendrogeomorphic method estimated an average accumulation rate of 1.27 cm year(-1) translating to a sediment loss rate of 46.1 metric ton year(-1) from the training areas. However, sediment export to sinkholes was estimated to be much greater (118.6 metric ton year(-1)) via the bottle method. These data suggest that the latter method provided a more accurate estimate since accumulation was greater in the center of sinkholes compared to the periphery where dendrogeomorphic data were collected. Vegetation data were not tightly correlated with sedimentation rates, suggesting that further research is needed to identify a viable proxy for direct measures of sediment accumulation in this extreme deposition environment. Mitigation activities for the sinkholes at Ft. Knox's tank training area, and other heavily disturbed karst environments where extreme sedimentation exists, should consider focusing on flow path and splay area management.

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.

Gross domestic product estimation based on electricity utilization by artificial neural network

NASA Astrophysics Data System (ADS)

Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.

2018-01-01

The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.

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

Observations and estimates of wave-driven water level extremes at the Marshall Islands

NASA Astrophysics Data System (ADS)

Merrifield, M. A.; Becker, J. M.; Ford, M.; Yao, Y.

2014-10-01

Wave-driven extreme water levels are examined for coastlines protected by fringing reefs using field observations obtained in the Republic of the Marshall Islands. The 2% exceedence water level near the shoreline due to waves is estimated empirically for the study sites from breaking wave height at the outer reef and by combining separate contributions from setup, sea and swell, and infragravity waves, which are estimated based on breaking wave height and water level over the reef flat. Although each component exhibits a tidal dependence, they sum to yield a 2% exceedence level that does not. A hindcast based on the breaking wave height parameterization is used to assess factors leading to flooding at Roi-Namur caused by an energetic swell event during December 2008. Extreme water levels similar to December 2008 are projected to increase significantly with rising sea level as more wave and tide events combine to exceed inundation threshold levels.

Assigning historic responsibility for extreme weather events

NASA Astrophysics Data System (ADS)

Otto, Friederike E. L.; Skeie, Ragnhild B.; Fuglestvedt, Jan S.; Berntsen, Terje; Allen, Myles R.

2017-11-01

Recent scientific advances make it possible to assign extreme events to human-induced climate change and historical emissions. These developments allow losses and damage associated with such events to be assigned country-level responsibility.

Stream Response to an Extreme Defoliation Event

NASA Astrophysics Data System (ADS)

Gold, A.; Loffredo, J.; Addy, K.; Bernhardt, E. S.; Berdanier, A. B.; Schroth, A. W.; Inamdar, S. P.; Bowden, W. B.

2017-12-01

Extreme climatic events are known to profoundly impact stream flow and stream fluxes. These events can also exert controls on insect outbreaks, which may create marked changes in stream characteristics. The invasive Gypsy Moth (Lymantria dispar dispar) experiences episodic infestations based on extreme climatic conditions within the northeastern U.S. In most years, gypsy moth populations are kept in check by diseases. In 2016 - after successive years of unusually warm, dry spring and summer weather -gypsy moth caterpillars defoliated over half of Rhode Island's 160,000 forested ha. No defoliation of this magnitude had occurred for more than 30 years. We examined one RI headwater stream's response to the defoliation event in 2016 compared with comparable data in 2014 and 2015. Stream temperature and flow was gauged continuously by USGS and dissolved oxygen (DO) was measured with a YSI EXO2 sonde every 30 minutes during a series of deployments in the spring, summer and fall from 2014-2016. We used the single station, open channel method to estimate stream metabolism metrics. We also assessed local climate and stream temperature data from 2009-2016. We observed changes in stream responses during the defoliation event that suggest changes in ET, solar radiation and heat flux. Although the summer of 2016 had more drought stress (PDSI) than previous years, stream flow occurred throughout the summer, in contrast to several years with lower drought stress when stream flow ceased. Air temperature in 2016 was similar to prior years, but stream temperature was substantially higher than the prior seven years, likely due to the loss of canopy shading. DO declined dramatically in 2016 compared to prior years - more than the rising stream temperatures would indicate. Gross Primary Productivity was significantly higher during the year of the defoliation, indicating more total fixation of inorganic carbon from photo-autotrophs. In 2016, Ecosystem Respiration was also higher and Net Ecosystem Productivity indicated it is a heterotrophic stream. Extreme events and fluctuations in climate patterns in the region are expected to increase suggesting that further work on the effects of insect defoliation on forested streams is warranted.

Super Clausius-Clapeyron scaling of extreme hourly precipitation and its relation to large-scale atmospheric conditions

NASA Astrophysics Data System (ADS)

Lenderink, Geert; Barbero, Renaud; Loriaux, Jessica; Fowler, Hayley

2017-04-01

Present-day precipitation-temperature scaling relations indicate that hourly precipitation extremes may have a response to warming exceeding the Clausius-Clapeyron (CC) relation; for The Netherlands the dependency on surface dew point temperature follows two times the CC relation corresponding to 14 % per degree. Our hypothesis - as supported by a simple physical argument presented here - is that this 2CC behaviour arises from the physics of convective clouds. So, we think that this response is due to local feedbacks related to the convective activity, while other large scale atmospheric forcing conditions remain similar except for the higher temperature (approximately uniform warming with height) and absolute humidity (corresponding to the assumption of unchanged relative humidity). To test this hypothesis, we analysed the large-scale atmospheric conditions accompanying summertime afternoon precipitation events using surface observations combined with a regional re-analysis for the data in The Netherlands. Events are precipitation measurements clustered in time and space derived from approximately 30 automatic weather stations. The hourly peak intensities of these events again reveal a 2CC scaling with the surface dew point temperature. The temperature excess of moist updrafts initialized at the surface and the maximum cloud depth are clear functions of surface dew point temperature, confirming the key role of surface humidity on convective activity. Almost no differences in relative humidity and the dry temperature lapse rate were found across the dew point temperature range, supporting our theory that 2CC scaling is mainly due to the response of convection to increases in near surface humidity, while other atmospheric conditions remain similar. Additionally, hourly precipitation extremes are on average accompanied by substantial large-scale upward motions and therefore large-scale moisture convergence, which appears to accelerate with surface dew point. This increase in large-scale moisture convergence appears to be consequence of latent heat release due to the convective activity as estimated from the quasi-geostrophic omega equation. Consequently, most hourly extremes occur in precipitation events with considerable spatial extent. Importantly, this event size appears to increase rapidly at the highest dew point temperature range, suggesting potentially strong impacts of climatic warming.

Infrared Sensor System for Mobile-Robot Positioning in Intelligent Spaces

PubMed Central

Gorostiza, Ernesto Martín; Galilea, José Luis Lázaro; Meca, Franciso Javier Meca; Monzú, David Salido; Zapata, Felipe Espinosa; Puerto, Luis Pallarés

2011-01-01

The aim of this work was to position a Mobile Robot in an Intelligent Space, and this paper presents a sensorial system for measuring differential phase-shifts in a sinusoidally modulated infrared signal transmitted from the robot. Differential distances were obtained from these phase-shifts, and the position of the robot was estimated by hyperbolic trilateration. Due to the extremely severe trade-off between SNR, angle (coverage) and real-time response, a very accurate design and device selection was required to achieve good precision with wide coverage and acceptable robot speed. An I/Q demodulator was used to measure phases with one-stage synchronous demodulation to DC. A complete set of results from real measurements, both for distance and position estimations, is provided to demonstrate the validity of the system proposed, comparing it with other similar indoor positioning systems. PMID:22163907

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

Wilcox, Kevin R.; Shi, Zheng; Gherardi, Laureano A.

Climatic changes are altering Earth's hydrological cycle, resulting in altered precipitation amounts, increased interannual variability of precipitation, and more frequent extreme precipitation events. These trends will likely continue into the future, having substantial impacts on net primary productivity (NPP) and associated ecosystem services such as food production and carbon sequestration. Frequently, experimental manipulations of precipitation have linked altered precipitation regimes to changes in NPP. Yet, findings have been diverse and substantial uncertainty still surrounds generalities describing patterns of ecosystem sensitivity to altered precipitation. Additionally, we do not know whether previously observed correlations between NPP and precipitation remain accurate when precipitationmore » changes become extreme. We synthesized results from 83 case studies of experimental precipitation manipulations in grasslands worldwide. Here, we used meta-analytical techniques to search for generalities and asymmetries of aboveground NPP (ANPP) and belowground NPP (BNPP) responses to both the direction and magnitude of precipitation change. Sensitivity (i.e., productivity response standardized by the amount of precipitation change) of BNPP was similar under precipitation additions and reductions, but ANPP was more sensitive to precipitation additions than reductions; this was especially evident in drier ecosystems. Additionally, overall relationships between the magnitude of productivity responses and the magnitude of precipitation change were saturating in form. The saturating form of this relationship was likely driven by ANPP responses to very extreme precipitation increases, although there were limited studies imposing extreme precipitation change, and there was considerable variation among experiments. Finally, this highlights the importance of incorporating gradients of manipulations, ranging from extreme drought to extreme precipitation increases into future climate change experiments. Additionally, policy and land management decisions related to global change scenarios should consider how ANPP and BNPP responses may differ, and that ecosystem responses to extreme events might not be predicted from relationships found under moderate environmental changes.« less

Multiscale Measurement of Extreme Response Style

ERIC Educational Resources Information Center

Bolt, Daniel M.; Newton, Joseph R.

2011-01-01

This article extends a methodological approach considered by Bolt and Johnson for the measurement and control of extreme response style (ERS) to the analysis of rating data from multiple scales. Specifically, it is shown how the simultaneous analysis of item responses across scales allows for more accurate identification of ERS, and more effective…

Soil response to long-term projections of extreme temperature and precipitation in the southern La Plata Basin

NASA Astrophysics Data System (ADS)

Pántano, Vanesa C.; Penalba, Olga C.

2017-12-01

Projected changes were estimated considering the main variables which take part in soil-atmosphere interaction. The analysis was focused on the potential impact of these changes on soil hydric condition under extreme precipitation and evapotranspiration, using the combination of Global Climate Models (GCMs) and observational data. The region of study is the southern La Plata Basin that covers part of Argentine territory, where rainfed agriculture production is one of the most important economic activities. Monthly precipitation and maximum and minimum temperatures were used from high quality-controlled observed data from 46 meteorological stations and the ensemble of seven CMIP5 GCMs in two periods: 1970-2005 and 2065-2100. Projected changes in monthly effective temperature and precipitation were analysed. These changes were combined with observed series for each probabilistic interval. The result was used as input variables for the water balance model in order to obtain consequent soil hydric condition (deficit or excess). Effective temperature and precipitation are expected to increase according to the projections of GCMs, with few exceptions. The analysis revealed increase (decrease) in the prevalence of evapotranspiration over precipitation, during spring (winter). Projections for autumn months show precipitation higher than potential evapotranspiration more frequently. Under dry extremes, the analysis revealed higher projected deficit conditions, impacting on crop development. On the other hand, under wet extremes, excess would reach higher values only in particular months. During December, projected increase in temperatures reduces the impact of extreme high precipitation but favours deficit conditions, affecting flower-fructification stage of summer crops.

Pedestrian Injuries By Source: Serious and Disabling Injuries in US and European Cases

PubMed Central

Mallory, Ann; Fredriksson, Rikard; Rosén, Erik; Donnelly, Bruce

2012-01-01

US and European pedestrian crash cases were analyzed to determine frequency of injury by body region and by the vehicle component identified as the injury source. US pedestrian data was drawn from the Pedestrian Crash Data Study (PCDS). European pedestrian data was drawn from the German In-Depth Accident Study (GIDAS). Results were analyzed in terms of both serious injury (AIS 3+) and disabling injury estimated with the Functional Capacity Index (FCI). The results are presented in parallel for a more complete international perspective on injuries and injury sources. Lower extremity injury from bumper impact and head&face injury from windshield impact were the most frequent combinations for both serious and disabling injuries. Serious lower extremity injuries from bumper contact occurred in 43% of seriously injured pedestrian cases in US PCDS data and 35% of European GIDAS cases. Lower-extremity bumper injuries also account for more than 20% of disability in both datasets. Serious head &face injuries from windshield contact occur in 27% of PCDS and 15% of GIDAS serious injury cases. While bumper impacts primarily result in lower extremity injury and windshield impacts are most often associated with head & face injuries, the hood and hood leading edge are responsible for serious and disabling injuries to a number of different body regions. Therefore, while it is appropriate to focus on lower extremity injury when studying bumper performance and on head injury risk when studying windshield impact, pedestrian performance of other components may require better understanding of injury risk for multiple body regions. PMID:23169112

Extreme heat reduces and shifts United States premium wine production in the 21st century

PubMed Central

White, M. A.; Diffenbaugh, N. S.; Jones, G. V.; Pal, J. S.; Giorgi, F.

2006-01-01

Premium wine production is limited to regions climatically conducive to growing grapes with balanced composition and varietal typicity. Three central climatic conditions are required: (i) adequate heat accumulation; (ii) low risk of severe frost damage; and (iii) the absence of extreme heat. Although wine production is possible in an extensive climatic range, the highest-quality wines require a delicate balance among these three conditions. Although historical and projected average temperature changes are known to influence global wine quality, the potential future response of wine-producing regions to spatially heterogeneous changes in extreme events is largely unknown. Here, by using a high-resolution regional climate model forced by the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios A2 greenhouse gas emission scenario, we estimate that potential premium winegrape production area in the conterminous United States could decline by up to 81% by the late 21st century. While increases in heat accumulation will shift wine production to warmer climate varieties and/or lower-quality wines, and frost constraints will be reduced, increases in the frequency of extreme hot days (>35°C) in the growing season are projected to eliminate winegrape production in many areas of the United States. Furthermore, grape and wine production will likely be restricted to a narrow West Coast region and the Northwest and Northeast, areas currently facing challenges related to excess moisture. Our results not only imply large changes for the premium wine industry, but also highlight the importance of incorporating fine-scale processes and extreme events in climate-change impact studies. PMID:16840557

Use of dynamical downscaling to improve the simulation of Central U.S. warm season precipitation in CMIP5 models

NASA Astrophysics Data System (ADS)

Harding, Keith J.; Snyder, Peter K.; Liess, Stefan

2013-11-01

supporting exceptionally productive agricultural lands, the Central U.S. is susceptible to severe droughts and floods. Such precipitation extremes are expected to worsen with climate change. However, future projections are highly uncertain as global climate models (GCMs) generally fail to resolve precipitation extremes. In this study, we assess how well models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate summer means, variability, extremes, and the diurnal cycle of Central U.S. summer rainfall. Output from a subset of historical CMIP5 simulations are used to drive the Weather Research and Forecasting model to determine whether dynamical downscaling improves the representation of Central U.S. rainfall. We investigate which boundary conditions influence dynamically downscaled precipitation estimates and identify GCMs that can reasonably simulate precipitation when downscaled. The CMIP5 models simulate the seasonal mean and variability of summer rainfall reasonably well but fail to resolve extremes, the diurnal cycle, and the dynamic forcing of precipitation. Downscaling to 30 km improves these characteristics of precipitation, with the greatest improvement in the representation of extremes. Additionally, sizeable diurnal cycle improvements occur with higher (10 km) resolution and convective parameterization disabled, as the daily rainfall peak shifts 4 h closer to observations than 30 km resolution simulations. This lends greater confidence that the mechanisms responsible for producing rainfall are better simulated. Because dynamical downscaling can more accurately simulate these aspects of Central U.S. summer rainfall, policymakers can have added confidence in dynamically downscaled rainfall projections, allowing for more targeted adaptation and mitigation.

Multilevel eEmpirical Bayes modeling for improved estimation of toxicant formulations tosuppress parasitic sea lamprey in the Upper Great Lakes

USGS Publications Warehouse

Hatfield, Laura A.; Gutreuter, Steve; Boogaard, Michael A.; Carlin, Bradley P.

2011-01-01

Estimation of extreme quantal-response statistics, such as the concentration required to kill 99.9% of test subjects (LC99.9), remains a challenge in the presence of multiple covariates and complex study designs. Accurate and precise estimates of the LC99.9 for mixtures of toxicants are critical to ongoing control of a parasitic invasive species, the sea lamprey, in the Laurentian Great Lakes of North America. The toxicity of those chemicals is affected by local and temporal variations in water chemistry, which must be incorporated into the modeling. We develop multilevel empirical Bayes models for data from multiple laboratory studies. Our approach yields more accurate and precise estimation of the LC99.9 compared to alternative models considered. This study demonstrates that properly incorporating hierarchical structure in laboratory data yields better estimates of LC99.9 stream treatment values that are critical to larvae control in the field. In addition, out-of-sample prediction of the results of in situ tests reveals the presence of a latent seasonal effect not manifest in the laboratory studies, suggesting avenues for future study and illustrating the importance of dual consideration of both experimental and observational data.

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.

Sensitivities of the hydrologic cycle to model physics, grid resolution, and ocean type in the aquaplanet Community Atmosphere Model

NASA Astrophysics Data System (ADS)

Benedict, James J.; Medeiros, Brian; Clement, Amy C.; Pendergrass, Angeline G.

2017-06-01

Precipitation distributions and extremes play a fundamental role in shaping Earth's climate and yet are poorly represented in many global climate models. Here, a suite of idealized Community Atmosphere Model (CAM) aquaplanet simulations is examined to assess the aquaplanet's ability to reproduce hydroclimate statistics of real-Earth configurations and to investigate sensitivities of precipitation distributions and extremes to model physics, horizontal grid resolution, and ocean type. Little difference in precipitation statistics is found between aquaplanets using time-constant sea-surface temperatures and those implementing a slab ocean model with a 50 m mixed-layer depth. In contrast, CAM version 5.3 (CAM5.3) produces more time mean, zonally averaged precipitation than CAM version 4 (CAM4), while CAM4 generates significantly larger precipitation variance and frequencies of extremely intense precipitation events. The largest model configuration-based precipitation sensitivities relate to choice of horizontal grid resolution in the selected range 1-2°. Refining grid resolution has significant physics-dependent effects on tropical precipitation: for CAM4, time mean zonal mean precipitation increases along the Equator and the intertropical convergence zone (ITCZ) narrows, while for CAM5.3 precipitation decreases along the Equator and the twin branches of the ITCZ shift poleward. Increased grid resolution also reduces light precipitation frequencies and enhances extreme precipitation for both CAM4 and CAM5.3 resulting in better alignment with observational estimates. A discussion of the potential implications these hydrologic cycle sensitivities have on the interpretation of precipitation statistics in future climate projections is also presented.