Impacts of Climate Variability and Change on Flood Frequency Analysis for Transportation Design

DOT National Transportation Integrated Search

2010-09-01

Planning for construction of roads and bridges over rivers or floodplains includes a hydrologic analysis of rainfall amount and intensity : for a defined period. Infrastructure design must be based on accurate rainfall estimates how much (intensi...

Spatial variability of extreme rainfall at radar subpixel scale

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Marra, Francesco; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo

2018-01-01

Extreme rainfall is quantified in engineering practice using Intensity-Duration-Frequency curves (IDF) that are traditionally derived from rain-gauges and more recently also from remote sensing instruments, such as weather radars. These instruments measure rainfall at different spatial scales: rain-gauge samples rainfall at the point scale while weather radar averages precipitation on a relatively large area, generally around 1 km2. As such, a radar derived IDF curve is representative of the mean areal rainfall over a given radar pixel and neglects the within-pixel rainfall variability. In this study, we quantify subpixel variability of extreme rainfall by using a novel space-time rainfall generator (STREAP model) that downscales in space the rainfall within a given radar pixel. The study was conducted using a unique radar data record (23 years) and a very dense rain-gauge network in the Eastern Mediterranean area (northern Israel). Radar-IDF curves, together with an ensemble of point-based IDF curves representing the radar subpixel extreme rainfall variability, were developed fitting Generalized Extreme Value (GEV) distributions to annual rainfall maxima. It was found that the mean areal extreme rainfall derived from the radar underestimate most of the extreme values computed for point locations within the radar pixel (on average, ∼70%). The subpixel variability of rainfall extreme was found to increase with longer return periods and shorter durations (e.g. from a maximum variability of 10% for a return period of 2 years and a duration of 4 h to 30% for 50 years return period and 20 min duration). For the longer return periods, a considerable enhancement of extreme rainfall variability was found when stochastic (natural) climate variability was taken into account. Bounding the range of the subpixel extreme rainfall derived from radar-IDF can be of major importance for different applications that require very local estimates of rainfall extremes.

Investigation of summer monsoon rainfall variability in Pakistan

NASA Astrophysics Data System (ADS)

Hussain, Mian Sabir; Lee, Seungho

2016-08-01

This study analyzes the inter-annual and intra-seasonal rainfall variability in Pakistan using daily rainfall data during the summer monsoon season (June to September) recorded from 1980 to 2014. The variability in inter-annual monsoon rainfall ranges from 20 % in northeastern regions to 65 % in southwestern regions of Pakistan. The analysis reveals that the transition of the negative and positive anomalies was not uniform in the investigated dataset. In order to acquire broad observations of the intra-seasonal variability, an objective criterion, the pre-active period, active period and post-active periods of the summer monsoon rainfall have demarcated. The analysis also reveals that the rainfall in June has no significant contribution to the increase in intra-seasonal rainfall in Pakistan. The rainfall has, however, been enhanced in the summer monsoon in August. The rainfall of September demonstrates a sharp decrease, resulting in a high variability in the summer monsoon season. A detailed examination of the intra-seasonal rainfall also reveals frequent amplitude from late July to early August. The daily normal rainfall fluctuates significantly with its maximum in the Murree hills and its minimum in the northwestern Baluchistan.

Validation of satellite-based rainfall in Kalahari

NASA Astrophysics Data System (ADS)

Lekula, Moiteela; Lubczynski, Maciek W.; Shemang, Elisha M.; Verhoef, Wouter

2018-06-01

Water resources management in arid and semi-arid areas is hampered by insufficient rainfall data, typically obtained from sparsely distributed rain gauges. Satellite-based rainfall estimates (SREs) are alternative sources of such data in these areas. In this study, daily rainfall estimates from FEWS-RFE∼11 km, TRMM-3B42∼27 km, CMOPRH∼27 km and CMORPH∼8 km were evaluated against nine, daily rain gauge records in Central Kalahari Basin (CKB), over a five-year period, 01/01/2001-31/12/2005. The aims were to evaluate the daily rainfall detection capabilities of the four SRE algorithms, analyze the spatio-temporal variability of rainfall in the CKB and perform bias-correction of the four SREs. Evaluation methods included scatter plot analysis, descriptive statistics, categorical statistics and bias decomposition. The spatio-temporal variability of rainfall, was assessed using the SREs' mean annual rainfall, standard deviation, coefficient of variation and spatial correlation functions. Bias correction of the four SREs was conducted using a Time-Varying Space-Fixed bias-correction scheme. The results underlined the importance of validating daily SREs, as they had different rainfall detection capabilities in the CKB. The FEWS-RFE∼11 km performed best, providing better results of descriptive and categorical statistics than the other three SREs, although bias decomposition showed that all SREs underestimated rainfall. The analysis showed that the most reliable SREs performance analysis indicator were the frequency of "miss" rainfall events and the "miss-bias", as they directly indicated SREs' sensitivity and bias of rainfall detection, respectively. The Time Varying and Space Fixed (TVSF) bias-correction scheme, improved some error measures but resulted in the reduction of the spatial correlation distance, thus increased, already high, spatial rainfall variability of all the four SREs. This study highlighted SREs as valuable source of daily rainfall data providing good spatio-temporal data coverage especially suitable for areas with limited rain gauges, such as the CKB, but also emphasized SREs' drawbacks, creating avenue for follow up research.

Decision tree analysis of factors influencing rainfall-related building damage

NASA Astrophysics Data System (ADS)

Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.

2014-04-01

Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998-2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.

Exploratory analysis of rainfall events in Coimbra, Portugal: variability of raindrop characteristics

NASA Astrophysics Data System (ADS)

Carvalho, S. C. P.; de Lima, M. I. P.; de Lima, J. L. M. P.

2012-04-01

Laser disdrometers can monitor efficiently rainfall characteristics at small temporal scales, providing data on rain intensity, raindrop diameter and fall speed, and raindrop counts over time. This type of data allows for the increased understanding of the rainfall structure at small time scales. Of particular interest for many hydrological applications is the characterization of the properties of extreme events, including the intra-event variability, which are affected by different factors (e.g. geographical location, rainfall generating mechanisms). These properties depend on the microphysical, dynamical and kinetic processes that interact to produce rain. In this study we explore rainfall data obtained during two years with a laser disdrometer installed in the city of Coimbra, in the centre region of mainland Portugal. The equipment was developed by Thies Clima. The data temporal resolution is one-minute. Descriptive statistics of time series of raindrop diameter (D), fall speed, kinetic energy, and rain rate were studied at the event scale; for different variables, the average, maximum, minimum, median, variance, standard deviation, quartile, coefficient of variation, skewness and kurtosis were determined. The empirical raindrop size distribution, N(D), was also calculated. Additionally, the parameterization of rainfall was attempted by investigating the applicability of different theoretical statistical distributions to fit the empirical data (e.g. exponential, gamma and lognormal distributions). As expected, preliminary results show that rainfall properties and structure vary with rainfall type and weather conditions over the year. Although only two years were investigated, already some insight into different rain events' structure was obtained.

The interaction rainfall vs. weight as determinant of total mercury concentration in fish from a tropical estuary.

PubMed

Barletta, M; Lucena, L R R; Costa, M F; Barbosa-Cintra, S C T; Cysneiros, F J A

2012-08-01

Mercury loads in tropical estuaries are largely controlled by the rainfall regime that may cause biodilution due to increased amounts of organic matter (both live and non-living) in the system. Top predators, as Trichiurus lepturus, reflect the changing mercury bioavailability situations in their muscle tissues. In this work two variables [fish weight (g) and monthly total rainfall (mm)] are presented as being important predictors of total mercury concentration (T-Hg) in fish muscle. These important explanatory variables were identified by a Weibull Regression model, which best fit the dataset. A predictive model using readily available variables as rainfall is important, and can be applied for human and ecological health assessments and decisions. The main contribution will be to further protect vulnerable groups as pregnant women and children. Nature conservation directives could also improve by considering monitoring sample designs that include this hypothesis, helping to establish complete and detailed mercury contamination scenarios. Copyright © 2012 Elsevier Ltd. All rights reserved.

A comprehensive analysis of coherent rainfall patterns in China and potential drivers. Part I: Interannual variability

NASA Astrophysics Data System (ADS)

Stephan, Claudia Christine; Klingaman, Nicholas Pappas; Vidale, Pier Luigi; Turner, Andrew George; Demory, Marie-Estelle; Guo, Liang

2018-06-01

Interannual rainfall variability in China affects agriculture, infrastructure and water resource management. To improve its understanding and prediction, many studies have associated precipitation variability with particular causes for specific seasons and regions. Here, a consistent and objective method, Empirical Orthogonal Teleconnection (EOT) analysis, is applied to 1951-2007 high-resolution precipitation observations over China in all seasons. Instead of maximizing the explained space-time variance, the method identifies regions in China that best explain the temporal variability in domain-averaged rainfall. The EOT method is validated by the reproduction of known relationships to the El Niño Southern Oscillation (ENSO): high positive correlations with ENSO are found in eastern China in winter, along the Yangtze River in summer, and in southeast China during spring. New findings include that wintertime rainfall variability along the southeast coast is associated with anomalous convection over the tropical eastern Atlantic and communicated to China through a zonal wavenumber-three Rossby wave. Furthermore, spring rainfall variability in the Yangtze valley is related to upper-tropospheric midlatitude perturbations that are part of a Rossby wave pattern with its origin in the North Atlantic. A circumglobal wave pattern in the northern hemisphere is also associated with autumn precipitation variability in eastern areas. The analysis is objective, comprehensive, and produces timeseries that are tied to specific locations in China. This facilitates the interpretation of associated dynamical processes, is useful for understanding the regional hydrological cycle, and allows the results to serve as a benchmark for assessing general circulation models.

Rainfall variability and extremes over southern Africa: assessment of a climate model to reproduce daily extremes

NASA Astrophysics Data System (ADS)

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

2009-04-01

It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset.

Performance of Sorghum Varieties under Variable Rainfall in Central Tanzania

PubMed Central

Tumbo, S. D.; Kihupi, N. I.; Rwehumbiza, Filbert B.

2017-01-01

Rainfall variability has a significant impact on crop production with manifestations in frequent crop failure in semiarid areas. This study used the parameterized APSIM crop model to investigate how rainfall variability may affect yields of improved sorghum varieties based on long-term historical rainfall and projected climate. Analyses of historical rainfall indicate a mix of nonsignificant and significant trends on the onset, cessation, and length of the growing season. The study confirmed that rainfall variability indeed affects yields of improved sorghum varieties. Further analyses of simulated sorghum yields based on seasonal rainfall distribution indicate the concurrence of lower grain yields with the 10-day dry spells during the cropping season. Simulation results for future sorghum response, however, show that impacts of rainfall variability on sorghum will be overridden by temperature increase. We conclude that, in the event where harms imposed by moisture stress in the study area are not abated, even improved sorghum varieties are likely to perform poorly. PMID:28536708

Performance of Sorghum Varieties under Variable Rainfall in Central Tanzania.

PubMed

Msongaleli, Barnabas M; Tumbo, S D; Kihupi, N I; Rwehumbiza, Filbert B

2017-01-01

Rainfall variability has a significant impact on crop production with manifestations in frequent crop failure in semiarid areas. This study used the parameterized APSIM crop model to investigate how rainfall variability may affect yields of improved sorghum varieties based on long-term historical rainfall and projected climate. Analyses of historical rainfall indicate a mix of nonsignificant and significant trends on the onset, cessation, and length of the growing season. The study confirmed that rainfall variability indeed affects yields of improved sorghum varieties. Further analyses of simulated sorghum yields based on seasonal rainfall distribution indicate the concurrence of lower grain yields with the 10-day dry spells during the cropping season. Simulation results for future sorghum response, however, show that impacts of rainfall variability on sorghum will be overridden by temperature increase. We conclude that, in the event where harms imposed by moisture stress in the study area are not abated, even improved sorghum varieties are likely to perform poorly.

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes

PubMed Central

Ghosh, Subimal; Vittal, H.; Sharma, Tarul; Karmakar, Subhankar; Kasiviswanathan, K. S.; Dhanesh, Y.; Sudheer, K. P.; Gunthe, S. S.

2016-01-01

India’s agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins. PMID:27463092

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes.

PubMed

Ghosh, Subimal; Vittal, H; Sharma, Tarul; Karmakar, Subhankar; Kasiviswanathan, K S; Dhanesh, Y; Sudheer, K P; Gunthe, S S

2016-01-01

India's agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins.

Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas - a review

NASA Astrophysics Data System (ADS)

Cristiano, Elena; ten Veldhuis, Marie-claire; van de Giesen, Nick

2017-07-01

In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

Rainfall variability and extremes over southern Africa: Assessment of a climate model to reproduce daily extremes

NASA Astrophysics Data System (ADS)

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

2009-04-01

It is increasingly accepted that that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The subcontinent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite derived rainfall data from the Microwave Infra-Red Algorithm (MIRA). This dataset covers the period from 1993-2002 and the whole of southern Africa at a spatial resolution of 0.1 degree longitude/latitude. The ability of a climate model to simulate current climate provides some indication of how much confidence can be applied to its future predictions. In this paper, simulations of current climate from the UK Meteorological Office Hadley Centre's climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. This concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of rainfall variability over southern Africa. Secondly, the ability of the model to reproduce daily rainfall extremes will be assessed, again by a comparison with extremes from the MIRA dataset. The paper will conclude by discussing the user needs of satellite rainfall retrievals from a climate change modelling prospective.

Influences of Appalachian orography on heavy rainfall and rainfall variability associated with the passage of hurricane Isabel by ensemble simulations

NASA Astrophysics Data System (ADS)

Oldaker, Guy; Liu, Liping; Lin, Yuh-Lang

2017-12-01

This study focuses on the heavy rainfall event associated with hurricane Isabel's (2003) passage over the Appalachian mountains of the eastern United States. Specifically, an ensemble consisting of two groups of simulations using the Weather Research and Forecasting model (WRF), with and without topography, is performed to investigate the orographic influences on heavy rainfall and rainfall variability. In general, the simulated ensemble mean with full terrain is able to reproduce the key observed 24-h rainfall amount and distribution, while the flat-terrain mean lacks in this respect. In fact, 30-h rainfall amounts are reduced by 75% with the removal of topography. Rainfall variability is also significantly increased with the presence of orography. Further analysis shows that the complex interaction between the hurricane and terrain along with contributions from varied microphysics, cumulus parametrization, and planetary boundary layer schemes have a pronounced effect on rainfall and rainfall variability. This study follows closely with a previous study, but for a different TC case of Isabel (2003). It is an important sensitivity test for a different TC in a very different environment. This study reveals that the rainfall variability behaves similarly, even with different settings of the environment.

Monsoon variability, crop water requirement, and crop planning for kharif rice in Sagar Island, India.

PubMed

Mandal, S; Choudhury, B U; Satpati, L N

2015-12-01

In the Sagar Island of Bay of Bengal, rainfed lowland rice is the major crop, grown solely depending on erratic distribution of southwest monsoon (SM) rainfall. Lack of information on SM rainfall variability and absence of crop scheduling accordingly results in frequent occurrence of intermittent water stress and occasional crop failure. In the present study, we analyzed long period (1982-2010) SM rainfall behavior (onset, withdrawal, rainfall and wetness indices, dry and wet spells), crop water requirement (CWR, by Food and Agriculture Organization (FAO) 56), and probability of weekly rainfall occurrence (by two-parameter gamma distribution) to assess the variability and impact on water availability, CWR, and rice productivity. Finally, crop planning was suggested to overcome monsoon uncertainties on water availability and rice productivity. Study revealed that the normal onset and withdrawal weeks for SM rainfall were 22nd ± 1 and 43rd ± 2 meteorological weeks (MW), respectively. However, effective monsoon rainfall started at 24th MW (rainfall 92.7 mm, p > 56.7 % for 50 mm rainfall) and was terminated by the end of 40th MW (rainfall 90.7 mm, p < 59.6 % for 50 mm rainfall). During crop growth periods (seed to seed, 21st to 45th MW), the island received an average weekly rainfall of 65.1 ± 25.9 mm, while the corresponding weekly CWR was 47.8 ± 5.4 mm. Despite net water surplus of 353.9 mm during crop growth periods, there was a deficit of 159.5 mm water during MW of 18-23 (seedling raising) and MW of 41-45 (flowering to maturity stages). Water stress was observed in early lag vegetative stage of crop growth (32nd MW). The total dry spell frequency during panicle initiation and heading stage was computed as 40 of which 6 dry spells were >7 days in duration and reflected a significant (p < 0.05) increasing trend (at 0.22 days year(-1)) over the years (1982-2010). The present study highlights the adaptive capacity of crop planning including abiotic stress-tolerant cultivars to monsoon rainfall variability for sustaining rainfed rice production vis-à-vis food and livelihood security in vulnerable islands of coastal ecosystem.

Adapting to the unpredictable: reproductive biology of vertebrates in the Australian wet-dry tropics.

PubMed

Shine, Richard; Brown, Gregory P

2008-01-27

In the wet-dry tropics of northern Australia, temperatures are high and stable year-round but monsoonal rainfall is highly seasonal and variable both annually and spatially. Many features of reproduction in vertebrates of this region may be adaptations to dealing with this unpredictable variation in precipitation, notably by (i) using direct proximate (rainfall-affected) cues to synchronize the timing and extent of breeding with rainfall events, (ii) placing the eggs or offspring in conditions where they will be buffered from rainfall extremes, and (iii) evolving developmental plasticity, such that the timing and trajectory of embryonic differentiation flexibly respond to local conditions. For example, organisms as diverse as snakes (Liasis fuscus, Acrochordus arafurae), crocodiles (Crocodylus porosus), birds (Anseranas semipalmata) and wallabies (Macropus agilis) show extreme annual variation in reproductive rates, linked to stochastic variation in wet season rainfall. The seasonal timing of initiation and cessation of breeding in snakes (Tropidonophis mairii) and rats (Rattus colletti) also varies among years, depending upon precipitation. An alternative adaptive route is to buffer the effects of rainfall variability on offspring by parental care (including viviparity) or by judicious selection of nest sites in oviparous taxa without parental care. A third type of adaptive response involves flexible embryonic responses (including embryonic diapause, facultative hatching and temperature-dependent sex determination) to incubation conditions, as seen in squamates, crocodilians and turtles. Such flexibility fine-tunes developmental rates and trajectories to conditions--especially, rainfall patterns--that are not predictable at the time of oviposition.

Evaluation of Rainfall-Runoff Models for Mediterranean Subcatchments

NASA Astrophysics Data System (ADS)

Cilek, A.; Berberoglu, S.; Donmez, C.

2016-06-01

The development and the application of rainfall-runoff models have been a corner-stone of hydrological research for many decades. The amount of rainfall and its intensity and variability control the generation of runoff and the erosional processes operating at different scales. These interactions can be greatly variable in Mediterranean catchments with marked hydrological fluctuations. The aim of the study was to evaluate the performance of rainfall-runoff model, for rainfall-runoff simulation in a Mediterranean subcatchment. The Pan-European Soil Erosion Risk Assessment (PESERA), a simplified hydrological process-based approach, was used in this study to combine hydrological surface runoff factors. In total 128 input layers derived from data set includes; climate, topography, land use, crop type, planting date, and soil characteristics, are required to run the model. Initial ground cover was estimated from the Landsat ETM data provided by ESA. This hydrological model was evaluated in terms of their performance in Goksu River Watershed, Turkey. It is located at the Central Eastern Mediterranean Basin of Turkey. The area is approximately 2000 km2. The landscape is dominated by bare ground, agricultural and forests. The average annual rainfall is 636.4mm. This study has a significant importance to evaluate different model performances in a complex Mediterranean basin. The results provided comprehensive insight including advantages and limitations of modelling approaches in the Mediterranean environment.

Temporal and spatial characteristics of annual and seasonal rainfall in Malawi

NASA Astrophysics Data System (ADS)

Ngongondo, Cosmo; Xu, Chong-Yu; Gottschalk, Lars; Tallaksen, Lena M.; Alemaw, Berhanu

2010-05-01

An understanding of the temporal and spatial characteristics of rainfall is central to water resources planning and management. However, such information is often limited in many developing countries like Malawi. In an effort to bridge the information gap, this study examined the temporal and spatial charecteristics of rainfall in Malawi. Rainfall readings from 42 stations across Malawi from 1960 to 2006 were analysed at monthly, annual and seasonal scales. The Malawian rainfall season lasts from November to April. The data were firstly subjected to quality checks through the cumulative deviations test and the Standard Normal Homogeinity Test (SNHT). Monthly distribution in a typical year, called heterogeneity, was investigated using the Precipitation Concentration Index (PCI). Further, normalized precipitation anomaly series of annual rainfall series (AR) and the PCI (APCI) were used to test for interannual rainfall variability. Spatial variability was characterised by fitting the Spatial Correlation function (SCF). The nonparametric Mann-Kendall statistic was used to investigate the temporal trends of the various rainfall variables. The results showed that 40 of the stations passed both data quality tests. For the two stations that failed, the data were adjusted using nearby stations. Annual and seasonal rainfall were found to be characterised by high spatial variation. The country mean annual rainfall was 1095 mm with mean interannual variability of 26%. The highland areas to the north and southeast of the country exhibited the highest rainfall and lowest interannual variability. Lowest rainfall coupled with high interannual variability was found in the Lower Shire basin, in the southern part of Malawi. This simillarity is the pattern of annual and seasonal rainfall should be expected because all stations had over 90% of their observed annual rainfall in the six month period between November and April. Monthly rainfall was found to be highly variable both temporally and spatially. None of the stations have stable monthly rainfall regimes (mean PCI of less than 10). Stations with the highest mean rainfall were found to have a lower interannual variability. The rainfall stations showed low spatial correlations for annual, monthly as well as seasonal timescales indicating that the data may not be suitable for spatial interpolation. However, some structure (i.e. lower correlation with distance) could be observed when aggregating the data at 50 mile intervals. The annual and seasonal rainfall series were dominated by negative trends. The spatial distribution of the trends can be described as heterogeneous, although most of the stations in the southern region have negative trends. At the monthly timescale, 37 of the stations show a negative trend with four of the stations, all in the south, showing significant negative trends. On the other hand, only 5 stations show positive trends with only one significant trend in the south. Keywords: Malawi, rainfall trends, spatial variation

The impact of inter-annual rainfall variability on African savannas changes with mean rainfall.

PubMed

Synodinos, Alexis D; Tietjen, Britta; Lohmann, Dirk; Jeltsch, Florian

2018-01-21

Savannas are mixed tree-grass ecosystems whose dynamics are predominantly regulated by resource competition and the temporal variability in climatic and environmental factors such as rainfall and fire. Hence, increasing inter-annual rainfall variability due to climate change could have a significant impact on savannas. To investigate this, we used an ecohydrological model of stochastic differential equations and simulated African savanna dynamics along a gradient of mean annual rainfall (520-780 mm/year) for a range of inter-annual rainfall variabilities. Our simulations produced alternative states of grassland and savanna across the mean rainfall gradient. Increasing inter-annual variability had a negative effect on the savanna state under dry conditions (520 mm/year), and a positive effect under moister conditions (580-780 mm/year). The former resulted from the net negative effect of dry and wet extremes on trees. In semi-arid conditions (520 mm/year), dry extremes caused a loss of tree cover, which could not be recovered during wet extremes because of strong resource competition and the increased frequency of fires. At high mean rainfall (780 mm/year), increased variability enhanced savanna resilience. Here, resources were no longer limiting and the slow tree dynamics buffered against variability by maintaining a stable population during 'dry' extremes, providing the basis for growth during wet extremes. Simultaneously, high rainfall years had a weak marginal benefit on grass cover due to density-regulation and grazing. Our results suggest that the effects of the slow tree and fast grass dynamics on tree-grass interactions will become a major determinant of the savanna vegetation composition with increasing rainfall variability. Copyright © 2017 Elsevier Ltd. All rights reserved.

Distributed modelling of water resources in the Lower Jordan River Basin - from present day variability to suitability for new water sources

NASA Astrophysics Data System (ADS)

Gunkel, Anne; Lange, Jens

2010-05-01

The Middle East is characterized by a high temporal and spatial variability of rainfall. As a result, water resources are not reliable and severe drought events are frequent, worsening the natural water scarcity. Single high magnitude events may dominate the water balance of entire seasons - a fact that is poorly represented in the assessments of available water resources that are normally based on long term averages. Therefore, a distributed hydrological model with a high temporal and spatial resolution is applied to the Lower Jordan River basin (LJRB). The focus is hereby to capture the variability of rainfall and to investigate how this signal is amplified in the hydrological cycle in this arid and semi arid environment. Rainfall variability is addressed through a volume scanning rainfall radar providing precipitation data with a resolution of 5 minutes for entire seasons that serves as input to a conceptual hydrological model. The raw radar data recorded by a C-Band system was pre-corrected by a multiple regression approach prior to regionalization to the LJRB, ground truthing with rainfall station data and conditional merging. Despite certain uncertainties, the data documents the accentuated rainfall variability in the entire LJRB. In order to include the full range of present rainfall variability, one average and two extreme seasons (wet and dry) are studied. Hydrological modelling is undertaken with a new modelling tool created by coupling two hydrological models, TRAIN and ZIN, complementing each other in respect to the addressed processes and water fluxes. The resulting modelling tool enables conceptual modelling of the processes relevant for semi-arid / arid environments with a high temporal and spatial resolution. The model is applied to the large scale LJRB (16,000 km²) in order to simulate all components of the water balance for three rainy seasons representing the present climate variability. Under given conditions of low data availability, the results give a basin wide view on the availability of surface water resources without human intervention with a high resolution in time (5 min) and space (up to 250 x 250 m²). The scarcity of water resources in many areas within the region is illustrated and detailed maps of the water balance components reveal spatial pattern of water availability characterizing the different potentials of regions or sub basins for water management options. Moreover, comparing different climate conditions provides valuable information for water management, including insights into the relation between green and blue water. For instance, runoff generation and percolation react stronger to changes in precipitation than evapotranspiration and the changes in runoff and percolation are considerably higher than the differences in rainfall between the three years. This amplification of rainfall variability by the hydrological cycle is significant for water management. Based on the results for current conditions, the impact of different scenarios and management options is analyzed, e.g. the effect of land use changes or the suitability of different regions for rainwater harvesting, one of the urgently needed new water sources.

Seasonal Scale Convective-Stratiform Pricipitation Variabilities at Tropics

NASA Astrophysics Data System (ADS)

S, Sreekanth T.

begin{center} Large Seasonal Scale Convective-Stratiform Pricipitation Variabilities at Tropics Sreekanth T S*, Suby Symon*, G. Mohan Kumar (1) and V Sasi Kumar (2) *Centre for Earth Science Studies, Akkulam, Thiruvananthapuram (1) D-330, Swathi Nagar, West Fort, Thiruvananthapuram 695023 (2) 32. NCC Nagar Peroorkada, Thiruvananthapuram ABSTRACT This study investigates the variabilities of convective and stratiform rainfall from 2011 to 2013 at a tropical coastal station in three seasons viz Pre-Monsoon (March-May), Monsoon (June-September) and Post-Monsoon (October-December). Understanding the climatological variability of these two dominant forms of precipitation and their implications in the total rainfall were the main objectives of this investigation. Variabilities in the frequency & duration of events, rain rate & total number of rain drops distribution in different events and the accumulated amount of rain water were analysed. Based on the ground & radar observations from optical & impact disdrometers, Micro Rain Radar and Atmospheric Electric Field Mill, precipitation events were classified into convective and stratiform in three seasons. Classification was done by the method followed by Testud et al (2001) and as an additional information electrical behaviour of clouds from Atmospheric Electric Field Mill is also used. Events which could not be included in both types were termed as 'mixed precipitation' and were included separately. Diurnal variability of the total rainfall in each seasons were also examined. For both convective and stratiform rainfall there exist distinct day-night differences. During nocturnal hours convective rain draged more attention. In all seasons almost 70% of rain duration and 60% of rain events of convective origin were confined to nocturnal hours. But stratiform rain was not affected by diurnal variations greatly because night time occurrences of stratiform duration and events were less than 50%. Also in Monsoon above 35% of rain duration is from mixed precipitation category.

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

NASA Astrophysics Data System (ADS)

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

2009-04-01

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

Organization of vertical shear of wind and daily variability of monsoon rainfall

NASA Astrophysics Data System (ADS)

Gouda, K. C.; Goswami, P.

2016-10-01

Very little is known about the mechanisms that govern the day to day variability of the Indian summer monsoon (ISM) rainfall; in the current dominant view, the daily rainfall is essentially a result of chaotic dynamics. Most studies in the past have thus considered monsoon in terms of its seasonal (June-September) or monthly rainfall. We show here that the daily rainfall in June is associated with vertical shear of horizontal winds at specific scales. While vertical shear had been used in the past to investigate interannual variability of seasonal rainfall, rarely any effort has been made to examine daily rainfall. Our work shows that, at least during June, the daily rainfall variability of ISM rainfall is associated with a large scale dynamical coherence in the sense that the vertical shear averaged over large spatial extents are significantly correlated with area-averaged daily rainfall. An important finding from our work is the existence of a clearly delineated monsoon shear domain (MSD) with strong coherence between area-averaged shear and area-averaged daily rainfall in June; this association of daily rainfall is not significant with shear over only MSD. Another important feature is that the association between daily rainfall and vertical shear is present only during the month of June. Thus while ISM (June-September) is a single seasonal system, it is important to consider the dynamics and variation of June independently of the seasonal ISM rainfall. The association between large-scale organization of circulation and daily rainfall is suggested as a basis for attempting prediction of daily rainfall by ensuring accurate simulation of wind shear.

Impact of La Niña and La Niña Modoki on Indonesia rainfall variability

NASA Astrophysics Data System (ADS)

Hidayat, R.; Juniarti, MD; Ma’rufah, U.

2018-05-01

La Niña events are indicated by cooling SST in central and eastern equatorial Pacific. While La Niña Modoki occurrences are indicated by cooling SST in central Pacific and warming SST in western and eastern equatorial Pacific. These two events are influencing rainfall variability in several regions including Indonesia. The objective of this study is to analyse the impact of La Niña and La Niña Modoki on Indonesian rainfall variability. We found the Nino 3.4 index is highly correlated (r = -0.95) with Indonesian rainfall. Positive rainfall anomalies up to 200 mm/month occurred mostly in Indonesian region during La Niña events, but in DJF several areas of Sumatera, Kalimantan and eastern Indonesia tend to have negative rainfall. During La Niña Modoki events, positive rainfall anomaly (up to 50 mm/month) occurred in Sumatera Island, Kalimantan, Java and eastern Indonesia in DJF and up to 175 mm/month occurred only in Java Island in MAM season. La Niña events have strong cooling SST in central and eastern equatorial Pacific (-1.5°C) in DJF. While La Niña Modoki events warming SST occurred in western and eastern equatorial Pacific (0.75°C) and cooling SST in central Pacific (- 0.75°C) in DJF and MAM. Walker circulation in La Niña Modoki events (on DJF and MAM) showed strong convergence in eastern Pacific, and weak convergence in western Pacific (Indonesia).

Spatio-temporal analysis of annual rainfall in Crete, Greece

NASA Astrophysics Data System (ADS)

Varouchakis, Emmanouil A.; Corzo, Gerald A.; Karatzas, George P.; Kotsopoulou, Anastasia

2018-03-01

Analysis of rainfall data from the island of Crete, Greece was performed to identify key hydrological years and return periods as well as to analyze the inter-annual behavior of the rainfall variability during the period 1981-2014. The rainfall spatial distribution was also examined in detail to identify vulnerable areas of the island. Data analysis using statistical tools and spectral analysis were applied to investigate and interpret the temporal course of the available rainfall data set. In addition, spatial analysis techniques were applied and compared to determine the rainfall spatial distribution on the island of Crete. The analysis presented that in contrast to Regional Climate Model estimations, rainfall rates have not decreased, while return periods vary depending on seasonality and geographic location. A small but statistical significant increasing trend was detected in the inter-annual rainfall variations as well as a significant rainfall cycle almost every 8 years. In addition, statistically significant correlation of the island's rainfall variability with the North Atlantic Oscillation is identified for the examined period. On the other hand, regression kriging method combining surface elevation as secondary information improved the estimation of the annual rainfall spatial variability on the island of Crete by 70% compared to ordinary kriging. The rainfall spatial and temporal trends on the island of Crete have variable characteristics that depend on the geographical area and on the hydrological period.

The multi-parameter remote measurement of rainfall

NASA Technical Reports Server (NTRS)

Atlas, D.; Ulbrich, C. W.; Meneghini, R.

1982-01-01

The measurement of rainfall by remote sensors is investigated. One parameter radar rainfall measurement is limited because both reflectivity and rain rate are dependent on at least two parameters of the drop size distribution (DSD), i.e., representative raindrop size and number concentration. A generalized rain parameter diagram is developed which includes a third distribution parameter, the breadth of the DSD, to better specify rain rate and all possible remote variables. Simulations show the improvement in accuracy attainable through the use of combinations of two and three remote measurables. The spectrum of remote measurables is reviewed. These include path integrated techniques of radiometry and of microwave and optical attenuation.

Mechanisms for Diurnal Variability of Global Tropical Rainfall Observed from TRMM

NASA Technical Reports Server (NTRS)

Yang, Song; Smith, Eric A.

2004-01-01

The behavior and various controls of diurnal variability in tropical-subtropical rainfall are investigated using Tropical Rainfall Measuring Mission (TRMM) precipitation measurements retrieved from: (1) TRMM Microwave Imager (TMI), (2) Precipitation Radar (PR), and (3) TMI/PR Combined, standard level 2 algorithms for the 1998 annual cycle. Results show that the diurnal variability characteristics of precipitation are consistent for all three algorithms, providing assurance that TRMM retrievals are providing consistent estimates of rainfall variability. As anticipated, most ocean areas exhibit more rainfall at night, while over most land areas rainfall peaks during daytime ,however, various important exceptions are found. The dominant feature of the oceanic diurnal cycle is a rainfall maximum in late-evening/early-morning (LE-EM) hours, while over land the dominant maximum occurs in the mid- to late-afternoon (MLA). In conjunction with these maxima are pronounced seasonal variations of the diurnal amplitudes. Amplitude analysis shows that the diurnal pattern and its seasonal evolution are closely related to the rainfall accumulation pattern and its seasonal evolution. In addition, the horizontal distribution of diurnal variability indicates that for oceanic rainfall there is a secondary MLA maximum, co-existing with the LE-EM maximum, at latitudes dominated by large scale convergence and deep convection. Analogously, there is a preponderance for an LE-EM maximum over land, co-existing with the stronger MLA maximum, although it is not evident that this secondary continental feature is closely associated with the large scale circulation. The ocean results clearly indicate that rainfall diurnal variability associated with large scale convection is an integral part of the atmospheric general circulation.

Understanding extreme rainfall events in Australia through historical data

NASA Astrophysics Data System (ADS)

Ashcroft, Linden; Karoly, David John

2016-04-01

Historical climate data recovery is still an emerging field in the Australian region. The majority of Australia's instrumental climate analyses begin in 1900 for rainfall and 1910 for temperature, particularly those focussed on extreme event analysis. This data sparsity for the past in turn limits our understanding of long-term climate variability, constraining efforts to predict the impact of future climate change. To address this need for improved historical data in Australia, a new network of recovered climate observations has recently been developed, centred on the highly populated southeastern Australian region (Ashcroft et al., 2014a, 2014b). The dataset includes observations from more than 39 published and unpublished sources and extends from British settlement in 1788 to the formation of the Australian Bureau of Meteorology in 1908. Many of these historical sources provide daily temperature and rainfall information, providing an opportunity to improve understanding of the multidecadal variability of Australia's extreme events. In this study we combine the historical data for three major Australian cities - Melbourne, Sydney and Adelaide - with modern observations to examine extreme rainfall variability over the past 174 years (1839-2013). We first explore two case studies, combining instrumental and documentary evidence to support the occurrence of severe storms in Sydney in 1841 and 1844. These events appear to be at least as extreme as Sydney's modern 24-hour rainfall record. Next we use a suite of rainfall indices to assess the long-term variability of rainfall in southeastern Australia. In particular, we focus on the stationarity of the teleconnection between the El Niño-Southern Oscillation (ENSO) phenomenon and extreme rainfall events. Using ENSO reconstructions derived from both palaeoclimatic and documentary sources, we determine the historical relationship between extreme rainfall in southeastern Australia and ENSO, and examine whether or not this relationship has remained stable since the early to mid-19th century. Ashcroft, L., Gergis, J., Karoly, D.J., 2014a. A historical climate dataset for southeastern Australia, 1788-1859. Geosci. Data J. 1, 158-178. doi:10.1002/gdj3.19 Ashcroft, L., Karoly, D.J., Gergis, J., 2014b. Southeastern Australian climate variability 1860-2009: A multivariate analysis. Int. J. Climatol. 34, 1928-1944. doi:10.1002/joc.3812

Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Funk, Chris

2014-01-01

Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.

The influence of ENSO, PDO and PNA on secular rainfall variations in Hawai`i

NASA Astrophysics Data System (ADS)

Frazier, Abby G.; Elison Timm, Oliver; Giambelluca, Thomas W.; Diaz, Henry F.

2017-11-01

Over the last century, significant declines in rainfall across the state of Hawai`i have been observed, and it is unknown whether these declines are due to natural variations in climate, or manifestations of human-induced climate change. Here, a statistical analysis of the observed rainfall variability was applied as first step towards better understanding causes for these long-term trends. Gridded seasonal rainfall from 1920 to 2012 is used to perform an empirical orthogonal function (EOF) analysis. The leading EOF components are correlated with three indices of natural climate variations (El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Pacific North American (PNA)), and multiple linear regression (MLR) is used to model the leading components with climate indices. PNA is the dominant mode of wet season (November-April) variability, while ENSO is most significant in the dry season (May-October). To assess whether there is an anthropogenic influence on rainfall, two methods are used: a linear trend term is included in the MLR, and pattern correlation coefficients (PCC) are calculated between recent rainfall trends and future changes in rainfall projected by downscaling methods. PCC results indicate that recent observed rainfall trends in the wet season are positively correlated with future expected changes in rainfall, while dry season PCC results do not show a clear pattern. The MLR results, however, show that the trend term adds significantly to model skill only in the dry season. Overall, MLR and PCC results give weak and inconclusive evidence for detection of anthropogenic signals in the observed rainfall trends.

Do we really use rainfall observations consistent with reality in hydrological modelling?

NASA Astrophysics Data System (ADS)

Ciampalini, Rossano; Follain, Stéphane; Raclot, Damien; Crabit, Armand; Pastor, Amandine; Moussa, Roger; Le Bissonnais, Yves

2017-04-01

Spatial and temporal patterns in rainfall control how water reaches soil surface and interacts with soil properties (i.e., soil wetting, infiltration, saturation). Once a hydrological event is defined by a rainfall with its spatiotemporal variability and by some environmental parameters such as soil properties (including land use, topographic and anthropic features), the evidence shows that each parameter variation produces different, specific outputs (e.g., runoff, flooding etc.). In this study, we focus on the effect of rainfall patterns because, due to the difficulty to dispose of detailed data, their influence in modelling is frequently underestimated or neglected. A rainfall event affects a catchment non uniformly, it is spatially localized and its pattern moves in space and time. The way and the time how the water reaches the soil and saturates it respect to the geometry of the catchment deeply influences soil saturation, runoff, and then sediment delivery. This research, approaching a hypothetical, simple case, aims to stimulate the debate on the reliability of the rainfall quality used in hydrological / soil erosion modelling. We test on a small catchment of the south of France (Roujan, Languedoc Roussillon) the influence of rainfall variability with the use of a HD hybrid hydrological - soil erosion model, combining a cinematic wave with the St. Venant equation and a simplified "bucket" conceptual model for ground water, able to quantify the effect of different spatiotemporal patterns of a very-high-definition synthetic rainfall. Results indicate that rainfall spatiotemporal patterns are crucial simulating an erosive event: differences between spatially uniform rainfalls, as frequently adopted in simulations, and some hypothetical rainfall patterns here applied, reveal that the outcome of a simulated event can be highly underestimated.

Infiltration and Runoff Measurements on Steep Burned Hillslopes Using a Rainfall Simulator with Variable Rain Intensities

USGS Publications Warehouse

Kinner, David A.; Moody, John A.

2008-01-01

Multiple rainfall intensities were used in rainfall-simulation experiments designed to investigate the infiltration and runoff from 1-square-meter plots on burned hillslopes covered by an ash layer of varying thickness. The 1-square-meter plots were on north- and south-facing hillslopes in an area burned by the Overland fire northwest of Boulder near Jamestown on the Front Range of Colorado. A single-nozzle, wide-angle, multi-intensity rain simulator was developed to investigate the infiltration and runoff on steep (30- to 40-percent gradient) burned hillslopes covered with ash. The simulated rainfall was evaluated for spatial variability, drop size, and kinetic energy. Fourteen rainfall simulations, at three intensities (about 20 millimeters per hour [mm/h], 35 mm/h, and 50 mm/h), were conducted on four plots. Measurements during and after the simulations included runoff, rainfall, suspended-sediment concentrations, surface ash layer thickness, soil moisture, soil grain size, soil lost on ignition, and plot topography. Runoff discharge reached a steady state within 7 to 26 minutes. Steady infiltration rates with the 50-mm/h application rainfall intensity approached 20?35 mm/h. If these rates are projected to rainfall application intensities used in many studies of burned area runoff production (about 80 mm/h), the steady discharge rates are on the lower end of measurements from other studies. Experiments using multiple rainfall intensities (three) suggest that runoff begins at rainfall intensities around 20 mm/h at the 1-square-meter scale, an observation consistent with a 10-mm/h rainfall intensity threshold needed for runoff initiation that has been reported in the literature.

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

NASA Astrophysics Data System (ADS)

Santos, Monica; Fragoso, Marcelo

2010-05-01

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

Impacts of rainfall variability and expected rainfall changes on cost-effective adaptation of water systems to climate change.

PubMed

van der Pol, T D; van Ierland, E C; Gabbert, S; Weikard, H-P; Hendrix, E M T

2015-05-01

Stormwater drainage and other water systems are vulnerable to changes in rainfall and runoff and need to be adapted to climate change. This paper studies impacts of rainfall variability and changing return periods of rainfall extremes on cost-effective adaptation of water systems to climate change given a predefined system performance target, for example a flood risk standard. Rainfall variability causes system performance estimates to be volatile. These estimates may be used to recurrently evaluate system performance. This paper presents a model for this setting, and develops a solution method to identify cost-effective investments in stormwater drainage adaptations. Runoff and water levels are simulated with rainfall from stationary rainfall distributions, and time series of annual rainfall maxima are simulated for a climate scenario. Cost-effective investment strategies are determined by dynamic programming. The method is applied to study the choice of volume for a storage basin in a Dutch polder. We find that 'white noise', i.e. trend-free variability of rainfall, might cause earlier re-investment than expected under projected changes in rainfall. The risk of early re-investment may be reduced by increasing initial investment. This can be cost-effective if the investment involves fixed costs. Increasing initial investments, therefore, not only increases water system robustness to structural changes in rainfall, but could also offer insurance against additional costs that would occur if system performance is underestimated and re-investment becomes inevitable. Copyright © 2015 Elsevier Ltd. All rights reserved.

Untangling Trends and Drivers of Changing River Discharge Along Florida's Gulf Coast

NASA Astrophysics Data System (ADS)

Glodzik, K.; Kaplan, D. A.; Klarenberg, G.

2017-12-01

Along the relatively undeveloped Big Bend coastline of Florida, discharge in many rivers and springs is decreasing. The causes are unclear, though they likely include a combination of groundwater extraction for water supply, climate variability, and altered land use. Saltwater intrusion from altered freshwater influence and sea level rise is causing transformative ecosystem impacts along this flat coastline, including coastal forest die-off and oyster reef collapse. A key uncertainty for understanding river discharge change is predicting discharge from rainfall, since Florida's karstic bedrock stores large amounts of groundwater, which has a long residence time. This study uses Dynamic Factor Analysis (DFA), a multivariate data reduction technique for time series, to find common trends in flow and reveal hydrologic variables affecting flow in eight Big Bend rivers since 1965. The DFA uses annual river flows as response time series, and climate data (annual rainfall and evapotranspiration by watershed) and climatic indices (El Niño Southern Oscillation [ENSO] Index and North Atlantic Oscillation [NAO] Index) as candidate explanatory variables. Significant explanatory variables (one evapotranspiration and three rainfall time series) explained roughly 50% of discharge variation across rivers. Significant trends (representing unexplained variation) were shared among rivers, with geographical grouping of five northern rivers and three southern rivers, along with a strong downward trend affecting six out of eight systems. ENSO and NAO had no significant impact. Advancing knowledge of these dynamics is necessary for forecasting how altered rainfall and temperatures from climate change may impact flows. Improved forecasting is especially important given Florida's reliance on groundwater extraction to support its growing population.

High temporal resolution of extreme rainfall rate variability and the acoustic classification of rainfall

NASA Astrophysics Data System (ADS)

Nystuen, Jeffrey A.; Amitai, Eyal

2003-04-01

The underwater sound generated by raindrop splashes on a water surface is loud and unique allowing detection, classification and quantification of rainfall. One of the advantages of the acoustic measurement is that the listening area, an effective catchment area, is proportional to the depth of the hydrophone and can be orders of magnitude greater than other in situ rain gauges. This feature allows high temporal resolution of the rainfall measurement. A series of rain events with extremely high rainfall rates, over 100 mm/hr, is examined acoustically. Rapid onset and cessation of rainfall intensity are detected within the convective cells of these storms with maximum 5-s resolution values exceeding 1000 mm/hr. The probability distribution functions (pdf) for rainfall rate occurrence and water volume using the longer temporal resolutions typical of other instruments do not include these extreme values. The variance of sound intensity within different acoustic frequency bands can be used as an aid to classify rainfall type. Objective acoustic classification algorithms are proposed. Within each rainfall classification the relationship between sound intensity and rainfall rate is nearly linear. The reflectivity factor, Z, also has a linear relationship with rainfall rate, R, for each rainfall classification.

Using Empirical Orthogonal Teleconnections to Analyze Interannual Precipitation Variability in China

NASA Astrophysics Data System (ADS)

Stephan, C.; Klingaman, N. P.; Vidale, P. L.; Turner, A. G.; Demory, M. E.; Guo, L.

2017-12-01

Interannual rainfall variability in China affects agriculture, infrastructure and water resource management. A consistent and objective method, Empirical Orthogonal Teleconnection (EOT) analysis, is applied to precipitation observations over China in all seasons. Instead of maximizing the explained space-time variance, the method identifies regions in China that best explain the temporal variability in domain-averaged rainfall. It produces known teleconnections, that include high positive correlations with ENSO in eastern China in winter, along the Yangtze River in summer, and in southeast China during spring. New findings include that variability along the southeast coast in winter, in the Yangtze valley in spring, and in eastern China in autumn, are associated with extratropical Rossby wave trains. The same analysis is applied to six climate simulations of the Met Office Unified Model with and without air-sea coupling and at various horizontal resolutions of 40, 90 and 200 km. All simulations reproduce the observed patterns of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are all patterns associated with the observed physical mechanism. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. Finer resolution does not improve the fidelity of these patterns or their associated mechanisms. Evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient; attention must be paid to associated mechanisms.

Regionalization of monthly rainfall erosivity patternsin Switzerland

NASA Astrophysics Data System (ADS)

Schmidt, Simon; Alewell, Christine; Panagos, Panos; Meusburger, Katrin

2016-10-01

One major controlling factor of water erosion is rainfall erosivity, which is quantified as the product of total storm energy and a maximum 30 min intensity (I30). Rainfall erosivity is often expressed as R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). As rainfall erosivity is closely correlated with rainfall amount and intensity, the rainfall erosivity of Switzerland can be expected to have a regional characteristic and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modeling approach to assess simultaneously spatial and monthly patterns of rainfall erosivity. So far only national seasonal means and regional annual means exist for Switzerland. We used a network of 87 precipitation gauging stations with a 10 min temporal resolution to calculate long-term monthly mean R-factors. Stepwise generalized linear regression (GLM) and leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal patterns of the R-factor for each month across Switzerland. The monthly R-factor is mapped by summarizing the predicted R-factor of the regression equation and the corresponding residues of the regression, which are interpolated by ordinary kriging (regression-kriging). As spatial covariates, a variety of precipitation indicator data has been included such as snow depths, a combination product of hourly precipitation measurements and radar observations (CombiPrecip), daily Alpine precipitation (EURO4M-APGD), and monthly precipitation sums (RhiresM). Topographic parameters (elevation, slope) were also significant explanatory variables for single months. The comparison of the 12 monthly rainfall erosivity maps showed a distinct seasonality with the highest rainfall erosivity in summer (June, July, and August) influenced by intense rainfall events. Winter months have the lowest rainfall erosivity. A proportion of 62 % of the total annual rainfall erosivity is identified within four months only (June-September). The highest erosion risk can be expected in July, where not only rainfall erosivity but also erosivity density is high. In addition to the intra-annual temporal regime, a spatial variability of this seasonality was detectable between different regions of Switzerland. The assessment of the dynamic behavior of the R-factor is valuable for the identification of susceptible seasons and regions.

Bivariate Rainfall and Runoff Analysis Using Shannon Entropy Theory

NASA Astrophysics Data System (ADS)

Rahimi, A.; Zhang, L.

2012-12-01

Rainfall-Runoff analysis is the key component for many hydrological and hydraulic designs in which the dependence of rainfall and runoff needs to be studied. It is known that the convenient bivariate distribution are often unable to model the rainfall-runoff variables due to that they either have constraints on the range of the dependence or fixed form for the marginal distributions. Thus, this paper presents an approach to derive the entropy-based joint rainfall-runoff distribution using Shannon entropy theory. The distribution derived can model the full range of dependence and allow different specified marginals. The modeling and estimation can be proceeded as: (i) univariate analysis of marginal distributions which includes two steps, (a) using the nonparametric statistics approach to detect modes and underlying probability density, and (b) fitting the appropriate parametric probability density functions; (ii) define the constraints based on the univariate analysis and the dependence structure; (iii) derive and validate the entropy-based joint distribution. As to validate the method, the rainfall-runoff data are collected from the small agricultural experimental watersheds located in semi-arid region near Riesel (Waco), Texas, maintained by the USDA. The results of unviariate analysis show that the rainfall variables follow the gamma distribution, whereas the runoff variables have mixed structure and follow the mixed-gamma distribution. With this information, the entropy-based joint distribution is derived using the first moments, the first moments of logarithm transformed rainfall and runoff, and the covariance between rainfall and runoff. The results of entropy-based joint distribution indicate: (1) the joint distribution derived successfully preserves the dependence between rainfall and runoff, and (2) the K-S goodness of fit statistical tests confirm the marginal distributions re-derived reveal the underlying univariate probability densities which further assure that the entropy-based joint rainfall-runoff distribution are satisfactorily derived. Overall, the study shows the Shannon entropy theory can be satisfactorily applied to model the dependence between rainfall and runoff. The study also shows that the entropy-based joint distribution is an appropriate approach to capture the dependence structure that cannot be captured by the convenient bivariate joint distributions. Joint Rainfall-Runoff Entropy Based PDF, and Corresponding Marginal PDF and Histogram for W12 Watershed The K-S Test Result and RMSE on Univariate Distributions Derived from the Maximum Entropy Based Joint Probability Distribution;

Modelled rainfall skill assessment against a 1000-year time/space isotope dendro-climatology for southern Africa

NASA Astrophysics Data System (ADS)

Woodborne, Stephan; Hall, Grant; Zhang, Qiong

2016-04-01

Palaeoclimate reconstruction using isotopic analysis of tree growth increments has yielded a 1000-year record of rainfall variability in southern Africa. Isotope dendro-climatology reconstructions from baobab trees (Adansonia digitata) provide evidence for rainfall variability from the arid Namib Desert and the Limpopo River Valley. Isotopic analysis of a museum specimen of a yellowwood tree (Podocarps falcatus) yields another record from the southwestern part of the subcontinent. Combined with the limited classic denro-climatologies available in the region these records yield palaeo-rainfall variability in the summer and winter rainfall zones as well as the hyper-arid zone over the last 1000 years. Coherent shifts in all of the records indicate synoptic changes in the westerlies, the inter-tropical convergence zone, and the Congo air boundary. The most substantial rainfall shift takes place at about 1600 CE at the onset of the Little Ice Age. Another distinctive feature of the record is a widespread phenomenon that occurs shortly after 1810 CE that in southern Africa corresponds with a widespread social upheaval known as the Difequane or Mfekane. Large scale forcing of the system includes sea-surface temperatures in the Agulhas Current, the El Nino Southern Oscillation and the Southern Annular Mode. The Little Ice Age and Mfekane climate shifts result from different forcing mechanisms, and the rainfall response in the different regions at these times do not have a fixed phase relationship. This complexity provides a good scenario to test climate models. A first order (wetter versus drier) comparison between each of the tree records and a 1000-year palaeoclimate model simulation for the Little Ice Age and Mfekane transitions demonstrates a generally good correspondence.

Changes in Intense Precipitation Events in West Africa and the central U.S. under Global Warming

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

Cook, Kerry H.; Vizy, Edward

The purpose of the proposed project is to improve our understanding of the physical processes and large-scale connectivity of changes in intense precipitation events (high rainfall rates) under global warming in West Africa and the central U.S., including relationships with low-frequency modes of variability. This is in response to the requested subject area #2 “simulation of climate extremes under a changing climate … to better quantify the frequency, duration, and intensity of extreme events under climate change and elucidate the role of low frequency climate variability in modulating extremes.” We will use a regional climate model and emphasize an understandingmore » of the physical processes that lead to an intensification of rainfall. The project objectives are as follows: 1. Understand the processes responsible for simulated changes in warm-season rainfall intensity and frequency over West Africa and the Central U.S. associated with greenhouse gas-induced global warming 2. Understand the relationship between changes in warm-season rainfall intensity and frequency, which generally occur on regional space scales, and the larger-scale global warming signal by considering modifications of low-frequency modes of variability. 3. Relate changes simulated on regional space scales to global-scale theories of how and why atmospheric moisture levels and rainfall should change as climate warms.« less

Daily Rainfall Simulation Using Climate Variables and Nonhomogeneous Hidden Markov Model

NASA Astrophysics Data System (ADS)

Jung, J.; Kim, H. S.; Joo, H. J.; Han, D.

2017-12-01

Markov chain is an easy method to handle when we compare it with other ones for the rainfall simulation. However, it also has limitations in reflecting seasonal variability of rainfall or change on rainfall patterns caused by climate change. This study applied a Nonhomogeneous Hidden Markov Model(NHMM) to consider these problems. The NHMM compared with a Hidden Markov Model(HMM) for the evaluation of a goodness of the model. First, we chose Gum river basin in Korea to apply the models and collected daily rainfall data from the stations. Also, the climate variables of geopotential height, temperature, zonal wind, and meridional wind date were collected from NCEP/NCAR reanalysis data to consider external factors affecting the rainfall event. We conducted a correlation analysis between rainfall and climate variables then developed a linear regression equation using the climate variables which have high correlation with rainfall. The monthly rainfall was obtained by the regression equation and it became input data of NHMM. Finally, the daily rainfall by NHMM was simulated and we evaluated the goodness of fit and prediction capability of NHMM by comparing with those of HMM. As a result of simulation by HMM, the correlation coefficient and root mean square error of daily/monthly rainfall were 0.2076 and 10.8243/131.1304mm each. In case of NHMM, the correlation coefficient and root mean square error of daily/monthly rainfall were 0.6652 and 10.5112/100.9865mm each. We could verify that the error of daily and monthly rainfall simulated by NHMM was improved by 2.89% and 22.99% compared with HMM. Therefore, it is expected that the results of the study could provide more accurate data for hydrologic analysis. Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(2017R1A2B3005695)

Modelling rainfall amounts using mixed-gamma model for Kuantan district

NASA Astrophysics Data System (ADS)

Zakaria, Roslinazairimah; Moslim, Nor Hafizah

2017-05-01

An efficient design of flood mitigation and construction of crop growth models depend upon good understanding of the rainfall process and characteristics. Gamma distribution is usually used to model nonzero rainfall amounts. In this study, the mixed-gamma model is applied to accommodate both zero and nonzero rainfall amounts. The mixed-gamma model presented is for the independent case. The formulae of mean and variance are derived for the sum of two and three independent mixed-gamma variables, respectively. Firstly, the gamma distribution is used to model the nonzero rainfall amounts and the parameters of the distribution (shape and scale) are estimated using the maximum likelihood estimation method. Then, the mixed-gamma model is defined for both zero and nonzero rainfall amounts simultaneously. The formulae of mean and variance for the sum of two and three independent mixed-gamma variables derived are tested using the monthly rainfall amounts from rainfall stations within Kuantan district in Pahang Malaysia. Based on the Kolmogorov-Smirnov goodness of fit test, the results demonstrate that the descriptive statistics of the observed sum of rainfall amounts is not significantly different at 5% significance level from the generated sum of independent mixed-gamma variables. The methodology and formulae demonstrated can be applied to find the sum of more than three independent mixed-gamma variables.

Effects of rainfall spatial variability and intermittency on shallow landslide triggering patterns at a catchment scale

NASA Astrophysics Data System (ADS)

von Ruette, J.; Lehmann, P.; Or, D.

2014-10-01

The occurrence of shallow landslides is often associated with intense and prolonged rainfall events, where infiltrating water reduces soil strength and may lead to abrupt mass release. Despite general understanding of the role of rainfall water in slope stability, the prediction of rainfall-induced landslides remains a challenge due to natural heterogeneity that affect hydrologic loading patterns and the largely unobservable internal progressive failures. An often overlooked and potentially important factor is the role of rainfall variability in space and time on landslide triggering that is often obscured by coarse information (e.g., hourly radar data at spatial resolution of a few kilometers). To quantify potential effects of rainfall variability on failure dynamics, spatial patterns, landslide numbers and volumes, we employed a physically based "Catchment-scale Hydromechanical Landslide Triggering" (CHLT) model for a study area where a summer storm in 2002 triggered 51 shallow landslides. In numerical experiments based on the CHLT model, we applied the measured rainfall amount of 53 mm in different artificial spatiotemporal rainfall patterns, resulting in between 30 and 100 landslides and total released soil volumes between 3000 and 60,000 m3 for the various scenarios. Results indicate that low intensity rainfall below soil's infiltration capacity resulted in the largest mechanical perturbation. This study illustrates how small-scale rainfall variability that is often overlooked by present operational rainfall data may play a key role in shaping landslide patterns.

Seasonal precipitation forecasting for the Melbourne region using a Self-Organizing Maps approach

NASA Astrophysics Data System (ADS)

Pidoto, Ross; Wallner, Markus; Haberlandt, Uwe

2017-04-01

The Melbourne region experiences highly variable inter-annual rainfall. For close to a decade during the 2000s, below average rainfall seriously affected the environment, water supplies and agriculture. A seasonal rainfall forecasting model for the Melbourne region based on the novel approach of a Self-Organizing Map has been developed and tested for its prediction performance. Predictor variables at varying lead times were first assessed for inclusion within the model by calculating their importance via Random Forests. Predictor variables tested include the climate indices SOI, DMI and N3.4, in addition to gridded global sea surface temperature data. Five forecasting models were developed: an annual model and four seasonal models, each individually optimized for performance through Pearson's correlation r and the Nash-Sutcliffe Efficiency. The annual model showed a prediction performance of r = 0.54 and NSE = 0.14. The best seasonal model was for spring, with r = 0.61 and NSE = 0.31. Autumn was the worst performing seasonal model. The sea surface temperature data contributed fewer predictor variables compared to climate indices. Most predictor variables were supplied at a minimum lead, however some predictors were found at lead times of up to a year.

Flexible Strategies for Coping with Rainfall Variability: Seasonal Adjustments in Cropped Area in the Ganges Basin

PubMed Central

Siderius, Christian; Biemans, Hester; van Walsum, Paul E. V.; van Ierland, Ekko C.; Kabat, Pavel; Hellegers, Petra J. G. J.

2016-01-01

One of the main manifestations of climate change will be increased rainfall variability. How to deal with this in agriculture will be a major societal challenge. In this paper we explore flexibility in land use, through deliberate seasonal adjustments in cropped area, as a specific strategy for coping with rainfall variability. Such adjustments are not incorporated in hydro-meteorological crop models commonly used for food security analyses. Our paper contributes to the literature by making a comprehensive model assessment of inter-annual variability in crop production, including both variations in crop yield and cropped area. The Ganges basin is used as a case study. First, we assessed the contribution of cropped area variability to overall variability in rice and wheat production by applying hierarchical partitioning on time-series of agricultural statistics. We then introduced cropped area as an endogenous decision variable in a hydro-economic optimization model (WaterWise), coupled to a hydrology-vegetation model (LPJmL), and analyzed to what extent its performance in the estimation of inter-annual variability in crop production improved. From the statistics, we found that in the period 1999–2009 seasonal adjustment in cropped area can explain almost 50% of variability in wheat production and 40% of variability in rice production in the Indian part of the Ganges basin. Our improved model was well capable of mimicking existing variability at different spatial aggregation levels, especially for wheat. The value of flexibility, i.e. the foregone costs of choosing not to crop in years when water is scarce, was quantified at 4% of gross margin of wheat in the Indian part of the Ganges basin and as high as 34% of gross margin of wheat in the drought-prone state of Rajasthan. We argue that flexibility in land use is an important coping strategy to rainfall variability in water stressed regions. PMID:26934389

The evaluation of rainfall influence on combined sewer overflows characteristics: the Berlin case study.

PubMed

Sandoval, S; Torres, A; Pawlowsky-Reusing, E; Riechel, M; Caradot, N

2013-01-01

The present study aims to explore the relationship between rainfall variables and water quality/quantity characteristics of combined sewer overflows (CSOs), by the use of multivariate statistical methods and online measurements at a principal CSO outlet in Berlin (Germany). Canonical correlation results showed that the maximum and average rainfall intensities are the most influential variables to describe CSO water quantity and pollutant loads whereas the duration of the rainfall event and the rain depth seem to be the most influential variables to describe CSO pollutant concentrations. The analysis of partial least squares (PLS) regression models confirms the findings of the canonical correlation and highlights three main influences of rainfall on CSO characteristics: (i) CSO water quantity characteristics are mainly influenced by the maximal rainfall intensities, (ii) CSO pollutant concentrations were found to be mostly associated with duration of the rainfall and (iii) pollutant loads seemed to be principally influenced by dry weather duration before the rainfall event. The prediction quality of PLS models is rather low (R² < 0.6) but results can be useful to explore qualitatively the influence of rainfall on CSO characteristics.

Thermodynamic ocean-atmosphere Coupling and the Predictability of Nordeste rainfall

NASA Astrophysics Data System (ADS)

Chang, P.; Saravanan, R.; Giannini, A.

2003-04-01

The interannual variability of rainfall in the northeastern region of Brazil, or Nordeste, is known to be very strongly correlated with sea surface temperature (SST) variability, of Atlantic and Pacific origin. For this reason the potential predictability of Nordeste rainfall is high. The current generation of state-of-the-art atmospheric models can replicate the observed rainfall variability with high skill when forced with the observed record of SST variability. The correlation between observed and modeled indices of Nordeste rainfall, in the AMIP-style integrations with two such models (NSIPP and CCM3) analyzed here, is of the order of 0.8, i.e. the models explain about 2/3 of the observed variability. Assuming that thermodynamic, ocean-atmosphere heat exchange plays the dominant role in tropical Atlantic SST variability on the seasonal to interannual time scale, we analyze its role in Nordeste rainfall predictability using an atmospheric general circulation model coupled to a slab ocean model. Predictability experiments initialized with observed December SST show that thermodynamic coupling plays a significant role in enhancing the persistence of SST anomalies, both in the tropical Pacific and in the tropical Atlantic. We show that thermodynamic coupling is sufficient to provide fairly accurate forecasts of tropical Atlantic SST in the boreal spring that are significantly better than the persistence forecasts. The consequences for the prediction of Nordeste rainfall are analyzed.

Climate variability and environmental stress in the Sudan-Sahel zone of West Africa.

PubMed

Mertz, Ole; D'haen, Sarah; Maiga, Abdou; Moussa, Ibrahim Bouzou; Barbier, Bruno; Diouf, Awa; Diallo, Drissa; Da, Evariste Dapola; Dabi, Daniel

2012-06-01

Environmental change in the Sudan-Sahel region of West Africa (SSWA) has been much debated since the droughts of the 1970s. In this article we assess climate variability and environmental stress in the region. Households in Senegal, Mali, Burkina Faso, Niger, and Nigeria were asked about climatic changes and their perceptions were compared across north-south and west-east rainfall gradients. More than 80% of all households found that rainfall had decreased, especially in the wettest areas. Increases in wind speeds and temperature were perceived by an overall 60-80% of households. Contrary to household perceptions, observed rainfall patterns showed an increasing trend over the past 20 years. However, August rainfall declined, and could therefore potentially explain the contrasting negative household perceptions of rainfall trends. Most households reported degradation of soils, water resources, vegetation, and fauna, but more so in the 500-900 mm zones. Adaptation measures to counter environmental degradation included use of manure, reforestation, soil and water conservation, and protection of fauna and vegetation. The results raise concerns for future environmental management in the region, especially in the 500-900 mm zones and the western part of SSWA.

Statistical Determination of Rainfall-Runoff Erosivity Indices for Single Storms in the Chinese Loess Plateau

PubMed Central

Zheng, Mingguo; Chen, Xiaoan

2015-01-01

Correlation analysis is popular in erosion- or earth-related studies, however, few studies compare correlations on a basis of statistical testing, which should be conducted to determine the statistical significance of the observed sample difference. This study aims to statistically determine the erosivity index of single storms, which requires comparison of a large number of dependent correlations between rainfall-runoff factors and soil loss, in the Chinese Loess Plateau. Data observed at four gauging stations and five runoff experimental plots were presented. Based on the Meng’s tests, which is widely used for comparing correlations between a dependent variable and a set of independent variables, two methods were proposed. The first method removes factors that are poorly correlated with soil loss from consideration in a stepwise way, while the second method performs pairwise comparisons that are adjusted using the Bonferroni correction. Among 12 rainfall factors, I 30 (the maximum 30-minute rainfall intensity) has been suggested for use as the rainfall erosivity index, although I 30 is equally correlated with soil loss as factors of I 20, EI 10 (the product of the rainfall kinetic energy, E, and I 10), EI 20 and EI 30 are. Runoff depth (total runoff volume normalized to drainage area) is more correlated with soil loss than all other examined rainfall-runoff factors, including I 30, peak discharge and many combined factors. Moreover, sediment concentrations of major sediment-producing events are independent of all examined rainfall-runoff factors. As a result, introducing additional factors adds little to the prediction accuracy of the single factor of runoff depth. Hence, runoff depth should be the best erosivity index at scales from plots to watersheds. Our findings can facilitate predictions of soil erosion in the Loess Plateau. Our methods provide a valuable tool while determining the predictor among a number of variables in terms of correlations. PMID:25781173

Monthly Rainfall Erosivity Assessment for Switzerland

NASA Astrophysics Data System (ADS)

Schmidt, Simon; Meusburger, Katrin; Alewell, Christine

2016-04-01

Water erosion is crucially controlled by rainfall erosivity, which is quantified out of the kinetic energy of raindrop impact and associated surface runoff. Rainfall erosivity is often expressed as the R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). Just like precipitation, the rainfall erosivity of Switzerland has a characteristic seasonal dynamic throughout the year. This inter-annual variability is to be assessed by a monthly and seasonal modelling approach. We used a network of 86 precipitation gauging stations with a 10-minute temporal resolution to calculate long-term average monthly R-factors. Stepwise regression and Monte Carlo Cross Validation (MCCV) was used to select spatial covariates to explain the spatial pattern of R-factor for each month across Switzerland. The regionalized monthly R-factor is mapped by its individual regression equation and the ordinary kriging interpolation of its residuals (Regression-Kriging). As covariates, a variety of precipitation indicator data has been included like snow height, a combination of hourly gauging measurements and radar observations (CombiPrecip), mean monthly alpine precipitation (EURO4M-APGD) and monthly precipitation sums (Rhires). Topographic parameters were also significant explanatory variables for single months. The comparison of all 12 monthly rainfall erosivity maps showed seasonality with highest rainfall erosivity in summer (June, July, and August) and lowest rainfall erosivity in winter months. Besides the inter-annual temporal regime, a seasonal spatial variability was detectable. Spatial maps of monthly rainfall erosivity are presented for the first time for Switzerland. The assessment of the spatial and temporal dynamic behaviour of the R-factor is valuable for the identification of more susceptible seasons and regions as well as for the application of selective erosion control measures. A combination with monthly vegetation cover (C-factor) maps would enable the assessment of seasonal dynamics of erosion processes in Switzerland.

Statistical determination of rainfall-runoff erosivity indices for single storms in the Chinese Loess Plateau.

PubMed

Zheng, Mingguo; Chen, Xiaoan

2015-01-01

Correlation analysis is popular in erosion- or earth-related studies, however, few studies compare correlations on a basis of statistical testing, which should be conducted to determine the statistical significance of the observed sample difference. This study aims to statistically determine the erosivity index of single storms, which requires comparison of a large number of dependent correlations between rainfall-runoff factors and soil loss, in the Chinese Loess Plateau. Data observed at four gauging stations and five runoff experimental plots were presented. Based on the Meng's tests, which is widely used for comparing correlations between a dependent variable and a set of independent variables, two methods were proposed. The first method removes factors that are poorly correlated with soil loss from consideration in a stepwise way, while the second method performs pairwise comparisons that are adjusted using the Bonferroni correction. Among 12 rainfall factors, I30 (the maximum 30-minute rainfall intensity) has been suggested for use as the rainfall erosivity index, although I30 is equally correlated with soil loss as factors of I20, EI10 (the product of the rainfall kinetic energy, E, and I10), EI20 and EI30 are. Runoff depth (total runoff volume normalized to drainage area) is more correlated with soil loss than all other examined rainfall-runoff factors, including I30, peak discharge and many combined factors. Moreover, sediment concentrations of major sediment-producing events are independent of all examined rainfall-runoff factors. As a result, introducing additional factors adds little to the prediction accuracy of the single factor of runoff depth. Hence, runoff depth should be the best erosivity index at scales from plots to watersheds. Our findings can facilitate predictions of soil erosion in the Loess Plateau. Our methods provide a valuable tool while determining the predictor among a number of variables in terms of correlations.

Prediction model for peninsular Indian summer monsoon rainfall using data mining and statistical approaches

NASA Astrophysics Data System (ADS)

Vathsala, H.; Koolagudi, Shashidhar G.

2017-01-01

In this paper we discuss a data mining application for predicting peninsular Indian summer monsoon rainfall, and propose an algorithm that combine data mining and statistical techniques. We select likely predictors based on association rules that have the highest confidence levels. We then cluster the selected predictors to reduce their dimensions and use cluster membership values for classification. We derive the predictors from local conditions in southern India, including mean sea level pressure, wind speed, and maximum and minimum temperatures. The global condition variables include southern oscillation and Indian Ocean dipole conditions. The algorithm predicts rainfall in five categories: Flood, Excess, Normal, Deficit and Drought. We use closed itemset mining, cluster membership calculations and a multilayer perceptron function in the algorithm to predict monsoon rainfall in peninsular India. Using Indian Institute of Tropical Meteorology data, we found the prediction accuracy of our proposed approach to be exceptionally good.

Vegetation response to rainfall seasonality and interannual variability in tropical dry forests

NASA Astrophysics Data System (ADS)

Feng, X.; Silva Souza, R. M.; Souza, E.; Antonino, A.; Montenegro, S.; Porporato, A. M.

2015-12-01

We analyzed the response of tropical dry forests to seasonal and interannual rainfall variability, focusing on the caatinga biome in semi-arid in Northeast Brazil. We selected four sites across a gradient of rainfall amount and seasonality and analyzed daily rainfall and biweekly Normalized Difference Vegetation Index (NDVI) in the period 2000-2014. The seasonal and interannual rainfall statistics were characterized using recently developed metrics describing duration, location, and intensity of wet season and compared them with those of NDVI time series and modelled soil moisture. A model of NDVI was also developed and forced by different rainfall scenarios (combination amount of rainfall and duration of wet season). The results show that the caatinga tends to have a more stable response characterized by longer and less variable growing seasons (of duration 3.1±0.1 months) compared to the rainfall wet seasons (2.0±0.5 months). Even for more extreme rainfall conditions, the ecosystem shows very little sensitivity to duration of wet season in relation to the amount of rainfall, however the duration of wet season is most evident for wetter sites. This ability of the ecosystem in buffering the interannual variability of rainfall is corroborated by the stability of the centroid location of the growing season compared to the wet season for all sites. The maximal biomass production was observed at intermediate levels of seasonality, suggesting a possible interesting trade-off in the effects of intensity (i.e., amount) and duration of the wet season on vegetation growth.

Relationships between atmospheric circulation indices and rainfall in Northern Algeria and comparison of observed and RCM-generated rainfall

NASA Astrophysics Data System (ADS)

Taibi, S.; Meddi, M.; Mahé, G.; Assani, A.

2017-01-01

This work aims, as a first step, to analyze rainfall variability in Northern Algeria, in particular extreme events, during the period from 1940 to 2010. Analysis of annual rainfall shows that stations in the northwest record a significant decrease in rainfall since the 1970s. Frequencies of rainy days for each percentile (5th, 10th, 25th, 50th, 75th, 90th, 95th, and 99th) and each rainfall interval class (1-5, 5-10, 10-20, 20-50, and ≥50 mm) do not show a significant change in the evolution of daily rainfall. The Tenes station is the only one to show a significant decrease in the frequency of rainy days up to the 75th percentile and for the 10-20-mm interval class. There is no significant change in the temporal evolution of extreme events in the 90th, 95th, and 99th percentiles. The relationships between rainfall variability and general atmospheric circulation indices for interannual and extreme event variability are moderately influenced by the El Niño-Southern Oscillation and Mediterranean Oscillation. Significant correlations are observed between the Southern Oscillation Index and annual rainfall in the northwestern part of the study area, which is likely linked with the decrease in rainfall in this region. Seasonal rainfall in Northern Algeria is affected by the Mediterranean Oscillation and North Atlantic Oscillation in the west. The ENSEMBLES regional climate models (RCMs) are assessed using the bias method to test their ability to reproduce rainfall variability at different time scales. The Centre National de Recherches Météorologiques (CNRM), Czech Hydrometeorological Institute (CHMI), Eidgenössische Technische Hochschule Zürich (ETHZ), and Forschungszentrum Geesthacht (GKSS) models yield the least biased results.

A sensitivity study of the coupled simulation of the Northeast Brazil rainfall variability

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu

2007-06-01

Two long-term coupled ocean-land-atmosphere simulations with slightly different parameterization of the diagnostic shallow inversion clouds in the atmospheric general circulation model (AGCM) of the Center for Ocean-Land-Atmosphere Studies (COLA) coupled climate model are compared for their annual cycle and interannual variability of the northeast Brazil (NEB) rainfall variability. It is seen that the solar insolation affected by the changes to the shallow inversion clouds results in large scale changes to the gradients of the SST and the surface pressure. The latter in turn modulates the surface convergence and the associated Atlantic ITCZ precipitation and the NEB annual rainfall variability. In contrast, the differences in the NEB interannual rainfall variability between the two coupled simulations is attributed to their different remote ENSO forcing.

Rainfall variation and child health: effect of rainfall on diarrhea among under 5 children in Rwanda, 2010.

PubMed

Mukabutera, Assumpta; Thomson, Dana; Murray, Megan; Basinga, Paulin; Nyirazinyoye, Laetitia; Atwood, Sidney; Savage, Kevin P; Ngirimana, Aimable; Hedt-Gauthier, Bethany L

2016-08-05

Diarrhea among children under 5 years of age has long been a major public health concern. Previous studies have suggested an association between rainfall and diarrhea. Here, we examined the association between Rwandan rainfall patterns and childhood diarrhea and the impact of household sanitation variables on this relationship. We derived a series of rain-related variables in Rwanda based on daily rainfall measurements and hydrological models built from daily precipitation measurements collected between 2009 and 2011. Using these data and the 2010 Rwanda Demographic and Health Survey database, we measured the association between total monthly rainfall, monthly rainfall intensity, runoff water and anomalous rainfall and the occurrence of diarrhea in children under 5 years of age. Among the 8601 children under 5 years of age included in the survey, 13.2 % reported having diarrhea within the 2 weeks prior to the survey. We found that higher levels of runoff were protective against diarrhea compared to low levels among children who lived in households with unimproved toilet facilities (OR = 0.54, 95 % CI: [0.34, 0.87] for moderate runoff and OR = 0.50, 95 % CI: [0.29, 0.86] for high runoff) but had no impact among children in household with improved toilets. Our finding that children in households with unimproved toilets were less likely to report diarrhea during periods of high runoff highlights the vulnerabilities of those living without adequate sanitation to the negative health impacts of environmental events.

High Resolution Flash Flood Forecasting Using a Wireless Sensor Network in the Dallas-Fort Worth Metroplex

NASA Astrophysics Data System (ADS)

Bartos, M. D.; Kerkez, B.; Noh, S.; Seo, D. J.

2017-12-01

In this study, we develop and evaluate a high resolution urban flash flood monitoring system using a wireless sensor network (WSN), a real-time rainfall-runoff model, and spatially-explicit radar rainfall predictions. Flooding is the leading cause of natural disaster fatalities in the US, with flash flooding in particular responsible for a majority of flooding deaths. While many riverine flood models have been operationalized into early warning systems, there is currently no model that is capable of reliably predicting flash floods in urban areas. Urban flash floods are particularly difficult to model due to a lack of rainfall and runoff data at appropriate scales. To address this problem, we develop a wide-area flood-monitoring wireless sensor network for the Dallas-Fort Worth metroplex, and use this network to characterize rainfall-runoff response over multiple heterogeneous catchments. First, we deploy a network of 22 wireless sensor nodes to collect real-time stream stage measurements over catchments ranging from 2-80 km2 in size. Next, we characterize the rainfall-runoff response of each catchment by combining stream stage data with gage and radar-based precipitation measurements. Finally, we demonstrate the potential for real-time flash flood prediction by joining the derived rainfall-runoff models with real-time radar rainfall predictions. We find that runoff response is highly heterogeneous among catchments, with large variabilities in runoff response detected even among nearby gages. However, when spatially-explicit rainfall fields are included, spatial variability in runoff response is largely captured. This result highlights the importance of increased spatial coverage for flash flood prediction.

A 507-year rainfall and runoff reconstruction for the Monsoonal North West, Australia derived from remote paleoclimate archives

NASA Astrophysics Data System (ADS)

Verdon-Kidd, Danielle C.; Hancock, Gregory R.; Lowry, John B.

2017-11-01

The Monsoonal North West (MNW) region of Australia faces a number of challenges adapting to anthropogenic climate change. These have the potential to impact on a range of industries, including agricultural, pastoral, mining and tourism. However future changes to rainfall regimes remain uncertain due to the inability of Global Climate Models to adequately capture the tropical weather/climate processes that are known to be important for this region. Compounding this is the brevity of the instrumental rainfall record for the MNW, which is unlikely to represent the full range of climatic variability. One avenue for addressing this issue (the focus of this paper) is to identify sources of paleoclimate information that can be used to reconstruct a plausible pre-instrumental rainfall history for the MNW. Adopting this approach we find that, even in the absence of local sources of paleoclimate data at a suitable temporal resolution, remote paleoclimate records can resolve 25% of the annual variability observed in the instrumental rainfall record. Importantly, the 507-year rainfall reconstruction developed using the remote proxies displays longer and more intense wet and dry periods than observed during the most recent 100 years. For example, the maximum number of consecutive years of below (above) average rainfall is 90% (40%) higher in the rainfall reconstruction than during the instrumental period. Further, implications for flood and drought risk are studied via a simple GR1A rainfall runoff model, which again highlights the likelihood of extremes greater than that observed in the limited instrumental record, consistent with previous paleoclimate studies elsewhere in Australia. Importantly, this research can assist in informing climate related risks to infrastructure, agriculture and mining, and the method can readily be applied to other regions in the MNW and beyond.

What rainfall events trigger landslides on the West Coast US?

NASA Astrophysics Data System (ADS)

Biasutti, Michela; Seager, Richard; Kirschbaum, Dalia

2016-04-01

A dataset of landslide occurrences compiled by collating google news reports covers 9 full years of data. We show that, while this compilation cannot provide consistent and widespread monitoring everywhere, it is adequate to capture the distribution of events in the major urban areas of the West Coast US and it can be used to provide a quantitative relationship between landslides and rainfall events. The case of the Seattle metropolitan area is presented as an example. The landslide dataset shows a clear seasonality in landslide occurrence, corresponding to the seasonality of rainfall, modified by the accumulation of soil moisture as winter progresses. Interannual variability of landslide occurrences is also linked to interannual variability of monthly rainfall. In most instances, landslides are clustered on consecutive days or at least within the same pentad and correspond to days of large rainfall accumulation at the regional scale. A joint analysis of the landslide data and of the high-resolution PRISM daily rainfall accumulation shows that on days when landslides occurred, the distribution of rainfall was shifted, with rainfall accumulation higher than 10mm/day being more common. Accumulations above 50mm/day much increase the probability of landslides, including the possibility of a major landslide event (one with multiple landslides in a day). The synoptic meteorological conditions associated with these major events show a mid-tropospheric ridge to the south of the target area steering a surface low and bringing enhanced precipitable water towards the Pacific North West. The interaction of the low-level flow with the local orography results in instances of a strong Puget Sound Convergence Zone, with widespread rainfall accumulation above 30mm/day and localized maxima as high as 100mm/day or more.

Daily variability of rainfall and emergency department visits of acute gastrointestinal illness in North Carolina, 2006-2008

EPA Science Inventory

Background & Aims: Projections based on climate models suggest that the frequency of extreme rainfall events will continue to rise over the next several decades. We aim to investigate the temporal relationship between daily variability of rainfall and acute gastrointestinal illne...

Variability of rainfall over small areas

NASA Technical Reports Server (NTRS)

Runnels, R. C.

1983-01-01

A preliminary investigation was made to determine estimates of the number of raingauges needed in order to measure the variability of rainfall in time and space over small areas (approximately 40 sq miles). The literature on rainfall variability was examined and the types of empirical relationships used to relate rainfall variations to meteorological and catchment-area characteristics were considered. Relations between the coefficient of variation and areal-mean rainfall and area have been used by several investigators. These parameters seemed reasonable ones to use in any future study of rainfall variations. From a knowledge of an appropriate coefficient of variation (determined by the above-mentioned relations) the number rain gauges needed for the precise determination of areal-mean rainfall may be calculated by statistical estimation theory. The number gauges needed to measure the coefficient of variation over a 40 sq miles area, with varying degrees of error, was found to range from 264 (10% error, mean precipitation = 0.1 in) to about 2 (100% error, mean precipitation = 0.1 in).

Climate Change Impact on Rainfall: How will Threaten Wheat Yield?

NASA Astrophysics Data System (ADS)

Tafoughalti, K.; El Faleh, E. M.; Moujahid, Y.; Ouargaga, F.

2018-05-01

Climate change has a significant impact on the environmental condition of the agricultural region. Meknes has an agrarian economy and wheat production is of paramount importance. As most arable area are under rainfed system, Meknes is one of the sensitive regions to rainfall variability and consequently to climate change. Therefore, the use of changes in rainfall is vital for detecting the influence of climate system on agricultural productivity. This article identifies rainfall temporal variability and its impact on wheat yields. We used monthly rainfall records for three decades and wheat yields records of fifteen years. Rainfall variability is assessed utilizing the precipitation concentration index and the variation coefficient. The association between wheat yields and cumulative rainfall amounts of different scales was calculated based on a regression model. The analysis shown moderate seasonal and irregular annual rainfall distribution. Yields fluctuated from 210 to 4500 Kg/ha with 52% of coefficient of variation. The correlation results shows that wheat yields are strongly correlated with rainfall of the period January to March. This investigation concluded that climate change is altering wheat yield and it is crucial to adept the necessary adaptation to challenge the risk.

Optimal traits of plant hydraulic capacitance as an adaptation to hydroclimatic variability

NASA Astrophysics Data System (ADS)

Hartzell, S. R.; Bartlett, M. S., Jr.; Porporato, A. M.

2016-12-01

Hydraulic capacitance allows plants to uptake and store water when it is abundant. This stored water is utilized during periods of water stress, decreasing tissue damage and increasing carbon assimilation. By providing a more consistent and readily accessible water supply, it buffers water stress variability across daily and seasonal timescales. The rate of plant water storage and withdrawal varies widely between plant species and is principally governed by several plant hydraulic parameters, principally the hydraulic capacitance, the total water storage capacity, and the conductance between xylem and water storage tissue. The timescale of the plant response to changes in environmental conditions may be related to the timescale of relevant environmental variability. For example, the Baobab tree (Adansonia), which grows in an environment with very strong seasonal rainfall variability, has a relatively long timescale of hydraulic response, while an evergreen tree such as Pinus taeda, which mainly contends with daily and inter-rainfall moisture variability, has a much shorter timescale of hydraulic response. Here a model of hydraulic capacitance is coupled to a resistance model of soil-plant-atmosphere continuum. We force this model with stochastic rainfall and examine plant responses to moisture variability at various timescales. Optimal plant hydraulic properties are examined as a function of mean soil moisture (daily variability), mean period between rainfall events (inter-rainfall variability), and seasonal rainfall variability, and the relative importance of each type of variability in shaping plant water use strategies is assessed. Results are compared to typical hydraulic parameters of plants growing under specific environmental conditions. Values of hydraulic traits which optimize carbon assimilation and water use efficiency are found; these values are dependent on mean environmental conditions as well as the timescale of environmental variability.

Patterns and perceptions of climate change in a biodiversity conservation hotspot.

PubMed

Hartter, Joel; Stampone, Mary D; Ryan, Sadie J; Kirner, Karen; Chapman, Colin A; Goldman, Abraham

2012-01-01

Quantifying local people's perceptions to climate change, and their assessments of which changes matter, is fundamental to addressing the dual challenge of land conservation and poverty alleviation in densely populated tropical regions To develop appropriate policies and responses, it will be important not only to anticipate the nature of expected changes, but also how they are perceived, interpreted and adapted to by local residents. The Albertine Rift region in East Africa is one of the world's most threatened biodiversity hotspots due to dense smallholder agriculture, high levels of land and resource pressures, and habitat loss and conversion. Results of three separate household surveys conducted in the vicinity of Kibale National Park during the late 2000s indicate that farmers are concerned with variable precipitation. Many survey respondents reported that conditions are drier and rainfall timing is becoming less predictable. Analysis of daily rainfall data for the climate normal period 1981 to 2010 indicates that total rainfall both within and across seasons has not changed significantly, although the timing and transitions of seasons has been highly variable. Results of rainfall data analysis also indicate significant changes in the intra-seasonal rainfall distribution, including longer dry periods within rainy seasons, which may contribute to the perceived decrease in rainfall and can compromise food security. Our results highlight the need for fine-scale climate information to assist agro-ecological communities in developing effective adaptive management.

Patterns and Perceptions of Climate Change in a Biodiversity Conservation Hotspot

PubMed Central

Hartter, Joel; Stampone, Mary D.; Ryan, Sadie J.; Kirner, Karen; Chapman, Colin A.; Goldman, Abraham

2012-01-01

Quantifying local people's perceptions to climate change, and their assessments of which changes matter, is fundamental to addressing the dual challenge of land conservation and poverty alleviation in densely populated tropical regions To develop appropriate policies and responses, it will be important not only to anticipate the nature of expected changes, but also how they are perceived, interpreted and adapted to by local residents. The Albertine Rift region in East Africa is one of the world's most threatened biodiversity hotspots due to dense smallholder agriculture, high levels of land and resource pressures, and habitat loss and conversion. Results of three separate household surveys conducted in the vicinity of Kibale National Park during the late 2000s indicate that farmers are concerned with variable precipitation. Many survey respondents reported that conditions are drier and rainfall timing is becoming less predictable. Analysis of daily rainfall data for the climate normal period 1981 to 2010 indicates that total rainfall both within and across seasons has not changed significantly, although the timing and transitions of seasons has been highly variable. Results of rainfall data analysis also indicate significant changes in the intra-seasonal rainfall distribution, including longer dry periods within rainy seasons, which may contribute to the perceived decrease in rainfall and can compromise food security. Our results highlight the need for fine-scale climate information to assist agro-ecological communities in developing effective adaptive management. PMID:22384244

Reply to comment by Fred L. Ogden et al. on "Beyond the SCS-CN method: A theoretical framework for spatially lumped rainfall-runoff response"

NASA Astrophysics Data System (ADS)

Bartlett, M. S.; Parolari, A. J.; McDonnell, J. J.; Porporato, A.

2017-07-01

Though Ogden et al. list several shortcomings of the original SCS-CN method, fit for purpose is a key consideration in hydrological modelling, as shown by the adoption of SCS-CN method in many design standards. The theoretical framework of Bartlett et al. [2016a] reveals a family of semidistributed models, of which the SCS-CN method is just one member. Other members include event-based versions of the Variable Infiltration Capacity (VIC) model and TOPMODEL. This general model allows us to move beyond the limitations of the original SCS-CN method under different rainfall-runoff mechanisms and distributions for soil and rainfall variability. Future research should link this general model approach to different hydrogeographic settings, in line with the call for action proposed by Ogden et al.

Errors and uncertainties in regional climate simulations of rainfall variability over Tunisia: a multi-model and multi-member approach

NASA Astrophysics Data System (ADS)

Fathalli, Bilel; Pohl, Benjamin; Castel, Thierry; Safi, Mohamed Jomâa

2018-02-01

Temporal and spatial variability of rainfall over Tunisia (at 12 km spatial resolution) is analyzed in a multi-year (1992-2011) ten-member ensemble simulation performed using the WRF model, and a sample of regional climate hindcast simulations from Euro-CORDEX. RCM errors and skills are evaluated against a dense network of local rain gauges. Uncertainties arising, on the one hand, from the different model configurations and, on the other hand, from internal variability are furthermore quantified and ranked at different timescales using simple spread metrics. Overall, the WRF simulation shows good skill for simulating spatial patterns of rainfall amounts over Tunisia, marked by strong altitudinal and latitudinal gradients, as well as the rainfall interannual variability, in spite of systematic errors. Mean rainfall biases are wet in both DJF and JJA seasons for the WRF ensemble, while they are dry in winter and wet in summer for most of the used Euro-CORDEX models. The sign of mean annual rainfall biases over Tunisia can also change from one member of the WRF ensemble to another. Skills in regionalizing precipitation over Tunisia are season dependent, with better correlations and weaker biases in winter. Larger inter-member spreads are observed in summer, likely because of (1) an attenuated large-scale control on Mediterranean and Tunisian climate, and (2) a larger contribution of local convective rainfall to the seasonal amounts. Inter-model uncertainties are globally stronger than those attributed to model's internal variability. However, inter-member spreads can be of the same magnitude in summer, emphasizing the important stochastic nature of the summertime rainfall variability over Tunisia.

Climate Factors as Important Determinants of Dengue Incidence in Curaçao.

PubMed

Limper, M; Thai, K T D; Gerstenbluth, I; Osterhaus, A D M E; Duits, A J; van Gorp, E C M

2016-03-01

Macro- and microclimates may have variable impact on dengue incidence in different settings. We estimated the short-term impact and delayed effects of climate variables on dengue morbidity in Curaçao. Monthly dengue incidence data from 1999 to 2009 were included to estimate the short-term influences of climate variables by employing wavelet analysis, generalized additive models (GAM) and distributed lag nonlinear models (DLNM) on rainfall, temperature and relative humidity in relation to dengue incidence. Dengue incidence showed a significant irregular 4-year multi-annual cycle associated with climate variables. Based on GAM, temperature showed a U-shape, while humidity and rainfall exhibited a dome-shaped association, suggesting that deviation from mean temperature increases and deviation from mean humidity and rainfall decreases dengue incidence, respectively. Rainfall was associated with an immediate increase in dengue incidence of 4.1% (95% CI: 2.2-8.1%) after a 10-mm increase, with a maximum increase of 6.5% (95% CI: 3.2-10.0%) after 1.5 month lag. A 1 °C decrease of mean temperature was associated with a RR of 17.4% (95% CI: 11.2-27.0%); the effect was inversed for a 1°C increase of mean temperature (RR= 0.457, 95% CI: 0.278-0.752). Climate variables are important determinants of dengue incidence and provide insight into its short-term effects. An increase in mean temperature was associated with lower dengue incidence, whereas lower temperatures were associated with higher dengue incidence. © 2015 Blackwell Verlag GmbH.

Estimation of streamflow, base flow, and nitrate-nitrogen loads in Iowa using multiple linear regression models

USGS Publications Warehouse

Schilling, K.E.; Wolter, C.F.

2005-01-01

Nineteen variables, including precipitation, soils and geology, land use, and basin morphologic characteristics, were evaluated to develop Iowa regression models to predict total streamflow (Q), base flow (Qb), storm flow (Qs) and base flow percentage (%Qb) in gauged and ungauged watersheds in the state. Discharge records from a set of 33 watersheds across the state for the 1980 to 2000 period were separated into Qb and Qs. Multiple linear regression found that 75.5 percent of long term average Q was explained by rainfall, sand content, and row crop percentage variables, whereas 88.5 percent of Qb was explained by these three variables plus permeability and floodplain area variables. Qs was explained by average rainfall and %Qb was a function of row crop percentage, permeability, and basin slope variables. Regional regression models developed for long term average Q and Qb were adapted to annual rainfall and showed good correlation between measured and predicted values. Combining the regression model for Q with an estimate of mean annual nitrate concentration, a map of potential nitrate loads in the state was produced. Results from this study have important implications for understanding geomorphic and land use controls on streamflow and base flow in Iowa watersheds and similar agriculture dominated watersheds in the glaciated Midwest. (JAWRA) (Copyright ?? 2005).

The cross wavelet analysis of dengue fever variability influenced by meteorological conditions

NASA Astrophysics Data System (ADS)

Lin, Yuan-Chien; Yu, Hwa-Lung; Lee, Chieh-Han

2015-04-01

The multiyear variation of meteorological conditions induced by climate change causes the changing diffusion pattern of infectious disease and serious epidemic situation. Among them, dengue fever is one of the most serious vector-borne diseases distributed in tropical and sub-tropical regions. Dengue virus is transmitted by several species of mosquito and causing lots amount of human deaths every year around the world. The objective of this study is to investigate the impact of meteorological variables to the temporal variation of dengue fever epidemic in southern Taiwan. Several extreme and average indices of meteorological variables, i.e. temperature and humidity, were used for this analysis, including averaged, maximum and minimum temperature, and average rainfall, maximum 1-hr rainfall, and maximum 24-hr rainfall. This study plans to identify and quantify the nonlinear relationship of meteorological variables and dengue fever epidemic, finding the non-stationary time-frequency relationship and phase lag effects of those time series from 1998-2011 by using cross wavelet method. Results show that meteorological variables all have a significant time-frequency correlation region to dengue fever epidemic in frequency about one year (52 weeks). The associated phases can range from 0 to 90 degrees (0-13 weeks lag from meteorological factors to dengue incidences). Keywords: dengue fever, cross wavelet analysis, meteorological factor

Variability of rainfall over Lake Kariba catchment area in the Zambezi river basin, Zimbabwe

NASA Astrophysics Data System (ADS)

Muchuru, Shepherd; Botai, Joel O.; Botai, Christina M.; Landman, Willem A.; Adeola, Abiodun M.

2016-04-01

In this study, average monthly and annual rainfall totals recorded for the period 1970 to 2010 from a network of 13 stations across the Lake Kariba catchment area of the Zambezi river basin were analyzed in order to characterize the spatial-temporal variability of rainfall across the catchment area. In the analysis, the data were subjected to intervention and homogeneity analysis using the Cumulative Summation (CUSUM) technique and step change analysis using rank-sum test. Furthermore, rainfall variability was characterized by trend analysis using the non-parametric Mann-Kendall statistic. Additionally, the rainfall series were decomposed and the spectral characteristics derived using Cross Wavelet Transform (CWT) and Wavelet Coherence (WC) analysis. The advantage of using the wavelet-based parameters is that they vary in time and can therefore be used to quantitatively detect time-scale-dependent correlations and phase shifts between rainfall time series at various localized time-frequency scales. The annual and seasonal rainfall series were homogeneous and demonstrated no apparent significant shifts. According to the inhomogeneity classification, the rainfall series recorded across the Lake Kariba catchment area belonged to category A (useful) and B (doubtful), i.e., there were zero to one and two absolute tests rejecting the null hypothesis (at 5 % significance level), respectively. Lastly, the long-term variability of the rainfall series across the Lake Kariba catchment area exhibited non-significant positive and negative trends with coherent oscillatory modes that are constantly locked in phase in the Morlet wavelet space.

SUB-PIXEL RAINFALL VARIABILITY AND THE IMPLICATIONS FOR UNCERTAINTIES IN RADAR RAINFALL ESTIMATES

EPA Science Inventory

Radar estimates of rainfall are subject to significant measurement uncertainty. Typically, uncertainties are measured by the discrepancies between real rainfall estimates based on radar reflectivity and point rainfall records of rain gauges. This study investigates how the disc...

A Bayesian Hierarchical Modeling Approach to Predicting Flow in Ungauged Basins

NASA Astrophysics Data System (ADS)

Gronewold, A.; Alameddine, I.; Anderson, R. M.

2009-12-01

Recent innovative approaches to identifying and applying regression-based relationships between land use patterns (such as increasing impervious surface area and decreasing vegetative cover) and rainfall-runoff model parameters represent novel and promising improvements to predicting flow from ungauged basins. In particular, these approaches allow for predicting flows under uncertain and potentially variable future conditions due to rapid land cover changes, variable climate conditions, and other factors. Despite the broad range of literature on estimating rainfall-runoff model parameters, however, the absence of a robust set of modeling tools for identifying and quantifying uncertainties in (and correlation between) rainfall-runoff model parameters represents a significant gap in current hydrological modeling research. Here, we build upon a series of recent publications promoting novel Bayesian and probabilistic modeling strategies for quantifying rainfall-runoff model parameter estimation uncertainty. Our approach applies alternative measures of rainfall-runoff model parameter joint likelihood (including Nash-Sutcliffe efficiency, among others) to simulate samples from the joint parameter posterior probability density function. We then use these correlated samples as response variables in a Bayesian hierarchical model with land use coverage data as predictor variables in order to develop a robust land use-based tool for forecasting flow in ungauged basins while accounting for, and explicitly acknowledging, parameter estimation uncertainty. We apply this modeling strategy to low-relief coastal watersheds of Eastern North Carolina, an area representative of coastal resource waters throughout the world because of its sensitive embayments and because of the abundant (but currently threatened) natural resources it hosts. Consequently, this area is the subject of several ongoing studies and large-scale planning initiatives, including those conducted through the United States Environmental Protection Agency (USEPA) total maximum daily load (TMDL) program, as well as those addressing coastal population dynamics and sea level rise. Our approach has several advantages, including the propagation of parameter uncertainty through a nonparametric probability distribution which avoids common pitfalls of fitting parameters and model error structure to a predetermined parametric distribution function. In addition, by explicitly acknowledging correlation between model parameters (and reflecting those correlations in our predictive model) our model yields relatively efficient prediction intervals (unlike those in the current literature which are often unnecessarily large, and may lead to overly-conservative management actions). Finally, our model helps improve understanding of the rainfall-runoff process by identifying model parameters (and associated catchment attributes) which are most sensitive to current and future land use change patterns. Disclaimer: Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.

Secular spring rainfall variability at local scale over Ethiopia: trend and associated dynamics

NASA Astrophysics Data System (ADS)

Tsidu, Gizaw Mengistu

2017-10-01

Spring rainfall secular variability is studied using observations, reanalysis, and model simulations. The joint coherent spatio-temporal secular variability of gridded monthly gauge rainfall over Ethiopia, ERA-Interim atmospheric variables and sea surface temperature (SST) from Hadley Centre Sea Ice and SST (HadISST) data set is extracted using multi-taper method singular value decomposition (MTM-SVD). The contemporaneous associations are further examined using partial Granger causality to determine presence of causal linkage between any of the climate variables. This analysis reveals that only the northwestern Indian Ocean secular SST anomaly has direct causal links with spring rainfall over Ethiopia and mean sea level pressure (MSLP) over Africa inspite of the strong secular covariance of spring rainfall, SST in parts of subtropical Pacific, Atlantic, Indian Ocean and MSLP. High secular rainfall variance and statistically significant linear trend show consistently that there is a massive decline in spring rain over southern Ethiopia. This happened concurrently with significant buildup of MSLP over East Africa, northeastern Africa including parts of the Arabian Peninsula, some parts of central Africa and SST warming over all ocean basins with the exception of the ENSO regions. The east-west pressure gradient in response to the Indian Ocean warming led to secular southeasterly winds over the Arabian Sea, easterly over central Africa and equatorial Atlantic. These flows weakened climatological northeasterly flow over the Arabian Sea and southwesterly flow over equatorial Atlantic and Congo basins which supply moisture into the eastern Africa regions in spring. The secular divergent flow at low level is concurrent with upper level convergence due to the easterly secular anomalous flow. The mechanisms through which the northwestern Indian Ocean secular SST anomaly modulates rainfall are further explored in the context of East Africa using a simplified atmospheric general circulation model (AGCM) coupled to mixed-layer oceanic model. The rainfall anomaly (with respect to control simulation), forced by the northwestern Indian Ocean secular SST anomaly and averaged over the 30-year period, exhibits prevalence of dry conditions over East and equatorial Africa in agreement with observation. The atmospheric response to secular SST warming anomaly led to divergent flow at low levels and subsidence at the upper troposphere over regions north of 5° S on the continent and vice versa over the Indian Ocean. This surface difluence over East Africa, in addition to its role in suppressing convective activity, deprives the region of moisture supply from the Indian Ocean as well as the Atlantic and Congo basins.

Variability of the recent climate of eastern Africa

NASA Astrophysics Data System (ADS)

Schreck, Carl J., III; Semazzi, Fredrick H. M.

2004-05-01

The primary objective of this study is to investigate the recent variability of the eastern African climate. The region of interest is also known as the Greater Horn of Africa (GHA), and comprises the countries of Burundi, Djibouti, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, Sudan, Uganda, and Tanzania.The analysis was based primarily on the construction of empirical orthogonal functions (EOFs) of gauge rainfall data and on CPC Merged Analysis of Precipitation (CMAP) data, derived from a combination of rain-gauge observations and satellite estimates. The investigation is based on the period 1961-2001 for the short rains season of eastern Africa of October through to December. The EOF analysis was supplemented by projection of National Centers for Environmental Prediction wind data onto the rainfall eigenmodes to understand the rainfall-circulation relationships. Furthermore, correlation and composite analyses have been performed with the Climatic Research Unit globally averaged surface-temperature time series to explore the potential relationship between the climate of eastern Africa and global warming.The most dominant mode of variability (EOF1) based on CMAP data over eastern Africa corresponds to El Niño-southern oscillation (ENSO) climate variability. It is associated with above-normal rainfall amounts during the short rains throughout the entire region, except for Sudan. The corresponding anomalous low-level circulation is dominated by easterly inflow from the Indian Ocean, and to a lesser extent the Congo tropical rain forest, into the positive rainfall anomaly region that extends across most of eastern Africa. The easterly inflow into eastern Africa is part of diffluent outflow from the maritime continent during the warm ENSO events. The second eastern African EOF (trend mode) is associated with decadal variability. In distinct contrast from the ENSO mode pattern, the trend mode is characterized by positive rainfall anomalies over the northern sector of eastern Africa and opposite conditions over the southern sector. This rainfall trend mode eluded detection in previous studies that did not include recent decades of data, because the signal was still relatively weak. The wind projection onto this mode indicates that the primary flow that feeds the positive anomaly region over the northern part of eastern Africa emanates primarily from the rainfall-deficient southern region of eastern Africa and Sudan. Although we do not assign attribution of the trend mode to global warming (in part because of the relatively short period of analysis), the evidence, based on our results and previous studies, strongly suggests a potential connection.

Analysis of spatial autocorrelation patterns of heavy and super-heavy rainfall in Iran

NASA Astrophysics Data System (ADS)

Rousta, Iman; Doostkamian, Mehdi; Haghighi, Esmaeil; Ghafarian Malamiri, Hamid Reza; Yarahmadi, Parvane

2017-09-01

Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran's heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord G i statistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran, have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.

Potential Predictability and Prediction Skill for Southern Peru Summertime Rainfall

NASA Astrophysics Data System (ADS)

WU, S.; Notaro, M.; Vavrus, S. J.; Mortensen, E.; Block, P. J.; Montgomery, R. J.; De Pierola, J. N.; Sanchez, C.

2016-12-01

The central Andes receive over 50% of annual climatological rainfall during the short period of January-March. This summertime rainfall exhibits strong interannual and decadal variability, including severe drought events that incur devastating societal impacts and cause agricultural communities and mining facilities to compete for limited water resources. An improved seasonal prediction skill of summertime rainfall would aid in water resource planning and allocation across the water-limited southern Peru. While various underlying mechanisms have been proposed by past studies for the drivers of interannual variability in summertime rainfall across southern Peru, such as the El Niño-Southern Oscillation (ENSO), Madden Julian Oscillation (MJO), and extratropical forcings, operational forecasts continue to be largely based on rudimentary ENSO-based indices, such as NINO3.4, justifying further exploration of predictive skill. In order to bridge this gap between the understanding of driving mechanisms and the operational forecast, we performed systematic studies on the predictability and prediction skill of southern Peru summertime rainfall by constructing statistical forecast models using best available weather station and reanalysis datasets. At first, by assuming the first two empirical orthogonal functions (EOFs) of summertime rainfall are predictable, the potential predictability skill was evaluated for southern Peru. Then, we constructed a simple regression model, based on the time series of tropical Pacific sea-surface temperatures (SSTs), and a more advanced Linear Inverse Model (LIM), based on the EOFs of tropical ocean SSTs and large-scale atmosphere variables from reanalysis. Our results show that the LIM model consistently outperforms the more rudimentary regression models on the forecast skill of domain averaged precipitation index and individual station indices. The improvement of forecast correlation skill ranges from 10% to over 200% for different stations. Further analysis shows that this advantage of LIM is likely to arise from its representation of local zonal winds and the position of Intertropical Convergence Zone (ITCZ).

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

NASA Astrophysics Data System (ADS)

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

2007-12-01

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

The significance of spatial variability of rainfall on streamflow: A synthetic analysis at the Upper Lee catchment, UK

NASA Astrophysics Data System (ADS)

Pechlivanidis, Ilias; McIntyre, Neil; Wheater, Howard

2017-04-01

Rainfall, one of the main inputs in hydrological modeling, is a highly heterogeneous process over a wide range of scales in space, and hence the ignorance of the spatial rainfall information could affect the simulated streamflow. Calibration of hydrological model parameters is rarely a straightforward task due to parameter equifinality and parameters' 'nature' to compensate for other uncertainties, i.e. structural and forcing input. In here, we analyse the significance of spatial variability of rainfall on streamflow as a function of catchment scale and type, and antecedent conditions using the continuous time, semi-distributed PDM hydrological model at the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments ranging in scale from 25 to 1040 km2, and further assessed by artificially changing the catchment characteristics and translating these to model parameters with uncertainty using model regionalisation. Synthetic rainfall events are introduced to directly relate the change in simulated streamflow to the spatial variability of rainfall. Overall, we conclude that the antecedent catchment wetness and catchment type play an important role in controlling the significance of the spatial distribution of rainfall on streamflow. Results show a relationship between hydrograph characteristics (streamflow peak and volume) and the degree of spatial variability of rainfall for the impermeable catchments under dry antecedent conditions, although this decreases at larger scales; however this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on streamflow varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Finally, hydrograph responses to different spatial patterns in rainfall depend on assumptions used for model parameter estimation and also the spatial variation in parameters indicating the need of an uncertainty framework in such investigation.

Constraining continuous rainfall simulations for derived design flood estimation

NASA Astrophysics Data System (ADS)

Woldemeskel, F. M.; Sharma, A.; Mehrotra, R.; Westra, S.

2016-11-01

Stochastic rainfall generation is important for a range of hydrologic and water resources applications. Stochastic rainfall can be generated using a number of models; however, preserving relevant attributes of the observed rainfall-including rainfall occurrence, variability and the magnitude of extremes-continues to be difficult. This paper develops an approach to constrain stochastically generated rainfall with an aim of preserving the intensity-durationfrequency (IFD) relationships of the observed data. Two main steps are involved. First, the generated annual maximum rainfall is corrected recursively by matching the generated intensity-frequency relationships to the target (observed) relationships. Second, the remaining (non-annual maximum) rainfall is rescaled such that the mass balance of the generated rain before and after scaling is maintained. The recursive correction is performed at selected storm durations to minimise the dependence between annual maximum values of higher and lower durations for the same year. This ensures that the resulting sequences remain true to the observed rainfall as well as represent the design extremes that may have been developed separately and are needed for compliance reasons. The method is tested on simulated 6 min rainfall series across five Australian stations with different climatic characteristics. The results suggest that the annual maximum and the IFD relationships are well reproduced after constraining the simulated rainfall. While our presentation focusses on the representation of design rainfall attributes (IFDs), the proposed approach can also be easily extended to constrain other attributes of the generated rainfall, providing an effective platform for post-processing of stochastic rainfall generators.

Connecting spatial and temporal scales of tropical precipitation in observations and the MetUM-GA6

NASA Astrophysics Data System (ADS)

Martin, Gill M.; Klingaman, Nicholas P.; Moise, Aurel F.

2017-01-01

This study analyses tropical rainfall variability (on a range of temporal and spatial scales) in a set of parallel Met Office Unified Model (MetUM) simulations at a range of horizontal resolutions, which are compared with two satellite-derived rainfall datasets. We focus on the shorter scales, i.e. from the native grid and time step of the model through sub-daily to seasonal, since previous studies have paid relatively little attention to sub-daily rainfall variability and how this feeds through to longer scales. We find that the behaviour of the deep convection parametrization in this model on the native grid and time step is largely independent of the grid-box size and time step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid boxes/time steps of little or no rain. In contrast, switching off the deep convection parametrization, albeit at an unrealistic resolution for resolving tropical convection, results in very persistent (for limited periods), but very sporadic, rainfall. In both cases, spatial and temporal averaging smoothes out this intermittency. On the ˜ 100 km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the configurations with parametrized convection agree fairly well with those from satellite-derived rainfall estimates, while at ˜ 10-day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. Ultimately, such work will shed light on how uncertainties in modelling small-/short-scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of small-/short-scale processes.

Combined effect of MJO, ENSO and IOD on the intraseasonal variability of northeast monsoon rainfall over south peninsular India

NASA Astrophysics Data System (ADS)

Sreekala, P. P.; Rao, S. Vijaya Bhaskara; Rajeevan, K.; Arunachalam, M. S.

2018-02-01

The present study has examined the combined effect of MJO, ENSO and IOD on the intraseasonal and interannual variability of northeast monsoon rainfall over south peninsular India. The study has revealed that the intraseasonal variation of daily rainfall over south peninsular India during NEM season is associated with various phases of eastward propagating MJO life cycle. Positive rainfall anomaly over south peninsular India and surrounding Indian Ocean (IO) is observed during the strong MJO phases 2, 3 and 4; and negative rainfall anomaly during the strong MJO phases 5,6,7,8 and 1. Above normal (below normal) convection over south peninsular India and suppressed convection over east Indian and West Pacific Ocean, high pressure (low pressure) anomaly over West Pacific Ocean, Positive (negative) SST anomalies over equatorial East and Central Pacific Ocean and easterly wind anomaly (westerly anomaly) over equatorial Indian Ocean are the observed features during the first three MJO (5, 6, 7) phases and all these features are observed in the excess (drought) NEMR composite. This suggests that a similar mode of physical mechanism is responsible for the intraseasonal and interannual variability of northeast monsoon rainfall. The number of days during the first three phases (last four phases) of MJO, where the enhanced convection and positive rainfall anomaly is over Indian Ocean (East Indian ocean and West Pacific Ocean), is more (less) during El Nino and IOD years and less during La Nina and NIOD years and vice versa. The observed excess (deficit) rainfall anomaly over west IO and south peninsular India and deficit (excess) rainfall anomaly over east IO including Bay of Bengal and West Pacific Ocean suggest that the more (less) number of first three phases during El Nino and IOD (La Nina and Negative IOD) is due to the interaction between eastward moving MJO and strong easterlies over equatorial IO present during El Nino and IOD years. This interaction would inhibit the development of long duration MJO and would result in short duration high frequency MJO type which confined over Indian Ocean and south peninsular India and hence make all the El Nino and IOD years to be excess rainfall years for NEM season.

Rainfall and streamflow from small tree-covered and fern-covered and burned watersheds in Hawaii

Treesearch

H. W. Anderson; P. D. Duffy; Teruo Yamamoto

1966-01-01

Streamflow from two 30-acre watersheds near Honolulu was studied by using principal components regression analysis. Models using data on monthly, storm, and peak discharges were tested against several variables expressing amount and intensity of rainfall, and against variables expressing antecedent rainfall. Explained variation ranged from 78 to 94 percent. The...

Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

NASA Astrophysics Data System (ADS)

Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

2016-05-01

Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

The Influence of ENSO to the Rainfall Variability in North Sumatra Province

NASA Astrophysics Data System (ADS)

Irwandi, H.; Pusparini, N.; Ariantono, J. Y.; Kurniawan, R.; Tari, C. A.; Sudrajat, A.

2018-04-01

The El Niño Southern Oscillation (ENSO) is a global phenomenon that affects the variability of rainfall in North Sumatra. The influence of ENSO will be different for each region. This review will analyse the influence of ENSO activity on seasonal and annual rainfall variability. In this research, North Sumatra Province will be divided into 4 (four) regions based on topographical conditions, such as: East Coast (EC), East Slope (ES), Mountains (MT), and West Coast (WC). The method used was statistical and descriptive analysis. Data used in this research were rainfall data from 15 stations / climate observation posts which spread in North Sumatera region and also anomaly data of Nino 3.4 region from period 1981-2016. The results showed that the active El Niño had an effect on the decreasing the rainfall during the period of DJF, JJA and SON in East Coast, East Slope, and Mountains with the decreasing of average percentage of annual rainfall up to 7%. On the contrary, the active La Nina had an effect on the addition of rainfall during the period DJF and JJA in the East Coast and Mountains with the increasing of average percentage of annual rainfall up to 6%.

Extreme Monsoon Rainfall Signatures Preserved in the Invasive Terrestrial Gastropod Lissachatina fulica

NASA Astrophysics Data System (ADS)

Ghosh, Prosenjit; Rangarajan, Ravi; Thirumalai, Kaustubh; Naggs, Fred

2017-11-01

Indian summer monsoon (ISM) rainfall lasts for a period of 4 months with large variations recorded in terms of rainfall intensity during its period between June and September. Proxy reconstructions of past ISM rainfall variability are required due to the paucity of long instrumental records. However, reconstructing subseasonal rainfall is extremely difficult using conventional hydroclimate proxies due to inadequate sample resolution. Here, we demonstrate the utility of the stable oxygen isotope composition of gastropod shells in reconstructing past rainfall on subseasonal timescales. We present a comparative isotopic study on present day rainwater and stable isotope ratios of precipitate found in the incremental growth bands of giant African land snail Lissachatina fulica (Bowdich) from modern day (2009) and in the historical past (1918). Isotopic signatures present in the growth bands allowed for the identification of ISM rainfall variability in terms of its active and dry spells in the modern as well as past gastropod record. Our results demonstrate the utility of gastropod growth band stable isotope ratios in semiquantitative reconstructions of seasonal rainfall patterns. High resolution climate records extracted from gastropod growth band stable isotopes (museum and archived specimens) can expand the scope for understanding past subseasonal-to-seasonal climate variability.

Can we improve streamflow simulation by using higher resolution rainfall information?

NASA Astrophysics Data System (ADS)

Lobligeois, Florent; Andréassian, Vazken; Perrin, Charles

2013-04-01

The catchment response to rainfall is the interplay between space-time variability of precipitation, catchment characteristics and antecedent hydrological conditions. Precipitation dominates the high frequency hydrological response, and its simulation is thus dependent on the way rainfall is represented. One of the characteristics which distinguishes distributed from lumped models is their ability to represent explicitly the spatial variability of precipitation and catchment characteristics. The sensitivity of runoff hydrographs to the spatial variability of forcing data has been a major concern of researchers over the last three decades. However, although the literature on the relationship between spatial rainfall and runoff response is abundant, results are contrasted and sometimes contradictory. Several studies concluded that including information on rainfall spatial distribution improves discharge simulation (e.g. Ajami et al., 2004, among others) whereas other studies showed the lack of significant improvement in simulations with better information on rainfall spatial pattern (e.g. Andréassian et al., 2004, among others). The difficulties to reach a clear consensus is mainly due to the fact that each modeling study is implemented only on a few catchments whereas the impact of the spatial distribution of rainfall on runoff is known to be catchment and event characteristics-dependent. Many studies are virtual experiments and only compare flow simulations, which makes it difficult to reach conclusions transposable to real-life case studies. Moreover, the hydrological rainfall-runoff models differ between the studies and the parameterization strategies sometimes tend to advantage the distributed approach (or the lumped one). Recently, Météo-France developed a rainfall reanalysis over the whole French territory at the 1-kilometer resolution and the hourly time step over a 10-year period combining radar data and raingauge measurements: weather radar data were corrected and adjusted with both hourly and daily raingauge data. Based on this new high resolution product, we propose a framework to evaluate the improvements in streamflow simulation by using higher resolution rainfall information. Semi-distributed modelling is performed for different spatial resolution of precipitation forcing: from lumped to semi-distributed simulations. Here we do not work on synthetic (simulated) streamflow, but with actual measurements, on a large set of 181 French catchments representing a variety of size and climate. The rainfall-runoff model is re-calibrated for each resolution of rainfall spatial distribution over a 5-year sub-period and evaluated on the complementary sub-period in validation mode. The results are analysed by catchment classes based on catchment area and for various types of rainfall events based on the spatial variability of precipitation. References Ajami, N. K., Gupta, H. V, Wagener, T. & Sorooshian, S. (2004) Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. Journal of Hydrology 298(1-4), 112-135. Andréassian, V., Oddos, A., Michel, C., Anctil, F., Perrin, C. & Loumagne, C. (2004) Impact of spatial aggregation of inputs and parameters on the efficiency of rainfall-runoff models: A theoretical study using chimera watersheds. Water Resources Research 40(5), 1-9.

Rainfall erosivity in the Fukushima Prefecture: implications for radiocesium mobilization and migration

NASA Astrophysics Data System (ADS)

Laceby, J. Patrick; Chartin, Caroline; Degan, Francesca; Onda, Yuichi; Evrard, Olivier; Cerdan, Olivier; Ayrault, Sophie

2015-04-01

The Fukushima Dai-ichi nuclear power plant (FDNPP) accident in March 2011 led to the fallout of predominantly radiocesium (137Cs and 134Cs) on soils of the Fukushima Prefecture. This radiocesium was primarily fixated to fine soil particles. Subsequently, rainfall and snow melt run-off events result in significant quantities of radiocesium being eroded and transported throughout the coastal catchments and ultimately exported to the Pacific Ocean. Erosion models, such as the Universal Soil Loss Equation (USLE), relate rainfall directly to soil erosion in that an increase in rainfall one month will directly result in a proportional increase in sediment generation. Understanding the rainfall regime of the region is therefore fundamental to modelling and predicting long-term radiocesium export. Here, we analyze rainfall data for ~40 stations within a 100 km radius of the FDNPP. First we present general information on the rainfall regime in the region based on monthly and annual rainfall totals. Second we present general information on rainfall erosivity, the R-factor of the USLE equation and its relationship to the general rainfall data. Third we examine rainfall trends over the last 100 years at several of the rainfall stations to understand temporal trends and whether ~20 years of data is sufficient to calculate the R-factor for USLE models. Fourth we present monthly R-factor maps for the Fukushima coastal catchments impacted by the FDNPP accident. The variability of the rainfall in the region, particularly during the typhoon season, is likely resulting in a similar variability in the transfer and migration of radiocesium throughout the coastal catchments of the Fukushima Prefecture. Characterizing the region's rainfall variability is fundamental to modelling sediment and the concomitant radiocesium migration and transfer throughout these catchments and ultimately to the Pacific Ocean.

Sustainable management for rangelands in a variable climate: evidence and insights from northern Australia.

PubMed

O'Reagain, P J; Scanlan, J C

2013-03-01

Inter-annual rainfall variability is a major challenge to sustainable and productive grazing management on rangelands. In Australia, rainfall variability is particularly pronounced and failure to manage appropriately leads to major economic loss and environmental degradation. Recommended strategies to manage sustainably include stocking at long-term carrying capacity (LTCC) or varying stock numbers with forage availability. These strategies are conceptually simple but difficult to implement, given the scale and spatial heterogeneity of grazing properties and the uncertainty of the climate. This paper presents learnings and insights from northern Australia gained from research and modelling on managing for rainfall variability. A method to objectively estimate LTCC in large, heterogeneous paddocks is discussed, and guidelines and tools to tactically adjust stocking rates are presented. The possible use of seasonal climate forecasts (SCF) in management is also considered. Results from a 13-year grazing trial in Queensland show that constant stocking at LTCC was far more profitable and largely maintained land condition compared with heavy stocking (HSR). Variable stocking (VAR) with or without the use of SCF was marginally more profitable, but income variability was greater and land condition poorer than constant stocking at LTCC. Two commercial scale trials in the Northern Territory with breeder cows highlighted the practical difficulties of variable stocking and provided evidence that heavier pasture utilisation rates depress reproductive performance. Simulation modelling across a range of regions in northern Australia also showed a decline in resource condition and profitability under heavy stocking rates. Modelling further suggested that the relative value of variable v. constant stocking depends on stocking rate and land condition. Importantly, variable stocking may possibly allow slightly higher stocking rates without pasture degradation. Enterprise-level simulations run for breeder herds nevertheless show that poor economic performance can occur under constant stocking and even under variable stocking in some circumstances. Modelling and research results both suggest that a form of constrained flexible stocking should be applied to manage for climate variability. Active adaptive management and research will be required as future climate changes make managing for rainfall variability increasingly challenging.

Rainfall erosivity and sediment load over the Poyang Lake Basin under variable climate and human activities since the 1960s

NASA Astrophysics Data System (ADS)

Gu, Chaojun; Mu, Xingmin; Gao, Peng; Zhao, Guangju; Sun, Wenyi; Yu, Qiang

2018-03-01

Accelerated soil erosion exerts adverse effects on water and soil resources. Rainfall erosivity reflects soil erosion potential driven by rainfall, which is essential for soil erosive risk assessment. This study investigated the spatiotemporal variation of rainfall erosivity and its impacts on sediment load over the largest freshwater lake basin of China (the Poyang Lake Basin, abbreviate to PYLB). The spatiotemporal variations of rainfall erosivity from 1961 to 2014 based on 57 meteorological stations were detected using the Mann-Kendall test, linear regression, and kriging interpolation method. The sequential t test analysis of regime shift (STARS) was employed to identify the abrupt changes of sediment load, and the modified double mass curve was used to assess the impacts of rainfall erosivity variability on sediment load. It was found that there was significant increase (P < 0.05) in rainfall erosivity in winter due to the significant increase in January over the last 54 years, whereas no trend in year and other seasons. Annual sediment load into the Poyang Lake (PYL) decreased significantly (P < 0.01) between 1961 and 2014, and the change-points were identified in both 1985 and 2003. It was found that take annual rainfall erosivity as the explanatory variables of the double mass curves is more reasonable than annual rainfall and erosive rainfall. The estimation via the modified double mass curve demonstrated that compared with the period before change-point (1961-1984), the changes of rainfall erosivity increased 8.0 and 2.1% of sediment load during 1985-2002 and 2003-2014, respectively. Human activities decreased 50.2 and 69.7% of sediment load during the last two periods, which indicated effects of human activities on sediment load change was much larger than that of rainfall erosivity variability in the PYLB.

RainyDay: An Online, Open-Source Tool for Physically-based Rainfall and Flood Frequency Analysis

NASA Astrophysics Data System (ADS)

Wright, D.; Yu, G.; Holman, K. D.

2017-12-01

Flood frequency analysis in ungaged or changing watersheds typically requires rainfall intensity-duration-frequency (IDF) curves combined with hydrologic models. IDF curves only depict point-scale rainfall depth, while true rainstorms exhibit complex spatial and temporal structures. Floods result from these rainfall structures interacting with watershed features such as land cover, soils, and variable antecedent conditions as well as river channel processes. Thus, IDF curves are traditionally combined with a variety of "design storm" assumptions such as area reduction factors and idealized rainfall space-time distributions to translate rainfall depths into inputs that are suitable for flood hydrologic modeling. The impacts of such assumptions are relatively poorly understood. Meanwhile, modern precipitation estimates from gridded weather radar, grid-interpolated rain gages, satellites, and numerical weather models provide more realistic depictions of rainfall space-time structure. Usage of such datasets for rainfall and flood frequency analysis, however, are hindered by relatively short record lengths. We present RainyDay, an open-source stochastic storm transposition (SST) framework for generating large numbers of realistic rainfall "scenarios." SST "lengthens" the rainfall record by temporal resampling and geospatial transposition of observed storms to extract space-time information from regional gridded rainfall data. Relatively short (10-15 year) records of bias-corrected radar rainfall data are sufficient to estimate rainfall and flood events with much longer recurrence intervals including 100-year and 500-year events. We describe the SST methodology as implemented in RainyDay and compare rainfall IDF results from RainyDay to conventional estimates from NOAA Atlas 14. Then, we demonstrate some of the flood frequency analysis properties that are possible when RainyDay is integrated with a distributed hydrologic model, including robust estimation of flood hazards in a changing watershed. The U.S. Bureau of Reclamation is supporting the development of a web-based variant of RainyDay, a "beta" version of which is available at http://her.cee.wisc.edu/projects/rainyday/.

Estimation of the fractional coverage of rainfall in climate models

NASA Technical Reports Server (NTRS)

Eltahir, E. A. B.; Bras, R. L.

1993-01-01

The fraction of the grid cell area covered by rainfall, mu, is an essential parameter in descriptions of land surface hydrology in climate models. A simple procedure is presented for estimating this fraction, based on extensive observations of storm areas and rainfall volumes. Storm area and rainfall volume are often linearly related; this relation can be used to compute the storm area from the volume of rainfall simulated by a climate model. A formula is developed for computing mu, which describes the dependence of the fractional coverage of rainfall on the season of the year, the geographical region, rainfall volume, and the spatial and temporal resolution of the model. The new formula is applied in computing mu over the Amazon region. Significant temporal variability in the fractional coverage of rainfall is demonstrated. The implications of this variability for the modeling of land surface hydrology in climate models are discussed.

Exploring public databases to characterize urban flood risks in Amsterdam

NASA Astrophysics Data System (ADS)

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

2015-04-01

Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to decide upon investment to reduce their impacts. Obvious flooding factors affecting flood risk include sewer systems performance and urban topography. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall and socioeconomic characteristics may help to explain probability and impacts of urban flooding. Several public databases were analyzed: complaints about flooding made by citizens, rainfall depths (15 min and 100 Ha spatio-temporal resolution), grids describing number of inhabitants, income, and housing price (1Ha and 25Ha resolution); and buildings age. Data analysis was done using Python and GIS programming, and included spatial indexing of data, cluster analysis, and multivariate regression on the complaints. Complaints were used as a proxy to characterize flooding impacts. The cluster analysis, run for all the variables except the complaints, grouped part of the grid-cells of central Amsterdam into a highly differentiated group, covering 10% of the analyzed area, and accounting for 25% of registered complaints. The configuration of the analyzed variables in central Amsterdam coincides with a high complaint count. Remaining complaints were evenly dispersed along other groups. An adjusted R2 of 0.38 in the multivariate regression suggests that explaining power can improve if additional variables are considered. While rainfall intensity explained 4% of the incidence of complaints, population density and building age significantly explained around 20% each. Data mining of public databases proved to be a valuable tool to identify factors explaining variability in occurrence of urban pluvial flooding, though additional variables must be considered to fully explain flood risk variability.

Prediction of spatially explicit rainfall intensity-duration thresholds for post-fire debris-flow generation in the western United States

NASA Astrophysics Data System (ADS)

Staley, Dennis; Negri, Jacquelyn; Kean, Jason

2016-04-01

Population expansion into fire-prone steeplands has resulted in an increase in post-fire debris-flow risk in the western United States. Logistic regression methods for determining debris-flow likelihood and the calculation of empirical rainfall intensity-duration thresholds for debris-flow initiation represent two common approaches for characterizing hazard and reducing risk. Logistic regression models are currently being used to rapidly assess debris-flow hazard in response to design storms of known intensities (e.g. a 10-year recurrence interval rainstorm). Empirical rainfall intensity-duration thresholds comprise a major component of the United States Geological Survey (USGS) and the National Weather Service (NWS) debris-flow early warning system at a regional scale in southern California. However, these two modeling approaches remain independent, with each approach having limitations that do not allow for synergistic local-scale (e.g. drainage-basin scale) characterization of debris-flow hazard during intense rainfall. The current logistic regression equations consider rainfall a unique independent variable, which prevents the direct calculation of the relation between rainfall intensity and debris-flow likelihood. Regional (e.g. mountain range or physiographic province scale) rainfall intensity-duration thresholds fail to provide insight into the basin-scale variability of post-fire debris-flow hazard and require an extensive database of historical debris-flow occurrence and rainfall characteristics. Here, we present a new approach that combines traditional logistic regression and intensity-duration threshold methodologies. This method allows for local characterization of both the likelihood that a debris-flow will occur at a given rainfall intensity, the direct calculation of the rainfall rates that will result in a given likelihood, and the ability to calculate spatially explicit rainfall intensity-duration thresholds for debris-flow generation in recently burned areas. Our approach synthesizes the two methods by incorporating measured rainfall intensity into each model variable (based on measures of topographic steepness, burn severity and surface properties) within the logistic regression equation. This approach provides a more realistic representation of the relation between rainfall intensity and debris-flow likelihood, as likelihood values asymptotically approach zero when rainfall intensity approaches 0 mm/h, and increase with more intense rainfall. Model performance was evaluated by comparing predictions to several existing regional thresholds. The model, based upon training data collected in southern California, USA, has proven to accurately predict rainfall intensity-duration thresholds for other areas in the western United States not included in the original training dataset. In addition, the improved logistic regression model shows promise for emergency planning purposes and real-time, site-specific early warning. With further validation, this model may permit the prediction of spatially-explicit intensity-duration thresholds for debris-flow generation in areas where empirically derived regional thresholds do not exist. This improvement would permit the expansion of the early-warning system into other regions susceptible to post-fire debris flow.

The variability of the rainfall rate as a function of area

NASA Astrophysics Data System (ADS)

Jameson, A. R.; Larsen, M. L.

2016-01-01

Distributions of drop sizes can be expressed as DSD = Nt × PSD, where Nt is the total number of drops in a sample and PSD is the frequency distribution of drop diameters (D). Their discovery permitted remote sensing techniques for rainfall estimation using radars and satellites measuring over large domains of several kilometers. Because these techniques depend heavily on higher moments of the PSD, there has been a bias toward attributing the variability of the intrinsic rainfall rates R over areas (σR) to the variability of the PSDs. While this variability does increase up to a point with increasing domain dimension L, the variability of the rainfall rate R also depends upon the variability in the total number of drops Nt. We show that while the importance of PSDs looms large for small domains used in past studies, it is the variability of Nt that dominates the variability of R as L increases to 1 km and beyond. The PSDs contribute to the variability of R through the relative dispersion of χ = D3Vt, where Vt is the terminal fall speed of drops of diameter D. However, the variability of χ is inherently limited because drop sizes and fall speeds are physically limited. In contrast, it is shown that the variance of Nt continuously increases as the domain expands for physical reasons explained below. Over domains larger than around 1 km, it is shown that Nt dominates the variance of the rainfall rate with increasing L regardless of the PSD.

Regional changes in extreme monsoon rainfall deficit and excess in India

NASA Astrophysics Data System (ADS)

Pal, Indrani; Al-Tabbaa, Abir

2010-04-01

With increasing concerns about climate change, the need to understand the nature and variability of monsoon climatic conditions and to evaluate possible future changes becomes increasingly important. This paper deals with the changes in frequency and magnitudes of extreme monsoon rainfall deficiency and excess in India from 1871 to 2005. Five regions across India comprising variable climates were selected for the study. Apart from changes in individual regions, changing tendencies in extreme monsoon rainfall deficit and excess were also determined for the Indian region as a whole. The trends and their significance were assessed using non-parametric Mann-Kendall technique. The results show that intra-region variability for extreme monsoon seasonal precipitation is large and mostly exhibited a negative tendency leading to increasing frequency and magnitude of monsoon rainfall deficit and decreasing frequency and magnitude of monsoon rainfall excess.

Assessing the importance of rainfall uncertainty on hydrological models with different spatial and temporal scale

NASA Astrophysics Data System (ADS)

Nossent, Jiri; Pereira, Fernando; Bauwens, Willy

2015-04-01

Precipitation is one of the key inputs for hydrological models. As long as the values of the hydrological model parameters are fixed, a variation of the rainfall input is expected to induce a change in the model output. Given the increased awareness of uncertainty on rainfall records, it becomes more important to understand the impact of this input - output dynamic. Yet, modellers often still have the intention to mimic the observed flow, whatever the deviation of the employed records from the actual rainfall might be, by recklessly adapting the model parameter values. But is it actually possible to vary the model parameter values in such a way that a certain (observed) model output can be generated based on inaccurate rainfall inputs? Thus, how important is the rainfall uncertainty for the model output with respect to the model parameter importance? To address this question, we apply the Sobol' sensitivity analysis method to assess and compare the importance of the rainfall uncertainty and the model parameters on the output of the hydrological model. In order to be able to treat the regular model parameters and input uncertainty in the same way, and to allow a comparison of their influence, a possible approach is to represent the rainfall uncertainty by a parameter. To tackle the latter issue, we apply so called rainfall multipliers on hydrological independent storm events, as a probabilistic parameter representation of the possible rainfall variation. As available rainfall records are very often point measurements at a discrete time step (hourly, daily, monthly,…), they contain uncertainty due to a latent lack of spatial and temporal variability. The influence of the latter variability can also be different for hydrological models with different spatial and temporal scale. Therefore, we perform the sensitivity analyses on a semi-distributed model (SWAT) and a lumped model (NAM). The assessment and comparison of the importance of the rainfall uncertainty and the model parameters is achieved by considering different scenarios for the included parameters and the state of the models.

Are revised models better models? A skill score assessment of regional interannual variability

NASA Astrophysics Data System (ADS)

Sperber, Kenneth R.; Participating AMIP Modelling Groups

1999-05-01

Various skill scores are used to assess the performance of revised models relative to their original configurations. The interannual variability of all-India, Sahel and Nordeste rainfall and summer monsoon windshear is examined in integrations performed under the experimental design of the Atmospheric Model Intercomparison Project. For the indices considered, the revised models exhibit greater fidelity at simulating the observed interannual variability. Interannual variability of all-India rainfall is better simulated by models that have a more realistic rainfall climatology in the vicinity of India, indicating the beneficial effect of reducing systematic model error.

Are revised models better models? A skill score assessment of regional interannual variability

NASA Astrophysics Data System (ADS)

Participating AMIP Modelling Groups,; Sperber, Kenneth R.

Various skill scores are used to assess the performance of revised models relative to their original configurations. The interannual variability of all-India, Sahel and Nordeste rainfall and summer monsoon windshear is examined in integrations performed under the experimental design of the Atmospheric Model Intercomparison Project. For the indices considered, the revised models exhibit greater fidelity at simulating the observed interannual variability. Interannual variability of all-India rainfall is better simulated by models that have a more realistic rainfall climatology in the vicinity of India, indicating the beneficial effect of reducing systematic model error.

Exploratory Long-Range Models to Estimate Summer Climate Variability over Southern Africa.

NASA Astrophysics Data System (ADS)

Jury, Mark R.; Mulenga, Henry M.; Mason, Simon J.

1999-07-01

Teleconnection predictors are explored using multivariate regression models in an effort to estimate southern African summer rainfall and climate impacts one season in advance. The preliminary statistical formulations include many variables influenced by the El Niño-Southern Oscillation (ENSO) such as tropical sea surface temperatures (SST) in the Indian and Atlantic Oceans. Atmospheric circulation responses to ENSO include the alternation of tropical zonal winds over Africa and changes in convective activity within oceanic monsoon troughs. Numerous hemispheric-scale datasets are employed to extract predictors and include global indexes (Southern Oscillation index and quasi-biennial oscillation), SST principal component scores for the global oceans, indexes of tropical convection (outgoing longwave radiation), air pressure, and surface and upper winds over the Indian and Atlantic Oceans. Climatic targets include subseasonal, area-averaged rainfall over South Africa and the Zambezi river basin, and South Africa's annual maize yield. Predictors and targets overlap in the years 1971-93, the defined training period. Each target time series is fitted by an optimum group of predictors from the preceding spring, in a linear multivariate formulation. To limit artificial skill, predictors are restricted to three, providing 17 degrees of freedom. Models with colinear predictors are screened out, and persistence of the target time series is considered. The late summer rainfall models achieve a mean r2 fit of 72%, contributed largely through ENSO modulation. Early summer rainfall cross validation correlations are lower (61%). A conceptual understanding of the climate dynamics and ocean-atmosphere coupling processes inherent in the exploratory models is outlined.Seasonal outlooks based on the exploratory models could help mitigate the impacts of southern Africa's fluctuating climate. It is believed that an advance warning of drought risk and seasonal rainfall prospects will improve the economic growth potential of southern Africa and provide additional security for food and water supplies.

Exploring the relationship between malaria, rainfall intermittency, and spatial variation in rainfall seasonality

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Wimberly, M. C.; Henebry, G. M.; Senay, G. B.

2014-12-01

Malaria is a major public health problem throughout tropical regions of the world. Successful prevention and treatment of malaria requires an understanding of the environmental factors that affect the life cycle of both the malaria pathogens, protozoan parasites, and its vectors, anopheline mosquitos. Because the egg, larval, and pupal stages of mosquito development occur in aquatic habitats, information about the spatial and temporal distribution of rainfall is critical for modeling malaria risk. Potential sources of hydrological data include satellite-derived rainfall estimates (TRMM and GPM), evapotranspiration derived from a simplified surface energy balance, and estimates of soil moisture and fractional water cover from passive microwave imagery. Previous studies have found links between malaria cases and total monthly or weekly rainfall in areas where both are highly seasonal. However it is far from clear that monthly or weekly summaries are the best metrics to use to explain malaria outbreaks. It is possible that particular temporal or spatial patterns of rainfall result in better mosquito habitat and thus higher malaria risk. We used malaria case data from the Amhara region of Ethiopia and satellite-derived rainfall estimates to explore the relationship between malaria outbreaks and rainfall with the goal of identifying the most useful rainfall metrics for modeling malaria occurrence. First, we explored spatial variation in the seasonal patterns of both rainfall and malaria cases in Amhara. Second, we assessed the relative importance of different metrics of rainfall intermittency, including alternation of wet and dry spells, the strength of intensity fluctuations, and spatial variability in these measures, in determining the length and severity of malaria outbreaks. We also explored the sensitivity of our results to the choice of method for describing rainfall intermittency and the spatial and temporal scale at which metrics were calculated. Results demonstrate that information about the seasonality and intermittency of rainfall has the potential to improve our understanding of malaria epidemiology and improve our ability to forecast malaria outbreaks.

The impact of inter-annual rainfall variability on food production in the Ganges basin

NASA Astrophysics Data System (ADS)

Siderius, Christian; Biemans, Hester; van Walsum, Paul; hellegers, Petra; van Ierland, Ekko; Kabat, Pavel

2014-05-01

Rainfall variability is expected to increase in the coming decades as the world warms. Especially in regions already water stressed, a higher rainfall variability will jeopardize food security. Recently, the impact of inter-annual rainfall variability has received increasing attention in regional to global analysis on water availability and food security. But the description of the dynamics behind it is still incomplete in most models. Contemporary land surface and hydrological models used for such analyses describe variability in production primarily as a function of yield, a process driven by biophysical parameters, thereby neglecting yearly variations in cropped area, a process driven largely by management decisions. Agricultural statistics for northern India show that the latter process could explain up to 40% of the observed inter-annual variation in food production in various states. We added a simple dynamic land use decision module to a land surface model (LPJmL) and analyzed to what extent this improved the estimation of variability in food production. Using this improved modelling framework we then assessed if and at which scale rainfall variability affects meeting the food self-sufficiency threshold. Early results for the Ganges Basin indicate that, while on basin level variability in crop production is still relatively low, several districts and states are highly affected (RSTD > 50%). Such insight can contribute to better recommendations on the most effective measures, at the most appropriate scale, to buffer variability in food production.

Spatio-temporal variability and trends of precipitation and extreme rainfall events in Ethiopia in 1980-2010

NASA Astrophysics Data System (ADS)

Gummadi, Sridhar; Rao, K. P. C.; Seid, Jemal; Legesse, Gizachew; Kadiyala, M. D. M.; Takele, Robel; Amede, Tilahun; Whitbread, Anthony

2017-12-01

This article summarizes the results from an analysis conducted to investigate the spatio-temporal variability and trends in the rainfall over Ethiopia over a period of 31 years from 1980 to 2010. The data is mostly observed station data supplemented by bias-corrected AgMERRA climate data. Changes in annual and Belg (March-May) and Kiremt (June to September) season rainfalls and rainy days have been analysed over the entire Ethiopia. Rainfall is characterized by high temporal variability with coefficient of variation (CV, %) varying from 9 to 30% in the annual, 9 to 69% during the Kiremt season and 15-55% during the Belg season rainfall amounts. Rainfall variability increased disproportionately as the amount of rainfall declined from 700 to 100 mm or less. No significant trend was observed in the annual rainfall amounts over the country, but increasing and decreasing trends were observed in the seasonal rainfall amounts in some areas. A declining trend is also observed in the number of rainy days especially in Oromia, Benishangul-Gumuz and Gambella regions. Trends in seasonal rainfall indicated a general decline in the Belg season and an increase in the Kiremt season rainfall amounts. The increase in rainfall during the main Kiremt season along with the decrease in the number of rainy days leads to an increase in extreme rainfall events over Ethiopia. The trends in the 95th-percentile rainfall events illustrate that the annual extreme rainfall events are increasing over the eastern and south-western parts of Ethiopia covering Oromia and Benishangul-Gumuz regions. During the Belg season, extreme rainfall events are mostly observed over central Ethiopia extending towards the southern part of the country while during the Kiremt season, they are observed over parts of Oromia, (covering Borena, Guji, Bali, west Harerge and east Harerge), Somali, Gambella, southern Tigray and Afar regions. Changes in the intensity of extreme rainfall events are mostly observed over south-eastern parts of Ethiopia extending to the south-west covering Somali and Oromia regions. Similar trends are also observed in the greatest 3-, 5- and 10-day rainfall amounts. Changes in the consecutive dry and wet days showed that consecutive wet days during Belg and Kiremt seasons decreased significantly in many areas in Ethiopia while consecutive dry days increased. The consistency in the trends over large spatial areas confirms the robustness of the trends and serves as a basis for understanding the projected changes in the climate. These results were discussed in relation to their significance to agriculture.

Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA

NASA Astrophysics Data System (ADS)

Machado, Luiz A. T.; Calheiros, Alan J. P.; Biscaro, Thiago; Giangrande, Scott; Silva Dias, Maria A. F.; Cecchini, Micael A.; Albrecht, Rachel; Andreae, Meinrat O.; Araujo, Wagner F.; Artaxo, Paulo; Borrmann, Stephan; Braga, Ramon; Burleyson, Casey; Eichholz, Cristiano W.; Fan, Jiwen; Feng, Zhe; Fisch, Gilberto F.; Jensen, Michael P.; Martin, Scot T.; Pöschl, Ulrich; Pöhlker, Christopher; Pöhlker, Mira L.; Ribaud, Jean-François; Rosenfeld, Daniel; Saraiva, Jaci M. B.; Schumacher, Courtney; Thalman, Ryan; Walter, David; Wendisch, Manfred

2018-05-01

This study provides an overview of precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin near Manaus during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. This study takes advantage of the numerous measurement platforms and instrument systems operating during both campaigns to sample cloud structure and environmental conditions during 2014 and 2015; the rainfall variability among seasons, aerosol loading, land surface type, and topography has been carefully characterized using these data. Differences between the wet and dry seasons were examined from a variety of perspectives. The rainfall rates distribution, total amount of rainfall, and raindrop size distribution (the mass-weighted mean diameter) were quantified over both seasons. The dry season generally exhibited higher rainfall rates than the wet season and included more intense rainfall periods. However, the cumulative rainfall during the wet season was 4 times greater than that during the total dry season rainfall, as shown in the total rainfall accumulation data. The typical size and life cycle of Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as were differences in these systems between the seasons. Moreover, monthly mean thermodynamic and dynamic variables were analysed using radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to atmospheric aerosol loading was discussed with regard to mass-weighted mean diameter and rain rate. This topic was evaluated only during the wet season due to the insignificant statistics of rainfall events for different aerosol loading ranges and the low frequency of precipitation events during the dry season. The impacts of aerosols on cloud droplet diameter varied based on droplet size. For the wet season, we observed no dependence between land surface type and rain rate. However, during the dry season, urban areas exhibited the largest rainfall rate tail distribution, and deforested regions exhibited the lowest mean rainfall rate. Airplane measurements were taken to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. Vertical motion was not correlated with cloud droplet sizes, but cloud droplet concentration correlated linearly with vertical motion. Clouds over forested areas contained larger droplets than clouds over pastures at all altitudes. Finally, the connections between topography and rain rate were evaluated, with higher rainfall rates identified at higher elevations during the dry season.

Assessing the Change in Rainfall Characteristics and Trends for the Southern African ITCZ Region

NASA Astrophysics Data System (ADS)

Baumberg, Verena; Weber, Torsten; Helmschrot, Jörg

2015-04-01

Southern Africa is strongly influenced by the movement and intensity of the Intertropical Convergence Zone (ITCZ) thus determining the climate in this region with distinct seasonal and inter-annual rainfall dynamics. The amount and variability of rainfall affect the various ecosystems by controlling the hydrological system, regulating water availability and determining agricultural practices. Changes in rainfall characteristics potentially caused by climate change are of uppermost relevance for both ecosystem functioning and human well-being in this region and, thus, need to be investigated. To analyse the rainfall variability governed by the ITCZ in southern Africa, observational daily rainfall datasets with a high spatial resolution of 0.25° x 0.25° (about 28 km x 28 km) from satellite-based Tropical Rainfall Measuring Mission (TRMM) and Global Land Data Assimilation System (GLDAS) are used. These datasets extend from 1998 to 2008 and 1948 to 2010, respectively, and allow for the assessment of rainfall characteristics over different spatial and temporal scales. Furthermore, a comparison of TRMM and GLDAS and, where available, with observed data will be made to determine the differences of both datasets. In order to quantify the intra- and inner-annual variability of rainfall, the amount of total rainfall, duration of rainy seasons and number of dry spells along with further indices are calculated from the observational datasets. Over the southern African ITCZ region, the rainfall characteristics change moving from wetter north to the drier south, but also from west to east, i.e. the coast to the interior. To address expected spatial and temporal variabilities, the assessment of changes in the rainfall parameters will be carried out for different transects in zonal and meridional directions over the region affected by the ITCZ. Revealing trends over more than 60 years, the results will help to identify and understand potential impacts of climate change on rainfall characteristics for the southern African ITCZ region. The findings of this study will feed into various ecosystem assessment and biodiversity change studies in Angola and Zambia.

Evaluation of Stochastic Rainfall Models in Capturing Climate Variability for Future Drought and Flood Risk Assessment

NASA Astrophysics Data System (ADS)

Chowdhury, A. F. M. K.; Lockart, N.; Willgoose, G. R.; Kuczera, G. A.; Kiem, A.; Nadeeka, P. M.

2016-12-01

One of the key objectives of stochastic rainfall modelling is to capture the full variability of climate system for future drought and flood risk assessment. However, it is not clear how well these models can capture the future climate variability when they are calibrated to Global/Regional Climate Model data (GCM/RCM) as these datasets are usually available for very short future period/s (e.g. 20 years). This study has assessed the ability of two stochastic daily rainfall models to capture climate variability by calibrating them to a dynamically downscaled RCM dataset in an east Australian catchment for 1990-2010, 2020-2040, and 2060-2080 epochs. The two stochastic models are: (1) a hierarchical Markov Chain (MC) model, which we developed in a previous study and (2) a semi-parametric MC model developed by Mehrotra and Sharma (2007). Our hierarchical model uses stochastic parameters of MC and Gamma distribution, while the semi-parametric model uses a modified MC process with memory of past periods and kernel density estimation. This study has generated multiple realizations of rainfall series by using parameters of each model calibrated to the RCM dataset for each epoch. The generated rainfall series are used to generate synthetic streamflow by using a SimHyd hydrology model. Assessing the synthetic rainfall and streamflow series, this study has found that both stochastic models can incorporate a range of variability in rainfall as well as streamflow generation for both current and future periods. However, the hierarchical model tends to overestimate the multiyear variability of wet spell lengths (therefore, is less likely to simulate long periods of drought and flood), while the semi-parametric model tends to overestimate the mean annual rainfall depths and streamflow volumes (hence, simulated droughts are likely to be less severe). Sensitivity of these limitations of both stochastic models in terms of future drought and flood risk assessment will be discussed.

Analysis of the influence of rainfall variables on urban effluents concentrations and fluxes in wet weather

NASA Astrophysics Data System (ADS)

Gooré Bi, Eustache; Monette, Frédéric; Gasperi, Johnny

2015-04-01

Urban rainfall runoff has been a topic of increasing importance over the past years, a result of both the increase in impervious land area arising from constant urban growth and the effects of climate change on urban drainage. The main goal of the present study is to assess and analyze the correlations between rainfall variables and common indicators of urban water quality, namely event mean concentrations (EMCs) and event fluxes (EFs), in order to identify and explain the impacts of each of the main rainfall variables on the generation process of urban pollutants during wet periods. To perform this analysis, runoff from eight summer rainfall events that resulted in combined sewer overflow (CSO) was sampled simultaneously from two distinct catchment areas in order to quantify discharges at the respective outfalls. Pearson statistical analysis of total suspended solids (TSS), chemical oxygen demand (COD), carbonaceous biochemical oxygen demand at 5 days (CBOD5), total phosphorus (Ptot) and total kedjal nitrogen (N-TKN) showed significant correlations (ρ = 0.05) between dry antecedent time (DAT) and EMCs on one hand, and between total rainfall (TR) and the volume discharged (VD) during EFs, on the other. These results show that individual rainfall variables strongly affect either EMCs or EFs and are good predictors to consider when selecting variables for statistical modeling of urban runoff quality. The results also show that in a combined sewer network, there is a linear relationship between TSS event fluxes and COD, CBOD5, Ptot, and N-TKN event fluxes; this explains 97% of the variability of these pollutants which adsorb onto TSS during wet weather, which therefore act as tracers. Consequently, the technological solution selected for TSS removal will also lead to a reduction of these pollutants. Given the huge volumes involved, urban runoffs contribute substantially to pollutant levels in receiving water bodies, a situation which, in a climate change context, may get much worse as a result of more frequent, shorter, but more intense rainfall events.

Southern Hemisphere rainfall variability over the past 200 years

NASA Astrophysics Data System (ADS)

Gergis, Joëlle; Henley, Benjamin J.

2017-04-01

This study presents an analysis of three palaeoclimate rainfall reconstructions from the Southern Hemisphere regions of south-eastern Australia (SEA), southern South Africa (SAF) and southern South America (SSA). We provide a first comparison of rainfall variations in these three regions over the past two centuries, with a focus on identifying synchronous wet and dry periods. Despite the uncertainties associated with the spatial and temporal limitations of the rainfall reconstructions, we find evidence of dynamically-forced climate influences. An investigation of the twentieth century relationship between regional rainfall and the large-scale climate circulation features of the Pacific, Indian and Southern Ocean regions revealed that Indo-Pacific variations of the El Niño-Southern Oscillation (ENSO) and the Indian Ocean dipole dominate rainfall variability in SEA and SAF, while the higher latitude Southern Annular Mode (SAM) exerts a greater influence in SSA. An assessment of the stability of the regional rainfall-climate circulation modes over the past two centuries revealed a number of non-stationarities, the most notable of which occurs during the early nineteenth century around 1820. This corresponds to a time when the influence of ENSO on SEA, SAF and SSA rainfall weakens and there is a strengthening of the influence of SAM. We conclude by advocating the use of long-term palaeoclimate data to estimate decadal rainfall variability for future water resource management.

Is the Aquarius sea surface salinity variability representative?

NASA Astrophysics Data System (ADS)

Carton, J.; Grodsky, S.

2016-12-01

The leading mode of the Aquarius monthly anomalous sea surface salinity (SSS) is evaluated within the 50S-50N belt, where SSS retrieval accuracy is higher. This mode accounts for about 18% of the variance and resembles a pattern of the ENSO-induced anomalous rainfall. The leading mode of SSS variability deducted from a longer JAMSTEC analysis also accounts for about 17% of the variance and has very similar spatial pattern and almost a perfect correspondence of its temporal principal component to the SOI index. In that sense, the Aquarius SSS variability at low and middle latitudes is representative of SSS variability that may be obtained from longer records. This is explained by the fact that during the Aquarius period (2011-2015), the SOI index changed significantly from La Nina toward El Nino state, thus spanning a significant range of its characteristic variations. Multivariate EOF analysis of anomalous SSS and SST suggests that ENSO-induced shift in the tropical Pacific rainfall produces negatively correlated variability of temperature and salinity, which are expected if the anomalous surface flux (stronger rainfall coincident with less downward radiation) drives the system. But, anomalous SSS and SST are positively correlated in some areas including the northwestern Atlantic shelf (north of the Gulfstream) and the Pacific sector adjusting to the California peninsula. This positive correlation is indicative of an advection driven regime that is analyzed separately.

The local and global climate forcings induced inhomogeneity of Indian rainfall.

PubMed

Nair, P J; Chakraborty, A; Varikoden, H; Francis, P A; Kuttippurath, J

2018-04-16

India is home for more than a billion people and its economy is largely based on agrarian society. Therefore, rainfall received not only decides its livelihood, but also influences its water security and economy. This situation warrants continuous surveillance and analysis of Indian rainfall. These kinds of studies would also help forecasters to better tune their models for accurate weather prediction. Here, we introduce a new method for estimating variability and trends in rainfall over different climate regions of India. The method based on multiple linear regression helps to assess contributions of different remote and local climate forcings to seasonal and regional inhomogeneity in rainfall. We show that the Indian Summer Monsoon Rainfall (ISMR) variability is governed by Eastern and Central Pacific El Niño Southern Oscillation, equatorial zonal winds, Atlantic zonal mode and surface temperatures of the Arabian Sea and Bay of Bengal, and the North East Monsoon Rainfall variability is controlled by the sea surface temperature of the North Atlantic and extratropial oceans. Also, our analyses reveal significant positive trends (0.43 mm/day/dec) in the North West for ISMR in the 1979-2017 period. This study cautions against the significant changes in Indian rainfall in a perspective of global climate change.

Vertical Variability of Rain Drop Size Distribution from Micro Rain Radar Measurements during IFloodS

NASA Astrophysics Data System (ADS)

Adirosi, Elisa; Tokay, Ali; Roberto, Nicoletta; Gorgucci, Eugenio; Montopoli, Mario; Baldini, Luca

2017-04-01

Ground based weather radars are highly used to generate rainfall products for meteorological and hydrological applications. However, weather radar quantitative rainfall estimation is obtained at a certain altitude that depends mainly on the radar elevation angle and on the distance from the radar. Therefore, depending on the vertical variability of rainfall, a time-height ambiguity between radar measurement and rainfall at the ground can affect the rainfall products. The vertically pointing radars (such as the Micro Rain Radar, MRR) are great tool to investigate the vertical variability of rainfall and its characteristics and ultimately, to fill the gap between the ground level and the first available radar elevation. Furthermore, the knowledge of rain Drop Size Distribution (DSD) variability is linked to the well-known problem of the non-uniform beam filling that is one of the main uncertainties of Global Precipitation Measurement (GPM) mission Dual frequency Precipitation Radar (DPR). During GPM Ground Validation Iowa Flood Studies (IFloodS) field experiment, data collected with 2D video disdrometers (2DVD), Autonomous OTT Parsivel2 Units (APU), and MRR profilers at different sites were available. In three different sites co-located APU, 2DVD and MRR are available and covered by the S-band Dual Polarimetric Doppler radar (NPOL). The first elevation height of the radar beam varies, among the three sites, between 70 m and 1100 m. The IFloodS set-up has been used to compare disdrometers, MRR and NPOL data and to evaluate the uncertainties of those measurements. First, the performance of disdrometers and MRR in determining different rainfall parameters at ground has been evaluated and then the MRR based parameters have been compared with the ones obtained from NPOL data at the lowest elevations. Furthermore, the vertical variability of DSD and integral rainfall parameters within the MRR bins (from ground to 1085 m each 35 m) has been investigated in order to provide some insight on the variability of the rainfall microphysical characteristics within about 1 km above the ground.

Predictability of Seasonal Rainfall over the Greater Horn of Africa

NASA Astrophysics Data System (ADS)

Ngaina, J. N.

2016-12-01

The El Nino-Southern Oscillation (ENSO) is a primary mode of climate variability in the Greater of Africa (GHA). The expected impacts of climate variability and change on water, agriculture, and food resources in GHA underscore the importance of reliable and accurate seasonal climate predictions. The study evaluated different model selection criteria which included the Coefficient of determination (R2), Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and the Fisher information approximation (FIA). A forecast scheme based on the optimal model was developed to predict the October-November-December (OND) and March-April-May (MAM) rainfall. The predictability of GHA rainfall based on ENSO was quantified based on composite analysis, correlations and contingency tables. A test for field-significance considering the properties of finiteness and interdependence of the spatial grid was applied to avoid correlations by chance. The study identified FIA as the optimal model selection criterion. However, complex model selection criteria (FIA followed by BIC) performed better compared to simple approach (R2 and AIC). Notably, operational seasonal rainfall predictions over the GHA makes of simple model selection procedures e.g. R2. Rainfall is modestly predictable based on ENSO during OND and MAM seasons. El Nino typically leads to wetter conditions during OND and drier conditions during MAM. The correlations of ENSO indices with rainfall are statistically significant for OND and MAM seasons. Analysis based on contingency tables shows higher predictability of OND rainfall with the use of ENSO indices derived from the Pacific and Indian Oceans sea surfaces showing significant improvement during OND season. The predictability based on ENSO for OND rainfall is robust on a decadal scale compared to MAM. An ENSO-based scheme based on an optimal model selection criterion can thus provide skillful rainfall predictions over GHA. This study concludes that the negative phase of ENSO (La Niña) leads to dry conditions while the positive phase of ENSO (El Niño) anticipates enhanced wet conditions

A paleoclimate rainfall reconstruction in the Murray-Darling Basin (MDB), Australia: 1. Evaluation of different paleoclimate archives, rainfall networks, and reconstruction techniques

NASA Astrophysics Data System (ADS)

Ho, Michelle; Kiem, Anthony S.; Verdon-Kidd, Danielle C.

2015-10-01

From ˜1997 to 2009 the Murray-Darling Basin (MDB), Australia's largest water catchment and reputed "food bowl," experienced a severe drought termed the "Millennium Drought" or "Big Dry" followed by devastating floods in the austral summers of 2010/2011, 2011/2012, and 2012/2013. The magnitude and severity of these extreme events highlight the limitations associated with assessing hydroclimatic risk based on relatively short instrumental records (˜100 years). An option for extending hydroclimatic records is through the use of paleoclimate records. However, there are few in situ proxies of rainfall or streamflow suitable for assessing hydroclimatic risk in Australia and none are available in the MDB. In this paper, available paleoclimate records are reviewed and those of suitable quality for hydroclimatic risk assessments are used to develop preinstrumental information for the MDB. Three different paleoclimate reconstruction techniques are assessed using two instrumental rainfall networks: (1) corresponding to rainfall at locations where rainfall-sensitive Australian paleoclimate archives currently exist and (2) corresponding to rainfall at locations identified as being optimal for explaining MDB rainfall variability. It is shown that the optimized rainfall network results in a more accurate model of MDB rainfall compared to reconstructions based on rainfall at locations where paleoclimate rainfall proxies currently exist. This highlights the importance of first identifying key locations where existing and as yet unrealized paleoclimate records will be most useful in characterizing variability. These results give crucial insight as to where future investment and research into developing paleoclimate proxies for Australia could be most beneficial, with respect to better understanding instrumental, preinstrumental and potential future variability in the MDB.

Spectral analysis of temporal non-stationary rainfall-runoff processes

NASA Astrophysics Data System (ADS)

Chang, Ching-Min; Yeh, Hund-Der

2018-04-01

This study treats the catchment as a block box system with considering the rainfall input and runoff output being a stochastic process. The temporal rainfall-runoff relationship at the catchment scale is described by a convolution integral on a continuous time scale. Using the Fourier-Stieltjes representation approach, a frequency domain solution to the convolution integral is developed to the spectral analysis of runoff processes generated by temporal non-stationary rainfall events. It is shown that the characteristic time scale of rainfall process increases the runoff discharge variability, while the catchment mean travel time constant plays the role in reducing the variability of runoff discharge. Similar to the behavior of groundwater aquifers, catchments act as a low-pass filter in the frequency domain for the rainfall input signal.

The influence of multiyear drought on the annual rainfall-runoff relationship: An Australian perspective

NASA Astrophysics Data System (ADS)

Saft, Margarita; Western, Andrew W.; Zhang, Lu; Peel, Murray C.; Potter, Nick J.

2015-04-01

Most current long-term (decadal and longer) hydrological predictions implicitly assume that hydrological processes are stationary even under changing climate. However, in practice, we suspect that changing climatic conditions may affect runoff generation processes and cause changes in the rainfall-runoff relationship. In this article, we investigate whether temporary but prolonged (i.e., of the order of a decade) shifts in rainfall result in changes in rainfall-runoff relationships at the catchment scale. Annual rainfall and runoff records from south-eastern Australia are used to examine whether interdecadal climate variability induces changes in hydrological behavior. We test statistically whether annual rainfall-runoff relationships are significantly different during extended dry periods, compared with the historical norm. The results demonstrate that protracted drought led to a significant shift in the rainfall-runoff relationship in ˜44% of the catchment-dry periods studied. The shift led to less annual runoff for a given annual rainfall, compared with the historical relationship. We explore linkages between cases where statistically significant changes occurred and potential explanatory factors, including catchment properties and characteristics of the dry period (e.g., length, precipitation anomalies). We find that long-term drought is more likely to affect transformation of rainfall to runoff in drier, flatter, and less forested catchments. Understanding changes in the rainfall-runoff relationship is important for accurate streamflow projections and to help develop adaptation strategies to deal with multiyear droughts.

Sensitivity of Latent Heating Profiles to Environmental Conditions: Implications for TRMM and Climate Research

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Einaudi, Franco (Technical Monitor)

2000-01-01

The Tropical Rainfall Measuring Mission (TRMM) as a part of NASA's Earth System Enterprise is the first mission dedicated to measuring tropical rainfall through microwave and visible sensors, and includes the first spaceborne rain radar. Tropical rainfall comprises two-thirds of global rainfall. It is also the primary distributor of heat through the atmosphere's circulation. It is this circulation that defines Earth's weather and climate. Understanding rainfall and its variability is crucial to understanding and predicting global climate change. Weather and climate models need an accurate assessment of the latent heating released as tropical rainfall occurs. Currently, cloud model-based algorithms are used to derive latent heating based on rainfall structure. Ultimately, these algorithms can be applied to actual data from TRMM. This study investigates key underlying assumptions used in developing the latent heating algorithms. For example, the standard algorithm is highly dependent on a system's rainfall amount and structure. It also depends on an a priori database of model-derived latent heating profiles based on the aforementioned rainfall characteristics. Unanswered questions remain concerning the sensitivity of latent heating profiles to environmental conditions (both thermodynamic and kinematic), regionality, and seasonality. This study investigates and quantifies such sensitivities and seeks to determine the optimal latent heating profile database based on the results. Ultimately, the study seeks to produce an optimized latent heating algorithm based not only on rainfall structure but also hydrometeor profiles.

Markov modulated Poisson process models incorporating covariates for rainfall intensity.

PubMed

Thayakaran, R; Ramesh, N I

2013-01-01

Time series of rainfall bucket tip times at the Beaufort Park station, Bracknell, in the UK are modelled by a class of Markov modulated Poisson processes (MMPP) which may be thought of as a generalization of the Poisson process. Our main focus in this paper is to investigate the effects of including covariate information into the MMPP model framework on statistical properties. In particular, we look at three types of time-varying covariates namely temperature, sea level pressure, and relative humidity that are thought to be affecting the rainfall arrival process. Maximum likelihood estimation is used to obtain the parameter estimates, and likelihood ratio tests are employed in model comparison. Simulated data from the fitted model are used to make statistical inferences about the accumulated rainfall in the discrete time interval. Variability of the daily Poisson arrival rates is studied.

Estimating the Risk of Domestic Water Source Contamination following Precipitation Events

PubMed Central

Eisenhauer, Ian F.; Hoover, Christopher M.; Remais, Justin V.; Monaghan, Andrew; Celada, Marco; Carlton, Elizabeth J.

2016-01-01

Climate change is expected to increase precipitation extremes, threatening water quality. In low resource settings, it is unclear which water sources are most vulnerable to contamination following rainfall events. We evaluated the relationship between rainfall and drinking water quality in southwest Guatemala where heavy rainfall is frequent and access to safe water is limited. We surveyed 59 shallow household wells, measured precipitation, and calculated simple hydrological variables. We compared Escherichia coli concentration at wells where recent rainfall had occurred versus had not occurred, and evaluated variability in the association between rainfall and E. coli concentration under different conditions using interaction models. Rainfall in the past 24 hours was associated with greater E. coli concentrations, with the strongest association between rainfall and fecal contamination at wells where pigs were nearby. Because of the small sample size, these findings should be considered preliminary, but provide a model to evaluate vulnerability to climate change. PMID:27114298

Indian Ocean dipole and rainfall drive a Moran effect in East Africa malaria transmission.

PubMed

Chaves, Luis Fernando; Satake, Akiko; Hashizume, Masahiro; Minakawa, Noboru

2012-06-15

Patterns of concerted fluctuation in populations-synchrony-can reveal impacts of climatic variability on disease dynamics. We examined whether malaria transmission has been synchronous in an area with a common rainfall regime and sensitive to the Indian Ocean Dipole (IOD), a global climatic phenomenon affecting weather patterns in East Africa. We studied malaria synchrony in 5 15-year long (1984-1999) monthly time series that encompass an altitudinal gradient, approximately 1000 m to 2000 m, along Lake Victoria basin. We quantified the association patterns between rainfall and malaria time series at different altitudes and across the altitudinal gradient encompassed by the study locations. We found a positive seasonal association of rainfall with malaria, which decreased with altitude. By contrast, IOD and interannual rainfall impacts on interannual disease cycles increased with altitude. Our analysis revealed a nondecaying synchrony of similar magnitude in both malaria and rainfall, as expected under a Moran effect, supporting a role for climatic variability on malaria epidemic frequency, which might reflect rainfall-mediated changes in mosquito abundance. Synchronous malaria epidemics call for the integration of knowledge on the forcing of malaria transmission by environmental variability to develop robust malaria control and elimination programs.

Droughts, rainfall and rural water supply in northern Nigeria

NASA Astrophysics Data System (ADS)

Tarhule, Aondover Augustine

Knowledge concerning various aspects of drought and water scarcity is required to predict, and to articulate strategies to minimize the effects of future events. This thesis investigated different aspects of droughts and rainfall variability at several time scales and described the dynamics of water supply and use in a rural village in northeastern Nigeria. The parallel existence of measured climatic records and information on famine/folklore events is utilized to calibrate the historical information against the measured data. It is shown that famines or historical droughts occurred when the cumulative deficit of rainfall fell below 1.3 times the standard deviation of the long-term mean rainfall. The study demonstrated that famine chronologies are adequate proxy for drought events, providing a means for the reconstruction of the drought/climatic history of the region. Analysis of recent changes in annual rainfall characteristics show that the series of annual rainfall and number of rain days experienced a discontinuity during the 1960's, caused largely by the decrease in the frequency of moderate to high intensity rain events. The periods prior to and after the change point are homogenous and provide an objective basis for the estimation of changes in rainfall characteristics, drought parameters and for demarcating the region into sub-zones. Rainfall variability was unaffected by the abrupt change. Furthermore, the variability is independently distributed and adequately described by the normal distribution. This allows estimates of the probability of various magnitudes or thresholds of variability. The effects of droughts and rainfall variability are most strongly felt in rural areas. Analysis of the patterns of water supply and use in a typical rural village revealed that the hydrologic system is driven by the local rainfall. Perturbations in the rains propagate through the system with short lag time between the various components. Where fadama aquifers occur, they offer a major supplement of water for six to seven months during the dry season. Under traditional systems, the pattern of water withdrawal from the fadama aquifers is designed to accommodate the diverse interests of different groups and to minimize the potential for conflict. The results contribute to our understanding of drought and water scarcity and are useful in various practical applications.

To what extent does variability of historical rainfall series influence extreme event statistics of sewer system surcharge and overflows?

PubMed

Schaarup-Jensen, K; Rasmussen, M R; Thorndahl, S

2009-01-01

In urban drainage modelling long-term extreme statistics has become an important basis for decision-making e.g. in connection with renovation projects. Therefore it is of great importance to minimize the uncertainties with regards to long-term prediction of maximum water levels and combined sewer overflow (CSO) in drainage systems. These uncertainties originate from large uncertainties regarding rainfall inputs, parameters, and assessment of return periods. This paper investigates how the choice of rainfall time series influences the extreme events statistics of max water levels in manholes and CSO volumes. Traditionally, long-term rainfall series, from a local rain gauge, are unavailable. In the present case study, however, long and local rain series are available. 2 rainfall gauges have recorded events for approximately 9 years at 2 locations within the catchment. Beside these 2 gauges another 7 gauges are located at a distance of max 20 kilometers from the catchment. All gauges are included in the Danish national rain gauge system which was launched in 1976. The paper describes to what extent the extreme events statistics based on these 9 series diverge from each other and how this diversity can be handled, e.g. by introducing an "averaging procedure" based on the variability within the set of statistics. All simulations are performed by means of the MOUSE LTS model.

The stochastic runoff-runon process: Extending its analysis to a finite hillslope

NASA Astrophysics Data System (ADS)

Jones, O. D.; Lane, P. N. J.; Sheridan, G. J.

2016-10-01

The stochastic runoff-runon process models the volume of infiltration excess runoff from a hillslope via the overland flow path. Spatial variability is represented in the model by the spatial distribution of rainfall and infiltration, and their ;correlation scale;, that is, the scale at which the spatial correlation of rainfall and infiltration become negligible. Notably, the process can produce runoff even when the mean rainfall rate is less than the mean infiltration rate, and it displays a gradual increase in net runoff as the rainfall rate increases. In this paper we present a number of contributions to the analysis of the stochastic runoff-runon process. Firstly we illustrate the suitability of the process by fitting it to experimental data. Next we extend previous asymptotic analyses to include the cases where the mean rainfall rate equals or exceeds the mean infiltration rate, and then use Monte Carlo simulation to explore the range of parameters for which the asymptotic limit gives a good approximation on finite hillslopes. Finally we use this to obtain an equation for the mean net runoff, consistent with our asymptotic results but providing an excellent approximation for finite hillslopes. Our function uses a single parameter to capture spatial variability, and varying this parameter gives us a family of curves which interpolate between known upper and lower bounds for the mean net runoff.

Indian Monsoon Rainfall Variability During the Common Era: Implications on the Ancient Civilization

NASA Astrophysics Data System (ADS)

Pothuri, D.

2017-12-01

Indian monsoon rainfall variability was reconstructed during last two millennia by using the δ18Ow from a sediment core in the Krishna-Godavari Basin. Higher δ18Ow values during Dark Age Cold Period (DACP) (1550 to 1250 years BP) and Little Ice Age (LIA) (700 to 200 years BP) represent less Indian monsoon rainfall. Whereas during Medieval Warm Period (MWP) (1200 to 800 years BP) and major portion of Roman Warm Period (RWP) 2000 to 1550 years BP) document more rainfall in the Indian subcontinent as evident from lower δ18Ow values. A significant correlation exist between the Bay of Bengal (BoB) sea surface temperature (SST) and Indian monsoon proxy (i.e. δ18Ow), which suggests that; (i) the forcing mechanism of the Indian monsoon rainfall variability during last two millennia was controlled by the thermal contrast between the Indian Ocean and Asian Land Mass, and (ii) the evaporation processes in the BoB and associated SST are strongly coupled with the Indian Monsoon variability over the last two millennia.

The Effects Of Urban Landscape Patterns On Rainfall-Runoff Processes At Small Scale

NASA Astrophysics Data System (ADS)

Chen, L.

2016-12-01

Many studies have indicated that urban landscape change may alter rainfall-runoff processes. However, how urban landscape pattern affect this process is little addressed. In this study, the hydrological effects of landscape pattern on rainfall-runoff processes at small-scale was explored. Twelve residential blocks with independent drainage systems in Beijing were selected as case study areas. Impervious metrics of these blocks, i.e., total impervious area (TIA) and directly connected impervious area (DCIA), were identified. A drainage index describing catchment general drainage load and the overland flow distance, Ad, was estimated and used as one of the landscape spatial metrics. Three scenarios were designed to test the potential influence of impervious surface pattern on runoff processes. Runoff variables including total and peak runoff depth (Qt and Qp) were simulated under different rainfall conditions by Storm Water Management Model (SWMM). The relationship between landscape patterns and runoff variables were analyzed, and further among the three scenarios. The results demonstrated that, in small urban blocks, spatial patterns have inherent influences on rainfall-runoff processes. Specifically, (1) Imperviousness acts as effective indicators in predicting both Qt and Qp. As rainfall intensity increases, the major affecting factor changes from DCIA to TIA for both Qt and Qp; (2) Increasing the size of drainage area dominated by each drainage inlet will benefit the block peak flow mitigation; (3) Different spatial concentrations of impervious surfaces have inherent influences on Qp, when impervious surfaces located away from the outlet can reduce the peak flow discharge. These findings may provide insights into the role of urban landscape patterns in driving rainfall-runoff responses in urbanization, which is essential for urban planning and stormwater management.

Variability in rainfall at monitoring stations and derivation of a long-term rainfall intensity record in the Grand Canyon Region, Arizona, USA

USGS Publications Warehouse

Caster, Joshua J.; Sankey, Joel B.

2016-04-11

In this study, we examine rainfall datasets of varying temporal length, resolution, and spatial distribution to characterize rainfall depth, intensity, and seasonality for monitoring stations along the Colorado River within Marble and Grand Canyons. We identify maximum separation distances between stations at which rainfall measurements might be most useful for inferring rainfall characteristics at other locations. We demonstrate a method for applying relations between daily rainfall depth and intensity, from short-term high-resolution data to lower-resolution longer-term data, to synthesize a long-term record of daily rainfall intensity from 1950–2012. We consider the implications of our spatio-temporal characterization of rainfall for understanding local landscape change in sedimentary deposits and archaeological sites, and for better characterizing past and present rainfall and its potential role in overland flow erosion within the canyons. We find that rainfall measured at stations within the river corridor is spatially correlated at separation distances of tens of kilometers, and is not correlated at the large elevation differences that separate stations along the Colorado River from stations above the canyon rim. These results provide guidance for reasonable separation distances at which rainfall measurements at stations within the Grand Canyon region might be used to infer rainfall at other nearby locations along the river. Like other rugged landscapes, spatial variability between rainfall measured at monitoring stations appears to be influenced by canyon and rim physiography and elevation, with preliminary results suggesting the highest elevation landform in the region, the Kaibab Plateau, may function as an important orographic influence. Stations at specific locations within the canyons and along the river, such as in southern (lower) Marble Canyon and eastern (upper) Grand Canyon, appear to have strong potential to receive high-intensity rainfall that can generate runoff which may erode alluvium. The characterization of past and present rainfall variability in this study will be useful for future studies that evaluate more spatially continuous datasets in order to better understand the rainfall dynamics within this, and potentially other, deep canyons.

TRMM Fire Algorithm, Product and Applications

NASA Technical Reports Server (NTRS)

Ji, Yi-Min; Stocker, Erich

2003-01-01

Land fires are frequent menaces to human lives and property. They also change the state of the vegetation and contribute to the climate forcing by releasing large amount of aerosols and greenhouse gases into the atmosphere. This paper summarizes methodologies of detecting global land fires from the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Scanner FIRS) measurements. The TRMM Science Data and Information System (TSDIS) fire products include global images of daily hot spots and monthly fire counts at 0.5 deg. x 0.5 deg. resolution, as well as text fiies that details necessary information of all fire pixels. The information includes date, orbit number, pixel number, local time, solar zenith angle, latitude, longitude, reflectance of visible/near infrared channels, brightness temperatures of infrared channels, as well as background brightness temperatures of infrared channels. These products have been archived since January 1998. The TSDIS fire products are compared with the coincidental European Commission (EC) Joint Research Center (JRC) 1 km AVHRR fire products. Analyses of the TSDIS monthly fire products during the period from 1998 to 2003 manifested seasonal cycles of biomass fires over Southeast Asia, Africa, North America and South America. The data also showed interannual variations associated with the 98/99 ENS0 cycle in Central America and the Indonesian region. In order to understand the variability of global land fires and their effects on the distribution of atmospheric aerosols, statistical methods were applied to the TSDIS fire products as well as to the Total Ozone Mapping Spectrometer (TOMS) aerosol index products for a period of five years from January 1998 to December 2002. The variability of global atmospheric aerosol is consistent with the fire variations over these regions during this period. The correlation between fire count and TOMS aerosol index is about 0.55 for fire pixels in Southeast Asia, Indonesia, and Africa. Parallel statistical analyses such as Empirical Orthogonal Function (EOF) analysis and Singular Spectrum Analysis (SSA) methods were applied to pentad TRMM fire data and TOMS aerosol data. The EOF analyses showed contrast between North and South hemispheres and also inter- continental transitions in Africa and America. EOF and SSA analyses also identified 25-60 day intra-seasonal oscillations that were superimposed on the annual cycles of both fire and aerosol data. The intra-seasonal variability of fires showed similarity of tropical rainfall oscillation modes. The TRMM fire products were also compared to the coincident TRMh4 rainfall and other rainfall products to investigate the interaction between rainfall and fire. The results indicate that the annual, interannual and intraseasonal variability of fire are dominated by global rainfall variations. However, the feedback of fire to the rainfall occurrence at regional scale for certain regions is also evident.

Factors Influencing the Sahelian Paradox at the Local Watershed Scale: Causal Inference Insights

NASA Astrophysics Data System (ADS)

Van Gordon, M.; Groenke, A.; Larsen, L.

2017-12-01

While the existence of paradoxical rainfall-runoff and rainfall-groundwater correlations are well established in the West African Sahel, the hydrologic mechanisms involved are poorly understood. In pursuit of mechanistic explanations, we perform a causal inference analysis on hydrologic variables in three watersheds in Benin and Niger. Using an ensemble of techniques, we compute the strength of relationships between observational soil moisture, runoff, precipitation, and temperature data at seasonal and event timescales. Performing analysis over a range of time lags allows dominant time scales to emerge from the relationships between variables. By determining the time scales of hydrologic connectivity over vertical and lateral space, we show differences in the importance of overland and subsurface flow over the course of the rainy season and between watersheds. While previous work on the paradoxical hydrologic behavior in the Sahel focuses on surface processes and infiltration, our results point toward the importance of subsurface flow to rainfall-runoff relationships in these watersheds. The hypotheses generated from our ensemble approach suggest that subsequent explorations of mechanistic hydrologic processes in the region include subsurface flow. Further, this work highlights how an ensemble approach to causal analysis can reveal nuanced relationships between variables even in poorly understood hydrologic systems.

Using CHIRPS Rainfall Dataset to detect rainfall trends in West Africa

NASA Astrophysics Data System (ADS)

Blakeley, S. L.; Husak, G. J.

2016-12-01

In West Africa, agriculture is often rain-fed, subjecting agricultural productivity and food availability to climate variability. Agricultural conditions will change as warming temperatures increase evaporative demand, and with a growing population dependent on the food supply, farmers will become more reliant on improved adaptation strategies. Development of such adaptation strategies will need to consider West African rainfall trends to remain relevant in a changing climate. Here, using the CHIRPS rainfall product (provided by the Climate Hazards Group at UC Santa Barbara), I examine trends in West African rainfall variability. My analysis will focus on seasonal rainfall totals, the structure of the rainy season, and the distribution of rainfall. I then use farmer-identified drought years to take an in-depth analysis of intra-seasonal rainfall irregularities. I will also examine other datasets such as potential evapotranspiration (PET) data, other remotely sensed rainfall data, rain gauge data in specific locations, and remotely sensed vegetation data. Farmer bad year data will also be used to isolate "bad" year markers in these additional datasets to provide benchmarks for identification in the future of problematic rainy seasons.

Tropical Rainfall Measuring Mission: Monitoring the Global Tropics for 3 Years and Beyond. 1.1

NASA Technical Reports Server (NTRS)

Shepherd, Marshall; Starr, David OC. (Technical Monitor)

2001-01-01

The Tropical Rainfall Measuring Mission (TRMM) was launched in November 1997 as a joint U.S.-Japanese mission to advance understanding of the global energy and water cycle by providing distributions of rainfall and latent heating over the global tropics. As a part of NASA's Earth System Enterprise, TRMM seeks to understand the mechanisms through which changes in tropical rainfall influence global circulation. Additionally, a goal is to improve the ability to model these processes in order to predict global circulations and rainfall variability at monthly and longer time scales. Such understanding has implications for assessing climate processes related to El Nino/La Nina and Global Warming. TRMM has also provided unexpected and exciting new knowledge and applications in areas related to hurricane monitoring, lightning, pollution, hydrology, and other areas. This CD-ROM includes a self-contained PowerPoint presentation that provides an overview of TRMM and significant science results; a set of data movies or animation; and listings of current TRMM-related publications in the literature.

Uncertainties on the definition of critical rainfall patterns for debris-flows triggering. Results from the Rebaixader monitoring site (Central Pyrenees)

NASA Astrophysics Data System (ADS)

Hürlimann, Marcel; Abancó, Clàudia; Moya, Jose; Berenguer, Marc

2015-04-01

Empirical rainfall thresholds are a widespread technique in debris-flow hazard assessment and can be established by statistical analysis of historic data. Typically, data from one or several rain gauges located nearby the affected catchment is used to define the triggering conditions. However, this procedure has been demonstrated not to be accurate enough due to the spatial variability of convective rainstorms. In 2009, a monitoring system was installed in the Rebaixader catchment, Central Pyrenees (Spain). Since then, 28 torrential flows (debris flows and debris floods) have occurred and rainfall data of 25 of them are available with a 5-minutes frequency of recording ("event rainfalls"). Other 142 rainfalls that did not trigger events ("no event rainfalls) were also collected and analysed. The goal of this work was threefold: a) characterize rainfall episodes in the Rebaixader catchment and compare rainfall data that triggered torrential events and others that did not; b) define and test Intensity-Duration (ID) thresholds using rainfall data measured inside the catchment; c) estimate the uncertainty derived from the use of rain gauges located outside the catchment based on the spatial correlation depicted by radar rainfall maps. The results of the statistical analysis showed that the parameters that more distinguish between the two populations of rainfalls are the rainfall intensities, the mean rainfall and the total precipitation. On the other side, the storm duration and the antecedent rainfall are not significantly different between "event rainfalls" and "no event rainfalls". Four different ID rainfall thresholds were derived based on the dataset of the first 5 years and tested using the 2014 dataset. The results of the test indicated that the threshold corresponding to the 90% percentile showed the best performance. Weather radar data was used to analyse the spatial variability of the triggering rainfalls. The analysis indicates that rain gauges outside the catchment may be considered useful or not to describe the rainfall depending on the type of rainfall. For widespread rainfalls, further rain gauges can give a reliable measurement, because the spatial correlation decreases slowly with the distance between the rain gauge and the debris-flow initiation area. Contrarily, local storm cells show higher space-time variability and, therefore, representative rainfall measurements are obtained only by the closest rain gauges. In conclusion, the definition of rainfall thresholds is a delicate task. When the rainfall records are coming from gauges that are outside the catchment under consideration, the data should be carefully analysed and crosschecked with radar data (especially for small convective cells).

Relative Contributions of Mean-State Shifts and ENSO-Driven Variability to Precipitation Changes in a Warming Climate

NASA Technical Reports Server (NTRS)

Bonfils, Celine J. W.; Santer, Benjamin D.; Phillips, Thomas J.; Marvel, Kate; Leung, L. Ruby; Doutriaux, Charles; Capotondi, Antonietta

2015-01-01

El Niño-Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with coupled general circulation models (CGCMs) to investigate how regional precipitation in the twenty-first century may be affected by changes in both ENSO-driven precipitation variability and slowly evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of twentieth-century climate change. Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in twenty-first-century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with twentieth-century observations and more stationary during the twenty-first century. Finally, the model-predicted twenty-first-century rainfall response to cENSO is decomposed into the sum of three terms: 1) the twenty-first-century change in the mean state of precipitation, 2) the historical precipitation response to the cENSO pattern, and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. By examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.

Relative Contributions of Mean-State Shifts and ENSO-Driven Variability to Precipitation Changes in a Warming Climate

NASA Technical Reports Server (NTRS)

Bonfils, Celine J. W.; Santer, Benjamin D.; Phillips, Thomas J.; Marvel, Kate; Leung, L. Ruby; Doutriaux, Charles; Capotondi, Antonietta

2015-01-01

The El Nino-Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with Coupled General Circulation Models (CGCMs) to investigate how regional precipitation in the 21st century may be affected by changes in both ENSO-driven precipitation variability and slowly-evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of 20th century climate change. Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in 21st century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with 20th century observations and more stationary during the 21st century. Finally, the model-predicted 21st century rainfall response to cENSO is decomposed into the sum of three terms: 1) the 21st century change in the mean state of precipitation; 2) the historical precipitation response to the cENSO pattern; and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. By examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.

Impact of rainfall spatial variability on Flash Flood Forecasting

NASA Astrophysics Data System (ADS)

Douinot, Audrey; Roux, Hélène; Garambois, Pierre-André; Larnier, Kevin

2014-05-01

According to the United States National Hazard Statistics database, flooding and flash flooding have caused the largest number of deaths of any weather-related phenomenon over the last 30 years (Flash Flood Guidance Improvement Team, 2003). Like the storms that cause them, flash floods are very variable and non-linear phenomena in time and space, with the result that understanding and anticipating flash flood genesis is far from straightforward. In the U.S., the Flash Flood Guidance (FFG) estimates the average number of inches of rainfall for given durations required to produce flash flooding in the indicated county. In Europe, flash flood often occurred on small catchments (approximately 100 km2) and it has been shown that the spatial variability of rainfall has a great impact on the catchment response (Le Lay and Saulnier, 2007). Therefore, in this study, based on the Flash flood Guidance method, rainfall spatial variability information is introduced in the threshold estimation. As for FFG, the threshold is the number of millimeters of rainfall required to produce a discharge higher than the discharge corresponding to the first level (yellow) warning of the French flood warning service (SCHAPI: Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations). The indexes δ1 and δ2 of Zoccatelli et al. (2010), based on the spatial moments of catchment rainfall, are used to characterize the rainfall spatial distribution. Rainfall spatial variability impacts on warning threshold and on hydrological processes are then studied. The spatially distributed hydrological model MARINE (Roux et al., 2011), dedicated to flash flood prediction is forced with synthetic rainfall patterns of different spatial distributions. This allows the determination of a warning threshold diagram: knowing the spatial distribution of the rainfall forecast and therefore the 2 indexes δ1 and δ2, the threshold value is read on the diagram. A warning threshold diagram is built for each studied catchment. The proposed methodology is applied on three Mediterranean catchments often submitted to flash floods. The new forecasting method as well as the Flash Flood Guidance method (uniform rainfall threshold) are tested on 25 flash floods events that had occurred on those catchments. Results show a significant impact of rainfall spatial variability. Indeed, it appears that the uniform rainfall threshold (FFG threshold) always overestimates the observed rainfall threshold. The difference between the FFG threshold and the proposed threshold ranges from 8% to 30%. The proposed methodology allows the calculation of a threshold more representative of the observed one. However, results strongly depend on the related event duration and on the catchment properties. For instance, the impact of the rainfall spatial variability seems to be correlated with the catchment size. According to these results, it seems to be interesting to introduce information on the catchment properties in the threshold calculation. Flash Flood Guidance Improvement Team, 2003. River Forecast Center (RFC) Development Management Team. Final Report. Office of Hydrologic Development (OHD), Silver Spring, Mary-land. Le Lay, M. and Saulnier, G.-M., 2007. Exploring the signature of climate and landscape spatial variabilities in flash flood events: Case of the 8-9 September 2002 Cévennes-Vivarais catastrophic event. Geophysical Research Letters, 34(L13401), doi:10.1029/2007GL029746. Roux, H., Labat, D., Garambois, P.-A., Maubourguet, M.-M., Chorda, J. and Dartus, D., 2011. A physically-based parsimonious hydrological model for flash floods in Mediterranean catchments. Nat. Hazards Earth Syst. Sci. J1 - NHESS, 11(9), 2567-2582. Zoccatelli, D., Borga, M., Zanon, F., Antonescu, B. and Stancalie, G., 2010. Which rainfall spatial information for flash flood response modelling? A numerical investigation based on data from the Carpathian range, Romania. Journal of Hydrology, 394(1-2), 148-161.

The Aggregate Description of Semi-Arid Vegetation with Precipitation-Generated Soil Moisture Heterogeneity

NASA Technical Reports Server (NTRS)

White, Cary B.; Houser, Paul R.; Arain, Altaf M.; Yang, Zong-Liang; Syed, Kamran; Shuttleworth, W. James

1997-01-01

Meteorological measurements in the Walnut Gulch catchment in Arizona were used to synthesize a distributed, hourly-average time series of data across a 26.9 by 12.5 km area with a grid resolution of 480 m for a continuous 18-month period which included two seasons of monsoonal rainfall. Coupled surface-atmosphere model runs established the acceptability (for modelling purposes) of assuming uniformity in all meteorological variables other than rainfall. Rainfall was interpolated onto the grid from an array of 82 recording rain gauges. These meteorological data were used as forcing variables for an equivalent array of stand-alone Biosphere-Atmosphere Transfer Scheme (BATS) models to describe the evolution of soil moisture and surface energy fluxes in response to the prevalent, heterogeneous pattern of convective precipitation. The calculated area-average behaviour was compared with that given by a single aggregate BATS simulation forced with area-average meteorological data. Heterogeneous rainfall gives rise to significant but partly compensating differences in the transpiration and the intercepted rainfall components of total evaporation during rain storms. However, the calculated area-average surface energy fluxes given by the two simulations in rain-free conditions with strong heterogeneity in soil moisture were always close to identical, a result which is independent of whether default or site-specific vegetation and soil parameters were used. Because the spatial variability in soil moisture throughout the catchment has the same order of magnitude as the amount of rain failing in a typical convective storm (commonly 10% of the vegetation's root zone saturation) in a semi-arid environment, non-linearitv in the relationship between transpiration and the soil moisture available to the vegetation has limited influence on area-average surface fluxes.

Modelling Ecuador's rainfall distribution according to geographical characteristics.

NASA Astrophysics Data System (ADS)

Tobar, Vladimiro; Wyseure, Guido

2017-04-01

It is known that rainfall is affected by terrain characteristics and some studies had focussed on its distribution over complex terrain. Ecuador's temporal and spatial rainfall distribution is affected by its location on the ITCZ, the marine currents in the Pacific, the Amazon rainforest, and the Andes mountain range. Although all these factors are important, we think that the latter one may hold a key for modelling spatial and temporal distribution of rainfall. The study considered 30 years of monthly data from 319 rainfall stations having at least 10 years of data available. The relatively low density of stations and their location in accessible sites near to main roads or rivers, leave large and important areas ungauged, making it not appropriate to rely on traditional interpolation techniques to estimate regional rainfall for water balance. The aim of this research was to come up with a useful model for seasonal rainfall distribution in Ecuador based on geographical characteristics to allow its spatial generalization. The target for modelling was the seasonal rainfall, characterized by nine percentiles for each one of the 12 months of the year that results in 108 response variables, later on reduced to four principal components comprising 94% of the total variability. Predictor variables for the model were: geographic coordinates, elevation, main wind effects from the Amazon and Coast, Valley and Hill indexes, and average and maximum elevation above the selected rainfall station to the east and to the west, for each one of 18 directions (50-135°, by 5°) adding up to 79 predictors. A multiple linear regression model by the Elastic-net algorithm with cross-validation was applied for each one of the PC as response to select the most important ones from the 79 predictor variables. The Elastic-net algorithm deals well with collinearity problems, while allowing variable selection in a blended approach between the Ridge and Lasso regression. The model fitting produced explained variances of 59%, 81%, 49% and 17% for PC1, PC2, PC3 and PC4, respectively, backing up the hypothesis of good correlation between geographical characteristics and seasonal rainfall patterns (comprised in the four principal components). With the obtained coefficients from the regression, the 108 rainfall percentiles for each station were back estimated giving very good results when compared with the original ones, with an overall 60% explained variance.

Modeling Rainfall-Runoff Dynamics in Tropical, Urban Socio-Hydrological Systems: Green Infrastructure and Variable Precipitation Interception

NASA Astrophysics Data System (ADS)

Nytch, C. J.; Meléndez-Ackerman, E. J.

2014-12-01

There is a pressing need to generate spatially-explicit models of rainfall-runoff dynamics in the urban humid tropics that can characterize flow pathways and flood magnitudes in response to erratic precipitation events. To effectively simulate stormwater runoff processes at multiple scales, complex spatio-temporal parameters such as rainfall, evapotranspiration, and antecedent soil moisture conditions must be accurately represented, in addition to uniquely urban factors including stormwater conveyance structures and connectivity between green and gray infrastructure elements. In heavily urbanized San Juan, Puerto Rico, stream flashiness and frequent flooding are major issues, yet still lacking is a hydrological analysis that models the generation and movement of fluvial and pluvial stormwater through the watershed. Our research employs a novel and multifaceted approach to dealing with this problem that integrates 1) field-based rainfall interception and infiltration methodologies to quantify the hydrologic functions of natural and built infrastructure in San Juan; 2) remote sensing analysis to produce a fine-scale typology of green and gray cover types in the city and determine patterns of spatial distribution and connectivity; 3) assessment of precipitation and streamflow variability at local and basin-wide scales using satellite and radar precipitation estimates in concert with rainfall and stream gauge point data and participatory flood mapping; 4) simulation of historical, present-day, and future stormwater runoff scenarios with a fully distributed hydrologic model that couples diverse components of urban socio-hydrological systems from formal and informal knowledge sources; and 5) bias and uncertainty analysis of parameters and model structure within a Bayesian hierarchical framework. Preliminary results from the rainfall interception study suggest that canopy structure and leaf area index of different tree species contribute to variable throughfall and stemflow responses. Additional investigations are pending. The findings from this work will help inform urban planning and design, and build adaptive capacity to reduce flood vulnerability in the context of a changing climate.

The Impact of Model and Rainfall Forcing Errors on Characterizing Soil Moisture Uncertainty in Land Surface Modeling

NASA Technical Reports Server (NTRS)

Maggioni, V.; Anagnostou, E. N.; Reichle, R. H.

2013-01-01

The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

Reconstruction of rainfall in Zafra (southwest Spain) from 1750 to 1840 from documentary sources

NASA Astrophysics Data System (ADS)

Fernández-Fernández, M. I.; Gallego, M. C.; Domínguez-Castro, F.; Vaquero, J. M.; Moreno González, J. M.; Castillo Durán, J.

2011-11-01

This work presents the first high-resolution reconstruction of rainfall in southwestern Spain during the period 1750-1840. The weather descriptions used are weekly reports describing the most relevant events that occurred in the Duchy of Feria. An index was defined to characterise the weekly rainfall. Monthly indices were obtained by summing the corresponding weekly indices, obtaining cumulative monthly rainfall indices. The reconstruction method consisted of establishing a linear correlation between the monthly rainfall index and monthly instrumental data (1960-1990). The correlation coefficients were greater than 0.80 for all months. The rainfall reconstruction showed major variability similar to natural variability. The reconstructed rainfall series in Zafra was compared with the rainfall series of Cadiz, Gibraltar and Lisbon for the period 1750-1840, with all four series found to have a similar pattern. The influence of the North Atlantic Oscillation (NAO) on the winter rainfall reconstruction was found to behave similarly to that of modern times. Other studies described are of the SLP values over the entire North Atlantic in the months with extreme values of rainfall, and unusual meteorological events (hail, frost, storms and snowfall) in the reports of the Duchy of Feria.

Tree ring reconstructed rainfall over the southern Amazon Basin

NASA Astrophysics Data System (ADS)

Lopez, Lidio; Stahle, David; Villalba, Ricardo; Torbenson, Max; Feng, Song; Cook, Edward

2017-07-01

Moisture sensitive tree ring chronologies of Centrolobium microchaete have been developed from seasonally dry forests in the southern Amazon Basin and used to reconstruct wet season rainfall totals from 1799 to 2012, adding over 150 years of rainfall estimates to the short instrumental record for the region. The reconstruction is correlated with the same atmospheric variables that influence the instrumental measurements of wet season rainfall. Anticyclonic circulation over midlatitude South America promotes equatorward surges of cold and relatively dry extratropical air that converge with warm moist air to form deep convection and heavy rainfall over this sector of the southern Amazon Basin. Interesting droughts and pluvials are reconstructed during the preinstrumental nineteenth and early twentieth centuries, but the tree ring reconstruction suggests that the strong multidecadal variability in instrumental and reconstructed wet season rainfall after 1950 may have been unmatched since 1799.

Characterizing multiscale variability of zero intermittency in spatial rainfall

NASA Technical Reports Server (NTRS)

Kumar, Praveen; Foufoula-Georgiou, Efi

1994-01-01

In this paper the authors study how zero intermittency in spatial rainfall, as described by the fraction of area covered by rainfall, changes with spatial scale of rainfall measurement or representation. A statistical measure of intermittency that describes the size distribution of 'voids' (nonrainy areas imbedded inside rainy areas) as a function of scale is also introduced. Morphological algorithms are proposed for reconstructing rainfall intermittency at fine scales given the intermittency at coarser scales. These algorithms are envisioned to be useful in hydroclimatological studies where the rainfall spatial variability at the subgrid scale needs to be reconstructed from the results of synoptic- or mesoscale meteorological numerical models. The developed methodologies are demsonstrated and tested using data from a severe springtime midlatitude squall line and a mild midlatitude winter storm monitored by a meteorological radar in Norman, Oklahoma.

SUBPIXEL-SCALE RAINFALL VARIABILITY AND THE EFFECTS ON SEPARATION OF RADAR AND GAUGE RAINFALL ERRORS

EPA Science Inventory

One of the primary sources of the discrepancies between radar-based rainfall estimates and rain gauge measurements is the point-area difference, i.e., the intrinsic difference in the spatial dimensions of the rainfall fields that the respective data sets are meant to represent. ...

Enhanced future variability during India's rainy season

NASA Astrophysics Data System (ADS)

Menon, Arathy; Levermann, Anders; Schewe, Jacob

2013-04-01

The Indian summer monsoon shapes the livelihood of a large share of the world's population. About 80% of annual precipitation over India occurs during the monsoon season from June through September. Next to its seasonal mean rainfall the day-to-day variability is crucial for the risk of flooding, national water supply and agricultural productivity. Here we show that the latest ensemble of climate model simulations, prepared for the IPCC's AR-5, consistently projects significant increases in day-to-day rainfall variability under unmitigated climate change. While all models show an increase in day-to-day variability, some models are more realistic in capturing the observed seasonal mean rainfall over India than others. While no model's monsoon rainfall exceeds the observed value by more than two standard deviations, half of the models simulate a significantly weaker monsoon than observed. The relative increase in day-to-day variability by the year 2100 ranges from 15% to 48% under the strongest scenario (RCP-8.5), in the ten models which capture seasonal mean rainfall closest to observations. The variability increase per degree of global warming is independent of the scenario in most models, and is 8% +/- 4% per K on average. This consistent projection across 20 comprehensive climate models provides confidence in the results and suggests the necessity of profound adaptation measures in the case of unmitigated climate change.

Intra-seasonal rainfall variability during the maize growing season in the northern lowlands of Lesotho

NASA Astrophysics Data System (ADS)

Tongwane, Mphethe Isaac; Moeletsi, Mokhele Edmond

2015-05-01

Intra-seasonal rainfall distribution was identified as a priority gap that needs to be addressed for southern Africa to cope with agro-meteorological risks. The region in the northwest of Lesotho is appropriate for crop cultivation due to its relatively favourable climatic conditions and soils. High rainfall variability is often blamed for poor agricultural production in this region. This study aims to determine the onset of rains, cessation of rains and rainy season duration using historical climate data. Temporal variability of these rainy season characteristics was also investigated. The earliest and latest onset dates of the rainy season are during the last week of October at Butha-Buthe and the third week of November at Mapoteng, respectively. Cessation of the season is predominantly in the first week of April making the season approximately 137-163 days long depending on the location. Average seasonal rainfall ranged from 474 mm at Mapoteng to 668 mm at Butha-Buthe. Onset and cessation of the rainfall season vary by 4-7 weeks and 1 week, respectively. Mean coefficient of variation of seasonal rainfall is 39 %, but monthly variations are higher. These variations make annual crop management and planning difficult each year. Trends show a decrease in the rainfall amounts but improvements in both the temporal distribution of annual rainfall, onset and cessation dates.

Combined effects of short-term rainfall patterns and soil texture on nitrogen cycling -- A Modeling Analysis

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

Gu, C.; Riley, W.J.

2009-11-01

Precipitation variability and magnitude are expected to change in many parts of the world over the 21st century. We examined the potential effects of intra-annual rainfall patterns on soil nitrogen (N) transport and transformation in the unsaturated soil zone using a deterministic dynamic modeling approach. The model (TOUGHREACT-N), which has been tested and applied in several experimental and observational systems, mechanistically accounts for microbial activity, soil-moisture dynamics that respond to precipitation variability, and gaseous and aqueous tracer transport in the soil. Here, we further tested and calibrated the model against data from a precipitation variability experiment in a tropical systemmore » in Costa Rica. The model was then used to simulate responses of soil moisture, microbial dynamics, nitrogen (N) aqueous and gaseous species, N leaching, and N trace-gas emissions to changes in rainfall patterns; the effect of soil texture was also examined. The temporal variability of nitrate leaching and NO, N{sub 2}, and N{sub 2}O effluxes were significantly influenced by rainfall dynamics. Soil texture combined with rainfall dynamics altered soil moisture dynamics, and consequently regulated soil N responses to precipitation changes. The clay loam soil more effectively buffered water stress during relatively long intervals between precipitation events, particularly after a large rainfall event. Subsequent soil N aqueous and gaseous losses showed either increases or decreases in response to increasing precipitation variability due to complex soil moisture dynamics. For a high rainfall scenario, high precipitation variability resulted in as high as 2.4-, 2.4-, 1.2-, and 13-fold increases in NH{sub 3}, NO, N{sub 2}O and NO{sub 3}{sup -} fluxes, respectively, in clay loam soil. In sandy loam soil, however, NO and N{sub 2}O fluxes decreased by 15% and 28%, respectively, in response to high precipitation variability. Our results demonstrate that soil N cycling responses to increasing precipitation variability depends on precipitation amount and soil texture, and that accurate prediction of future N cycling and gas effluxes requires models with relatively sophisticated representation of the relevant processes.« less

Hydroclimate temporal variability in a coastal Mediterranean watershed: the Tafna basin, North-West Algeria

NASA Astrophysics Data System (ADS)

Boulariah, Ouafik; Longobardi, Antonia; Meddi, Mohamed

2017-04-01

One of the major challenges scientists, practitioners and stakeholders are nowadays involved in, is to provide the worldwide population with reliable water supplies, protecting, at the same time, the freshwater ecosystems quality and quantity. Climate and land use changes undermine the balance between water demand and water availability, causing alteration of rivers flow regime. Knowledge of hydro-climate variables temporal and spatial variability is clearly helpful to plan drought and flood hazard mitigation strategies but also to adapt them to future environmental scenarios. The present study relates to the coastal semi-arid Tafna catchment, located in the North-West of Algeria, within the Mediterranean basin. The aim is the investigation of streamflow and rainfall indices temporal variability in six sub-basins of the large catchment Tafna, attempting to relate streamflow and rainfall changes. Rainfall and streamflow time series have been preliminary tested for data quality and homogeneity, through the coupled application of two-tailed t test, Pettitt test and Cumsum tests (significance level of 0.1, 0.05 and 0.01). Subsequently maximum annual daily rainfall and streamflow and average daily annual rainfall and streamflow time series have been derived and tested for temporal variability, through the application of the Mann Kendall and Sen's test. Overall maximum annual daily streamflow time series exhibit a negative trend which is however significant for only 30% of the station. Maximum annual daily rainfall also e exhibit a negative trend which is intend significant for the 80% of the stations. In the case of average daily annual streamflow and rainfall, the tendency for decrease in time is unclear and, in both cases, appear significant for 60% of stations.

Characteristics of aggregation of daily rainfall in a middle-latitudes region during a climate variability in annual rainfall amount

NASA Astrophysics Data System (ADS)

Lucero, Omar A.; Rozas, Daniel

Climate variability in annual rainfall occurs because the aggregation of daily rainfall changes. A topic open to debate is whether that change takes place because rainfall becomes more intense, or because it rains more often, or a combination of both. The answer to this question is of interest for water resources planning, hydrometeorological design, and agricultural management. Change in the number of rainy days can cause major disruptions in hydrological and ecological systems, with important economic and social effects. Furthermore, the characteristics of daily rainfall aggregation in ongoing climate variability provide a reference to evaluate the capability of GCM to simulate changes in the hydrologic cycle. In this research, we analyze changes in the aggregation of daily rainfall producing a climate positive trend in annual rainfall in central Argentina, in the southern middle-latitudes. This state-of-the-art agricultural region has a semiarid climate with dry and wet seasons. Weather effects in the region influence world-market prices of several crops. Results indicate that the strong positive trend in seasonal and annual rainfall amount is produced by an increase in number of rainy days. This increase takes place in the 3-month periods January-March (summer) and April-June (autumn). These are also the 3-month periods showing a positive trend in the mean of annual rainfall. The mean of the distribution of annual number of rainy day (ANRD) increased in 50% in a 36-year span (starting at 44 days/year). No statistically significant indications on time changes in the probability distribution of daily rainfall amount were found. Non-periodic fluctuations in the time series of annual rainfall were analyzed using an integral wavelet transform. Fluctuations with a time scale of about 10 and 20 years construct the trend in annual rainfall amount. These types of non-periodic fluctuations have been observed in other regions of the world. This suggests that results of this research could have further geographical validity.

Receiver Operating Characteristic Curve Analysis of Beach Water Quality Indicator Variables

PubMed Central

Morrison, Ann Michelle; Coughlin, Kelly; Shine, James P.; Coull, Brent A.; Rex, Andrea C.

2003-01-01

Receiver operating characteristic (ROC) curve analysis is a simple and effective means to compare the accuracies of indicator variables of bacterial beach water quality. The indicator variables examined in this study were previous day's Enterococcus density and antecedent rainfall at 24, 48, and 96 h. Daily Enterococcus densities and 15-min rainfall values were collected during a 5-year (1996 to 2000) study of four Boston Harbor beaches. The indicator variables were assessed for their ability to correctly classify water as suitable or unsuitable for swimming at a maximum threshold Enterococcus density of 104 CFU/100 ml. Sensitivity and specificity values were determined for each unique previous day's Enterococcus density and antecedent rainfall volume and used to construct ROC curves. The area under the ROC curve was used to compare the accuracies of the indicator variables. Twenty-four-hour antecedent rainfall classified elevated Enterococcus densities more accurately than previous day's Enterococcus density (P = 0.079). An empirically derived threshold for 48-h antecedent rainfall, corresponding to a sensitivity of 0.75, was determined from the 1996 to 2000 data and evaluated to ascertain if the threshold would produce a 0.75 sensitivity with independent water quality data collected in 2001 from the same beaches. PMID:14602593

Demographic patterns of a widespread long-lived tree are associated with rainfall and disturbances along rainfall gradients in SE Australia

PubMed Central

Cohn, Janet S; Lunt, Ian D; Bradstock, Ross A; Hua, Quan; McDonald, Simon

2013-01-01

Predicting species distributions with changing climate has often relied on climatic variables, but increasingly there is recognition that disturbance regimes should also be included in distribution models. We examined how changes in rainfall and disturbances along climatic gradients determined demographic patterns in a widespread and long-lived tree species, Callitris glaucophylla in SE Australia. We examined recruitment since 1950 in relation to annual (200–600 mm) and seasonal (summer, uniform, winter) rainfall gradients, edaphic factors (topography), and disturbance regimes (vertebrate grazing [tenure and species], fire). A switch from recruitment success to failure occurred at 405 mm mean annual rainfall, coincident with a change in grazing regime. Recruitment was lowest on farms with rabbits below 405 mm rainfall (mean = 0–0.89 cohorts) and highest on less-disturbed tenures with no rabbits above 405 mm rainfall (mean = 3.25 cohorts). Moderate levels of recruitment occurred where farms had no rabbits or less disturbed tenures had rabbits above and below 405 mm rainfall (mean = 1.71–1.77 cohorts). These results show that low annual rainfall and high levels of introduced grazing has led to aging, contracting populations, while higher annual rainfall with low levels of grazing has led to younger, expanding populations. This study demonstrates how demographic patterns vary with rainfall and spatial variations in disturbances, which are linked in complex ways to climatic gradients. Predicting changes in tree distribution with climate change requires knowledge of how rainfall and key disturbances (tenure, vertebrate grazing) will shift along climatic gradients. PMID:23919160

Critical scales to explain urban hydrological response: an application in Cranbrook, London

NASA Astrophysics Data System (ADS)

Cristiano, Elena; ten Veldhuis, Marie-Claire; Gaitan, Santiago; Ochoa Rodriguez, Susana; van de Giesen, Nick

2018-04-01

Rainfall variability in space and time, in relation to catchment characteristics and model complexity, plays an important role in explaining the sensitivity of hydrological response in urban areas. In this work we present a new approach to classify rainfall variability in space and time and we use this classification to investigate rainfall aggregation effects on urban hydrological response. Nine rainfall events, measured with a dual polarimetric X-Band radar instrument at the CAESAR site (Cabauw Experimental Site for Atmospheric Research, NL), were aggregated in time and space in order to obtain different resolution combinations. The aim of this work was to investigate the influence that rainfall and catchment scales have on hydrological response in urban areas. Three dimensionless scaling factors were introduced to investigate the interactions between rainfall and catchment scale and rainfall input resolution in relation to the performance of the model. Results showed that (1) rainfall classification based on cluster identification well represents the storm core, (2) aggregation effects are stronger for rainfall than flow, (3) model complexity does not have a strong influence compared to catchment and rainfall scales for this case study, and (4) scaling factors allow the adequate rainfall resolution to be selected to obtain a given level of accuracy in the calculation of hydrological response.

Interannual Tropical Rainfall Variability in General Circulation Model Simulations Associated with the Atmospheric Model Intercomparison Project.

NASA Astrophysics Data System (ADS)

Sperber, K. R.; Palmer, T. N.

1996-11-01

The interannual variability of rainfall over the Indian subcontinent, the African Sahel, and the Nordeste region of Brazil have been evaluated in 32 models for the period 1979-88 as part of the Atmospheric Model Intercomparison Project (AMIP). The interannual variations of Nordeste rainfall are the most readily captured, owing to the intimate link with Pacific and Atlantic sea surface temperatures. The precipitation variations over India and the Sahel are less well simulated. Additionally, an Indian monsoon wind shear index was calculated for each model. Evaluation of the interannual variability of a wind shear index over the summer monsoon region indicates that the models exhibit greater fidelity in capturing the large-scale dynamic fluctuations than the regional-scale rainfall variations. A rainfall/SST teleconnection quality control was used to objectively stratify model performance. Skill scores improved for those models that qualitatively simulated the observed rainfall/El Niño- Southern Oscillation SST correlation pattern. This subset of models also had a rainfall climatology that was in better agreement with observations, indicating a link between systematic model error and the ability to simulate interannual variations.A suite of six European Centre for Medium-Range Weather Forecasts (ECMWF) AMIP runs (differing only in their initial conditions) have also been examined. As observed, all-India rainfall was enhanced in 1988 relative to 1987 in each of these realizations. All-India rainfall variability during other years showed little or no predictability, possibly due to internal chaotic dynamics associated with intraseasonal monsoon fluctuations and/or unpredictable land surface process interactions. The interannual variations of Nordeste rainfall were best represented. The State University of New York at Albany/National Center for Atmospheric Research Genesis model was run in five initial condition realizations. In this model, the Nordeste rainfall variability was also best reproduced. However, for all regions the skill was less than that of the ECMWF model.The relationships of the all-India and Sahel rainfall/SST teleconnections with horizontal resolution, convection scheme closure, and numerics have been evaluated. Models with resolution T42 performed more poorly than lower-resolution models. The higher resolution models were predominantly spectral. At low resolution, spectral versus gridpoint numerics performed with nearly equal verisimilitude. At low resolution, moisture convergence closure was slightly more preferable than other convective closure techniques. At high resolution, the models that used moisture convergence closure performed very poorly, suggesting that moisture convergence may be problematic for models with horizontal resolution T42.

Indian summer monsoon variability forecasts in the North American multimodel ensemble

NASA Astrophysics Data System (ADS)

Singh, Bohar; Cash, Ben; Kinter, James L., III

2018-04-01

The representation of the seasonal mean and interannual variability of the Indian summer monsoon rainfall (ISMR) in nine global ocean-atmosphere coupled models that participated in the North American Multimodal Ensemble (NMME) phase 1 (NMME:1), and in nine global ocean-atmosphere coupled models participating in the NMME phase 2 (NMME:2) from 1982-2009, is evaluated over the Indo-Pacific domain with May initial conditions. The multi-model ensemble (MME) represents the Indian monsoon rainfall with modest skill and systematic biases. There is no significant improvement in the seasonal forecast skill or interannual variability of ISMR in NMME:2 as compared to NMME:1. The NMME skillfully predicts seasonal mean sea surface temperature (SST) and some of the teleconnections with seasonal mean rainfall. However, the SST-rainfall teleconnections are stronger in the NMME than observed. The NMME is not able to capture the extremes of seasonal mean rainfall and the simulated Indian Ocean-monsoon teleconnections are opposite to what are observed.

Biotic and abiotic factors affecting stemflow variability in downy oak and Scots pine stands in Mediterranean conditions

NASA Astrophysics Data System (ADS)

Cayuela, Carles; Garcia-Estringana, Pablo; Latron, Jérôme; Llorens, Pilar

2015-04-01

Although stemflow is only a small portion of rainfall, it may represent an important local input of water and nutrients at the plant stem. Previous studies have shown that stemflow has a significant influence on hydrological and biogeochemical processes. Stemflow volume is affected by many biotic factors as species, age, branch or bark characteristics. Moreover, the seasonality of the rainfall regime in Mediterranean areas, which includes both frontal rainfall events and short convective storms, can add complexity to the rainfall-stemflow relationship. This work investigates stemflow dynamics and the influence of biotic and abiotic factors on stemflow rates in two Mediterranean stands during the leafed period - from May to October. The monitored stands are a Downy oak forest (Quercus pubescens) and a Scots pine forest (Pinus sylvestris), both located in the Vallcebre research catchments (NE Spain, 42° 12'N, 1° 49'E). The monitoring design of each plot consists of 7 stemflow rings connected to tipping-buckets, bulk rainfall measured in a nearby clearing and meteorological conditions above the canopies. All data were recorded at 5 min interval. Biometric characteristics of the measured trees were also measured. The analysis of 39 rainfall events (65% smaller than 10 mm) shows that stemflow accounted for less than 1% of the bulk rainfall in both stands. Results also show that, on average, the rainfall amount required for the start of the stemflow and the time delay between the beginning of the precipitation and the start of stemflow are higher in the Downy oak forest. As suggested by stemflow funneling ratios, these differences might be linked to the canopy structure and bark water storage capacity of the trees, indicating that during low magnitude events, oaks have more difficulty to reach storage capacity. The role of other biotic and abiotic parameters on stemflow variability in both stands is still under investigation.

Describing rainfall in northern Australia using multiple climate indices

NASA Astrophysics Data System (ADS)

Wilks Rogers, Cassandra Denise; Beringer, Jason

2017-02-01

Savanna landscapes are globally extensive and highly sensitive to climate change, yet the physical processes and climate phenomena which affect them remain poorly understood and therefore poorly represented in climate models. Both human populations and natural ecosystems are highly susceptible to precipitation variation in these regions due to the effects on water and food availability and atmosphere-biosphere energy fluxes. Here we quantify the relationship between climate phenomena and historical rainfall variability in Australian savannas and, in particular, how these relationships changed across a strong rainfall gradient, namely the North Australian Tropical Transect (NATT). Climate phenomena were described by 16 relevant climate indices and correlated against precipitation from 1900 to 2010 to determine the relative importance of each climate index on seasonal, annual and decadal timescales. Precipitation trends, climate index trends and wet season characteristics have also been investigated using linear statistical methods. In general, climate index-rainfall correlations were stronger in the north of the NATT where annual rainfall variability was lower and a high proportion of rainfall fell during the wet season. This is consistent with a decreased influence of the Indian-Australian monsoon from the north to the south. Seasonal variation was most strongly correlated with the Australian Monsoon Index, whereas yearly variability was related to a greater number of climate indices, predominately the Tasman Sea and Indonesian sea surface temperature indices (both of which experienced a linear increase over the duration of the study) and the El Niño-Southern Oscillation indices. These findings highlight the importance of understanding the climatic processes driving variability and, subsequently, the importance of understanding the relationships between rainfall and climatic phenomena in the Northern Territory in order to project future rainfall patterns in the region.

Interannual Rainfall Variability in the Tropical Atlantic Region

NASA Technical Reports Server (NTRS)

Gu, Guojun

2005-01-01

Rainfall variability on seasonal and interannual-to-interdecadal time scales in the tropical Atlantic is quantified using a 25-year (1979-2003) monthly rainfall dataset from the Global Precipitation Climatology Project (GPCP). The ITCZ measured by monthly rainfall between 15-37.5 deg W attains its peak as moving to the northernmost latitude (4-10 deg N) during July-September in which the most total rainfall is observed in the tropical Atlantic basin (17.5 deg S-22.5 deg N, 15 deg-37.5 deg W); the ITCZ becomes weakest during January-February with the least total rainfall as it moves to the south. In contrast, rainfall variability on interannual to interdecadal time scales shows a quite different seasonal preference. The most intense interannual variability occurs during March-May when the ITCZ tends to be near the equator and becomes weaker. Significant, negative correlations between the ITCZ strength and latitude anomalies are observed during boreal spring and early summer. The ITCZ strength and total rainfall amount in the tropical Atlantic basin are significantly modulated by the Pacific El Nino and the Atlantic equatorial mode (or Atlantic Nino) particularly during boreal spring and summer; whereas the impact of the Atlantic interhemispheric mode is considerably weaker. Regarding the anomalous latitudes of the ITCZ, the influence can come from both local, i.e., the Atlantic interhemispheric and equatorial modes, and remote forcings, i. e., El Nino; however, a direct impact of El Nino on the latitudes of the ITCZ can only be found during April-July, not in winter and early spring in which the warmest SST anomalies are usually observed in the equatorial Pacific.

Characterizing response of total suspended solids and total phosphorus loading to weather and watershed characteristics for rainfall and snowmelt events in agricultural watersheds

USGS Publications Warehouse

Danz, Mari E.; Corsi, Steven; Brooks, Wesley R.; Bannerman, Roger T.

2013-01-01

Understanding the response of total suspended solids (TSS) and total phosphorus (TP) to influential weather and watershed variables is critical in the development of sediment and nutrient reduction plans. In this study, rainfall and snowmelt event loadings of TSS and TP were analyzed for eight agricultural watersheds in Wisconsin, with areas ranging from 14 to 110 km2 and having four to twelve years of data available. The data showed that a small number of rainfall and snowmelt runoff events accounted for the majority of total event loading. The largest 10% of the loading events for each watershed accounted for 73–97% of the total TSS load and 64–88% of the total TP load. More than half of the total annual TSS load was transported during a single event for each watershed at least one of the monitored years. Rainfall and snowmelt events were both influential contributors of TSS and TP loading. TSS loading contributions were greater from rainfall events at five watersheds, from snowmelt events at two watersheds, and nearly equal at one watershed. The TP loading contributions were greater from rainfall events at three watersheds, from snowmelt events at two watersheds and nearly equal at three watersheds. Stepwise multivariate regression models for TSS and TP event loadings were developed separately for rainfall and snowmelt runoff events for each individual watershed and for all watersheds combined by using a suite of precipitation, melt, temperature, seasonality, and watershed characteristics as predictors. All individual models and the combined model for rainfall events resulted in two common predictors as most influential for TSS and TP. These included rainfall depth and the antecedent baseflow. Using these two predictors alone resulted in an R2 greater than 0.7 in all but three individual models and 0.61 or greater for all individual models. The combined model yielded an R2 of 0.66 for TSS and 0.59 for TP. Neither the individual nor the combined models were substantially improved by using additional predictors. Snowmelt event models were statistically significant for individual and combined watershed models, but the model fits were not all as good as those for rainfall events (R2 between 0.19 and 0.87). Predictor selection varied from watershed to watershed, and the common variables that were selected were not always selected in the same order. Influential variables were commonly direct measures of moisture in the watershed such as snowmelt, rainfall + snowmelt, and antecedent baseflow, or measures of potential snowmelt volume in the watershed such as air temperature.

Variable rainfall intensity and tillage effects on runoff, sediment, and carbon losses from a loamy sand under simulated rainfall.

PubMed

Truman, C C; Strickland, T C; Potter, T L; Franklin, D H; Bosch, D D; Bednarz, C W

2007-01-01

The low-carbon, intensively cropped Coastal Plain soils of Georgia are susceptible to runoff, soil loss, and drought. Reduced tillage systems offer the best management tool for sustained row crop production. Understanding runoff, sediment, and chemical losses from conventional and reduced tillage systems is expected to improve if the effect of a variable rainfall intensity storm was quantified. Our objective was to quantify and compare effects of a constant (Ic) intensity pattern and a more realistic, observed, variable (Iv) rainfall intensity pattern on runoff (R), sediment (E), and carbon losses (C) from a Tifton loamy sand cropped to conventional-till (CT) and strip-till (ST) cotton (Gossypium hirsutum L.). Four treatments were evaluated: CT-Ic, CT-Iv, ST-Ic, and ST-Iv, each replicated three times. Field plots (n=12), each 2 by 3 m, were established on each treatment. Each 6-m2 field plot received simulated rainfall at a constant (57 mm h(-1)) or variable rainfall intensity pattern for 70 min (12-run ave.=1402 mL; CV=3%). The Iv pattern represented the most frequent occurring intensity pattern for spring storms in the region. Compared with CT, ST decreased R by 2.5-fold, E by 3.5-fold, and C by 7-fold. Maximum runoff values for Iv events were 1.6-fold higher than those for Ic events and occurred 38 min earlier. Values for Etot and Ctot for Iv events were 19-36% and 1.5-fold higher than corresponding values for Ic events. Values for Emax and Cmax for Iv events were 3-fold and 4-fold higher than corresponding values for Ic events. Carbon enrichment ratios (CER) were or=1.0 for CT plots (except for first 20 min). Maximum CER for CT-Ic, CT-Iv, ST-Ic, and ST-Iv were 2.0, 2.2, 1.0, and 1.2, respectively. Transport of sediment, carbon, and agrichemicals would be better understood if variable rainfall intensity patterns derived from natural rainfall were used in rainfall simulations to evaluate their fate and transport from CT and ST systems.

Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA

DOE PAGES

Machado, Luiz A. T.; Calheiros, Alan J. P.; Biscaro, Thiago; ...

2018-05-07

This study provides an overview of precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin near Manaus during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. Here, this study takes advantage of the numerous measurement platforms and instrument systems operating during both campaigns to sample cloud structure and environmental conditions during 2014 and 2015; the rainfall variability among seasons, aerosol loading, land surface type, and topography has been carefully characterized using these data. Differences between the wet and dry seasons were examined from a variety of perspectives. The rainfall rates distribution, total amount of rainfall, and raindrop size distribution (the mass-weightedmore » mean diameter) were quantified over both seasons. The dry season generally exhibited higher rainfall rates than the wet season and included more intense rainfall periods. However, the cumulative rainfall during the wet season was 4 times greater than that during the total dry season rainfall, as shown in the total rainfall accumulation data. The typical size and life cycle of Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as were differences in these systems between the seasons. Moreover, monthly mean thermodynamic and dynamic variables were analysed using radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to atmospheric aerosol loading was discussed with regard to mass-weighted mean diameter and rain rate. This topic was evaluated only during the wet season due to the insignificant statistics of rainfall events for different aerosol loading ranges and the low frequency of precipitation events during the dry season. The impacts of aerosols on cloud droplet diameter varied based on droplet size. For the wet season, we observed no dependence between land surface type and rain rate. However, during the dry season, urban areas exhibited the largest rainfall rate tail distribution, and deforested regions exhibited the lowest mean rainfall rate. Airplane measurements were taken to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. Vertical motion was not correlated with cloud droplet sizes, but cloud droplet concentration correlated linearly with vertical motion. Clouds over forested areas contained larger droplets than clouds over pastures at all altitudes. Finally, the connections between topography and rain rate were evaluated, with higher rainfall rates identified at higher elevations during the dry season.« less

Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA

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

Machado, Luiz A. T.; Calheiros, Alan J. P.; Biscaro, Thiago

This study provides an overview of precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin near Manaus during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. Here, this study takes advantage of the numerous measurement platforms and instrument systems operating during both campaigns to sample cloud structure and environmental conditions during 2014 and 2015; the rainfall variability among seasons, aerosol loading, land surface type, and topography has been carefully characterized using these data. Differences between the wet and dry seasons were examined from a variety of perspectives. The rainfall rates distribution, total amount of rainfall, and raindrop size distribution (the mass-weightedmore » mean diameter) were quantified over both seasons. The dry season generally exhibited higher rainfall rates than the wet season and included more intense rainfall periods. However, the cumulative rainfall during the wet season was 4 times greater than that during the total dry season rainfall, as shown in the total rainfall accumulation data. The typical size and life cycle of Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as were differences in these systems between the seasons. Moreover, monthly mean thermodynamic and dynamic variables were analysed using radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to atmospheric aerosol loading was discussed with regard to mass-weighted mean diameter and rain rate. This topic was evaluated only during the wet season due to the insignificant statistics of rainfall events for different aerosol loading ranges and the low frequency of precipitation events during the dry season. The impacts of aerosols on cloud droplet diameter varied based on droplet size. For the wet season, we observed no dependence between land surface type and rain rate. However, during the dry season, urban areas exhibited the largest rainfall rate tail distribution, and deforested regions exhibited the lowest mean rainfall rate. Airplane measurements were taken to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. Vertical motion was not correlated with cloud droplet sizes, but cloud droplet concentration correlated linearly with vertical motion. Clouds over forested areas contained larger droplets than clouds over pastures at all altitudes. Finally, the connections between topography and rain rate were evaluated, with higher rainfall rates identified at higher elevations during the dry season.« less

Extreme Events in China under Climate Change: Uncertainty and related impacts (CSSP-FOREX)

NASA Astrophysics Data System (ADS)

Leckebusch, Gregor C.; Befort, Daniel J.; Hodges, Kevin I.

2016-04-01

Suitable adaptation strategies or the timely initiation of related mitigation efforts in East Asia will strongly depend on robust and comprehensive information about future near-term as well as long-term potential changes in the climate system. Therefore, understanding the driving mechanisms associated with the East Asian climate is of major importance. The FOREX project (Fostering Regional Decision Making by the Assessment of Uncertainties of Future Regional Extremes and their Linkage to Global Climate System Variability for China and East Asia) focuses on the investigation of extreme wind and rainfall related events over Eastern Asia and their possible future changes. Here, analyses focus on the link between local extreme events and their driving weather systems. This includes the coupling between local rainfall extremes and tropical cyclones, the Meiyu frontal system, extra-tropical teleconnections and monsoonal activity. Furthermore, the relation between these driving weather systems and large-scale variability modes, e.g. NAO, PDO, ENSO is analysed. Thus, beside analysing future changes of local extreme events, the temporal variability of their driving weather systems and related large-scale variability modes will be assessed in current CMIP5 global model simulations to obtain more robust results. Beyond an overview of FOREX itself, first results regarding the link between local extremes and their steering weather systems based on observational and reanalysis data are shown. Special focus is laid on the contribution of monsoonal activity, tropical cyclones and the Meiyu frontal system on the inter-annual variability of the East Asian summer rainfall.

Relative contributions of mean-state shifts and ENSO-driven variability to precipitation changes in a warming climate

DOE PAGES

Bonfils, Celine J. W.; Santer, Benjamin D.; Phillips, Thomas J.; ...

2015-12-18

The El Niño–Southern Oscillation (ENSO) is an important driver of regional hydroclimate variability through far-reaching teleconnections. This study uses simulations performed with coupled general circulation models (CGCMs) to investigate how regional precipitation in the twenty-first century may be affected by changes in both ENSO-driven precipitation variability and slowly evolving mean rainfall. First, a dominant, time-invariant pattern of canonical ENSO variability (cENSO) is identified in observed SST data. Next, the fidelity with which 33 state-of-the-art CGCMs represent the spatial structure and temporal variability of this pattern (as well as its associated precipitation responses) is evaluated in simulations of twentieth-century climate change.more » Possible changes in both the temporal variability of this pattern and its associated precipitation teleconnections are investigated in twenty-first-century climate projections. Models with better representation of the observed structure of the cENSO pattern produce winter rainfall teleconnection patterns that are in better accord with twentieth-century observations and more stationary during the twenty-first century. Finally, the model-predicted twenty-first-century rainfall response to cENSO is decomposed into the sum of three terms: 1) the twenty-first-century change in the mean state of precipitation, 2) the historical precipitation response to the cENSO pattern, and 3) a future enhancement in the rainfall response to cENSO, which amplifies rainfall extremes. Lastly, by examining the three terms jointly, this conceptual framework allows the identification of regions likely to experience future rainfall anomalies that are without precedent in the current climate.« less

Precipitation interannual variability and predictions for West Africa from the National Multi-Model Ensemble dataset

NASA Astrophysics Data System (ADS)

Thiaw, W. M.

2013-12-01

The ability of coupled climate models from the national multi-model ensemble (NMME) dataset to reproduce the basic state and interannual variability of precipitation in West Africa and associated teleconnections is examined. The analysis is for the period 1982-2010 for most of the models, which corresponds to the NMME hindcast period, except for the CFS version 1 (CFSv1) which covers the period 1981-2009. The satellite based CPC African Rainfall Climatology (ARC2) data is used as proxy for observed rainfall and to validate the models. We examine rainfall patterns throughout the year. Models are able to reproduce the north-south migration of precipitation from winter and spring when the area of maximum precipitation is located in Central Africa and the Gulf of Guinea region to the summer when it is in northern Sub-Saharan Africa, and the later return to the south. Models also appropriately place precipitation over the Gulf of Guinea region during the equinoxes in MAM and OND. However, there are considerable differences in the representation of the intensities and locations of the rainfall. Three of the models including the two versions of the NCEP CFS and the NASA models also have a systematic dry (wet) bias over the Sahel (Gulf of Guinea region) during the summer rainfall season, while the others show alternating wet and dry biases across West Africa. All models have spatially averaged values of standard deviation lower than that observed. Models are also able to reproduce to some extent the main features of the precipitation variability maximum, but again with deficiencies in the amplitudes and locations. The areas of highest variability are generally depicted, but there are significant differences among the models, and even between the two versions of the CFS. Teleconnections in the models are investigated by first conducting an EOF in the precipitation anomaly fields and then perform a regression of the first or second EOF time series onto the global SST. Focusing on JAS rainfall season, only the CFSv1 and the NASA models were able to depict the dipole pattern between the Sahel and the Gulf of Guinea rainfall. However, none of the models was able to reproduce the observed upward trend of Sahel rainfall in the last decade. The relationship to SST is also examined. The observed influence of tropical north Atlantic SST on the Sahel rainfall is only partially represented even in the CFSv1, while the NASA model inconsistently emphasizes the role of the tropical South Atlantic. A majority of the models show a partial ENSO teleconnection combined with the tropical south Atlantic mode. However, observations indicate that the influence of ENSO on northern Sub-Saharan summer rainfall has been very weak over the past 30 years. Results for MAM, and OND are also presented. The influence of model errors on the predictions of African rainfall is presented. Canonical correlation analysis (CCA) is employed to correct the model simulations. A new ensemble based on models corrected forecasts is then formed and the results are presented.

Trends and homogeneity of monthly, seasonal, and annual rainfall over arid region of Rajasthan, India

NASA Astrophysics Data System (ADS)

Meena, Hari Mohan; Machiwal, Deepesh; Santra, Priyabrata; Moharana, Pratap Chandra; Singh, D. V.

2018-05-01

Knowledge of rainfall variability is important for regional-scale planning and management of water resources in agriculture. This study explores spatio-temporal variations, trends, and homogeneity in monthly, seasonal, and annual rainfall series of 62 stations located in arid region of Rajasthan, India using 55 year (1957-2011) data. Box-whisker plots indicate presence of outliers and extremes in annual rainfall, which made the distribution of annual rainfall right-skewed. Mean and coefficient of variation (CV) of rainfall reveals a high inter-annual variability (CV > 200%) in the western portion where the mean annual rainfall is very low. A general gradient of the mean monthly, seasonal, and annual rainfall is visible from northwest to southeast direction, which is orthogonal to the gradient of CV. The Sen's innovative trend test is found over-sensitive in evaluating statistical significance of the rainfall trends, while the Mann-Kendall test identifies significantly increasing rainfall trends in June and September. Rainfall in July shows prominently decreasing trends although none of them are found statistically significant. Monsoon and annual rainfall show significantly increasing trends at only four stations. The magnitude of trends indicates that the rainfall is increasing at a mean rate of 1.11, 2.85, and 2.89 mm year-1 in August, monsoon season, and annual series. The rainfall is found homogeneous over most of the area except for few stations situated in the eastern and northwest portions where significantly increasing trends are observed. Findings of this study indicate that there are few increasing trends in rainfall of this Indian arid region.

Rainfall Morphology in Semi-Tropical Convergence Zones

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Ferrier, Brad S.; Ray, Peter S.

2000-01-01

Central Florida is the ideal test laboratory for studying convergence zone-induced convection. The region regularly experiences sea breeze fronts and rainfall-induced outflow boundaries. The focus of this study is the common yet poorly-studied convergence zone established by the interaction of the sea breeze front and an outflow boundary. Previous studies have investigated mechanisms primarily affecting storm initiation by such convergence zones. Few have focused on rainfall morphology yet these storms contribute a significant amount precipitation to the annual rainfall budget. Low-level convergence and mid-tropospheric moisture have both been shown to correlate with rainfall amounts in Florida. Using 2D and 3D numerical simulations, the roles of low-level convergence and mid-tropospheric moisture in rainfall evolution are examined. The results indicate that time-averaged, vertical moisture flux (VMF) at the sea breeze front/outflow convergence zone is directly and linearly proportional to initial condensation rates. This proportionality establishes a similar relationship between VMF and initial rainfall. Vertical moisture flux, which encompasses depth and magnitude of convergence, is better correlated to initial rainfall production than surface moisture convergence. This extends early observational studies which linked rainfall in Florida to surface moisture convergence. The amount and distribution of mid-tropospheric moisture determines how rainfall associated with secondary cells develop. Rainfall amount and efficiency varied significantly over an observable range of relative humidities in the 850- 500 mb layer even though rainfall evolution was similar during the initial or "first-cell" period. Rainfall variability was attributed to drier mid-tropospheric environments inhibiting secondary cell development through entrainment effects. Observationally, 850-500 mb moisture structure exhibits wider variability than lower level moisture, which is virtually always present in Florida. A likely consequence of the variability in 850-500 moisture is a stronger statistical correlation to rainfall, which observational studies have noted. The study indicates that vertical moisture flux forcing at convergence zones is critical in determining rainfall in the initial stage of development but plays a decreasing role in rainfall evolution as the system matures. The mid-tropospheric moisture (e.g. environment) plays an increasing role in rainfall evolution as the system matures. This suggests the need to improve measurements of magnitude/depth of convergence and mid-tropospheric moisture distribution. It also highlights the need for better parameterization of entrainment and vertical moisture distribution in larger-scale models.

Impacts of rainfall spatial variability on hydrogeological response

NASA Astrophysics Data System (ADS)

Sapriza-Azuri, Gonzalo; Jódar, Jorge; Navarro, Vicente; Slooten, Luit Jan; Carrera, Jesús; Gupta, Hoshin V.

2015-02-01

There is currently no general consensus on how the spatial variability of rainfall impacts and propagates through complex hydrogeological systems. Most studies to date have focused on the effects of rainfall spatial variability (RSV) on river discharge, while paying little attention to other important aspects of system response. Here, we study the impacts of RSV on several responses of a hydrological model of an overexploited system. To this end, we drive a spatially distributed hydrogeological model for the semiarid Upper Guadiana basin in central Spain with stochastic daily rainfall fields defined at three different spatial resolutions (fine → 2.5 km × 2.5 km, medium → 50 km × 50 km, large → lumped). This enables us to investigate how (i) RSV at different spatial resolutions, and (ii) rainfall uncertainty, are propagated through the hydrogeological model of the system. Our results demonstrate that RSV has a significant impact on the modeled response of the system, by specifically affecting groundwater recharge and runoff generation, and thereby propagating through to various other related hydrological responses (river discharge, river-aquifer exchange, groundwater levels). These results call into question the validity of management decisions made using hydrological models calibrated or forced with spatially lumped rainfall.

Internal and International Mobility as Adaptation to Climatic Variability in Contemporary Mexico: Evidence from the Integration of Census and Satellite Data.

PubMed

Leyk, Stefan; Runfola, Dan; Nawrotzki, Raphael J; Hunter, Lori M; Riosmena, Fernando

2017-08-01

Migration provides a strategy for rural Mexican households to cope with, or adapt to, weather events and climatic variability. Yet prior studies on "environmental migration" in this context have not examined the differences between choices of internal (domestic) or international movement. In addition, much of the prior work relied on very coarse spatial scales to operationalize the environmental variables such as rainfall patterns. To overcome these limitations, we use fine-grain rainfall estimates derived from NASA's Tropical Rainfall Measuring Mission (TRMM) satellite. The rainfall estimates are combined with Population and Agricultural Census information to examine associations between environmental changes and municipal rates of internal and international migration 2005-2010. Our findings suggest that municipal-level rainfall deficits relative to historical levels are an important predictor of both international and internal migration, especially in areas dependent on seasonal rainfall for crop productivity. Although our findings do not contradict results of prior studies using coarse spatial resolution, they offer clearer results and a more spatially nuanced examination of migration as related to social and environmental vulnerability and thus higher degrees of confidence.

Does internal variability change in response to global warming? A large ensemble modelling study of tropical rainfall

NASA Astrophysics Data System (ADS)

Milinski, S.; Bader, J.; Jungclaus, J. H.; Marotzke, J.

2017-12-01

There is some consensus on mean state changes of rainfall under global warming; changes of the internal variability, on the other hand, are more difficult to analyse and have not been discussed as much despite their importance for understanding changes in extreme events, such as droughts or floodings. We analyse changes in the rainfall variability in the tropical Atlantic region. We use a 100-member ensemble of historical (1850-2005) model simulations with the Max Planck Institute for Meteorology Earth System Model (MPI-ESM1) to identify changes of internal rainfall variability. To investigate the effects of global warming on the internal variability, we employ an additional ensemble of model simulations with stronger external forcing (1% CO2-increase per year, same integration length as the historical simulations) with 68 ensemble members. The focus of our study is on the oceanic Atlantic ITCZ. We find that the internal variability of rainfall over the tropical Atlantic does change due to global warming and that these changes in variability are larger than changes in the mean state in some regions. From splitting the total variance into patterns of variability, we see that the variability on the southern flank of the ITCZ becomes more dominant, i.e. explaining a larger fraction of the total variance in a warmer climate. In agreement with previous studies, we find that changes in the mean state show an increase and narrowing of the ITCZ. The large ensembles allow us to do a statistically robust differentiation between the changes in variability that can be explained by internal variability and those that can be attributed to the external forcing. Furthermore, we argue that internal variability in a transient climate is only well defined in the ensemble domain and not in the temporal domain, which requires the use of a large ensemble.

Tropical Indian Ocean Variability Driving Southeast Australian Droughts

NASA Astrophysics Data System (ADS)

Ummenhofer, C. C.; England, M. H.; McIntosh, P. C.; Meyers, G. A.; Pook, M. J.; Risbey, J. S.; Sen Gupta, A.; Taschetto, A. S.

2009-04-01

Variability in the tropical Indian Ocean has widespread effects on rainfall in surrounding countries, including East Africa, India and Indonesia. The leading mode of tropical Indian Ocean variability, the Indian Ocean Dipole (IOD), is a coupled ocean-atmosphere mode characterized by sea surface temperature (SST) anomalies of opposite sign in the east and west of the basin with an associated large-scale atmospheric re-organisation. Earlier work has often focused on the positive phase of the IOD. However, we show here that the negative IOD phase is an important driver of regional rainfall variability and multi-year droughts. For southeastern Australia, we show that it is actually a lack of the negative IOD phase, rather than the positive IOD phase or Pacific variability, that provides the most robust explanation for recent drought conditions. Since 1995, a large region of Australia has been gripped by the most severe drought in living memory, the so-called "Big Dry". The ramifications for affected regions are dire, with acute water shortages for rural and metropolitan areas, record agricultural losses, the drying-out of two of Australia's major river systems and far-reaching ecosystem damage. Yet the drought's origins have remained elusive. For Southeast Australia, we show that the "Big Dry" and other iconic 20th Century droughts, including the Federation Drought (1895-1902) and World War II drought (1937-1945), are driven by tropical Indian Ocean variability, not Pacific Ocean conditions as traditionally assumed. Specifically, a conspicuous absence of characteristic Indian Ocean temperature conditions that are conducive to enhanced tropical moisture transport has deprived southeastern Australia of its normal rainfall quota. In the case of the "Big Dry", its unprecedented intensity is also related to recent above-average temperatures. Implications of recent non-uniform warming trends in the Indian Ocean and how that might affect ocean characteristics and climate in Indian Ocean rim countries are also discussed.

Optimization of rainfall networks using information entropy and temporal variability analysis

NASA Astrophysics Data System (ADS)

Wang, Wenqi; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Liu, Jiufu; Zou, Ying; He, Ruimin

2018-04-01

Rainfall networks are the most direct sources of precipitation data and their optimization and evaluation are essential and important. Information entropy can not only represent the uncertainty of rainfall distribution but can also reflect the correlation and information transmission between rainfall stations. Using entropy this study performs optimization of rainfall networks that are of similar size located in two big cities in China, Shanghai (in Yangtze River basin) and Xi'an (in Yellow River basin), with respect to temporal variability analysis. Through an easy-to-implement greedy ranking algorithm based on the criterion called, Maximum Information Minimum Redundancy (MIMR), stations of the networks in the two areas (each area is further divided into two subareas) are ranked during sliding inter-annual series and under different meteorological conditions. It is found that observation series with different starting days affect the ranking, alluding to the temporal variability during network evaluation. We propose a dynamic network evaluation framework for considering temporal variability, which ranks stations under different starting days with a fixed time window (1-year, 2-year, and 5-year). Therefore, we can identify rainfall stations which are temporarily of importance or redundancy and provide some useful suggestions for decision makers. The proposed framework can serve as a supplement for the primary MIMR optimization approach. In addition, during different periods (wet season or dry season) the optimal network from MIMR exhibits differences in entropy values and the optimal network from wet season tended to produce higher entropy values. Differences in spatial distribution of the optimal networks suggest that optimizing the rainfall network for changing meteorological conditions may be more recommended.

Accounting for interannual variability: A comparison of options for water resources climate change impact assessments

NASA Astrophysics Data System (ADS)

Johnson, Fiona; Sharma, Ashish

2011-04-01

Empirical scaling approaches for constructing rainfall scenarios from general circulation model (GCM) simulations are commonly used in water resources climate change impact assessments. However, these approaches have a number of limitations, not the least of which is that they cannot account for changes in variability or persistence at annual and longer time scales. Bias correction of GCM rainfall projections offers an attractive alternative to scaling methods as it has similar advantages to scaling in that it is computationally simple, can consider multiple GCM outputs, and can be easily applied to different regions or climatic regimes. In addition, it also allows for interannual variability to evolve according to the GCM simulations, which provides additional scenarios for risk assessments. This paper compares two scaling and four bias correction approaches for estimating changes in future rainfall over Australia and for a case study for water supply from the Warragamba catchment, located near Sydney, Australia. A validation of the various rainfall estimation procedures is conducted on the basis of the latter half of the observational rainfall record. It was found that the method leading to the lowest prediction errors varies depending on the rainfall statistic of interest. The flexibility of bias correction approaches in matching rainfall parameters at different frequencies is demonstrated. The results also indicate that for Australia, the scaling approaches lead to smaller estimates of uncertainty associated with changes to interannual variability for the period 2070-2099 compared to the bias correction approaches. These changes are also highlighted using the case study for the Warragamba Dam catchment.

Distributional changes in rainfall and river flow in Sarawak, Malaysia

NASA Astrophysics Data System (ADS)

Sa'adi, Zulfaqar; Shahid, Shamsuddin; Ismail, Tarmizi; Chung, Eun-Sung; Wang, Xiao-Jun

2017-11-01

Climate change may not change the rainfall mean, but the variability and extremes. Therefore, it is required to explore the possible distributional changes of rainfall characteristics over time. The objective of present study is to assess the distributional changes in annual and northeast monsoon rainfall (November-January) and river flow in Sarawak where small changes in rainfall or river flow variability/distribution may have severe implications on ecology and agriculture. A quantile regression-based approach was used to assess the changes of scale and location of empirical probability density function over the period 1980-2014 at 31 observational stations. The results indicate that diverse variation patterns exist at all stations for annual rainfall but mainly increasing quantile trend at the lowers, and higher quantiles for the month of January and December. The significant increase in annual rainfall is found mostly in the north and central-coastal region and monsoon month rainfalls in the interior and north of Sarawak. Trends in river flow data show that changes in rainfall distribution have affected higher quantiles of river flow in monsoon months at some of the basins and therefore more flooding. The study reveals that quantile trend can provide more information of rainfall change which may be useful for climate change mitigation and adaptation planning.

Global meteorological influences on the record UK rainfall of winter 2013-14

NASA Astrophysics Data System (ADS)

Knight, Jeff R.; Maidens, Anna; Watson, Peter A. G.; Andrews, Martin; Belcher, Stephen; Brunet, Gilbert; Fereday, David; Folland, Chris K.; Scaife, Adam A.; Slingo, Julia

2017-07-01

The UK experienced record average rainfall in winter 2013-14, leading to widespread and prolonged flooding. The immediate cause of this exceptional rainfall was a very strong and persistent cyclonic atmospheric circulation over the North East Atlantic Ocean. This was related to a very strong North Atlantic jet stream which resulted in numerous damaging wind storms. These exceptional meteorological conditions have led to renewed questions about whether anthropogenic climate change is noticeably influencing extreme weather. The regional weather pattern responsible for the extreme UK winter coincided with highly anomalous conditions across the globe. We assess the contributions from various possible remote forcing regions using sets of ocean-atmosphere model relaxation experiments, where winds and temperatures are constrained to be similar to those observed in winter 2013-14 within specified atmospheric domains. We find that influences from the tropics were likely to have played a significant role in the development of the unusual extra-tropical circulation, including a role for the tropical Atlantic sector. Additionally, a stronger and more stable stratospheric polar vortex, likely associated with a strong westerly phase of the stratospheric Quasi-Biennial Oscillation (QBO), appears to have contributed to the extreme conditions. While intrinsic climatic variability clearly has the largest effect on the generation of extremes, results from an analysis which segregates circulation-related and residual rainfall variability suggest that emerging climate change signals made a secondary contribution to extreme rainfall in winter 2013-14.

Combined effects of constant versus variable intensity simulated rainfall and reduced tillage management on cotton preemergence herbicide runoff.

PubMed

Potter, Thomas L; Truman, Clint C; Strickland, Timothy C; Bosch, David D; Webster, Theodore M; Franklin, Dorcas H; Bednarz, Craig W

2006-01-01

Pesticide runoff research relies heavily on rainfall simulation experiments. Most are conducted at a constant intensity, i.e., at a fixed rainfall rate; however, large differences in natural rainfall intensity is common. To assess implications we quantified runoff of two herbicides, fluometuron and pendimethalin, and applied preemergence after planting cotton on Tifton loamy sand. Rainfall at constant and variable intensity patterns representative of late spring thunderstorms in the Atlantic Coastal Plain region of Georgia (USA) were simulated on 6-m2 plots under strip- (ST) and conventional-tillage (CT) management. The variable pattern produced significantly higher runoff rates of both compounds from CT but not ST plots. However, on an event-basis, runoff totals (% applied) were not significantly different, with one exception: fluometuron runoff from CT plots. There was about 25% more fluometuron runoff with the variable versus the constant intensity pattern (P = 0.10). Study results suggest that conduct of simulations using variable intensity storm patterns may provide more representative rainfall simulation-based estimates of pesticide runoff and that the greatest impacts will be observed with CT. The study also found significantly more fluometuron in runoff from ST than CT plots. Further work is needed to determine whether this behavior may be generalized to other active ingredients with similar properties [low K(oc) (organic carbon partition coefficient) approximately 100 mL g(-1); high water solubility approximately 100 mg L(-1)]. If so, it should be considered when making tillage-specific herbicide recommendations to reduce runoff potential.

Exogenous factors matter when interpreting the results of an impact evaluation: a case study of rainfall and child health programme intervention in Rwanda.

PubMed

Mukabutera, Assumpta; Thomson, Dana R; Hedt-Gauthier, Bethany L; Atwood, Sidney; Basinga, Paulin; Nyirazinyoye, Laetitia; Savage, Kevin P; Habimana, Marcellin; Murray, Megan

2017-12-01

Public health interventions are often implemented at large scale, and their evaluation seems to be difficult because they are usually multiple and their pathways to effect are complex and subject to modification by contextual factors. We assessed whether controlling for rainfall-related variables altered estimates of the efficacy of a health programme in rural Rwanda and have a quantifiable effect on an intervention evaluation outcomes. We conducted a retrospective quasi-experimental study using previously collected cross-sectional data from the 2005 and 2010 Rwanda Demographic and Health Surveys (DHS), 2010 DHS oversampled data, monthly rainfall data collected from meteorological stations over the same period, and modelled output of long-term rainfall averages, soil moisture, and rain water run-off. Difference-in-difference models were used. Rainfall factors confounded the PIH intervention impact evaluation. When we adjusted our estimates of programme effect by controlling for a variety of rainfall variables, several effectiveness estimates changed by 10% or more. The analyses that did not adjust for rainfall-related variables underestimated the intervention effect on the prevalence of ARI by 14.3%, fever by 52.4% and stunting by 10.2%. Conversely, the unadjusted analysis overestimated the intervention's effect on diarrhoea by 56.5% and wasting by 80%. Rainfall-related patterns have a quantifiable effect on programme evaluation results and highlighted the importance and complexity of controlling for contextual factors in quasi-experimental design evaluations. © 2017 John Wiley & Sons Ltd.

Climate Variability and Yields of Major Staple Food Crops in Northern Ghana

NASA Astrophysics Data System (ADS)

Amikuzuno, J.

2012-12-01

Climate variability, the short-term fluctuations in average weather conditions, and agriculture affect each other. Climate variability affects the agroecological and growing conditions of crops and livestock, and is recently believed to be the greatest impediment to the realisation of the first Millennium Development Goal of reducing poverty and food insecurity in arid and semi-arid regions of developing countries. Conversely, agriculture is a major contributor to climate variability and change by emitting greenhouse gases and reducing the agroecology's potential for carbon sequestration. What however, is the empirical evidence of this inter-dependence of climate variability and agriculture in Sub-Sahara Africa? In this paper, we provide some insight into the long run relationship between inter-annual variations in temperature and rainfall, and annual yields of the most important staple food crops in Northern Ghana. Applying pooled panel data of rainfall, temperature and yields of the selected crops from 1976 to 2010 to cointegration and Granger causality models, there is cogent evidence of cointegration between seasonal, total rainfall and crop yields; and causality from rainfall to crop yields in the Sudano-Guinea Savannah and Guinea Savannah zones of Northern Ghana. This suggests that inter-annual yields of the crops have been influenced by the total mounts of rainfall in the planting season. Temperature variability over the study period is however stationary, and is suspected to have minimal effect if any on crop yields. Overall, the results confirm the appropriateness of our attempt in modelling long-term relationships between the climate and crop yield variables.

Global warming induced hybrid rainy seasons in the Sahel

NASA Astrophysics Data System (ADS)

Salack, Seyni; Klein, Cornelia; Giannini, Alessandra; Sarr, Benoit; Worou, Omonlola N.; Belko, Nouhoun; Bliefernicht, Jan; Kunstman, Harald

2016-10-01

The small rainfall recovery observed over the Sahel, concomitant with a regional climate warming, conceals some drought features that exacerbate food security. The new rainfall features include false start and early cessation of rainy seasons, increased frequency of intense daily rainfall, increasing number of hot nights and warm days and a decreasing trend in diurnal temperature range. Here, we explain these mixed dry/wet seasonal rainfall features which are called hybrid rainy seasons by delving into observed data consensus on the reduction in rainfall amount, its spatial coverage, timing and erratic distribution of events, and other atmospheric variables crucial in agro-climatic monitoring and seasonal forecasting. Further composite investigations of seasonal droughts, oceans warming and the regional atmospheric circulation nexus reveal that the low-to-mid-level atmospheric winds pattern, often stationary relative to either strong or neutral El-Niño-Southern-Oscillations drought patterns, associates to basin warmings in the North Atlantic and the Mediterranean Sea to trigger hybrid rainy seasons in the Sahel. More challenging to rain-fed farming systems, our results suggest that these new rainfall conditions will most likely be sustained by global warming, reshaping thereby our understanding of food insecurity in this region.

Uganda rainfall variability and prediction

NASA Astrophysics Data System (ADS)

Jury, Mark R.

2018-05-01

This study analyzes large-scale controls on Uganda's rainfall. Unlike past work, here, a May-October season is used because of the year-round nature of agricultural production, vegetation sensitivity to rainfall, and disease transmission. The Uganda rainfall record exhibits steady oscillations of ˜3 and 6 years over 1950-2013. Correlation maps at two-season lead time resolve the subtropical ridge over global oceans as an important feature. Multi-variate environmental predictors include Dec-May south Indian Ocean sea surface temperature, east African upper zonal wind, and South Atlantic wind streamfunction, providing a 33% fit to May-Oct rainfall time series. Composite analysis indicates that cool-phase El Niño Southern Oscillation supports increased May-Oct Uganda rainfall via a zonal overturning lower westerly/upper easterly atmospheric circulation. Sea temperature anomalies are positive in the east Atlantic and negative in the west Indian Ocean in respect of wet seasons. The northern Hadley Cell plays a role in limiting the northward march of the equatorial trough from May to October. An analysis of early season floods found that moist inflow from the west Indian Ocean converges over Uganda, generating diurnal thunderstorm clusters that drift southwestward producing high runoff.

Forty years experience in developing and using rainfall simulators under tropical and Mediterranean conditions

NASA Astrophysics Data System (ADS)

Pla-Sentís, Ildefonso; Nacci, Silvana

2010-05-01

Rainfall simulation has been used as a practical tool for evaluating the interaction of falling water drops on the soil surface, to measure both stability of soil aggregates to drop impact and water infiltration rates. In both cases it is tried to simulate the effects of natural rainfall, which usually occurs at very different, variable and erratic rates and intensities. One of the main arguments against the use of rainfall simulators is the difficulty to reproduce the size, final velocity and kinetic energy of the drops in natural rainfall. Since the early 70´s we have been developing and using different kinds of rainfall simulators, both at laboratory and field levels, and under tropical and Mediterranean soil and climate conditions, in flat and sloping lands. They have been mainly used to evaluate the relative effects of different land use and management, including different cropping systems, tillage practices, surface soil conditioning, surface covers, etc. on soil water infiltration, on runoff and on erosion. Our experience is that in any case it is impossible to reproduce the variable size distribution and terminal velocity of raindrops, and the variable changes in intensity of natural storms, under a particular climate condition. In spite of this, with the use of rainfall simulators it is possible to obtain very good information, which if it is properly interpreted in relation to each particular condition (land and crop management, rainfall characteristics, measurement conditions, etc.) may be used as one of the parameters for deducing and modelling soil water balance and soil moisture regime under different land use and management and variable climate conditions. Due to the possibility for a better control of the intensity of simulated rainfall and of the size of water drops, and the possibility to make more repeated measurements under very variable soil and land conditions, both in the laboratory and specially in the field, the better results have been obtained with small size 500-1000 cm2, easily dismantled, drop former simulators, than with larger, nozzle, or more sophisticated equipments. In this contribution there are presented some of the rainfall simulators developed and used by the main author, and some of the results obtained in different studies of practical problems under tropical and Mediterranean conditions. References Pla, I.,G.Campero, y R.Useche.1974.Physical degradación of agricultural soils in the Western Plains of Venezuela. "Trans.10th Int.Cong.Soil.Sci.Soc". 1:231-240. .Moscú Pla, I. 1975.Effects of bitumen emulsion and polyacrilamide on some physical properties of Venezuelan soils. En "Soil Sci. Soc. Am. Special Publication"• 7. 35-46. Madison. Wisconsin . (USA). Pla, I. 1977.Aggregate size and erosion control on sloping land treated with hydrophobic bitumen emulsion."Soil Conservation and Management in the Humid Tropics".109-115. John Wiley & Sons. Pla, I.1981.Simuladores de lluvia para el estudio de relaciones suelo-agua bajo agricultura de secano en los trópicos. Rev. Fac. Agron. XII(1-2):81-93.Maracay (Venezuela) Pla, I. 1986.A routine laboratory index to predict the effects of soil sealing on soil and water conservation. En "Assesment of Soil Surface Sealing and Crusting". 154-162.State Univ. of Ghent.Gante (Bélgica Pla, I., M.C. Ramos, S. Nacci, F. Fonseca y X. Abreu. 2005. Soil moisture regime in dryland vineyards of Catalunya (Spain) as influenced by climate, soil and land management. "Integrated Soil and Water Management for Orchard Development". FAO Land and Water Bulletin 10. 41-49. Roma (Italia).

Fitting monthly Peninsula Malaysian rainfall using Tweedie distribution

NASA Astrophysics Data System (ADS)

Yunus, R. M.; Hasan, M. M.; Zubairi, Y. Z.

2017-09-01

In this study, the Tweedie distribution was used to fit the monthly rainfall data from 24 monitoring stations of Peninsula Malaysia for the period from January, 2008 to April, 2015. The aim of the study is to determine whether the distributions within the Tweedie family fit well the monthly Malaysian rainfall data. Within the Tweedie family, the gamma distribution is generally used for fitting the rainfall totals, however the Poisson-gamma distribution is more useful to describe two important features of rainfall pattern, which are the occurrences (dry months) and the amount (wet months). First, the appropriate distribution of the monthly rainfall was identified within the Tweedie family for each station. Then, the Tweedie Generalised Linear Model (GLM) with no explanatory variable was used to model the monthly rainfall data. Graphical representation was used to assess model appropriateness. The QQ plots of quantile residuals show that the Tweedie models fit the monthly rainfall data better for majority of the stations in the west coast and mid land than those in the east coast of Peninsula. This significant finding suggests that the best fitted distribution depends on the geographical location of the monitoring station. In this paper, a simple model is developed for generating synthetic rainfall data for use in various areas, including agriculture and irrigation. We have showed that the data that were simulated using the Tweedie distribution have fairly similar frequency histogram to that of the actual data. Both the mean number of rainfall events and mean amount of rain for a month were estimated simultaneously for the case that the Poisson gamma distribution fits the data reasonably well. Thus, this work complements previous studies that fit the rainfall amount and the occurrence of rainfall events separately, each to a different distribution.

Projecting Changes in S. Florida Rainfall for the 21st century: Scenarios, Downscaling and Analysis

NASA Astrophysics Data System (ADS)

Cioffi, F.; Lall, U.; Monti, A.

2013-12-01

A Non-Homogeneous hidden Markov Models (NHMM) is developed using a 65-years record (1948-2012) of daily rainfall amount at nineteen stations in South Florida and re-analysis atmospheric fields of Temperature (T) at 1000 hPa, Geo Potential Height (GPH) at 1000 hPa, Meridional Winds (MW) and Zonal Winds (ZW) at 850 hPa, and Zonal Winds on the specific latitude of 27N (ZW27N) from 10 to 1000 hPa. The NHMM fitted is then used for predicting future rainfall patterns under global warming scenario (RCP8.5), using predictors from the CMCC-CMS simulations from 1950-2100. The model directly includes a consideration of seasonality through changes in the driving variables thus addressing the question of how future changes in seasonality of precipitation can also be modeled. The results of the simulations obtained by using the downscaling model NHMM, with predictors derived from the simulations of CMCC-CMS CGM, in the worst conditions of global warming as simulated by RCP8.5 scenario, seems to indicate that, as a consequence of increase of CO2 concentration and temperature, South Florida should be subjected to more frequent dry conditions for the most part of the year, due mainly to a reduction of number of wet days and, at the same time, the territory should be also affected by extreme rainfall events that are more intense than the present ones. What appears from results is an increases of rainfall variability. This scenario seems coherent with the trends of rainfall patterns observed in the XX century. An investigation on the causes of such hydrologic changes, and specifically on the role of North Atlantic Subtropical High is pursued.

The influence of climate, topography and land-use on the hydrology of ephemeral upland catchments

NASA Astrophysics Data System (ADS)

Daly, E.; Webb, J.; Dresel, E.

2016-12-01

We report on an on-going project aimed at determining the effects of climate variability and land use change on water resources in ephemeral productive catchments. Meteorological data (including rainfall, solar radiation, air temperature, humidity and wind speed), streamflow and groundwater levels were collected continuously for over five years in seven ephemeral catchments in southeastern Australia. The catchments, dominated by either pasture for grazing (four) or Eucalyptus globulus (blue gum) plantations of different ages (three), were located in three different geological settings. Rainfall varied from higher than the long-term average of this area for the initial years of the study period to much drier than the long-term average for the last two years. Groundwater levels in the farm sites remained stable or slightly increased through the study period, while levels declined in all the plantation catchments, where evapotranspiration rates were greater than rainfall. The trees intercept groundwater recharge and in some areas of the catchments directly access groundwater. Streamflow occurred mainly during winter, with short-term flows in summer caused by sporadic large rainfall events. Despite the large annual rainfall variability, flow rates in each year were similar in most catchments, with the duration of flow being important in determining the annual flow. The frequency rather than the amount of rainfall events determines the generation of streamflow in the two catchments with steeper slopes. The effect of the tree plantations on streamflow varied from a substantial reduction in one catchment to no effect in another, where the tree rows are oriented predominantly downslope, allowing greater runoff. In the third plantation catchment, geology is the main driver of runoff due to capture into underlying karst conduits.

On the Predictability of Northeast Monsoon Rainfall over South Peninsular India in General Circulation Models

NASA Astrophysics Data System (ADS)

Nair, Archana; Acharya, Nachiketa; Singh, Ankita; Mohanty, U. C.; Panda, T. C.

2013-11-01

In this study the predictability of northeast monsoon (Oct-Nov-Dec) rainfall over peninsular India by eight general circulation model (GCM) outputs was analyzed. These GCM outputs (forecasts for the whole season issued in September) were compared with high-resolution observed gridded rainfall data obtained from the India Meteorological Department for the period 1982-2010. Rainfall, interannual variability (IAV), correlation coefficients, and index of agreement were examined for the outputs of eight GCMs and compared with observation. It was found that the models are able to reproduce rainfall and IAV to different extents. The predictive power of GCMs was also judged by determining the signal-to-noise ratio and the external error variance; it was noted that the predictive power of the models was usually very low. To examine dominant modes of interannual variability, empirical orthogonal function (EOF) analysis was also conducted. EOF analysis of the models revealed they were capable of representing the observed precipitation variability to some extent. The teleconnection between the sea surface temperature (SST) and northeast monsoon rainfall was also investigated and results suggest that during OND the SST over the equatorial Indian Ocean, the Bay of Bengal, the central Pacific Ocean (over Nino3 region), and the north and south Atlantic Ocean enhances northeast monsoon rainfall. This observed phenomenon is only predicted by the CCM3v6 model.

Extreme climatic events drive mammal irruptions: regression analysis of 100-year trends in desert rainfall and temperature

PubMed Central

Greenville, Aaron C; Wardle, Glenda M; Dickman, Chris R

2012-01-01

Extreme climatic events, such as flooding rains, extended decadal droughts and heat waves have been identified increasingly as important regulators of natural populations. Climate models predict that global warming will drive changes in rainfall and increase the frequency and severity of extreme events. Consequently, to anticipate how organisms will respond we need to document how changes in extremes of temperature and rainfall compare to trends in the mean values of these variables and over what spatial scales the patterns are consistent. Using the longest historical weather records available for central Australia – 100 years – and quantile regression methods, we investigate if extreme climate events have changed at similar rates to median events, if annual rainfall has increased in variability, and if the frequency of large rainfall events has increased over this period. Specifically, we compared local (individual weather stations) and regional (Simpson Desert) spatial scales, and quantified trends in median (50th quantile) and extreme weather values (5th, 10th, 90th, and 95th quantiles). We found that median and extreme annual minimum and maximum temperatures have increased at both spatial scales over the past century. Rainfall changes have been inconsistent across the Simpson Desert; individual weather stations showed increases in annual rainfall, increased frequency of large rainfall events or more prolonged droughts, depending on the location. In contrast to our prediction, we found no evidence that intra-annual rainfall had become more variable over time. Using long-term live-trapping records (22 years) of desert small mammals as a case study, we demonstrate that irruptive events are driven by extreme rainfalls (>95th quantile) and that increases in the magnitude and frequency of extreme rainfall events are likely to drive changes in the populations of these species through direct and indirect changes in predation pressure and wildfires. PMID:23170202

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.

A predictive model for spotted wilt epidemics in peanut based on local weather conditions and the tomato spotted wilt virus risk index.

PubMed

Olatinwo, R O; Paz, J O; Brown, S L; Kemerait, R C; Culbreath, A K; Beasley, J P; Hoogenboom, G

2008-10-01

Tomato spotted wilt virus (TSWV), a member of the genus Tospovirus (family Bunyaviridae), is an important plant virus that causes severe damage to peanut (Arachis hypogaea) in the southeastern United States. Disease severity has been extremely variable in individual fields in Georgia, due to several factors including variability in weather patterns. A TSWV risk index has been developed by the University of Georgia to aid peanut growers with the assessment and avoidance of high risk situations. This study was conducted to examine the relationship between weather parameters and spotted wilt severity in peanut, and to develop a predictive model that integrates localized weather information into the risk index. On-farm survey data collected during 1999, 2002, 2004, and 2005 growing seasons, and derived weather variables during the same years were analyzed using nonlinear and multiple regression analyses. Meteorological data were obtained from the Georgia Automated Environmental Monitoring Network. The best model explained 61% of the variation in spotted wilt severity (square root transformed) as a function of the interactions between the TSWV risk index, the average daily temperature in April (TavA), the average daily minimum temperature between March and April (TminMA), the accumulated rainfall in March (RainfallM), the accumulated rainfall in April (RainfallA), the number of rain days in April (RainDayA), evapotranspiration in April (EVTA), and the number of days from 1 January to the planting date (JulianDay). Integrating this weather-based model with the TSWV risk index may help peanut growers more effectively manage tomato spotted wilt disease.

HOW STRONG IS THE RELATIONSHIP BETWEEN RAINFALL VARIABILITY AND CAATINGA PRODUCTIVITY? A CASE STUDY UNDER A CHANGING CLIMATE.

PubMed

Salimon, Cleber; Anderson, Liana

2017-05-22

Despite the knowledge of the influence of rainfall on vegetation dynamics in semiarid tropical Brazil, few studies address and explore quantitatively the various aspects of this relationship. Moreover, Northeast Brazil is expected to have its rainfall reduced by as much as 60% until the end of the 21st Century, under scenario AII of the IPCC Report 2010. We sampled and analyzed satellite-derived monthly rainfall and a vegetation index data for 40 sites with natural vegetation cover in Paraíba State, Brazil from 2001 to 2012. In addition, the anomalies for both variables were calculated. Rainfall variation explained as much as 50% of plant productivity, using the vegetation index as a proxy, and rainfall anomaly explained 80% of the vegetation productivity anomaly. In an extreme dry year (2012), with 65% less rainfall than average for the period 2001-2012, the vegetation index decreased by 25%. If such decrease persists in a long term trend in rainfall reduction, this could lead to a disruption in this ecosystem functioning and the dominant vegetation could become even more xeric or desert-like, bringing serious environmental, social and economical impacts.

Recharge characteristics of an unconfined aquifer from the rainfall-water table relationship

NASA Astrophysics Data System (ADS)

Viswanathan, M. N.

1984-02-01

The determination of recharge levels of unconfined aquifers, recharged entirely by rainfall, is done by developing a model for the aquifer that estimates the water-table levels from the history of rainfall observations and past water-table levels. In the present analysis, the model parameters that influence the recharge were not only assumed to be time dependent but also to have varying dependence rates for various parameters. Such a model is solved by the use of a recursive least-squares method. The variable-rate parameter variation is incorporated using a random walk model. From the field tests conducted at Tomago Sandbeds, Newcastle, Australia, it was observed that the assumption of variable rates of time dependency of recharge parameters produced better estimates of water-table levels compared to that with constant-recharge parameters. It was observed that considerable recharge due to rainfall occurred on the very same day of rainfall. The increase in water-table level was insignificant for subsequent days of rainfall. The level of recharge very much depends upon the intensity and history of rainfall. Isolated rainfalls, even of the order of 25 mm day -1, had no significant effect on the water-table levels.

Seasonal forecasts in the Sahel region: the use of rainfall-based predictive variables

NASA Astrophysics Data System (ADS)

Lodoun, Tiganadaba; Sanon, Moussa; Giannini, Alessandra; Traoré, Pierre Sibiry; Somé, Léopold; Rasolodimby, Jeanne Millogo

2014-08-01

In the Sahel region, seasonal predictions are crucial to alleviate the impacts of climate variability on populations' livelihoods. Agricultural planning (e.g., decisions about sowing date, fertilizer application date, and choice of crop or cultivar) is based on empirical predictive indices whose accuracy to date has not been scientifically proven. This paper attempts to statistically test whether the pattern of rainfall distribution over the May-July period contributes to predicting the real onset date and the nature (wet or dry) of the rainy season, as farmers believe. To that end, we considered historical records of daily rainfall from 51 stations spanning the period 1920-2008 and the different agro-climatic zones in Burkina Faso. We performed (1) principal component analysis to identify climatic zones, based on the patterns of intra-seasonal rainfall, (2) and linear discriminant analysis to find the best rainfall-based variables to distinguish between real and false onset dates of the rainy season, and between wet and dry seasons in each climatic zone. A total of nine climatic zones were identified in each of which, based on rainfall records from May to July, we derived linear discriminant functions to correctly predict the nature of a potential onset date of the rainy season (real or false) and that of the rainy season (dry or wet) in at least three cases out of five. These functions should contribute to alleviating the negative impacts of climate variability in the different climatic zones of Burkina Faso.

Modeling rainfall-runoff relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Modarres, R.; Ouarda, T. B. M. J.

2013-08-01

The traditional hydrologic time series approaches are used for modeling, simulating and forecasting conditional mean of hydrologic variables but neglect their time varying variance or the second order moment. This paper introduces the multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) modeling approach to show how the variance-covariance relationship between hydrologic variables varies in time. These approaches are also useful to estimate the dynamic conditional correlation between hydrologic variables. To illustrate the novelty and usefulness of MGARCH models in hydrology, two major types of MGARCH models, the bivariate diagonal VECH and constant conditional correlation (CCC) models are applied to show the variance-covariance structure and cdynamic correlation in a rainfall-runoff process. The bivariate diagonal VECH-GARCH(1,1) and CCC-GARCH(1,1) models indicated both short-run and long-run persistency in the conditional variance-covariance matrix of the rainfall-runoff process. The conditional variance of rainfall appears to have a stronger persistency, especially long-run persistency, than the conditional variance of streamflow which shows a short-lived drastic increasing pattern and a stronger short-run persistency. The conditional covariance and conditional correlation coefficients have different features for each bivariate rainfall-runoff process with different degrees of stationarity and dynamic nonlinearity. The spatial and temporal pattern of variance-covariance features may reflect the signature of different physical and hydrological variables such as drainage area, topography, soil moisture and ground water fluctuations on the strength, stationarity and nonlinearity of the conditional variance-covariance for a rainfall-runoff process.

Internally Generated and Externally Forced Multidecadal Oceanic Modes and their Influence on the Summer Rainfall over East Asia

NASA Astrophysics Data System (ADS)

Si, D.; Hu, A.

2017-12-01

The interdecadal oceanic variabilities can be generated from both internal and external processes, and these variabilities can significantly modulate our climate on global and regional scale, such as the warming slowdown in the early 21st century, and the rainfall in East Asia. By analyzing simulations from a unique Community Earth System Model (CESM) Large Ensemble (CESM_LE) project, we show that the Interdecadal Pacific Oscillation (IPO) is primarily an internally generated oceanic variability, while the Atlantic Multidecadal Oscillation (AMO) may be an oceanic variability generated by internal oceanic processes and modulated by external forcings in the 20th century. Although the observed relationship between IPO and the Yangtze-Huaihe River valley (YHRV) summer rainfall in China is well simulated in both the preindustrial control and 20th century ensemble, none of the 20th century ensemble members can reproduce the observed time evolution of both IPO and YHRV due to the unpredictable nature of IPO on multidecade timescale. On the other hand, although CESM_LE cannot reproduce the observed relationship between AMO and Huanghe River valley (HRV) summer rainfall of China in the preindustrial control simulation, this relationship in the 20th century simulations is well reproduced, and the chance to reproduce the observed time evolution of both AMO and HRV rainfall is about 30%, indicating the important role of the interaction between the internal processes and the external forcing to realistically simulate the AMO and HRV rainfall.

Interannual variability and predictability over the Arabian Penuinsula Winter monsoon region

NASA Astrophysics Data System (ADS)

Adnan Abid, Muhammad; Kucharski, Fred; Almazroui, Mansour; Kang, In-Sik

2016-04-01

Interannual winter rainfall variability and its predictability are analysed over the Arabian Peninsula region by using observed and hindcast datasets from the state-of-the-art European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal prediction System 4 for the period 1981-2010. An Arabian winter monsoon index (AWMI) is defined to highlight the Arabian Peninsula as the most representative region for the Northern Hemispheric winter dominating the summer rainfall. The observations show that the rainfall variability is relatively large over the northeast of the Arabian Peninsula. The correlation coefficient between the Nino3.4 index and rainfall in this region is 0.33, suggesting potentially some modest predictability, and indicating that El Nino increases and La Nina decreases the rainfall. Regression analysis shows that upper-level cyclonic circulation anomalies that are forced by El Nino Southern Oscillation (ENSO) are responsible for the winter rainfall anomalies over the Arabian region. The stronger (weaker) mean transient-eddy activity related to the upper-level trough induced by the warm (cold) sea-surface temperatures during El Nino (La Nina) tends to increase (decrease) the rainfall in the region. The model hindcast dataset reproduces the ENSO-rainfall connection. The seasonal mean predictability of the northeast Arabian rainfall index is 0.35. It is shown that the noise variance is larger than the signal over the Arabian Peninsula region, which tends to limit the prediction skill. The potential predictability is generally increased in ENSO years and is, in particular, larger during La Nina compared to El Nino years in the region. Furthermore, central Pacific ENSO events and ENSO events with weak signals in the Indian Ocean tend to increase predictability over the Arabian region.

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

Ghosh, Subimal; Das, Debasish; Kao, Shih-Chieh

Recent studies disagree on how rainfall extremes over India have changed in space and time over the past half century, as well as on whether the changes observed are due to global warming or regional urbanization. Although a uniform and consistent decrease in moderate rainfall has been reported, a lack of agreement about trends in heavy rainfall may be due in part to differences in the characterization and spatial averaging of extremes. Here we use extreme value theory to examine trends in Indian rainfall over the past half century in the context of long-term, low-frequency variability.We show that when generalizedmore » extreme value theory is applied to annual maximum rainfall over India, no statistically significant spatially uniform trends are observed, in agreement with previous studies using different approaches. Furthermore, our space time regression analysis of the return levels points to increasing spatial variability of rainfall extremes over India. Our findings highlight the need for systematic examination of global versus regional drivers of trends in Indian rainfall extremes, and may help to inform flood hazard preparedness and water resource management in the region.« less

Influences of rainfall variables and antecedent discharge on urban effluent concentrations and loads in wet weather.

PubMed

Xu, Zuxin; Xiong, Lijun; Li, Huaizheng; Liao, Zhengliang; Yin, Hailong; Wu, Jun; Xu, Jin; Chen, Hao

2017-04-01

For storm drainages inappropriately connected with sewage, wet weather discharge is a major factor that adversely affects receiving waters. A study of the wet weather influences of rainfall-discharge variables on storm drainages connected with sewage was conducted in the downtown Shanghai area (374 ha). Two indicators, event mean concentration (EMC) and event pollutant load per unit area (EPL), were used to describe the pollution discharge during 20 rain events. The study showed that the total rainfall and discharge volume were important factors that affect the EMCs and EPLs of the chemical oxygen demand, total phosphorus, and especially those of NH 4 + -N. The pollutant concentrations at the beginning of the discharge and the discharge period were also major factors that influence the EMCs of these three pollutants. Regression relationships between the rainfall-discharge variables and discharge volume/ EPLs (R 2 = 0.824-0.981) were stronger than the relationships between the rainfall-discharge variables and EMCs. These regression equations can be considered reliable in the system, with a relative validation error of less than ±10% for the discharge volume, and less than ±20% for the EPLs. The results presented in this paper provide guidance for effectively controlling pollution in similar storm drainages.

Spatial variability of mountain stream dynamics along the Ethiopian Rift Valley escarpment

NASA Astrophysics Data System (ADS)

Asfaha, Tesfaalem-Ghebreyohannes; Frankl, Amaury; Zenebe, Amanuel; Haile, Mitiku; Nyssen, Jan

2014-05-01

Changes in hydrogeomorphic characteristics of mountain streams are generally deemed to be controlled mainly by land use/cover changes and rainfall variability. This study investigates the spatial variability of peak discharge in relation to land cover, rainfall and topographic variables in eleven catchments of the Ethiopian Rift Valley escarpment (average slope gradient = 48% (± 13%). Rapid deforestation of the escarpment in the second half of the 20th century resulted in the occurrence of strong flash floods, transporting large amounts of discharge and sediment to the lower graben bottom. Due to integrated reforestation interventions as of the 1980s, many of these catchments do show improvement in vegetation cover at various degrees. Daily rainfall was measured using seven non-recording rain gauges, while peak stage discharges were measured after floods using crest stage gauges installed at eleven stream reaches. Peak discharges were calculated using the Manning's equation. Daily area-weighted rainfall was computed for each catchment using the Thiessen Polygon method. To estimate the vegetation cover of each catchment, the Normalized Difference Vegetation Index was calculated from Landsat TM imagery (mean = 0.14 ± 0.05). In the rainy season of 2012, there was a positive correlation between daily rainfall and peak discharge in each of the monitored catchments. In a multiple linear regression analysis (R² = 0.83; P<0.01), average daily peak discharge in all rivers was positively related with rainfall depth and catchment size and negatively with vegetation cover (as represented by average NDVI values). Average slope gradient of the catchments and Gravelius's compactness index did not show a statistically significant relation with peak discharge. This study shows that though the average vegetation cover of the catchments is still relatively low, differences in vegetation cover, together with rainfall variability plays a determining role in the amount of peak discharges in flashy mountain streams.

The Microphysical Structure of Extreme Precipitation as Inferred from Ground-Based Raindrop Spectra.

NASA Astrophysics Data System (ADS)

Uijlenhoet, Remko; Smith, James A.; Steiner, Matthias

2003-05-01

The controls on the variability of raindrop size distributions in extreme rainfall and the associated radar reflectivity-rain rate relationships are studied using a scaling-law formalism for the description of raindrop size distributions and their properties. This scaling-law formalism enables a separation of the effects of changes in the scale of the raindrop size distribution from those in its shape. Parameters controlling the scale and shape of the scaled raindrop size distribution may be related to the microphysical processes generating extreme rainfall. A global scaling analysis of raindrop size distributions corresponding to rain rates exceeding 100 mm h1, collected during the 1950s with the Illinois State Water Survey raindrop camera in Miami, Florida, reveals that extreme rain rates tend to be associated with conditions in which the variability of the raindrop size distribution is strongly number controlled (i.e., characteristic drop sizes are roughly constant). This means that changes in properties of raindrop size distributions in extreme rainfall are largely produced by varying raindrop concentrations. As a result, rainfall integral variables (such as radar reflectivity and rain rate) are roughly proportional to each other, which is consistent with the concept of the so-called equilibrium raindrop size distribution and has profound implications for radar measurement of extreme rainfall. A time series analysis for two contrasting extreme rainfall events supports the hypothesis that the variability of raindrop size distributions for extreme rain rates is strongly number controlled. However, this analysis also reveals that the actual shapes of the (measured and scaled) spectra may differ significantly from storm to storm. This implies that the exponents of power-law radar reflectivity-rain rate relationships may be similar, and close to unity, for different extreme rainfall events, but their prefactors may differ substantially. Consequently, there is no unique radar reflectivity-rain rate relationship for extreme rain rates, but the variability is essentially reduced to one free parameter (i.e., the prefactor). It is suggested that this free parameter may be estimated on the basis of differential reflectivity measurements in extreme rainfall.

Rainfall and tillage effects on transport of fecal bacteria and sex hormones 17beta-estradiol and testosterone from broiler litter applications to a Georgia Piedmont Ultisol.

PubMed

Jenkins, Michael B; Truman, Clint C; Siragusa, Gregory; Line, Eric; Bailey, J Stan; Frye, Jonathan; Endale, Dinku M; Franklin, Dorcas H; Schomberg, Harry H; Fisher, Dwight S; Sharpe, Ronald R

2008-09-15

Poultry litter provides nutrients for crop and pasture production; however, it also contains fecal bacteria, sex hormones (17beta-estradiol and testosterone) and antibiotic residues that may contaminate surface waters. Our objective was to quantify transport of fecal bacteria, estradiol, testosterone and antibiotic residues from a Cecil sandy loam managed since 1991 under no-till (NT) and conventional tillage (CT) to which either poultry litter (PL) or conventional fertilizer (CF) was applied based on the nitrogen needs of corn (Zea mays L) in the Southern Piedmont of NE Georgia. Simulated rainfall was applied for 60 min to 2 by 3-m field plots at a constant rate in 2004 and variable rate in 2005. Runoff was continuously measured and subsamples taken for determining flow-weighted concentrations of fecal bacteria, hormones, and antibiotic residues. Neither Salmonella, nor Campylobacter, nor antimicrobial residues were detected in litter, soil, or runoff. Differences in soil concentrations of fecal bacteria before and after rainfall simulations were observed only for Escherichia coli in the constant rainfall intensity experiment. Differences in flow-weighted concentrations were observed only for testosterone in both constant and variable intensity rainfall experiments, and were greatest for treatments that received poultry litter. Total loads of E. coli and fecal enterococci, were largest for both tillage treatments receiving poultry litter for the variable rainfall intensity. Load of testosterone was greatest for no-till plots receiving poultry litter under variable rainfall intensity. Poultry litter application rates commensurate for corn appeared to enhance only soil concentrations of E. coli, and runoff concentrations of testosterone above background levels.

Adapting to climate variability and change: experiences from cereal-based farming in the central rift and Kobo Valleys, Ethiopia.

PubMed

Kassie, Belay Tseganeh; Hengsdijk, Huib; Rötter, Reimund; Kahiluoto, Helena; Asseng, Senthold; Van Ittersum, Martin

2013-11-01

Small-holder farmers in Ethiopia are facing several climate related hazards, in particular highly variable rainfall with severe droughts which can have devastating effects on their livelihoods. Projected changes in climate are expected to aggravate the existing challenges. This study examines farmer perceptions on current climate variability and long-term changes, current adaptive strategies, and potential barriers for successful further adaptation in two case study regions-the Central Rift Valley (CRV) and Kobo Valley. The study was based on a household questionnaire, interviews with key stakeholders, and focus group discussions. The result revealed that about 99 % of the respondents at the CRV and 96 % at the Kobo Valley perceived an increase in temperature and 94 % at CRV and 91 % at the Kobo Valley perceived a decrease in rainfall over the last 20-30 years. Inter-annual and intraseasonal rainfall variability also has increased according to the farmers. The observed climate data (1977-2009) also showed an increasing trend in temperature and high inter-annual and intra-seasonal rainfall variability. In contrast to farmers' perceptions of a decrease in rainfall totals, observed rainfall data showed no statistically significant decline. The interaction among various bio-physical and socio-economic factors, changes in rainfall intensity and reduced water available to crops due to increased hot spells, may have influenced the perception of farmers with respect to rainfall trends. In recent decades, farmers in both the CRV and Kobo have changed farming practices to adapt to perceived climate change and variability, for example, through crop and variety choice, adjustment of cropping calendar, and in situ moisture conservation. These relatively low-cost changes in farm practices were within the limited adaptation capacity of farmers, which may be insufficient to deal with the impacts of future climate change. Anticipated climate change is expected to impose new risks outside the range of current experiences. To enable farmers to adapt to these impacts critical technological, institutional, and market-access constraints need to be removed. Inconsistencies between farmers' perceptions and observed climate trends (e.g., decrease in annual rainfall) could lead to sub-optimal or counterproductive adaptations, and therefore must be removed by better communication and capacity building, for example through Climate Field Schools. Enabling strategies, which are among others targeted at agricultural inputs, credit supply, market access, and strengthening of local knowledge and information services need to become integral part of government policies to assist farmers to adapt to the impacts of current and future climate change.

Adapting to Climate Variability and Change: Experiences from Cereal-Based Farming in the Central Rift and Kobo Valleys, Ethiopia

NASA Astrophysics Data System (ADS)

Kassie, Belay Tseganeh; Hengsdijk, Huib; Rötter, Reimund; Kahiluoto, Helena; Asseng, Senthold; Van Ittersum, Martin

2013-11-01

Small-holder farmers in Ethiopia are facing several climate related hazards, in particular highly variable rainfall with severe droughts which can have devastating effects on their livelihoods. Projected changes in climate are expected to aggravate the existing challenges. This study examines farmer perceptions on current climate variability and long-term changes, current adaptive strategies, and potential barriers for successful further adaptation in two case study regions—the Central Rift Valley (CRV) and Kobo Valley. The study was based on a household questionnaire, interviews with key stakeholders, and focus group discussions. The result revealed that about 99 % of the respondents at the CRV and 96 % at the Kobo Valley perceived an increase in temperature and 94 % at CRV and 91 % at the Kobo Valley perceived a decrease in rainfall over the last 20-30 years. Inter-annual and intraseasonal rainfall variability also has increased according to the farmers. The observed climate data (1977-2009) also showed an increasing trend in temperature and high inter-annual and intra-seasonal rainfall variability. In contrast to farmers’ perceptions of a decrease in rainfall totals, observed rainfall data showed no statistically significant decline. The interaction among various bio-physical and socio-economic factors, changes in rainfall intensity and reduced water available to crops due to increased hot spells, may have influenced the perception of farmers with respect to rainfall trends. In recent decades, farmers in both the CRV and Kobo have changed farming practices to adapt to perceived climate change and variability, for example, through crop and variety choice, adjustment of cropping calendar, and in situ moisture conservation. These relatively low-cost changes in farm practices were within the limited adaptation capacity of farmers, which may be insufficient to deal with the impacts of future climate change. Anticipated climate change is expected to impose new risks outside the range of current experiences. To enable farmers to adapt to these impacts critical technological, institutional, and market-access constraints need to be removed. Inconsistencies between farmers’ perceptions and observed climate trends (e.g., decrease in annual rainfall) could lead to sub-optimal or counterproductive adaptations, and therefore must be removed by better communication and capacity building, for example through Climate Field Schools. Enabling strategies, which are among others targeted at agricultural inputs, credit supply, market access, and strengthening of local knowledge and information services need to become integral part of government policies to assist farmers to adapt to the impacts of current and future climate change.

Indian summer monsoon rainfall: Dancing with the tunes of the sun

NASA Astrophysics Data System (ADS)

Hiremath, K. M.; Manjunath, Hegde; Soon, Willie

2015-02-01

There is strong statistical evidence that solar activity influences the Indian summer monsoon rainfall. To search for a physical link between the two, we consider the coupled cloud hydrodynamic equations, and derive an equation for the rate of precipitation that is similar to the equation of a forced harmonic oscillator, with cloud and rain water mixing ratios as forcing variables. Those internal forcing variables are parameterized in terms of the combined effect of external forcing as measured by sunspot and coronal hole activities with several well known solar periods (9, 13 and 27 days; 1.3, 5, 11 and 22 years). The equation is then numerically solved and the results show that the variability of the simulated rate of precipitation captures very well the actual variability of the Indian monsoon rainfall, yielding vital clues for a physical understanding that has so far eluded analyses based on statistical correlations alone. We also solved the precipitation equation by allowing for the effects of long-term variation of aerosols. We tentatively conclude that the net effects of aerosols variation are small, when compared to the solar factors, in terms of explaining the observed rainfall variability covering the full Indian monsoonal geographical domains.

Diagnostic evaluation of distributed physically based model at the REW scale (THREW) using rainfall-runoff event analysis

NASA Astrophysics Data System (ADS)

Tian, F.; Sivapalan, M.; Li, H.; Hu, H.

2007-12-01

The importance of diagnostic analysis of hydrological models is increasingly recognized by the scientific community (M. Sivapalan, et al., 2003; H. V. Gupta, et al., 2007). Model diagnosis refers to model structures and parameters being identified not only by statistical comparison of system state variables and outputs but also by process understanding in a specific watershed. Process understanding can be gained by the analysis of observational data and model results at the specific watershed as well as through regionalization. Although remote sensing technology can provide valuable data about the inputs, state variables, and outputs of the hydrological system, observational rainfall-runoff data still constitute the most accurate, reliable, direct, and thus a basic component of hydrology related database. One critical question in model diagnostic analysis is, therefore, what signature characteristic can we extract from rainfall and runoff data. To this date only a few studies have focused on this question, such as Merz et al. (2006) and Lana-Renault et al. (2007), still none of these studies related event analysis with model diagnosis in an explicit, rigorous, and systematic manner. Our work focuses on the identification of the dominant runoff generation mechanisms from event analysis of rainfall-runoff data, including correlation analysis and analysis of timing pattern. The correlation analysis involves the identification of the complex relationship among rainfall depth, intensity, runoff coefficient, and antecedent conditions, and the timing pattern analysis aims to identify the clustering pattern of runoff events in relation to the patterns of rainfall events. Our diagnostic analysis illustrates the changing pattern of runoff generation mechanisms in the DMIP2 test watersheds located in Oklahoma region, which is also well recognized by numerical simulations based on TsingHua Representative Elementary Watershed (THREW) model. The result suggests the usefulness of rainfall-runoff event analysis for model development as well as model diagnostics.

Rainfall estimation with TFR model using Ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Asyiqotur Rohmah, Nabila; Apriliani, Erna

2018-03-01

Rainfall fluctuation can affect condition of other environment, correlated with economic activity and public health. The increasing of global average temperature is influenced by the increasing of CO2 in the atmosphere, which caused climate change. Meanwhile, the forests as carbon sinks that help keep the carbon cycle and climate change mitigation. Climate change caused by rainfall intensity deviations can affect the economy of a region, and even countries. It encourages research on rainfall associated with an area of forest. In this study, the mathematics model that used is a model which describes the global temperatures, forest cover, and seasonal rainfall called the TFR (temperature, forest cover, and rainfall) model. The model will be discretized first, and then it will be estimated by the method of Ensemble Kalman Filter (EnKF). The result shows that the more ensembles used in estimation, the better the result is. Also, the accurateness of simulation result is influenced by measurement variable. If a variable is measurement data, the result of simulation is better.

Analysis of water-level fluctuations of the US Highway 90 retention pond, Madison, Florida

USGS Publications Warehouse

Bridges, W.C.

1985-01-01

A closed basin stormwater retention pond, located 1 mile west of Madison, Florida, has a maximum storage capacity of 134.1 acre-feet at the overtopping altitude of 100.2 feet. The maximum observed altitude (July 1982 to March 1984) was 99.52 feet (126.7 acre-feet) on March 28, 1984. This report provides a technique for simulating net monthly change-in-altitude in response to rainfall and evaporation. A regression equation was developed which relates net monthly change in altitude (dependent variable) to rainfall and evaporation (independent variables). Rainfall frequency curves were developed using a log-Pearson Type III distribution of the annual, January through April, June through August, and July monthly rainfall totals for the years 1908-72, 1974, 1976-82. The altitude of the retention pond increased almost 7 feet during the 4-month period January through April 1983. The rainfall total was 35.1 inches, and the recurrence interval exceeded the 100-year January-April rainfall. (USGS)

Missing pieces of the puzzle: understanding decadal variability of Sahel Rainfall

NASA Astrophysics Data System (ADS)

Vellinga, Michael; Roberts, Malcolm; Vidale, Pier-Luigi; Mizielinski, Matthew; Demory, Marie-Estelle; Schiemann, Reinhard; Strachan, Jane; Bain, Caroline

2015-04-01

The instrumental record shows that substantial decadal fluctuations affected Sahel rainfall from the West African monsoon throughout the 20th century. Climate models generally underestimate the magnitude of decadal Sahel rainfall changes compared to observations. This shows that the processes that control low-frequency Sahel rainfall change are misrepresented in most CMIP5-era climate models. Reliable climate information of future low-frequency rainfall changes thus remains elusive. Here we identify key processes that control the magnitude of the decadal rainfall recovery in the Sahel since the mid-1980s. We show its sensitivity to model resolution and physics in a suite of experiments with global HadGEM3 model configurations at resolutions between 130-25 km. The decadal rainfall trend increases with resolution and at 60-25 km falls within the observed range. Higher resolution models have stronger increases of moisture supply and of African Easterly wave activity. Easterly waves control the occurrence of strong organised rainfall events which carry most of the decadal trend. Weak rainfall events occur too frequently at all resolutions and at low resolution contribute substantially to the decadal trend. All of this behaviour is seen across CMIP5, including future scenarios. Additional simulations with a global 12km version of HadGEM3 show that treating convection explicitly dramatically improves the properties of Sahel rainfall systems. We conclude that interaction between convective scale and global scale processes is key to decadal rainfall changes in the Sahel. This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.Crown Copyright

Dynamic Rainfall Patterns and the Simulation of Changing Scenarios: A behavioral watershed response

NASA Astrophysics Data System (ADS)

Chu, M.; Guzman, J.; Steiner, J. L.; Hou, C.; Moriasi, D.

2015-12-01

Rainfall is one of the fundamental drivers that control hydrologic responses including runoff production and transport phenomena that consequently drive changes in aquatic ecosystems. Quantifying the hydrologic responses to changing scenarios (e.g., climate, land use, and management) using environmental models requires a realistic representation of probable rainfall in its most sensible spatio-temporal dimensions matching that of the phenomenon under investigation. Downscaling projected rainfall from global circulation models (GCMs) is the most common practice in deriving rainfall datasets to be used as main inputs to hydrologic models which in turn are used to assess the impacts of climate changes on ecosystems. Downscaling assumes that local climate is a combination of large-scale climatic/atmospheric conditions and local conditions. However, the representation of the latter is generally beyond the capacity of current GCMs. The main objective of this study was to develop and implement a synthetic rainfall generator to downscale expected rainfall trends to 1 x 1 km rainfall daily patterns that mimic the dynamic propagation of probability distribution functions (pdf) derived from historic rainfall data (rain-gauge or radar estimated). Future projections were determined based on actual and expected changes in the pdf and stochastic processes to account for variability. Watershed responses in terms of streamflow and nutrients loads were evaluated using synthetically generated rainfall patterns and actual data. The framework developed in this study will allow practitioners to generate rainfall datasets that mimic the temporal and spatial patterns exclusive to their study area under full disclosure of the uncertainties involved. This is expected to provide significantly more accurate environmental models than is currently available and would provide practitioners with ways to evaluate the spectrum of systemic responses to changing scenarios.

Documentary reconstruction of monsoon rainfall variability over western India, 1781-1860

NASA Astrophysics Data System (ADS)

Adamson, George C. D.; Nash, David J.

2014-02-01

Investigations into the climatic forcings that affect the long-term variability of the Indian summer monsoon are constrained by a lack of reliable rainfall data prior to the late nineteenth century. Extensive qualitative and quantitative meteorological information for the pre-instrumental period exists within historical documents, although these materials have been largely unexplored. This paper presents the first reconstruction of monsoon variability using documentary sources, focussing on western India for the period 1781-1860. Three separate reconstructions are generated, for (1) Mumbai, (2) Pune and (3) the area of Gujarat bordering the Gulf of Khambat. A composite chronology is then produced from the three reconstructions, termed the Western India Monsoon Rainfall reconstruction (WIMR). The WIMR exhibits four periods of generally deficient monsoon rainfall (1780-1785, 1799-1806, 1830-1838 and 1845-1857) and three of above-normal rainfall (1788-1794, 1813-1828 and 1839-1844). The WIMR shows good correspondence with a dendroclimatic drought reconstruction for Kerala, although agreement with the western Indian portion of the tree-ring derived Monsoon Asia Drought Atlas is less strong. The reconstruction is used to examine the long-term relationship between the El Nino-Southern Oscillation (ENSO) and monsoon rainfall over western India. This exhibits peaks and troughs in correlation over time, suggesting a regular long-term fluctuation. This may be an internal oscillation in the ENSO-monsoon system or may be related to volcanic aerosol forcings. Further reconstructions of monsoon rainfall are necessary to validate this. The study highlights uncertainties in existing published rainfall records for 1817-1846 for western India.

Soil conservation service curve number: How to take into account spatial and temporal variability

NASA Astrophysics Data System (ADS)

Rianna, M.; Orlando, D.; Montesarchio, V.; Russo, F.; Napolitano, F.

2012-09-01

The most commonly used method to evaluate rainfall excess, is the Soil Conservation Service (SCS) runoff curve number model. This method is based on the determination of the CN valuethat is linked with a hydrological soil group, cover type, treatment, hydrologic condition and antecedent runoff condition. To calculate the antecedent runoff condition the standard procedure needs to calculate the rainfall over the entire basin during the five days previous to the beginning of the event in order to simulate and then to use that volume of rainfall to calculate the antecedent moisture condition (AMC). This is necessary in order to obtain the correct curve number value. The value of the modified parameter is then kept constant throughout the whole event. The aim of this work is to evaluate the possibility of improving the curve number method. The various assumptions are focused on modifying those related to rainfall and the determination of an AMC condition and their role in the determination of the value of the curve number parameter. In order to consider the spatial variability we assumed that the rainfall which influences the AMC and the CN value does not account for the rainfall over the entire basin, but for the rainfall within a single cell where the basin domain is discretized. Furthermore, in order to consider the temporal variability of rainfall we assumed that the value of the CN of the single cell is not maintained constant during the whole event, but instead varies throughout it according to the time interval used to define the AMC conditions.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Throughfall and its spatial variability beneath xerophytic shrub canopies within water-limited arid desert ecosystems

NASA Astrophysics Data System (ADS)

Zhang, Ya-feng; Wang, Xin-ping; Hu, Rui; Pan, Yan-xia

2016-08-01

Throughfall is known to be a critical component of the hydrological and biogeochemical cycles of forested ecosystems with inherently temporal and spatial variability. Yet little is understood concerning the throughfall variability of shrubs and the associated controlling factors in arid desert ecosystems. Here we systematically investigated the variability of throughfall of two morphological distinct xerophytic shrubs (Caragana korshinskii and Artemisia ordosica) within a re-vegetated arid desert ecosystem, and evaluated the effects of shrub structure and rainfall characteristics on throughfall based on heavily gauged throughfall measurements at the event scale. We found that morphological differences were not sufficient to generate significant difference (P < 0.05) in throughfall between two studied shrub species under the same rainfall and meteorological conditions in our study area, with a throughfall percentage of 69.7% for C. korshinskii and 64.3% for A. ordosica. We also observed a highly variable patchy pattern of throughfall beneath individual shrub canopies, but the spatial patterns appeared to be stable among rainfall events based on time stability analysis. Throughfall linearly increased with the increasing distance from the shrub base for both shrubs, and radial direction beneath shrub canopies had a pronounced impact on throughfall. Throughfall variability, expressed as the coefficient of variation (CV) of throughfall, tended to decline with the increase in rainfall amount, intensity and duration, and stabilized passing a certain threshold. Our findings highlight the great variability of throughfall beneath the canopies of xerophytic shrubs and the time stability of throughfall pattern among rainfall events. The spatially heterogeneous and temporally stable throughfall is expected to generate a dynamic patchy distribution of soil moisture beneath shrub canopies within arid desert ecosystems.

The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert.

PubMed

Li, Bonan; Wang, Lixin; Kaseke, Kudzai F; Li, Lin; Seely, Mary K

2016-01-01

Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months' continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert.

The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert

PubMed Central

Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Li, Lin; Seely, Mary K.

2016-01-01

Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling results as well as sensitivity analyses provide soil moisture baseline information for future monitoring and the prediction of soil moisture patterns in the Namib Desert. PMID:27764203

Interannual Variability of Boreal Summer Rainfall in the Equatorial Atlantic

NASA Technical Reports Server (NTRS)

Gu, Guojun; Adler, Robert F.

2007-01-01

Tropical Atlantic rainfall patterns and variation during boreal summer [June-July-August (JJA)] are quantified by means of a 28-year (1979-2006) monthly precipitation dataset from the Global Precipitation Climatology Project (GPCP). Rainfall variability during boreal spring [March-April-May (MAM)] is also examined for comparison in that the most intense interannual variability is usually observed during this season. Comparable variabilities in the Intertropical Convergence Zone (ITCZ) strength and the basin-mean rainfall are found during both seasons. Interannual variations in the ITCZ's latitudinal location during JJA however are generally negligible, in contrasting to intense year-to-year fluctuations during MAM. Sea surface temperature (SST) oscillations along the equatorial region (usually called the Atlantic Nino events) and in the tropical north Atlantic (TNA) are shown to be the two major local factors modulating the tropical Atlantic climate during both seasons. During MAM, both SST modes tend to contribute to the formation of an evident interhemispheric SST gradient, thus inducing anomalous shifting of the ITCZ and then forcing a dipolar structure of rainfall anomalies across the equator primarily in the western basin. During JJA the impacts however are primarily on the ITCZ strength likely due to negligible changes in the ITCZ latitudinal location. The Atlantic Nino reaches its peak in JJA, while much weaker SST anomalies appear north of the equator in JJA than in MAM, showing decaying of the interhemispheric SST mode. SST anomalies in the tropical central-eastern Pacific (the El Nino events) have a strong impact on tropical Atlantic including both the tropical north Atlantic and the equatorial-southern Atlantic. However, anomalous warming in the tropical north Atlantic following positive SST anomalies in the tropical Pacific disappears during JJA because of seasonal changes in the large-scale circulation cutting off the ENSO influence passing through the mid-latitudes. Hence the anomalies associated with the tropical Pacific during JJA are forced through an anomalous Walker circulation primarily working on the western basin, and likely a lagged oceanic response in the equatorial region.

Water availability as a driver of spatial and temporal variability in vegetation in the La Mancha plain (Spain): Implications for the land-surface energy, water and carbon budget

NASA Astrophysics Data System (ADS)

Los, Sietse

2017-04-01

Vegetation is water limited in large areas of Spain and therefore a close link exists between vegetation greenness observed from satellite and moisture availability. Here we exploit this link to infer spatial and temporal variability in moisture from MODIS NDVI data and thermal data. Discrepancies in the precipitation - vegetation relationship indicate areas with an alternative supply of water (i.e. not rainfall), this can be natural where moisture is supplied by upwelling groundwater, or can be artificial where crops are irrigated. As a result spatial and temporal variability in vegetation in the La Mancha Plain appears closely linked to topography, geology, rainfall and land use. Crop land shows large variability in year-to-year vegetation greenness; for some areas this variability is linked to variability in rainfall but in other cases this variability is linked to irrigation. The differences in irrigation treatment within one plant functional type, in this case crops, will lead to errors in land surface models when ignored. The magnitude of these effects on the energy, carbon and water balance are assessed at the scale of 250 m to 200 km. Estimating the water balance correctly is of particular important since in some areas in Spain more water is used for irrigation than is supplemented by rainfall.

Predicting rainfall erosivity by momentum and kinetic energy in Mediterranean environment

NASA Astrophysics Data System (ADS)

Carollo, Francesco G.; Ferro, Vito; Serio, Maria A.

2018-05-01

Rainfall erosivity is an index that describes the power of rainfall to cause soil erosion and it is used around the world for assessing and predicting soil loss on agricultural lands. Erosivity can be represented in terms of both rainfall momentum and kinetic energy, both calculated per unit time and area. Contrasting results on the representativeness of these two variables are available: some authors stated that momentum and kinetic energy are practically interchangeable in soil loss estimation while other found that kinetic energy is the most suitable expression of rainfall erosivity. The direct and continuous measurements of momentum and kinetic energy by a disdrometer allow also to establish a relationship with rainfall intensity at the study site. At first in this paper a comparison between the momentum-rainfall intensity relationships measured at Palermo and El Teularet by an optical disdrometer is presented. For a fixed rainfall intensity the measurements showed that the rainfall momentum values measured at the two experimental sites are not coincident. However both datasets presented a threshold value of rainfall intensity over which the rainfall momentum assumes a quasi-constant value. Then the reliability of a theoretically deduced relationship, linking momentum, rainfall intensity and median volume diameter, is positively verified using measured raindrop size distributions. An analysis to assess which variable, momentum or kinetic energy per unit area and time, is the best predictor of erosivity in Italy and Spain was also carried out. This investigation highlighted that the rainfall kinetic energy per unit area and time can be substituted by rainfall momentum as index for estimating the rainfall erosivity, and this result does not depend on the site where precipitation occurs. Finally, rainfall intensity measurements and soil loss data collected from the bare plots equipped at Sparacia experimental area were used to verify the reliability of some rainfall erosivity indices and their ability to distinguish the type of involved soil erosion processes.

Climate change impacts on rainfall and temperature in sugarcane growing Upper Gangetic Plains of India

NASA Astrophysics Data System (ADS)

Verma, Ram Ratan; Srivastava, Tapendra Kumar; Singh, Pushpa

2018-01-01

Assessment of variability in climate extremes is crucial for managing their aftermath on crops. Sugarcane (Saccharum officinarum L.), a major C4 crop, dominates the Upper Gangetic Plain (UGP) in India and is vulnerable to both direct and indirect effects of changes in temperature and rainfall. The present study was taken up to assess the weekly, monthly, seasonal, and annual trends of rainfall and temperature variability during the period 1956-2015 (60 years) for envisaging the probabilities of different levels of rainfall suitable for sugarcane in UGP in the present climate scenario. The analysis revealed that 87% of total annual rainfall was received during southwest monsoon months (June-September) while post-monsoon (October to February) and pre-monsoon months (March-May) accounted for only 9.4 and 3.6%, respectively. There was a decline in both monthly and annual normal rainfall during the period 1986-2015 as compared to 1956-1985, and an annual rainfall deficiency of 205.3 mm was recorded. Maximum monthly normal rainfall deficiencies of 52.8, 84.2, and 54.0 mm were recorded during the months of July, August, and September, respectively, while a minimum rainfall deficiency of 2.2 mm was observed in November. There was a decline by 196.3 mm in seasonal normal rainfall during June-September (kharif). The initial probability of a week going dry was higher (> 70%) from the 1st to the 25th week; however, standard meteorological weeks (SMW) 26 to 37 had more than 50% probability of going wet. The normal annual maximum temperature (Tmax) decreased by 0.4 °C while normal annual minimum temperatures (Tmin) increased by 0.21 °C. Analysis showed that there was an increase in frequency of drought from 1986 onwards in the zone and a monsoon rainfall deficit by about 21.25% during June-September which coincided with tillering and grand growth stage of sugarcane. The imposed drought during the growth and elongation phase is emerging as a major constraint in realizing high cane productivity in the zone. Strategies for mitigating the negative impacts of rainfall and temperature variability on sugarcane productivity through improvement in existing adaptation strategies are proposed.

Effects of variable regolith depth, hydraulic properties, and rainfall on debris-flow initiation during the September 2013 northern Colorado Front Range rainstorm

NASA Astrophysics Data System (ADS)

Baum, R. L.; Coe, J. A.; Kean, J. W.; Jones, E. S.; Godt, J.

2015-12-01

Heavy rainfall during 9 - 13 September 2013 induced about 1100 debris flows in the foothills and mountains of the northern Colorado Front Range. Weathered bedrock was partially exposed in the basal surfaces of many of the shallow source areas at depths ranging from 0.2 to 5 m. Typical values of saturated hydraulic conductivity of soils and regolith units mapped in the source areas range from about 10-4 - 10-6 m/s, with a median value of 2.8 x 10-5 m/s based on number of source areas in each map unit. Rainfall intensities varied spatially and temporally, from 0 to 2.5 x 10-5 m/s (90 mm/hour), with two periods of relatively heavy rainfall on September 12 - 13. The distribution of debris flows appears to correlate with total storm rainfall, and reported times of greatest landslide activity coincide with times of heaviest rainfall. Process-based models of rainfall infiltration and slope stability (TRIGRS) representing the observed ranges of regolith depth, hydraulic conductivity, and rainfall intensity, provide additional insights about the timing and distribution of debris flows from this storm. For example, small debris flows from shallower source areas (<2 m) occurred late on September 11 and in the early morning of September 12, whereas large debris flows from deeper (3 - 5 m) source areas in the western part of the affected area occurred late on September 12. Timing of these flows can be understood in terms of the time required for pore pressure rise depending on regolith depth and rainfall intensity. The variable hydraulic properties combined with variable regolith depth and slope angles account for much of the observed range in timing in areas of similar rainfall intensity and duration. Modeling indicates that the greatest and most rapid pore pressure rise likely occurred in areas of highest rainfall intensity and amount. This is consistent with the largest numbers of debris flows occurring on steep canyon walls in areas of high total storm rainfall.

Panic anxiety, under the weather?

NASA Astrophysics Data System (ADS)

Bulbena, A.; Pailhez, G.; Aceña, R.; Cunillera, J.; Rius, A.; Garcia-Ribera, C.; Gutiérrez, J.; Rojo, C.

2005-03-01

The relationship between weather conditions and psychiatric disorders has been a continuous subject of speculation due to contradictory findings. This study attempts to further clarify this relationship by focussing on specific conditions such as panic attacks and non-panic anxiety in relation to specific meteorological variables. All psychiatric emergencies attended at a general hospital in Barcelona (Spain) during 2002 with anxiety as main complaint were classified as panic or non-panic anxiety according to strict independent and retrospective criteria. Both groups were assessed and compared with meteorological data (wind speed and direction, daily rainfall, temperature, humidity and solar radiation). Seasons and weekend days were also included as independent variables. Non-parametric statistics were used throughout since most variables do not follow a normal distribution. Logistic regression models were applied to predict days with and without the clinical condition. Episodes of panic were three times more common with the poniente wind (hot wind), twice less often with rainfall, and one and a half times more common in autumn than in other seasons. These three trends (hot wind, rainfall and autumn) were accumulative for panic episodes in a logistic regression formula. Significant reduction of episodes on weekends was found only for non-panic episodes. Panic attacks, unlike other anxiety episodes, in a psychiatric emergency department in Barcelona seem to show significant meteorotropism. Assessing specific disorders instead of overall emergencies or other variables of a more general quality could shed new light on the relationship between weather conditions and behaviour.

A Monte-Carlo Bayesian framework for urban rainfall error modelling

NASA Astrophysics Data System (ADS)

Ochoa Rodriguez, Susana; Wang, Li-Pen; Willems, Patrick; Onof, Christian

2016-04-01

Rainfall estimates of the highest possible accuracy and resolution are required for urban hydrological applications, given the small size and fast response which characterise urban catchments. While significant progress has been made in recent years towards meeting rainfall input requirements for urban hydrology -including increasing use of high spatial resolution radar rainfall estimates in combination with point rain gauge records- rainfall estimates will never be perfect and the true rainfall field is, by definition, unknown [1]. Quantifying the residual errors in rainfall estimates is crucial in order to understand their reliability, as well as the impact that their uncertainty may have in subsequent runoff estimates. The quantification of errors in rainfall estimates has been an active topic of research for decades. However, existing rainfall error models have several shortcomings, including the fact that they are limited to describing errors associated to a single data source (i.e. errors associated to rain gauge measurements or radar QPEs alone) and to a single representative error source (e.g. radar-rain gauge differences, spatial temporal resolution). Moreover, rainfall error models have been mostly developed for and tested at large scales. Studies at urban scales are mostly limited to analyses of propagation of errors in rain gauge records-only through urban drainage models and to tests of model sensitivity to uncertainty arising from unmeasured rainfall variability. Only few radar rainfall error models -originally developed for large scales- have been tested at urban scales [2] and have been shown to fail to well capture small-scale storm dynamics, including storm peaks, which are of utmost important for urban runoff simulations. In this work a Monte-Carlo Bayesian framework for rainfall error modelling at urban scales is introduced, which explicitly accounts for relevant errors (arising from insufficient accuracy and/or resolution) in multiple data sources (in this case radar and rain gauge estimates typically available at present), while at the same time enabling dynamic combination of these data sources (thus not only quantifying uncertainty, but also reducing it). This model generates an ensemble of merged rainfall estimates, which can then be used as input to urban drainage models in order to examine how uncertainties in rainfall estimates propagate to urban runoff estimates. The proposed model is tested using as case study a detailed rainfall and flow dataset, and a carefully verified urban drainage model of a small (~9 km2) pilot catchment in North-East London. The model has shown to well characterise residual errors in rainfall data at urban scales (which remain after the merging), leading to improved runoff estimates. In fact, the majority of measured flow peaks are bounded within the uncertainty area produced by the runoff ensembles generated with the ensemble rainfall inputs. REFERENCES: [1] Ciach, G. J. & Krajewski, W. F. (1999). On the estimation of radar rainfall error variance. Advances in Water Resources, 22 (6), 585-595. [2] Rico-Ramirez, M. A., Liguori, S. & Schellart, A. N. A. (2015). Quantifying radar-rainfall uncertainties in urban drainage flow modelling. Journal of Hydrology, 528, 17-28.

Application of satellite precipitation data to analyse and model arbovirus activity in the tropics

PubMed Central

2011-01-01

Background Murray Valley encephalitis virus (MVEV) is a mosquito-borne Flavivirus (Flaviviridae: Flavivirus) which is closely related to Japanese encephalitis virus, West Nile virus and St. Louis encephalitis virus. MVEV is enzootic in northern Australia and Papua New Guinea and epizootic in other parts of Australia. Activity of MVEV in Western Australia (WA) is monitored by detection of seroconversions in flocks of sentinel chickens at selected sample sites throughout WA. Rainfall is a major environmental factor influencing MVEV activity. Utilising data on rainfall and seroconversions, statistical relationships between MVEV occurrence and rainfall can be determined. These relationships can be used to predict MVEV activity which, in turn, provides the general public with important information about disease transmission risk. Since ground measurements of rainfall are sparse and irregularly distributed, especially in north WA where rainfall is spatially and temporally highly variable, alternative data sources such as remote sensing (RS) data represent an attractive alternative to ground measurements. However, a number of competing alternatives are available and careful evaluation is essential to determine the most appropriate product for a given problem. Results The Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 product was chosen from a range of RS rainfall products to develop rainfall-based predictor variables and build logistic regression models for the prediction of MVEV activity in the Kimberley and Pilbara regions of WA. Two models employing monthly time-lagged rainfall variables showed the strongest discriminatory ability of 0.74 and 0.80 as measured by the Receiver Operating Characteristics area under the curve (ROC AUC). Conclusions TMPA data provide a state-of-the-art data source for the development of rainfall-based predictive models for Flavivirus activity in tropical WA. Compared to ground measurements these data have the advantage of being collected spatially regularly, irrespective of remoteness. We found that increases in monthly rainfall and monthly number of days above average rainfall increased the risk of MVEV activity in the Pilbara at a time-lag of two months. Increases in monthly rainfall and monthly number of days above average rainfall increased the risk of MVEV activity in the Kimberley at a lag of three months. PMID:21255449

Historical analysis of interannual rainfall variability and trends in southeastern Brazil based on observational and remotely sensed data

NASA Astrophysics Data System (ADS)

Vásquez P., Isela L.; de Araujo, Lígia Maria Nascimento; Molion, Luiz Carlos Baldicero; de Araujo Abdalad, Mariana; Moreira, Daniel Medeiros; Sanchez, Arturo; Barbosa, Humberto Alves; Rotunno Filho, Otto Corrêa

2018-02-01

The Brazilian Southeast is considered a humid region. It is also prone to landslides and floods, a result of significant increases in rainfall during spring and summer caused by the South Atlantic Convergence Zone (SACZ). Recently, however, the region has faced a striking rainfall shortage, raising serious concerns regarding water availability. The present work endeavored to explain the meteorological drought that has led to hydrological imbalance and water scarcity in the region. Hodrick-Prescott smoothing and wavelet transform techniques were applied to long-term hydrologic and sea surface temperature (SST)—based climate indices monthly time series data in an attempt to detect cycles and trends that could help explain rainfall patterns and define a framework for improving the predictability of extreme events in the region. Historical observational hydrologic datasets available include monthly precipitation amounts gauged since 1888 and 1940 and stream flow measured since the 1930s. The spatial representativeness of rain gauges was tested against gridded rainfall satellite estimates from 2000 to 2015. The analyses revealed variability in four time scale domains—infra-annual, interannual, quasi-decadal and inter-decadal or multi-decadal. The strongest oscillations periods revealed were: for precipitation—8 months, 2, 8 and 32 years; for Pacific SST in the Niño-3.4 region—6 months, 2, 8 and 35.6 years, for North Atlantic SST variability—6 months, 2, 8 and 32 years and for Pacific Decadal Oscillation (PDO) index—6.19 months, 2.04, 8.35 and 27.31 years. Other periodicities less prominent but still statistically significant were also highlighted.

Along the Rainfall-Runoff Chain: From Scaling of Greatest Point Rainfall to Global Change Attribution

NASA Astrophysics Data System (ADS)

Fraedrich, K.

2014-12-01

Processes along the continental rainfall-runoff chain cover a wide range of time and space scales which are presented here combining observations (ranging from minutes to decades) and minimalist concepts. (i) Rainfall, which can be simulated by a censored first-order autoregressive process (vertical moisture fluxes), exhibits 1/f-spectra if presented as binary events (tropics), while extrema world wide increase with duration according to Jennings' scaling law. (ii) Runoff volatility (Yangtze) shows data collapse which, linked to an intra-annual 1/f-spectrum, is represented by a single function not unlike physical systems at criticality and the short and long return times of extremes are Weibull-distributed. Atmospheric and soil moisture variabilities are also discussed. (iii) Soil moisture (in a bucket), whose variability is interpreted by a biased coinflip Ansatz for rainfall events, adds an equation of state to energy and water flux balances comprising Budyko's frame work for quasi-stationary watershed analysis. Eco-hydrologic state space presentations in terms of surface flux ratios of energy excess (loss by sensible heat over supply by net radiation) versus water excess (loss by discharge over gain by precipitation) allow attributions of state change to external (or climate) and internal (or anthropogenic) causes. Including the vegetation-greenness index (NDVI) as an active tracer extends the eco-hydrologic state space analysis to supplement the common geographical presentations. Two examples demonstrate the approach combining ERA and MODIS data sets: (a) global geobotanic classification by combining first and second moments of the dryness ratio (net radiation over precipitation) and (b) regional attributions (Tibetan Plateau) of vegetation changes.

Accounting for Rainfall Spatial Variability in Prediction of Flash Floods

NASA Astrophysics Data System (ADS)

Saharia, M.; Kirstetter, P. E.; Gourley, J. J.; Hong, Y.; Vergara, H. J.

2016-12-01

Flash floods are a particularly damaging natural hazard worldwide in terms of both fatalities and property damage. In the United States, the lack of a comprehensive database that catalogues information related to flash flood timing, location, causative rainfall, and basin geomorphology has hindered broad characterization studies. First a representative and long archive of more than 20,000 flooding events during 2002-2011 is used to analyze the spatial and temporal variability of flash floods. We also derive large number of spatially distributed geomorphological and climatological parameters such as basin area, mean annual precipitation, basin slope etc. to identify static basin characteristics that influence flood response. For the same period, the National Severe Storms Laboratory (NSSL) has produced a decadal archive of Multi-Radar/Multi-Sensor (MRMS) radar-only precipitation rates at 1-km spatial resolution with 5-min temporal resolution. This provides an unprecedented opportunity to analyze the impact of event-level precipitation variability on flooding using a big data approach. To analyze the impact of sub-basin scale rainfall spatial variability on flooding, certain indices such as the first and second scaled moment of rainfall, horizontal gap, vertical gap etc. are computed from the MRMS dataset. Finally, flooding characteristics such as rise time, lag time, and peak discharge are linked to derived geomorphologic, climatologic, and rainfall indices to identify basin characteristics that drive flash floods. Next the model is used to predict flash flooding characteristics all over the continental U.S., specifically over regions poorly covered by hydrological observations. So far studies involving rainfall variability indices have only been performed on a case study basis, and a large scale approach is expected to provide a deeper insight into how sub-basin scale precipitation variability affects flooding. Finally, these findings are validated using the National Weather Service storm reports and a historical flood fatalities database. This analysis framework will serve as a baseline for evaluating distributed hydrologic model simulations such as the Flooded Locations And Simulated Hydrographs Project (FLASH) (http://flash.ou.edu).

Accounting for rainfall spatial variability in the prediction of flash floods

NASA Astrophysics Data System (ADS)

Saharia, Manabendra; Kirstetter, Pierre-Emmanuel; Gourley, Jonathan J.; Hong, Yang; Vergara, Humberto; Flamig, Zachary L.

2017-04-01

Flash floods are a particularly damaging natural hazard worldwide in terms of both fatalities and property damage. In the United States, the lack of a comprehensive database that catalogues information related to flash flood timing, location, causative rainfall, and basin geomorphology has hindered broad characterization studies. First a representative and long archive of more than 15,000 flooding events during 2002-2011 is used to analyze the spatial and temporal variability of flash floods. We also derive large number of spatially distributed geomorphological and climatological parameters such as basin area, mean annual precipitation, basin slope etc. to identify static basin characteristics that influence flood response. For the same period, the National Severe Storms Laboratory (NSSL) has produced a decadal archive of Multi-Radar/Multi-Sensor (MRMS) radar-only precipitation rates at 1-km spatial resolution with 5-min temporal resolution. This provides an unprecedented opportunity to analyze the impact of event-level precipitation variability on flooding using a big data approach. To analyze the impact of sub-basin scale rainfall spatial variability on flooding, certain indices such as the first and second scaled moment of rainfall, horizontal gap, vertical gap etc. are computed from the MRMS dataset. Finally, flooding characteristics such as rise time, lag time, and peak discharge are linked to derived geomorphologic, climatologic, and rainfall indices to identify basin characteristics that drive flash floods. The database has been subjected to rigorous quality control by accounting for radar beam height and percentage snow in basins. So far studies involving rainfall variability indices have only been performed on a case study basis, and a large scale approach is expected to provide a deeper insight into how sub-basin scale precipitation variability affects flooding. Finally, these findings are validated using the National Weather Service storm reports and a historical flood fatalities database. This analysis framework will serve as a baseline for evaluating distributed hydrologic model simulations such as the Flooded Locations And Simulated Hydrographs Project (FLASH) (http://flash.ou.edu).

Variability of East Asian summer monsoon precipitation during the Holocene and possible forcing mechanisms

NASA Astrophysics Data System (ADS)

Lu, Fuzhi; Ma, Chunmei; Zhu, Cheng; Lu, Huayu; Zhang, Xiaojian; Huang, Kangyou; Guo, Tianhong; Li, Kaifeng; Li, Lan; Li, Bing; Zhang, Wenqing

2018-03-01

Projecting how the East Asian summer monsoon (EASM) rainfall will change with global warming is essential for human sustainability. Reconstructing Holocene climate can provide critical insight into its forcing and future variability. However, quantitative reconstructions of Holocene summer precipitation are lacking for tropical and subtropical China, which is the core region of the EASM influence. Here we present high-resolution annual and summer rainfall reconstructions covering the whole Holocene based on the pollen record at Xinjie site from the lower Yangtze region. Summer rainfall was less seasonal and 30% higher than modern values at 10-6 cal kyr BP and gradually declined thereafter, which broadly followed the Northern Hemisphere summer insolation. Over the last two millennia, however, the summer rainfall has deviated from the downward trend of summer insolation. We argue that greenhouse gas forcing might have offset summer insolation forcing and contributed to the late Holocene rainfall anomaly, which is supported by the TraCE-21 ka transient simulation. Besides, tropical sea-surface temperatures could modulate summer rainfall by affecting evaporation of seawater. The rainfall pattern concurs with stalagmite and other proxy records from southern China but differs from mid-Holocene rainfall maximum recorded in arid/semiarid northern China. Summer rainfall in northern China was strongly suppressed by high-northern-latitude ice volume forcing during the early Holocene in spite of high summer insolation. In addition, the El Niño/Southern Oscillation might be responsible for droughts of northern China and floods of southern China during the late Holocene. Furthermore, quantitative rainfall reconstructions indicate that the Paleoclimate Modeling Intercomparison Project (PMIP) simulations underestimate the magnitude of Holocene precipitation changes. Our results highlight the spatial and temporal variability of the Holocene EASM precipitation and potential forcing mechanisms, which are very helpful for calibration of paleoclimate models and prediction of future precipitation changes in East Asia in the scenario of global warming.

Advancing a Model-Validated Statistical Method for Decomposing the Key Oceanic Drivers of Observed Regional Climate Variability and Evaluating Model Performance: Focus on North African Rainfall in CESM

NASA Astrophysics Data System (ADS)

Wang, F.; Notaro, M.; Yu, Y.; Mao, J.; Shi, X.; Wei, Y.

2016-12-01

North (N.) African rainfall is characterized by dramatic interannual to decadal variability with serious socio-economic ramifications. The Sahel and West African Monsoon (WAM) region experienced a dramatic shift to persistent drought by the late 1960s, while the Horn of Africa (HOA) underwent drying since the 1990s. Large disagreementregarding the dominant oceanic drivers of N. African hydrologic variability exists among modeling studies, leading to notable spread in Sahel summer rainfall projections for this century among Coupled Model Intercomparison Project models. In order to gain a deeper understanding of the oceanic drivers of N. African rainfall and establish a benchmark for model evaluation, a statistical method, the multivariate Generalized Equilibrium Feedback Assessment, is validated and applied to observations and a control run from the Community Earth System Model (CESM). This study represents the first time that the dominant oceanic drivers of N. African rainfall were evaluated and systematically compared between observations and model simulations. CESM and the observations consistently agree that tropical oceanic modes are the dominant controls of N. African rainfall. During the monsoon season, CESM and observations agree that an anomalously warm eastern tropical Pacific shifts the Walker Circulation eastward, with its descending branch supporting Sahel drying. CESM and the observations concur that a warmer tropical eastern Atlantic favors a southward-shifted Intertropical Convergence Zone, which intensifies WAM monsoonal rainfall. An observed reduction in Sahel rainfall accompanies this enhanced WAM rainfall, yet is confined to the Atlantic in CESM. During the short rains, both observations and CESM indicate that a positive phase of tropical Indian Ocean dipole (IOD) mode [anomalously warm (cold) in western (eastern) Indian] enhances HOA rainfall. The observed IOD impacts are limited to the short rains, while the simulated impacts are year-round.

Identification of MJO Signal on Various Elevation Station Rainfall in Southern Papua, Indonesia

NASA Astrophysics Data System (ADS)

Sakya, A. E.; Permana, D.; Makmur, E. E. S.; Handayani, A. S.; Hanggoro, W.; Setyadi, G.

2016-12-01

The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability in tropical rainfall on the large scale, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. The characteristic of the MJO during its propagation through the Maritime Continent has always been a challenge to comprehend despite decades of research attempts in that region. Unique topography over the Maritime Continent is believed to act as one of the vanguard of precipitation triggered by the MJO. Such condition leads to a maximize amplitude of the diurnal cycle of precipitation over land on phase 2 and 5, even before the arrival of the MJO. Papua in Indonesia is one of the wettest regions on Earth and is at the heart of the MJO envelope. Aiming to investigate the effect of topography and coastline distance on MJO in southern Papua, 14 years of rainfall data from 12 stations in PTFI AWS network at various elevations (9 meters to 4400 meters above sea level) have been utilized. The results show a strong MJO modulation in rainfall variability with variance of 30 - 100 days in the region. These results suggest a strong impact of MJO on rainfall at various elevations in southern Papua which confirm the previous studies. The peak rainfall rates were observed at phase 3 at lower elevation and coastline stations and phase 4 at middle and high elevation stations. The study also investigated the relationship between MJO phases and diurnal precipitation cycle at all stations. At low elevation and coastline stations, diurnal rainfall variation is more variable with high rainfall observed at afternoon to midnight and after midnight. This is due to the local effect of land-sea breeze system. While in middle and high elevation stations, rainfall peak was observed at afternoon to midnight. The results show the impact of MJO in diurnal rainfall variation at all stations.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

USGS Publications Warehouse

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-01-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-12-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Analysis of the sensitivity to rainfall spatio-temporal variability of an operational urban rainfall-runoff model in a multifractal framework

NASA Astrophysics Data System (ADS)

Gires, A.; Tchiguirinskaia, I.; Schertzer, D. J.; Lovejoy, S.

2011-12-01

In large urban areas, storm water management is a challenge with enlarging impervious areas. Many cities have implemented real time control (RTC) of their urban drainage system to either reduce overflow or limit urban contamination. A basic component of RTC is hydraulic/hydrologic model. In this paper we use the multifractal framework to suggest an innovative way to test the sensitivity of such a model to the spatio-temporal variability of its rainfall input. Indeed the rainfall variability is often neglected in urban context, being considered as a non-relevant issue at the scales involve. Our results show that on the contrary the rainfall variability should be taken into account. Universal multifractals (UM) rely on the concept of multiplicative cascade and are a standard tool to analyze and simulate with a reduced number of parameters geophysical processes that are extremely variable over a wide range of scales. This study is conducted on a 3 400 ha urban area located in Seine-Saint-Denis, in the North of Paris (France). We use the operational semi-distributed model that was calibrated by the local authority (Direction Eau et Assainnissement du 93) that is in charge of urban drainage. The rainfall data comes from the C-Band radar of Trappes operated by Météo-France. The rainfall event of February 9th, 2009 was used. A stochastic ensemble approach was implemented to quantify the uncertainty on discharge associated to the rainfall variability occurring at scales smaller than 1 km x 1 km x 5 min that is usually available with C-band radar networks. An analysis of the quantiles of the simulated peak flow showed that the uncertainty exceeds 20 % for upstream links. To evaluate a potential gain from a direct use of the rainfall data available at the resolution of X-band radar, we performed similar analysis of the rainfall fields of the degraded resolution of 9 km x 9 km x 20 min. The results show a clear decrease in uncertainty when the original resolution of C-band radar data is used. This analysis highlights the interest of implementing X-band radars in urban areas. Indeed such radars provide the rainfall data at a hectometric resolution that would enable a better nowcasting and management of storm water. The multifractal properties of the simulated hydrographs were analysed with the help of simulated rainfall fields of resolution 111 m x 111 m x 1 min, lasting 4 hours, and corresponding to a 5 year return period event. On the whole, the discharge exhibits a good scaling behaviour over the range 4 h - 5 min. Both UM parameters tend to be greater for the discharge than for the rainfall. The notion of maximum probable singularity was used to clarify the consequences on the assessment of extremes. It appears that the urban drainage network basically reproduces the extremes, or only slightly damps them, at least in terms of multifractal statistics. The results were obtained with the financial support from the EU FP7 SMARTesT Project and the Chair "Hydrology for Resilient Cities" (sponsored by Veolia) of Ecole des Ponts ParisTech.

Potential impact of climate variability on respiratory diseases in infant and children in Semarang

NASA Astrophysics Data System (ADS)

Budiyono; Rismawati; Jati, S. P.; Ginandjar, P.

2017-02-01

Temperature, humidity, and rainfall may influence respiratory disease, including acute respiratory infection (ARI) and pneumonia. In Semarang, the temperature and humidity has increased 0.1°C and 1.6% respectively during 2002-2011. ARI and pneumonia in children under 5 years had increased during 2012-2014. This study aimed to analyze the relationship of climate variability and ARI and pneumonia incidence. It was an ecological study. Subject consisted of patients visited primary health care of Bandarharjo from 2011 to 2015. Pneumonia was related to infants (<1-year-old) and children (1-4 years old), while ARI was related to children (≥5 years old). Data of climate was obtained from Agency for Meteorology, Climatology and Geophysics (BMKG) Semarang. Pearson correlation (α=0.05) was used to analyse the correlation of the 60 samples. Mean of temperature was 27.96° C, relative humidity was 74.73%, and rainfall was 179.98 mm/month. The total of ARI was 38523 cases and pneumonia was 1558 cases. Temperature, humidity, and rainfall had no correlation to pneumonia. Humidity had a significant correlation to ARI on female children and total ARI (r=0.3 and r=0.26; p-value=0.02 and 0.04 respectively). Rainfall and temperature had no correlation to total ARI. This study concluded humidity has potential impact to ARI.

Ecohydrology of managed ecosystems: Linking rainfall unpredictability, agronomic performance, and sustainable water use

NASA Astrophysics Data System (ADS)

Vico, Giulia; Porporato, Amilcare

2014-05-01

The field of ecohydrology, traditionally focusing on natural ecosystems, can offer the necessary quantitative tools to assess and compare the sustainability of agriculture across climates, soil types, crops, and irrigation strategies, including rainfall unpredictability. In particular, irrigation is one of the main strategies to enhance and stabilize agricultural productivity, but represents a cost in terms of often scarce water resources. Here, the sustainability of irrigated and rainfed agriculture is assessed by means of water productivity (defined as the ratio between yield and total supplied water), yields, water requirements, and their variability. These indicators are quantified using a probabilistic description of the soil water balance and crop development. Employing this framework, we interpret changes in water productivity as total water input is altered, in two staple crops (maize and wheat) grown under different soils, climates, and irrigation strategies. Climate change scenarios are explored by using the same approach and altering the rainfall statistics. For a given irrigation strategy, intermediate rainfall inputs leads to the highest variability in yield and irrigation water requirement - it is under these conditions that water management is most problematic. When considering the contrasting needs of limiting water requirements while ensuring adequate yields, micro-irrigation emerges as the most sustainable strategy at the field level, although consideration should be given to its profitability and long-term environmental implications.

East Asian Summer Monsoon Rainfall: A Historical Perspective of the 1998 Flood over Yangtze River

NASA Technical Reports Server (NTRS)

Weng, H.-Y.; Lau, K.-M.

1999-01-01

One of the main factors that might have caused the disastrous flood in China during 1998 summer is long-term variations that include a trend indicating increasing monsoon rainfall over the Yangtze River Valley. China's 160-station monthly rainfall anomaly for the summers of 1955-98 is analyzed for exploring such long-term variations. Singular value decomposition (SVD) between the summer rainfall and the global sea surface temperature (SST) anomalies reveals that the rainfall over Yangtze River Valley is closely related to global and regional SST variabilities at both interannual and interdecadal timescales. SVD1 mode links the above normal rainfall condition in central China to an El Nino-like SSTA distribution, varying on interannual timescale modified by a trend during the period. SVD3 mode links positive rainfall anomaly in Yangtze River Valley to the warm SST anomaly in the subtropical western Pacific, varying on interannual timescales modified by interdecadal timescales. This link tends to be stronger when the Nino3 area becomes colder and the western subtropical Pacific becomes warmer. The 1998 summer is a transition season when the 1997/98 El Nino event was in its decaying phase, and the SST in the Nino3 area emerged below normal anomaly while the subtropical western Pacific SST above normal. Thus, the first and third SVD modes become dominant in 1998 summer, favoring more Asian summer monsoon rainfall over the Yangtze River Valley.

Impacts of the ENSO Modoki and other Tropical Indo-Pacific Climate-Drivers on African Rainfall

PubMed Central

Preethi, B.; Sabin, T. P.; Adedoyin, J. A.; Ashok, K.

2015-01-01

The study diagnoses the relative impacts of the four known tropical Indo-Pacific drivers, namely, El Niño Southern Oscillation (ENSO), ENSO Modoki, Indian Ocean Dipole (IOD), and Indian Ocean Basin-wide mode (IOBM) on African seasonal rainfall variability. The canonical El Niño and El Niño Modoki are in general associated with anomalous reduction (enhancement) of rainfall in southern (northern) hemispheric regions during March-May season. However, both the El Niño flavours anomalously reduce the northern hemispheric rainfall during June-September. Interestingly, during boreal spring and summer, in many regions, the Indian Ocean drivers have influences opposite to those from tropical Pacific drivers. On the other hand, during the October-December season, the canonical El Niño and/or positive IOD are associated with an anomalous enhancement of rainfall in the Eastern Africa, while the El Niño Modoki events are associated with an opposite impact. In addition to the Walker circulation changes, the Indo-Pacific drivers influence the African rainfall through modulating jet streams. During boreal summer, the El Niño Modoki and canonical El Niño (positive IOD) tend to weaken (strengthen) the tropical easterly jet, and result in strengthening (weakening) and southward shift of African easterly jet. This anomalously reduces (enhances) rainfall in the tropical north, including Sahelian Africa. PMID:26567458

Impacts of the ENSO Modoki and other Tropical Indo-Pacific Climate-Drivers on African Rainfall.

PubMed

Preethi, B; Sabin, T P; Adedoyin, J A; Ashok, K

2015-11-16

The study diagnoses the relative impacts of the four known tropical Indo-Pacific drivers, namely, El Niño Southern Oscillation (ENSO), ENSO Modoki, Indian Ocean Dipole (IOD), and Indian Ocean Basin-wide mode (IOBM) on African seasonal rainfall variability. The canonical El Niño and El Niño Modoki are in general associated with anomalous reduction (enhancement) of rainfall in southern (northern) hemispheric regions during March-May season. However, both the El Niño flavours anomalously reduce the northern hemispheric rainfall during June-September. Interestingly, during boreal spring and summer, in many regions, the Indian Ocean drivers have influences opposite to those from tropical Pacific drivers. On the other hand, during the October-December season, the canonical El Niño and/or positive IOD are associated with an anomalous enhancement of rainfall in the Eastern Africa, while the El Niño Modoki events are associated with an opposite impact. In addition to the Walker circulation changes, the Indo-Pacific drivers influence the African rainfall through modulating jet streams. During boreal summer, the El Niño Modoki and canonical El Niño (positive IOD) tend to weaken (strengthen) the tropical easterly jet, and result in strengthening (weakening) and southward shift of African easterly jet. This anomalously reduces (enhances) rainfall in the tropical north, including Sahelian Africa.

Trends analysis of rainfall and rainfall extremes in Sarawak, Malaysia using modified Mann-Kendall test

NASA Astrophysics Data System (ADS)

Sa'adi, Zulfaqar; Shahid, Shamsuddin; Ismail, Tarmizi; Chung, Eun-Sung; Wang, Xiao-Jun

2017-11-01

This study assesses the spatial pattern of changes in rainfall extremes of Sarawak in recent years (1980-2014). The Mann-Kendall (MK) test along with modified Mann-Kendall (m-MK) test, which can discriminate multi-scale variability of unidirectional trend, was used to analyze the changes at 31 stations. Taking account of the scaling effect through eliminating the effect of autocorrelation, m-MK was employed to discriminate multi-scale variability of the unidirectional trends of the annual rainfall in Sarawak. It can confirm the significance of the MK test. The annual rainfall trend from MK test showed significant changes at 95% confidence level at five stations. The seasonal trends from MK test indicate an increasing rate of rainfall during the Northeast monsoon and a decreasing trend during the Southwest monsoon in some region of Sarawak. However, the m-MK test detected an increasing trend in annual rainfall only at one station and no significant trend in seasonal rainfall at any stations. The significant increasing trends of the 1-h maximum rainfall from the MK test are detected mainly at the stations located in the urban area giving concern to the occurrence of the flash flood. On the other hand, the m-MK test detected no significant trend in 1- and 3-h maximum rainfalls at any location. On the contrary, it detected significant trends in 6- and 72-h maximum rainfalls at a station located in the Lower Rajang basin area which is an extensive low-lying agricultural area and prone to stagnant flood. These results indicate that the trends in rainfall and rainfall extremes reported in Malaysia and surrounding region should be verified with m-MK test as most of the trends may result from scaling effect.

Design of a reliable and operational landslide early warning system at regional scale

NASA Astrophysics Data System (ADS)

Calvello, Michele; Piciullo, Luca; Gariano, Stefano Luigi; Melillo, Massimo; Brunetti, Maria Teresa; Peruccacci, Silvia; Guzzetti, Fausto

2017-04-01

Landslide early warning systems at regional scale are used to warn authorities, civil protection personnel and the population about the occurrence of rainfall-induced landslides over wide areas, typically through the prediction and measurement of meteorological variables. A warning model for these systems must include a regional correlation law and a decision algorithm. A regional correlation law can be defined as a functional relationship between rainfall and landslides; it is typically based on thresholds of rainfall indicators (e.g., cumulated rainfall, rainfall duration) related to different exceedance probabilities of landslide occurrence. A decision algorithm can be defined as a set of assumptions and procedures linking rainfall thresholds to warning levels. The design and the employment of an operational and reliable early warning system for rainfall-induced landslides at regional scale depend on the identification of a reliable correlation law as well as on the definition of a suitable decision algorithm. Herein, a five-step process chain addressing both issues and based on rainfall thresholds is proposed; the procedure is tested in a landslide-prone area of the Campania region in southern Italy. To this purpose, a database of 96 shallow landslides triggered by rainfall in the period 2003-2010 and rainfall data gathered from 58 rain gauges are used. First, a set of rainfall thresholds are defined applying a frequentist method to reconstructed rainfall conditions triggering landslides in the test area. In the second step, several thresholds at different exceedance probabilities are evaluated, and different percentile combinations are selected for the activation of three warning levels. Subsequently, within steps three and four, the issuing of warning levels is based on the comparison, over time and for each combination, between the measured rainfall and the pre-defined warning level thresholds. Finally, the optimal percentile combination to be employed in the regional early warning system is selected evaluating the model performance in terms of success and error indicators by means of the "event, duration matrix, performance" (EDuMaP) method.

Climate change and the impact of increased rainfall variability on sediment transport and catchment scale water quality

NASA Astrophysics Data System (ADS)

Hancock, G. R.; Willgoose, G. R.; Cohen, S.

2009-12-01

Recently there has been recognition that changing climate will affect rainfall and storm patterns with research directed to examine how the global hydrological cycle will respond to climate change. This study investigates the effect of different rainfall patterns on erosion and resultant water quality for a well studied tropical monsoonal catchment that is undisturbed by Europeans in the Northern Territory, Australia. Water quality has a large affect on a range of aquatic flora and fauna and a significant change in sediment could have impacts on the aquatic ecosystems. There have been several studies of the effect of climate change on rainfall patterns in the study area with projections indicating a significant increase in storm activity. Therefore it is important that the impact of this variability be assessed in terms of catchment hydrology, sediment transport and water quality. Here a numerical model of erosion and hydrology (CAESAR) is used to assess several different rainfall scenarios over a 1000 year modelled period. The results show that that increased rainfall amount and intensity increases sediment transport rates but predicted water quality was variable and non-linear but within the range of measured field data for the catchment and region. Therefore an assessment of sediment transport and water quality is a significant and complex issue that requires further understandings of the role of biophysical feedbacks such as vegetation as well as the role of humans in managing landscapes (i.e. controlled and uncontrolled fire). The study provides a robust methodology for assessing the impact of enhanced climate variability on sediment transport and water quality.

Recent variations in geopotential height associated with West African monsoon variability

NASA Astrophysics Data System (ADS)

Okoro, Ugochukwu K.; Chen, Wen; Nath, Debashis

2018-02-01

In the present study, the atmospheric circulation patterns associated with the seasonal West Africa (WA) monsoon (WAM) rainfall variability has been investigated. The observational rainfall data from the Climatic Research Unit (CRU) and atmospheric fields from the National Center for Environmental Prediction (NCEP) reanalysis 2, from 1979 to 2014, have been used. The rainfall variability extremes, classified as wet or dry years, are the outcomes of simultaneous 6-month SPI at the three rainfall zones, which shows increasing trends [Guinea Coast (GC = 0.012 year-1), Eastern Sudano Sahel (ESS = 0.045 year-1) and Western Sudano Sahel (WSS = 0.056 year-1) from Sen's slope]; however, it is significant only in the Sahel region (α = 0.05 and α = 0.001 at ESS and WSS, respectively, from Mann-Kendall test). The vertical profile of the geopotential height (GpH) during the wet and dry years reveals that the 700 hPa anomalies show remarkable pattern at about 8°N to 13°N. This shows varying correlation with the zonal averaged vertically integrated moisture flux convergence and rainfall anomalies, respectively, as well as the oceanic pulsations indexes [Ocean Nino Index (ONI) and South Atlantic Ocean dipole index (SAODI), significant from t test], identified as precursors to the Sahel and GC rainfall variability respectively. The role of GpH anomalies at 700 hPa has been identified as the facilitator to the West African Westerly Jet's input to the moisture flux transported over the WA. This is a new perspective of the circulation processes associated with WAM and serves as a basis for modeling investigations.

Could Malaria Control Programmes be Timed to Coincide with Onset of Rainfall?

PubMed

Komen, Kibii

2017-06-01

Malaria cases in South Africa's Northern Province of Limpopo have surpassed known endemic KwaZulu Natal and Mpumalanga Provinces. This paper applies statistical methods: regression analysis and impulse response function to understand the timing of impact and the length that such impacts last. Climate data (rainfall and temperature) are obtained from South African Weather Services (SAWs); global data from the European Centre for Medium-Range Weather Forecasts (ECMWF), while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province). Data collected span from January 1998 to July 2007. Signs of the coefficients are positive for rainfall and temperature and negative for their exponents. Three out of five independent variables consistently maintain a very high statistical level of significance. The coefficients for climate variables describe an inverted u-shape: parameters for the exponents of rainfall (-0.02, -0.01, -0.02, -0.00) and temperature (-46.61, -47.46, -48.14, -36.04) are both negative. A one standard deviation rise in rainfall (rainfall onset) increases malaria cases, and the effects become sustained for at least 3 months and conclude that onset of rainfall therefore triggers a 'malaria season'. Malaria control programme and early warning system should be intensified in the first 3 months following the onset of rainfall.

Global intensification in observed short-duration rainfall extremes

NASA Astrophysics Data System (ADS)

Fowler, H. J.; Lewis, E.; Guerreiro, S.; Blenkinsop, S.; Barbero, R.; Westra, S.; Lenderink, G.; Li, X.

2017-12-01

Extreme rainfall events are expected to intensify with a warming climate and this is currently driving extensive research. While daily rainfall extremes are widely thought to have increased globally in recent decades, changes in rainfall extremes on shorter timescales, often associated with flash flooding, have not been documented at global scale due to surface observational limitations and the lack of a global sub-daily rainfall database. The access to and use of such data remains a challenge. For the first time, we have synthesized across multiple data sources providing gauge-based sub-daily rainfall observations across the globe over the last 6 decades. This forms part of the INTENSE project (part of the World Climate Research Programme (WCRP)'s Grand Challenge on 'Understanding and Predicting Weather and Climate Extremes' and the Global Water and Energy Exchanges (GEWEX) Hydroclimate Project cross-cut on sub-daily rainfall). A set of global hydroclimatic indices have been produced based upon stakeholder recommendations including indices that describe maximum rainfall totals and timing, the intensity, duration and frequency of storms, frequency of storms above specific thresholds and information about the diurnal cycle. This will provide a unique global data resource on sub-daily precipitation whose derived indices will be freely available to the wider scientific community. Because of the physical connection between global warming and the moisture budget, we also sought to infer long-term changes in sub-daily rainfall extremes contingent on global mean temperature. Whereas the potential influence of global warming is uncertain at regional scales, where natural variability dominates, aggregating surface stations across parts of the world may increase the global warming-induced signal. Changes in terms of annual maximum rainfall across various resolutions ranging from 1-h to 24-h are presented and discussed.

Impacts of Different Soil Texture and Organic Content on Hydrological Performance of Bioretention

NASA Astrophysics Data System (ADS)

Gülbaz, Sezar; Melek Kazezyilmaz Alhan, Cevza

2015-04-01

The land development and increase in urbanization in a watershed has adverse effects such as flooding and water pollution on both surface water and groundwater resources. Low Impact Development (LID) Best Management Practices (BMPs) such as bioretentions, vegetated rooftops, rain barrels, vegetative swales and permeable pavements have been implemented in order to diminish adverse effects of urbanization. LID-BMP is a land planning method which is used to manage storm water runoff by reducing peak flows as well as simultaneously improving water quality. The aim of this study is developing a functional experimental setup called as Rainfall-Watershed-Bioretention (RWB) System in order to investigate and quantify the hydrological performance of bioretention. RWB System is constructed on the Istanbul University Campus and includes an artificial rainfall system, which allows for variable rainfall intensity, drainage area, which has controllable size and slope, and bioretention columns with different soil ratios. Four bioretention columns with different soil textures and organic content are constructed in order to investigate their effects on water quantity. Using RWB System, the runoff volume, hydrograph, peak flow rate and delay in peak time at the exit of bioretention columns may be quantified under various rainfalls in order to understand the role of soil types used in bioretention columns and rainfall intensities. The data obtained from several experiments conducted in RWB System are employed in establishing a relation among rainfall, surface runoff and flow reduction after bioretention. Moreover, the results are supported by mathematical models in order to explain the physical mechanism of bioretention. Following conclusions are reached based on the analyses carried out in this study: i) Results show that different local soil types in bioretention implementation affect surface runoff and peak flow considerably. ii) Rainfall intensity and duration affect peak flow reduction and arrival time and shape of the hydrograph. iii) A mathematical representation of the relation among the rainfall, surface runoff over the watershed and outflow from the bioretention is developed by incorporating kinematic wave equation into the modified Green-Ampt Method. The rainfall intensity in modified Green-Ampt method is represented by the inflow per unit surface area of bioretention which may be obtained from kinematic wave solution using the measured rainfall data. Variable rainfall cases may be taken into account by using the modified Green-Ampt method. Thus, employing the modified Green-Ampt method helps significantly in understanding and explaining the hydrological mechanism of a bioretention cell where the Darcy law or the classical Green-Ampt method is inadequate which works under constant rainfall intensities. Consequently, the rainfall is directly related with the outflow through the bioretention. This study discusses only the water quantity of bioretention.

Climate influence on dengue epidemics in Puerto Rico.

PubMed

Jury, Mark R

2008-10-01

The variability of the insect-borne disease dengue in Puerto Rico was studied in relation to climatic variables in the period 1979-2005. Annual and monthly reported dengue cases were compared with precipitation and temperature data. Results show that the incidence of dengue in Puerto Rico was relatively constant over time despite global warming, possibly due to the offsetting effects of declining rainfall, improving health care and little change in population. Seasonal fluctuations of dengue were driven by rainfall increases from May to November. Year-to-year variability in dengue cases was positively related to temperature, but only weakly associated with local rainfall and an index of El Nino Southern Oscillation (ENSO). Climatic conditions were mapped with respect to dengue cases and patterns in high and low years were compared. During epidemics, a low pressure system east of Florida draws warm humid air over the northwestern Caribbean. Long-term trends in past observed and future projected rainfall and temperatures were studied. Rainfall has declined slowly, but temperatures in the Caribbean are rising with the influence of global warming. Thus, dengue may increase in the future, and it will be necessary to anticipate dengue epidemics using climate forecasts, to reduce adverse health impacts.

How effective is the new generation of GPM satellite precipitation in characterizing the rainfall variability over Malaysia?

NASA Astrophysics Data System (ADS)

Mahmud, Mohd Rizaludin; Hashim, Mazlan; Reba, Mohd Nadzri Mohd

2017-08-01

We investigated the potential of the new generation of satellite precipitation product from the Global Precipitation Mission (GPM) to characterize the rainfall in Malaysia. Most satellite precipitation products have limited ability to precisely characterize the high dynamic rainfall variation that occurred at both time and scale in this humid tropical region due to the coarse grid size to meet the physical condition of the smaller land size, sub-continent and islands. Prior to the status quo, an improved satellite precipitation was required to accurately measure the rainfall and its distribution. Subsequently, the newly released of GPM precipitation product at half-hourly and 0.1° resolution served an opportunity to anticipate the aforementioned conflict. Nevertheless, related evidence was not found and therefore, this study made an initiative to fill the gap. A total of 843 rain gauges over east (Borneo) and west Malaysia (Peninsular) were used to evaluate the rainfall the GPM rainfall data. The assessment covered all critical rainy seasons which associated with Asian Monsoon including northeast (Nov. - Feb.), southwest (May - Aug.) and their subsequent inter-monsoon period (Mar. - Apr. & Sep. - Oct.). The ability of GPM to provide quantitative rainfall estimates and qualitative spatial rainfall patterns were analysed. Our results showed that the GPM had good capacity to depict the spatial rainfall patterns in less heterogeneous rainfall patterns (Spearman's correlation, 0.591 to 0.891) compared to the clustered one (r = 0.368 to 0.721). Rainfall intensity and spatial heterogeneity that is largely driven by seasonal monsoon has significant influence on GPM ability to resolve local rainfall patterns. In quantitative rainfall estimation, large errors can be primarily associated with the rainfall intensity increment. 77% of the error variation can be explained through rainfall intensity particularly the high intensity (> 35 mm d-1). A strong relationship between GPM rainfall and error was found from heavy ( 35 mm d-1) to violent rain (160 mm d-1). The output of this study provides reference regarding the performance of GPM data for respective hydrology studies in this region.

Proxy system modeling of tree-ring isotope chronologies over the Common Era

NASA Astrophysics Data System (ADS)

Anchukaitis, K. J.; LeGrande, A. N.

2017-12-01

The Asian monsoon can be characterized in terms of both precipitation variability and atmospheric circulation across a range of spatial and temporal scales. While multicentury time series of tree-ring widths at hundreds of sites across Asia provide estimates of past rainfall, the oxygen isotope ratios of annual rings may reveal broader regional hydroclimate and atmosphere-ocean dynamics. Tree-ring oxygen isotope chronologies from Monsoon Asia have been interpreted to reflect a local 'amount effect', relative humidity, source water and seasonality, and winter snowfall. Here, we use an isotope-enabled general circulation model simulation from the NASA Goddard Institute for Space Science (GISS) Model E and a proxy system model of the oxygen isotope composition of tree-ring cellulose to interpret the large-scale and local climate controls on δ 18O chronologies. Broad-scale dominant signals are associated with a suite of covarying hydroclimate variables including growing season rainfall amounts, relative humidity, and vapor pressure deficit. Temperature and source water influences are region-dependent, as are the simulated tree-ring isotope signals associated with the El Nino Southern Oscillation (ENSO) and large-scale indices of the Asian monsoon circulation. At some locations, including southern coastal Viet Nam, local precipitation isotope ratios and the resulting simulated δ 18O tree-ring chronologies reflect upstream rainfall amounts and atmospheric circulation associated with monsoon strength and wind anomalies.

How much of the interannual variability of East Asian summer rainfall is forced by SST?

NASA Astrophysics Data System (ADS)

He, Chao; Wu, Bo; Li, Chunhui; Lin, Ailan; Gu, Dejun; Zheng, Bin; Zhou, Tianjun

2016-07-01

It is widely accepted that the interannual variability of East Asian summer rainfall is forced by sea surface temperature (SST), and SST anomalies are widely used as predictors of East Asian summer rainfall. But it is still not very clear what percentage of the interannual rainfall variability is contributed by SST anomalies. In this study, Atmospheric general circulation model simulations forced by observed interannual varying SST are compared with those forced by the fixed annual cycle of SST climatology, and their ratios of interannual variance (IAV) are analyzed. The output of 12 models from the 5th Phase of Coupled Model Intercomparison Project (CMIP5) are adopted, and idealized experiments are done by Community Atmosphere Model version 4 (CAM4). Both the multi-model median of CMIP5 models and CAM4 experiments show that only about 18 % of the IAV of rainfall over East Asian land (EAL) is explained by SST, which is significantly lower than the tropical western Pacific, but comparable to the mid-latitude western Pacific. There is no significant difference between the southern part and the northern part of EAL in the percentages of SST contribution. The remote SST anomalies regulates rainfall over EAL probably by modulating the horizontal water vapor transport rather than the vertical motion, since the horizontal water vapor transport into EAL is strongly modulated by SST but the vertical motion over EAL is not. Previous studies argued about the relative importance of tropical Indian Ocean and tropical Pacific Ocean to East Asian summer rainfall anomalies. Our idealized experiments performed by CAM4 suggest that the contributions from these two ocean basins are comparable to each other, both of which account for approximately 6 % of the total IAV of rainfall over EAL.

Effects of ocean-atmosphere coupling on rainfall over the Indian Ocean and northwestern Pacific Ocean during boreal summer

NASA Astrophysics Data System (ADS)

Zhou, Z. Q.; Xie, S. P.; Zhou, W.

2016-12-01

Atmosphere general circulation model (AGCM), forced with specified SST, has been widely used in climate studies. On one hand, AGCM is much faster to run compared to coupled general circulation model (CGCM). Also, the identical SST forcing allows a clean evaluation of the atmospheric component of CGCM. On the other hand, the coupling between atmosphere and ocean is missed in such atmosphere-only simulations. It is not clear how such simplification could affect the simulate of the atmosphere. In this study, the impact of ocean-atmosphere coupling is studied by comparing a CGCM simulation with an AGCM simulation which is forced with monthly SSTs specified from the CGCM simulation. Particularly, we focus on the climatology and interannual variability of rainfall over the IONWP during boreal summer. The IONWP is a unique region with a strong negative correlation between sea surface temperature (SST) and rainfall during boreal summer on the interannual time scale. The lead/lag correlation analysis suggests a negative feedback of rainfall on SST, which is only reasonably captured by CGCMs. We find that the lack of the negative feedback in AGCM not only enhances the climatology and interannual variability of rainfall but also increases the internal variability of rainfall over the IONWP. A simple mechanism is proposed to explain such enhancement. In addition, AGCM is able to capture the large-scale rainfall pattern over the IONWP during boreal summer, this is because that rainfall here is caused by remote ENSO effect on the interannual time scale. Our results herein suggest that people should be more careful when using an AGCM for climate change studies.

Impact of Climatic Variability on Hydropower Reservoirs in the Paraiba Basin, Southeast of Brazil

NASA Astrophysics Data System (ADS)

Barros, A.; simoes, s

2002-05-01

During 2000/2001, a severe drought greatly reduced the volume of water available to Brazilian hydropower plants and lead to a national water rationing plan. To undestand the potential for climatic change in hydrological regimes and its impact on hydropower we chose the Paraiba Basin located in Southeast Brazil. Three important regional multi-purpose reservoirs are operating in this basin. Moreover, the Paraiba River is of great economic and environmental importance and also constitutes a major corridor connecting the two cities of Sao Paulo and Rio de Janeiro. We analyzed monthly and daily records for rainfall, streamflow and temperature using regression and variance analysis. Rainfall records do not show any significant trend since the 1930s/1940s. By contrast, analysis of seasonal patterns show that in the last twenty years rainfall has increased during autumn and winter (dry season) and decreased during spring and summer (rainy season). Comparison between rainfall and streaflow, from small catchment without man-made influences, shows a more pronounced deficit in streamflow when compared with rainfall. The shifts in seasonal rainfall could indicate a tendency towards a more uniform rainfall pattern and could serve to reduce the streamflow. However, the largest upward trends in temperature were found in the driest months (JJA). The increase in rainfall would not be sufficient to overcome increased of evaporation expect to the same period. Instead, such increase in evaporation could create an over more pronounced streamflow deficit. Climatic variability could be reducing water availability in these reservoirs especially in the driest months. To reduce the uncertainties in hydrological predictions, planners need to incorporate climatic variability, at the catchment scale, in order to accomodate the new conditions resulting from these changes.

Downscaling of Global Climate Change Estimates to Regional Scales: An Application to Iberian Rainfall in Wintertime.

NASA Astrophysics Data System (ADS)

von Storch, Hans; Zorita, Eduardo; Cubasch, Ulrich

1993-06-01

A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique.The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It is shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM).The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with the proposed statistical model. In an instantaneous `2 C02' doubling experiment, using the simulated change of the mean North Atlantic SLP field to predict Iberian rainfall yields, there is an insignificant increase of area-averaged rainfall of 1 mm/month, with maximum values of 4 mm/month in the northwest of the peninsula. In contrast, for the four GCM grid points representing the Iberian Peninsula, the change is 10 mm/month, with a minimum of 19 mm/month in the southwest. In the second experiment, with the IPCC scenario A ("business as usual") increase Of C02, the statistical-model results partially differ from the directly simulated rainfall changes: in the experimental range of 100 years, the area-averaged rainfall decreases by 7 mm/month (statistical model), and by 9 mm/month (GCM); at the same time the amplitude of the interdecadal variability is quite different.

Empirical models to predict the volumes of debris flows generated by recently burned basins in the western U.S.

USGS Publications Warehouse

Gartner, J.E.; Cannon, S.H.; Santi, P.M.; deWolfe, V.G.

2008-01-01

Recently burned basins frequently produce debris flows in response to moderate-to-severe rainfall. Post-fire hazard assessments of debris flows are most useful when they predict the volume of material that may flow out of a burned basin. This study develops a set of empirically-based models that predict potential volumes of wildfire-related debris flows in different regions and geologic settings. The models were developed using data from 53 recently burned basins in Colorado, Utah and California. The volumes of debris flows in these basins were determined by either measuring the volume of material eroded from the channels, or by estimating the amount of material removed from debris retention basins. For each basin, independent variables thought to affect the volume of the debris flow were determined. These variables include measures of basin morphology, basin areas burned at different severities, soil material properties, rock type, and rainfall amounts and intensities for storms triggering debris flows. Using these data, multiple regression analyses were used to create separate predictive models for volumes of debris flows generated by burned basins in six separate regions or settings, including the western U.S., southern California, the Rocky Mountain region, and basins underlain by sedimentary, metamorphic and granitic rocks. An evaluation of these models indicated that the best model (the Western U.S. model) explains 83% of the variability in the volumes of the debris flows, and includes variables that describe the basin area with slopes greater than or equal to 30%, the basin area burned at moderate and high severity, and total storm rainfall. This model was independently validated by comparing volumes of debris flows reported in the literature, to volumes estimated using the model. Eighty-seven percent of the reported volumes were within two residual standard errors of the volumes predicted using the model. This model is an improvement over previous models in that it includes a measure of burn severity and an estimate of modeling errors. The application of this model, in conjunction with models for the probability of debris flows, will enable more complete and rapid assessments of debris flow hazards following wildfire.

Climate and Leishmaniasis in French Guiana

PubMed Central

Roger, Amaury; Nacher, Mathieu; Hanf, Matthieu; Drogoul, Anne Sophie; Adenis, Antoine; Basurko, Celia; Dufour, Julie; Sainte Marie, Dominique; Blanchet, Denis; Simon, Stephane; Carme, Bernard; Couppié, Pierre

2013-01-01

To study the link between climatic variables and the incidence of leishmaniasis a study was conducted in Cayenne, French Guiana. Patients infected between January 1994 and December 2010. Meteorological data were studied in relation to the incidence of leishmaniasis using an ARIMA model. In the final model, the infections were negatively correlated with rainfall (with a 2-month lag) and with the number of days with rainfall > 50 mm (lags of 4 and 7 months). The variables that were positively correlated were temperature and the Multivariate El Niño Southern Oscillation Index with lags of 8 and 4 months, respectively. Significantly greater correlations were observed in March for rainfall and in November for the Multivariate El Niño/Southern Oscillation Index. Climate thus seems to be a non-negligible explanatory variable for the fluctuations of leishmaniasis. A decrease in rainfall is linked to increased cases 2 months later. This easily perceptible point could lead to an interesting prevention message. PMID:23939706

Skilful Seasonal Predictions of Summer European Rainfall

NASA Astrophysics Data System (ADS)

Dunstone, Nick; Smith, Doug; Scaife, Adam; Hermanson, Leon; Fereday, David; O'Reilly, Chris; Stirling, Alison; Eade, Rosie; Gordon, Margaret; MacLachlan, Craig; Woollings, Tim; Sheen, Katy; Belcher, Stephen

2018-04-01

Year-to-year variability in Northern European summer rainfall has profound societal and economic impacts; however, current seasonal forecast systems show no significant forecast skill. Here we show that skillful predictions are possible (r 0.5, p < 0.001) using the latest high-resolution Met Office near-term prediction system over 1960-2017. The model predictions capture both low-frequency changes (e.g., wet summers 2007-2012) and some of the large individual events (e.g., dry summer 1976). Skill is linked to predictable North Atlantic sea surface temperature variability changing the supply of water vapor into Northern Europe and so modulating convective rainfall. However, dynamical circulation variability is not well predicted in general—although some interannual skill is found. Due to the weak amplitude of the forced model signal (likely caused by missing or weak model responses), very large ensembles (>80 members) are required for skillful predictions. This work is promising for the development of European summer rainfall climate services.

Monsoon rainfall over India in June and link with northwest tropical pacific - June ISMR and link with northwest tropical pacific

NASA Astrophysics Data System (ADS)

Surendran, Sajani; Gadgil, Sulochana; Rajendran, Kavirajan; Varghese, Stella Jes; Kitoh, Akio

2018-03-01

Recent years have witnessed large interannual variation of all-India rainfall (AIR) in June, with intermittent large deficits and excesses. Variability of June AIR is found to have the strongest link with variation of rainfall over northwest tropical Pacific (NWTP), with AIR deficit (excess) associated with enhancement (suppression) of NWTP rainfall. This association is investigated using high-resolution Meteorological Research Institute model which shows high skill in simulating important features of Asian summer monsoon, its variability and the inverse relationship between NWTP rainfall and AIR. Analysis of the variation of NWTP rainfall shows that it is associated with a change in the latitudinal position of subtropical westerly jet over the region stretching from West of Tibetan Plateau (WTP) to NWTP and the phase of Rossby wave steered in it with centres over NWTP and WTP. In years with large rainfall excess/deficit, the strong link between AIR and NWTP rainfall exists through differences in Rossby wave phase steered in the jet. The positive phase of the WTP-NWTP pattern, with troughs over WTP and west of NWTP, tends to be associated with increased rainfall over NWTP and decreased AIR. This scenario is reversed in the opposite phase. Thus, the teleconnection between NWTP rainfall and AIR is a manifestation of the difference in the phase of Rossby wave between excess and deficit years, with centres over WTP and NWTP. This brings out the importance of prediction of phase of Rossby waves over WTP and NWTP in advance, for prediction of June rainfall over India.

Use of Machine Learning Techniques for Identification of Robust Teleconnections to East African Rainfall Variability

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, F. R.; Funk, C.

2014-01-01

Hidden Markov models can be used to investigate structure of subseasonal variability. East African short rain variability has connections to large-scale tropical variability. MJO - Intraseasonal variations connected with appearance of "wet" and "dry" states. ENSO/IOZM SST and circulation anomalies are apparent during years of anomalous residence time in the subseasonal "wet" state. Similar results found in previous studies, but we can interpret this with respect to variations of subseasonal wet and dry modes. Reveal underlying connections between MJO/IOZM/ENSO with respect to East African rainfall.

Evaluation of common bean lines for adaptation to high temperatures in Honduras

USDA-ARS?s Scientific Manuscript database

As in other regions worldwide, common bean (Phaseolus vulgaris L.) production in Central America and the Caribbean (CA/C) region is threatened by effects of climate change including increasing temperatures and drought due to variable rainfall patterns. One of the main alternatives for increasing ada...

Preliminary response of primary production and community composition to precipitation variation in a temperate grassland

USDA-ARS?s Scientific Manuscript database

a) Background/Questions/Methods Grassland ecosystems are water-limited and show the highest interannual ANPP variability across biomes. Changes in annual amounts or seasonality of rainfall may interact with soil texture to impact grassland ecosystem functions including net primary productivity (NPP...

Uncertainty evaluation of a regional real-time system for rain-induced landslides

NASA Astrophysics Data System (ADS)

Kirschbaum, Dalia; Stanley, Thomas; Yatheendradas, Soni

2015-04-01

A new prototype regional model and evaluation framework has been developed over Central America and the Caribbean region using satellite-based information including precipitation estimates, modeled soil moisture, topography, soils, as well as regionally available datasets such as road networks and distance to fault zones. The algorithm framework incorporates three static variables: a susceptibility map; a 24-hr rainfall triggering threshold; and an antecedent soil moisture variable threshold, which have been calibrated using historic landslide events. The thresholds are regionally heterogeneous and are based on the percentile distribution of the rainfall or antecedent moisture time series. A simple decision tree algorithm framework integrates all three variables with the rainfall and soil moisture time series and generates a landslide nowcast in real-time based on the previous 24 hours over this region. This system has been evaluated using several available landslide inventories over the Central America and Caribbean region. Spatiotemporal uncertainty and evaluation metrics of the model are presented here based on available landslides reports. This work also presents a probabilistic representation of potential landslide activity over the region which can be used to further refine and improve the real-time landslide hazard assessment system as well as better identify and characterize the uncertainties inherent in this type of regional approach. The landslide algorithm provides a flexible framework to improve hazard estimation and reduce uncertainty at any spatial and temporal scale.

Coupling of Indian and East Asian Monsoon Precipitation in July-August

NASA Astrophysics Data System (ADS)

Day, J. A.; Fung, I. Y.; Risi, C. M.

2014-12-01

Recent work suggests that summer rainfall in the Indian and East Asian monsoons results from different mechanisms. The onset of intense convection in India is mediated by Hadley Cell transitions, whereas frontal rainfall in China (most notably during Meiyu season in June) arises from forced meridional convergence and zonal heat transport in the wake of the Tibetan Plateau. However, the leading mode of July-August interannual rainfall variability for All-Asia (defined as the region within 68E-140E and 5N-45N) demonstrates a statistically significant coupling between monthly anomalies in India and China. In particular, positive anomalies along the Himalayan Foothills are associated with positive anomalies along the Yangtze River, and also with negative anomalies over central India and northern and southern China. The entire pattern reverses in dry years over the Himalayan Foothills. This coupling is not significantly correlated with ENSO, the leading mode of global interannual variability. We propose that a channel of moisture transport links the Bay of Bengal to the Yangtze River valley across the high terrain of the Yunnan Plateau, on the southeast edge of the Tibetan Plateau. This channel only activates in July, when the maximum of moist static energy (MSE) shifts to land, and weakens in September with the cooling of Bay of Bengal SST. Our mechanism is substantiated by analysis of output from the LMDZ5 model, which includes a high-resolution nested grid nudged to reanalysis, improving the simulation of the Indian Monsoon and performance near high topography. Potential changes in moisture transport across the Yunnan Plateau under 21st century warming conditions may lead to modified interannual variability of Asian rainfall.

Effects of rainfall partitioning in the seasonal and spatial variability of soil water content in a Mediterranean downy oak forest

NASA Astrophysics Data System (ADS)

Garcia-Estringana, P.; Latron, J.; Molina, A. J.; Llorens, P.

2012-04-01

Rainfall partitioning fluxes (throughfall and stemflow) have a large degree of temporal and spatial variability and may consequently lead to significant changes in the volume and composition of water that reach the understory and the soil. The objective of this work is to study the effect of rainfall partitioning on the seasonal and spatial variability of the soil water content in a Mediterranean downy oak forest (Quercus pubescens), located in the Vallcebre research catchments (42° 12'N, 1° 49'E). The monitoring design, started on July 2011, consists of a set of 20 automatic rain recorders and 40 automatic soil moisture probes located below the canopy. One hundred hemispheric photographs of the canopy were used to place the instruments at representative locations (in terms of canopy cover) within the plot. Bulk rainfall, stemflow and meteorological conditions above the forest cover are also automatically recorded. Canopy cover, in leaf and leafless periods, as well as biometric characteristics of the plot, are also regularly measured. This work presents the first results describing throughfall and soil moisture spatial variability during both the leaf and leafless periods. The main drivers of throughfall variability, as canopy structure and meteorological conditions are also analysed.

Interannual rainfall variability in the Amazon basin and sea-surface temperatures in the equatorial Pacific and the tropical Atlantic Oceans

NASA Astrophysics Data System (ADS)

Ronchail, Josyane; Cochonneau, Gérard; Molinier, Michel; Guyot, Jean-Loup; Chaves, Adriana Goretti De Miranda; Guimarães, Valdemar; de Oliveira, Eurides

2002-11-01

Rainfall variability in the Amazon basin is studied in relation to sea-surface temperatures (SSTs) in the equatorial Pacific and the northern and southern tropical Atlantic during the 1977-99 period, using the HiBAm original rainfall data set and complementary cluster and composite analyses.The northeastern part of the basin, north of 5 °S and east of 60 °W, is significantly related with tropical SSTs: a rainier wet season is observed when the equatorial Pacific and the northern (southern) tropical Atlantic are anomalously cold (warm). A shorter and drier wet season is observed during El Niño events and negative rainfall anomalies are also significantly associated with a warm northern Atlantic in the austral autumn and a cold southern Atlantic in the spring. The northeastern Amazon rainfall anomalies are closely related with El Niño-southern oscillation during the whole year, whereas the relationships with the tropical Atlantic SST anomalies are mainly observed during the autumn. A time-space continuity is observed between El Niño-related rainfall anomalies in the northeastern Amazon, those in the northern Amazon and south-eastern Amazon, and those in northern South America and in the Nordeste of Brazil.A reinforcement of certain rainfall anomalies is observed when specific oceanic events combine. For instance, when El Niño and cold SSTs in the southern Atlantic are associated, very strong negative anomalies are observed in the whole northern Amazon basin. Nonetheless, the comparison of the cluster and the composite analyses results shows that the rainfall anomalies in the northeastern Amazon are not always associated with tropical SST anomalies.In the southern and western Amazon, significant tropical SST-related rainfall anomalies are very few and spatially variable. The precipitation origins differ from those of the northeastern Amazon: land temperature variability, extratropical perturbations and moisture advection are important rainfall factors, as well as SSTs. This could partially explain why: (a) the above-mentioned signals weaken or disappear, with the exception of the relative dryness that is observed at the peak of an El Niño event and during the dry season when northern Atlantic SSTs are warmer than usual; (b) rainfall anomalies tend to resemble those of southeastern South America, noticeably at the beginning and the end of El Niño and La Niña events; (c) some strong excesses of rain are not associated with any SST anomalies and merit further investigation.

A stochastical event-based continuous time step rainfall generator based on Poisson rectangular pulse and microcanonical random cascade models

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Zha, Tingting; Schümberg, Sabine; Müller, Hannes; Maurer, Thomas; Hinz, Christoph

2017-04-01

Rainfall variability within a storm is of major importance for fast hydrological processes, e.g. surface runoff, erosion and solute dissipation from surface soils. To investigate and simulate the impacts of within-storm variabilities on these processes, long time series of rainfall with high resolution are required. Yet, observed precipitation records of hourly or higher resolution are in most cases available only for a small number of stations and only for a few years. To obtain long time series of alternating rainfall events and interstorm periods while conserving the statistics of observed rainfall events, the Poisson model can be used. Multiplicative microcanonical random cascades have been widely applied to disaggregate rainfall time series from coarse to fine temporal resolution. We present a new coupling approach of the Poisson rectangular pulse model and the multiplicative microcanonical random cascade model that preserves the characteristics of rainfall events as well as inter-storm periods. In the first step, a Poisson rectangular pulse model is applied to generate discrete rainfall events (duration and mean intensity) and inter-storm periods (duration). The rainfall events are subsequently disaggregated to high-resolution time series (user-specified, e.g. 10 min resolution) by a multiplicative microcanonical random cascade model. One of the challenges of coupling these models is to parameterize the cascade model for the event durations generated by the Poisson model. In fact, the cascade model is best suited to downscale rainfall data with constant time step such as daily precipitation data. Without starting from a fixed time step duration (e.g. daily), the disaggregation of events requires some modifications of the multiplicative microcanonical random cascade model proposed by Olsson (1998): Firstly, the parameterization of the cascade model for events of different durations requires continuous functions for the probabilities of the multiplicative weights, which we implemented through sigmoid functions. Secondly, the branching of the first and last box is constrained to preserve the rainfall event durations generated by the Poisson rectangular pulse model. The event-based continuous time step rainfall generator has been developed and tested using 10 min and hourly rainfall data of four stations in North-Eastern Germany. The model performs well in comparison to observed rainfall in terms of event durations and mean event intensities as well as wet spell and dry spell durations. It is currently being tested using data from other stations across Germany and in different climate zones. Furthermore, the rainfall event generator is being applied in modelling approaches aimed at understanding the impact of rainfall variability on hydrological processes. Reference Olsson, J.: Evaluation of a scaling cascade model for temporal rainfall disaggregation, Hydrology and Earth System Sciences, 2, 19.30

Heavy rainfall in Mediterranean cyclones. Part I: contribution of deep convection and warm conveyor belt

NASA Astrophysics Data System (ADS)

Flaounas, Emmanouil; Kotroni, Vassiliki; Lagouvardos, Konstantinos; Gray, Suzanne L.; Rysman, Jean-François; Claud, Chantal

2018-04-01

In this study, we provide an insight to the role of deep convection (DC) and the warm conveyor belt (WCB) as leading processes to Mediterranean cyclones' heavy rainfall. To this end, we use reanalysis data, lighting and satellite observations to quantify the relative contribution of DC and the WCB to cyclone rainfall, as well as to analyse the spatial and temporal variability of these processes with respect to the cyclone centre and life cycle. Results for the period 2005-2015 show that the relationship between cyclone rainfall and intensity has high variability and demonstrate that even intense cyclones may produce low rainfall amounts. However, when considering rainfall averages for cyclone intensity bins, a linear relationship was found. We focus on the 500 most intense tracked cyclones (responsible for about 40-50% of the total 11-year Mediterranean rainfall) and distinguish between the ones producing high and low rainfall amounts. DC and the WCB are found to be the main cause of rainfall for the former (producing up to 70% of cyclone rainfall), while, for the latter, DC and the WCB play a secondary role (producing up to 50% of rainfall). Further analysis showed that rainfall due to DC tends to occur close to the cyclones' centre and to their eastern sides, while the WCBs tend to produce rainfall towards the northeast. In fact, about 30% of rainfall produced by DC overlaps with rainfall produced by WCBs but this represents only about 8% of rainfall produced by WCBs. This suggests that a considerable percentage of DC is associated with embedded convection in WCBs. Finally, DC was found to be able to produce higher rain rates than WCBs, exceeding 50 mm in 3-h accumulated rainfall compared to a maximum of the order of 40 mm for WCBs. Our results demonstrate in a climatological framework the relationship between cyclone intensity and processes that lead to heavy rainfall, one of the most prominent environmental risks in the Mediterranean. Therefore, we set perspectives for a deeper analysis of the favourable atmospheric conditions that yield high impact weather.

A vectorial capacity product to monitor changing malaria transmission potential in epidemic regions of Africa

USGS Publications Warehouse

Ceccato, Pietro; Vancutsem, Christelle; Klaver, Robert; Rowland, James; Connor, Stephen J.

2012-01-01

Rainfall and temperature are two of the major factors triggering malaria epidemics in warm semi-arid (desert-fringe) and high altitude (highland-fringe) epidemic risk areas. The ability of the mosquitoes to transmit Plasmodium spp. is dependent upon a series of biological features generally referred to as vectorial capacity. In this study, the vectorial capacity model (VCAP) was expanded to include the influence of rainfall and temperature variables on malaria transmission potential. Data from two remote sensing products were used to monitor rainfall and temperature and were integrated into the VCAP model. The expanded model was tested in Eritrea and Madagascar to check the viability of the approach. The analysis of VCAP in relation to rainfall, temperature and malaria incidence data in these regions shows that the expanded VCAP correctly tracks the risk of malaria both in regions where rainfall is the limiting factor and in regions where temperature is the limiting factor. The VCAP maps are currently offered as an experimental resource for testing within Malaria Early Warning applications in epidemic prone regions of sub-Saharan Africa. User feedback is currently being collected in preparation for further evaluation and refinement of the VCAP model.

Rainfall spatiotemporal variability relation to wetlands hydroperiods

NASA Astrophysics Data System (ADS)

Serrano-Hidalgo, Carmen; Guardiola-Albert, Carolina; Fernandez-Naranjo, Nuria

2017-04-01

Doñana natural space (Southwestern Spain) is one of the largest protected wetlands in Europe. The wide marshes present in this natural space have such ecological value that this wetland has been declared a Ramsar reserve in 1982. Apart from the extensive marsh, there are also small lagoons and seasonally flooded areas which are likewise essential to maintain a wide variety of valuable habitats. Hydroperiod, the length of time each point remains flooded along an annual cycle, is a critical ecological parameter that shapes aquatic plants and animals distribution and determines available habitat for many of the living organisms in the marshes. Recently, there have been published two different works estimating the hydroperiod of Doñana lagoons with Landsat Time Series images (Cifuentes et al., 2015; Díaz-Delgado et al., 2016). In both works the flooding cycle hydroperiod in Doñana marshes reveals a flooding regime mainly driven by rainfall, evapotranspiration, topography and local hydrological management actions. The correlation found between rainfall and hydroperiod is studied differently in both works. While in one the rainfall is taken from one raingauge (Cifuentes et al., 2015), the one performed by Díaz-Delgado (2016) uses annual rainfall maps interpolated with the inverse of the distance method. The rainfall spatiotemporal variability in this area can be highly significant; however the amount of this importance has not been quantified at the moment. In the present work the geostatistical tool known as spatiotemporal variogram is used to study the rainfall spatiotemporal variability. The spacetime package implemented in R (Pebesma, 2012) facilities its computation from a high rainfall data base of more than 100 raingauges from 1950 to 2016. With the aid of these variograms the rainfall spatiotemporal variability is quantified. The principal aim of the present work is the study of the relation between the rainfall spatiotemporal variability and the hydroperiods of wetlands present in Doñana natural space. Key issues: spatiotemporal variability, geostatistics, hydroperiod, wetlands. References: Cifuentes, V., García, M.A., Checa, M.J. & Escudero, R. (2015). Estimación por teledetección de la superficie de la lámina de agua y los niveles de profundidad de las lagunas en los humedales de la Campiña Andaluza Central incluidos en la demarcación hidrográfica del Guadalquivir. Teledetección: Humedales y Espacios Protegidos. Presented in XVI Congreso de la Asociación Española de Teledetección. pp. 322-325. Sevilla 21-23 octubre 2015. http://ocs.ebd.csic.es/index.php/AET/2015/schedConf/presentations Díaz-Delgado, R., Carro, F., Herruzo, F. Q., Osuna, A., & Baena, M. (2016). Contribución del seguimiento ecológico a largo plazo a la investigación y la gestión en la plataforma LTSER-Doñana. Revista Ecosistemas, 25(1), 9-18. Pebesma, E. (2012). spacetime: Spatio-temporal data in r. Journal of Statistical Software, 51(7), 1-30.

Changing pattern of natural hazards due to extreme hydro-meteorological conditions (Apulia, southern Italy)

NASA Astrophysics Data System (ADS)

Polemio, Maurizio; Lonigro, Teresa

2013-04-01

Recent international researches have underlined the evidences of climate changes throughout the world. Among the consequences of climate change, there is the increase in the frequency and magnitude of natural disasters, such as droughts, windstorms, heat waves, landslides, floods and secondary floods (i.e. rapid accumulation or pounding of surface water with very low flow velocity). The Damaging Hydrogeological Events (DHEs) can be defined as the occurrence of one or more simultaneous aforementioned phenomena causing damages. They represent a serious problem, especially in DHE-prone areas with growing urbanisation. In these areas the increasing frequency of extreme hydrological events could be related to climate variations and/or urban development. The historical analysis of DHEs can support decision making and land-use planning, ultimately reducing natural risks. The paper proposes a methodology, based on both historical and time series approaches, used for describing the influence of climatic variability on the number of phenomena observed. The historical approach is finalised to collect phenomenon historical data. The historical flood and landslide data are important for the comprehension of the evolution of a study area and for the estimation of risk scenarios as a basis for civil protection purposes. Phenomenon historical data is useful for expanding the historical period of investigation in order to assess the occurrence trend of DHEs. The time series approach includes the collection and the statistical analysis of climatic and rainfall data (monthly rainfall, wet days, rainfall intensity, and temperature data together with the annual maximum of short-duration rainfall data, from 1 hour to 5 days), which are also used as a proxy for floods and landslides. The climatic and rainfall data are useful to characterise the climate variations and trends and to roughly assess the effects of these trends on river discharge and on the triggering of landslides. The time series approach is completed by tools to analyse simultaneously all data types. The methodology was tested considering a selected Italian region (Apulia, southern Italy). The data were collected in two databases: a damaging hydrogeological event database (1186 landslides and floods since 1918) and a climate database (from 1877; short-duration rainfall from 1921). A statistically significant decreasing trend of rainfall intensity and an increasing trend of temperature, landslides, and DHEs were observed. A generalised decreasing trend of short-duration rainfall was observed. If there is not an evident relationship between climate variability and the variability of DHE occurrences, the role of anthropogenic modifications (increasing use or misuse of flood- and landslide-prone areas) could be hypothesized to justify the increasing occurrences of floods and landslides.. This study identifies the advantages of a simplifying approach to reduce the intrinsic complexities of the spatial-temporal analysis of climate variability, permitting the simultaneous analysis of the modification of flood and landslide occurrences.

Influences of large-scale convection and moisture source on monthly precipitation isotope ratios observed in Thailand, Southeast Asia

NASA Astrophysics Data System (ADS)

Wei, Zhongwang; Lee, Xuhui; Liu, Zhongfang; Seeboonruang, Uma; Koike, Masahiro; Yoshimura, Kei

2018-04-01

Many paleoclimatic records in Southeast Asia rely on rainfall isotope ratios as proxies for past hydroclimatic variability. However, the physical processes controlling modern rainfall isotopic behaviors in the region is poorly constrained. Here, we combined isotopic measurements at six sites across Thailand with an isotope-incorporated atmospheric circulation model (IsoGSM) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to investigate the factors that govern the variability of precipitation isotope ratios in this region. Results show that rainfall isotope ratios are both correlated with local rainfall amount and regional outgoing longwave radiation, suggesting that rainfall isotope ratios in this region are controlled not only by local rain amount (amount effect) but also by large-scale convection. As a transition zone between the Indian monsoon and the western North Pacific monsoon, the spatial difference of observed precipitation isotope among different sites are associated with moisture source. These results highlight the importance of regional processes in determining rainfall isotope ratios in the tropics and provide constraints on the interpretation of paleo-precipitation isotope records in the context of regional climate dynamics.

Spatial and temporal variability in the R-5 infiltration data set: Déjà vu and rainfall-runoff simulations

NASA Astrophysics Data System (ADS)

Loague, Keith; Kyriakidis, Phaedon C.

1997-12-01

This paper is a continuation of the event-based rainfall-runoff model evaluation study reported by Loague and Freeze [1985[. Here we reevaluate the performance of a quasi-physically based rainfall-runoff model for three large events from the well-known R-5 catchment. Five different statistical criteria are used to quantitatively judge model performance. Temporal variability in the large R-5 infiltration data set [Loague and Gander, 1990] is filtered by working in terms of permeability. The transformed data set is reanalyzed via geostatistical methods to model the spatial distribution of permeability across the R-5 catchment. We present new estimates of the spatial distribution of infiltration that are in turn used in our rainfall-runoff simulations with the Horton rainfall-runoff model. The new rainfall-runoff simulations, complicated by reinfiltration impacts at the smaller scales of characterization, indicate that the near-surface hydrologic response of the R-5 catchment is most probably dominated by a combination of the Horton and Dunne overland flow mechanisms.

Current and Future Urban Stormwater Flooding Scenarios in the Southeast Florida Coasts

NASA Astrophysics Data System (ADS)

Huq, E.; Abdul-Aziz, O. I.

2016-12-01

This study computed rainfall-fed stormwater flooding under the historical and future reference scenarios for the Southeast Coasts Basin of Florida. A large-scale, mechanistic rainfall-runoff model was developed using the U.S. E.P.A. Storm Water Management Model (SWMM 5.1). The model parameterized important processes of urban hydrology, groundwater, and sea level, while including hydroclimatological variables and land use features. The model was calibrated and validated with historical streamflow data. It was then used to estimate the sensitivity of stormwater runoff to the reference changes in hydroclimatological variables (rainfall and evapotranspiration) and different land use/land cover features (imperviousness, roughness). Furthermore, historical (1970-2000) and potential 2050s stormwater budgets were also estimated for the Florida Southeast Coasts Basin by incorporating climatic projections from different GCMs and RCMs, as well as by using relevant projections of sea level and land use/cover. Comparative synthesis of the historical and future scenarios along with the results of sensitivity analysis can aid in efficient management of stormwater flooding for the southeast Florida coasts and similar urban centers under a changing regime of climate, sea level, land use/cover and hydrology.

The influence of El Niño-Southern Oscillation regimes on eastern African vegetation and its future implications under the RCP8.5 warming scenario

NASA Astrophysics Data System (ADS)

Fer, Istem; Tietjen, Britta; Jeltsch, Florian; Wolff, Christian

2017-09-01

The El Niño-Southern Oscillation (ENSO) is the main driver of the interannual variability in eastern African rainfall, with a significant impact on vegetation and agriculture and dire consequences for food and social security. In this study, we identify and quantify the ENSO contribution to the eastern African rainfall variability to forecast future eastern African vegetation response to rainfall variability related to a predicted intensified ENSO. To differentiate the vegetation variability due to ENSO, we removed the ENSO signal from the climate data using empirical orthogonal teleconnection (EOT) analysis. Then, we simulated the ecosystem carbon and water fluxes under the historical climate without components related to ENSO teleconnections. We found ENSO-driven patterns in vegetation response and confirmed that EOT analysis can successfully produce coupled tropical Pacific sea surface temperature-eastern African rainfall teleconnection from observed datasets. We further simulated eastern African vegetation response under future climate change as it is projected by climate models and under future climate change combined with a predicted increased ENSO intensity. Our EOT analysis highlights that climate simulations are still not good at capturing rainfall variability due to ENSO, and as we show here the future vegetation would be different from what is simulated under these climate model outputs lacking accurate ENSO contribution. We simulated considerable differences in eastern African vegetation growth under the influence of an intensified ENSO regime which will bring further environmental stress to a region with a reduced capacity to adapt effects of global climate change and food security.

Technical Report Series on Global Modeling and Data Assimilation. Volume 12; Comparison of Satellite Global Rainfall Algorithms

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Chang, Alfred T. C.; Chiu, Long S.

1997-01-01

Seventeen months of rainfall data (August 1987-December 1988) from nine satellite rainfall algorithms (Adler, Chang, Kummerow, Prabhakara, Huffman, Spencer, Susskind, and Wu) were analyzed to examine the uncertainty of satellite-derived rainfall estimates. The variability among algorithms, measured as the standard deviation computed from the ensemble of algorithms, shows regions of high algorithm variability tend to coincide with regions of high rain rates. Histograms of pattern correlation (PC) between algorithms suggest a bimodal distribution, with separation at a PC-value of about 0.85. Applying this threshold as a criteria for similarity, our analyses show that algorithms using the same sensor or satellite input tend to be similar, suggesting the dominance of sampling errors in these satellite estimates.

Rainfall variability in southern Spain on decadal to centennial time scales

NASA Astrophysics Data System (ADS)

Rodrigo, F. S.; Esteban-Parra, M. J.; Pozo-Vázquez, D.; Castro-Díez, Y.

2000-06-01

In this work a long rainfall series in Andalusia (southern Spain) is analysed. Methods of historical climatology were used to reconstruct a 500-year series from historical sources. Different statistical tools were used to detect and characterize significant changes in this series. Results indicate rainfall fluctuations, without abrupt changes, in the following alternating dry and wet phases: 1501-1589 dry, 1590-1649 wet, 1650-1775 dry, 1776-1937 wet and 1938-1997 dry. Possible causal mechanisms are discussed, emphasizing the important contribution of the North Atlantic Oscillation (NAO) to rainfall variability in the region. Solar activity is discussed in relation to the Maunder Minimum period, and finally the past and present are compared. Results indicate that the magnitude of fluctuations is similar in the past and present.

Rainfall and runoff variability in Ethiopia

NASA Astrophysics Data System (ADS)

Billi, Paolo; Fazzini, Massimiliano; Tadesse Alemu, Yonas; Ciampalini, Rossano

2014-05-01

Rainfall and river flow variability have been deeply investigated and and the impact of climate change on both is rather well known in Europe (EEA, 2012) or in other industrialized countries. Reports of international organizations (IPCC, 2012) and the scientific literature provide results and outlooks that were found contrasting and spatially incoherent (Manton et al., 2001; Peterson et al., 2002; Griffiths et al., 2003; Herath and Ratnayake, 2004) or weakened by limitation of data quality and quantity. According to IPCC (2012), in East Africa precipitation there are contrasting regional and seasonal variations and trends, though Easterling et al. (2000) and Seleshi and Camberlin (2006) report decreasing trends in heavy precipitation over parts of Ethiopia during the period 1965-2002. Literature on the impact of climate change on river flow is scarce in Africa and IPCC Technical Paper VI (IPCC, 2008) concluded that no evidence, based on instrumental records, has been found for a climate-driven globally widespread change in the magnitude/frequency of floods during the last decades (Rosenzweig et al., 2007), though increases in runoff and increased risk of flood events in East Africa are expected. Some papers have faced issues regarding rainfall and river flow variability in Ethiopia (e.g. Seleshi and Demaree, 1995; Osman and Sauerborn, 2002; Seleshi and Zanke, 2004; Meze-Hausken, 2004; Korecha and Barnston, 2006; Cheung et al., 2008) but their investigations are commonly geographically limited or used a small number of rain and flow gauges with the most recent data bound to the beginning of the last decade. In this study an attempt to depict rainfall and river flow variability, considering the longer as possible time series for the largest as possible number of meteo-stations and flow gauge evenly distributed across Ethiopia, is presented. 25 meteo-stations and 21 flow gauges with as much as possible continuous data records were selected. The length of the time series ranges between 35 to 50 and 9 to 49 years for rainfall and river flow, respectively. In order to improve the poor linear correlation model to describe rainfall gradient with altitude a simple topographic parameter is introduced capable to better depict the spatial variability of annual rainfall and its coefficient of variation. The small rains (Belg) were found to be much more unpredictable than the long, monsoon-type rains (Kiremt) and hence much more out of phase with the variation of annual precipitation amount that is significantly influenced by the Kiremt rains. In order to investigate the long term trends, rainfall anomalies were calculated as Z score for annual, Belg and Kiremt precipitation for all the stations and average values are calculated and plotted against time. The three Z trend lines obtained show no marked deviation from the mean as only an almost negligible decreasing trend is observed. Rainfall intensity in 24 hours is analyzed and the trend line of the maximum intensity averaged over the maximum value of each year recorded at each meteo-station is constructed. These data indicate a general decrease in daily rainfall intensity across Ethiopia with clear exceptions in a few selected areas. The same procedure, based on the Z scores, used to analyze rainfall variability is applied also to the river flow data and a similar result is obtained. If compared with rainfall, annual runoff shows a much wider range of variation among the study rivers. This issue is discussed and possible explanations are presented.

Relationships between Tropical Rainfall Events and Regional Annual Rainfall Anomalies

NASA Astrophysics Data System (ADS)

Painter, C.; Varble, A.; Zipser, E. J.

2016-12-01

Regional annual precipitation anomalies strongly impact the health of regional ecosystems, water resources, agriculture, and the probability of flood and drought conditions. Individual event characteristics, including rain rate, areal coverage, and stratiform fraction are also crucial in considering large-scale impacts on these resources. Therefore, forecasting individual event characteristics is important and could potentially be improved through correlation with longer and better predicted timescale environmental variables such as annual rainfall. This study examines twelve years of retrieved rainfall characteristics from the Tropical Rainfall Measuring Mission (TRMM) satellite at a 5° x 5° resolution between 35°N and 35°S, as a function of annual rainfall anomaly derived from Global Precipitation Climatology Project data. Rainfall event characteristics are derived at a system scale from the University of Utah TRMM Precipitation Features database and at a 5-km pixel scale from TRMM 2A25 products. For each 5° x 5° grid box and year, relationships between these characteristics and annual rainfall anomaly are derived. Additionally, years are separated into wet and dry groups for each grid box and are compared versus one another. Convective and stratiform rain rates, along with system area and volumetric rainfall, generally increase during wetter years, and this increase is most prominent over oceans. This is in agreement with recent studies suggesting that convective systems become larger and rainier when regional annual rainfall increases or when the climate warms. Over some land regions, on the other hand, system rain rate, volumetric rainfall, and area actually decrease as annual rainfall increases. Therefore, land and ocean regions generally exhibit different relationships. In agreement with recent studies of extreme rainfall in a changing climate, the largest and rainiest systems increase in relative size and intensity compared to average systems, and do so as a function of annual rainfall in most tropical regions. However, select land regions such as the Congo fail to follow this tendency. Changes in seasonal and diurnal cycles of PF characteristics as a function of regional annual rainfall anomaly are also analyzed.

Robust signals of future projections of Indian summer monsoon rainfall by IPCC AR5 climate models: Role of seasonal cycle and interannual variability

NASA Astrophysics Data System (ADS)

Jayasankar, C. B.; Surendran, Sajani; Rajendran, Kavirajan

2015-05-01

Coupled Model Intercomparison Project phase 5 (Fifth Assessment Report of Intergovernmental Panel on Climate Change) coupled global climate model Representative Concentration Pathway 8.5 simulations are analyzed to derive robust signals of projected changes in Indian summer monsoon rainfall (ISMR) and its variability. Models project clear future temperature increase but diverse changes in ISMR with substantial intermodel spread. Objective measures of interannual variability (IAV) yields nearly equal chance for future increase or decrease. This leads to discrepancy in quantifying changes in ISMR and variability. However, based primarily on the physical association between mean changes in ISMR and its IAV, and objective methods such as k-means clustering with Dunn's validity index, mean seasonal cycle, and reliability ensemble averaging, projections fall into distinct groups. Physically consistent groups of models with the highest reliability project future reduction in the frequency of light rainfall but increase in high to extreme rainfall and thereby future increase in ISMR by 0.74 ± 0.36 mm d-1, along with increased future IAV. These robust estimates of future changes are important for useful impact assessments.

Assessing future changes in the occurrence of rainfall-induced landslides at a regional scale.

PubMed

Gariano, S L; Rianna, G; Petrucci, O; Guzzetti, F

2017-10-15

According to the fifth report of the Intergovernmental Panel on Climate Change, an increase in the frequency and the intensity of extreme rainfall is expected in the Mediterranean area. Among different impacts, this increase might result in a variation in the frequency and the spatial distribution of rainfall-induced landslides, and in an increase in the size of the population exposed to landslide risk. We propose a method for the regional-scale evaluation of future variations in the occurrence of rainfall-induced landslides, in response to changes in rainfall regimes. We exploit information on the occurrence of 603 rainfall-induced landslides in Calabria, southern Italy, in the period 1981-2010, and daily rainfall data recorded in the same period in the region. Furthermore, we use high-resolution climate projections based on RCP4.5 and RCP8.5 scenarios. In particular, we consider the mean variations between a 30-year future period (2036-2065) and the reference period 1981-2010 in three variables assumed as proxy for landslide activity: annual rainfall, seasonal cumulated rainfall, and annual maxima of daily rainfall. Based on reliable correlations between landslide occurrence and weather variables estimated in the reference period, we assess future variations in rainfall-induced landslide occurrence for all the municipalities of Calabria. A +45.7% and +21.2% average regional variation in rainfall-induced landslide occurrence is expected in the region for the period 2036-2065, under the RCP4.5 and RCP8.5 scenario, respectively. We also investigate the future variations in the impact of rainfall-induced landslides on the population of Calabria. We find a +80.2% and +54.5% increase in the impact on the population for the period 2036-2065, under the RCP4.5 and RCP8.5 scenario, respectively. The proposed method is quantitative and reproducible, thus it can be applied in similar regions, where adequate landslide and rainfall information is available. Copyright © 2017 Elsevier B.V. All rights reserved.

Encounter risk analysis of rainfall and reference crop evapotranspiration in the irrigation district

NASA Astrophysics Data System (ADS)

Zhang, Jinping; Lin, Xiaomin; Zhao, Yong; Hong, Yang

2017-09-01

Rainfall and reference crop evapotranspiration are random but mutually affected variables in the irrigation district, and their encounter situation can determine water shortage risks under the contexts of natural water supply and demand. However, in reality, the rainfall and reference crop evapotranspiration may have different marginal distributions and their relations are nonlinear. In this study, based on the annual rainfall and reference crop evapotranspiration data series from 1970 to 2013 in the Luhun irrigation district of China, the joint probability distribution of rainfall and reference crop evapotranspiration are developed with the Frank copula function. Using the joint probability distribution, the synchronous-asynchronous encounter risk, conditional joint probability, and conditional return period of different combinations of rainfall and reference crop evapotranspiration are analyzed. The results show that the copula-based joint probability distributions of rainfall and reference crop evapotranspiration are reasonable. The asynchronous encounter probability of rainfall and reference crop evapotranspiration is greater than their synchronous encounter probability, and the water shortage risk associated with meteorological drought (i.e. rainfall variability) is more prone to appear. Compared with other states, there are higher conditional joint probability and lower conditional return period in either low rainfall or high reference crop evapotranspiration. For a specifically high reference crop evapotranspiration with a certain frequency, the encounter risk of low rainfall and high reference crop evapotranspiration is increased with the decrease in frequency. For a specifically low rainfall with a certain frequency, the encounter risk of low rainfall and high reference crop evapotranspiration is decreased with the decrease in frequency. When either the high reference crop evapotranspiration exceeds a certain frequency or low rainfall does not exceed a certain frequency, the higher conditional joint probability and lower conditional return period of various combinations likely cause a water shortage, but the water shortage is not severe.

Influence of high resolution rainfall data on the hydrological response of urban flat catchments

NASA Astrophysics Data System (ADS)

Cristiano, Elena; ten Veldhuis, Marie-claire; van de Giesen, Nick

2016-04-01

In the last decades, cities have become more and more urbanized and population density in urban areas is increased. At the same time, due to the climate changes, rainfall events present higher intensity and shorter duration than in the past. The increase of imperviousness degree, due to urbanization, combined with short and intense rainfall events, determinates a fast hydrological response of the urban catchment and in some cases it can lead to flooding. Urban runoff processes are sensitive to rainfall spatial and temporal variability and, for this reason, high resolution rainfall data are required as input for the hydrological model. A better knowledge of the hydrological response of system can help to prevent damages caused by flooding. This study aims to evaluate the sensitivity of urban hydrological response to spatial and temporal rainfall variability in urban areas, focusing especially on understanding the hydrological behaviour in lowland areas. In flat systems, during intense rainfall events, the flow in the sewer network can be pressurized and it can change direction, depending on the setting of pumping stations and CSOs (combined sewer overflow). In many cases these systems are also looped and it means that the water can follow different paths, depending on the pipe filling process. For these reasons, hydrological response of flat and looped catchments is particularly complex and it can be difficult characterize and predict it. A new dual polarimetric X-band weather radar, able to measure rainfall with temporal resolution of 1 min and spatial resolution of 100mX100m, was recently installed in the city of Rotterdam (NL). With this instrument, high resolution rainfall data were measured and used, in this work, as input for the hydrodynamic model. High detailed, semi-distributed hydrodynamic models of some districts of Rotterdam were used to investigate the hydrological response of flat catchments to high resolution rainfall data. In particular, the hydrological response of some subcatchments of the district of Kralingen was studied. Rainfall data were combined with level and discharge measurements at the pumping station that connects the sewer system with the waste water treatment plane. Using this data it was possible to study the water balance and to have a better idea of the amount of water that leave the system during a specific rainfall events. Results show that the hydrological response of flat and looped catchments is sensitive to spatial and temporal rainfall variability and it can be strongly influenced by rainfall event characteristics, such as intensity, velocity and intermittency of the storm.

Rainfall, runoff and sediment transport in a Mediterranean mountainous catchment.

PubMed

Tuset, J; Vericat, D; Batalla, R J

2016-01-01

The relation between rainfall, runoff, erosion and sediment transport is highly variable in Mediterranean catchments. Their relation can be modified by land use changes and climate oscillations that, ultimately, will control water and sediment yields. This paper analyses rainfall, runoff and sediment transport relations in a meso-scale Mediterranean mountain catchment, the Ribera Salada (NE Iberian Peninsula). A total of 73 floods recorded between November 2005 and November 2008 at the Inglabaga Sediment Transport Station (114.5 km(2)) have been analysed. Suspended sediment transport and flow discharge were measured continuously. Rainfall data was obtained by means of direct rain gauges and daily rainfall reconstructions from radar information. Results indicate that the annual sediment yield (2.3 t km(-1) y(-1) on average) and the flood-based runoff coefficients (4.1% on average) are low. The Ribera Salada presents a low geomorphological and hydrological activity compared with other Mediterranean mountain catchments. Pearson correlations between rainfall, runoff and sediment transport variables were obtained. The hydrological response of the catchment is controlled by the base flows. The magnitude of suspended sediment concentrations is largely correlated with flood magnitude, while sediment load is correlated with the amount of direct runoff. Multivariate analysis shows that total suspended load can be predicted by integrating rainfall and runoff variables. The total direct runoff is the variable with more weight in the equation. Finally, three main hydro-sedimentary phases within the hydrological year are defined in this catchment: (a) Winter, where the catchment produces only water and very little sediment; (b) Spring, where the majority of water and sediment is produced; and (c) Summer-Autumn, when little runoff is produced but significant amount of sediments is exported out of the catchment. Results show as land use and climate change may have an important role in modifying the cycles of water and sediment yields in Mediterranean mountain catchments. Copyright © 2015 Elsevier B.V. All rights reserved.

Contingency in the Direction and Mechanics of Soil Organic Matter Responses to Increased Rainfall

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

Berhe, Asmeret A.; Suttle, K. Blake; Burton, Sarah D.

2012-09-03

Shifts in regional precipitation patterns will be a major component of global climate change. Rainfall will show greater and more variable changes in response to rising earth surface temperatures than most other climatic variables, and will be a major driver of ecosystem change. We studied the consequences of predicted changes in California’s rainy season for storage and stabilization mechanisms of soil organic matter (SOM). In a controlled and replicated experiment, we amended rainfall over large plots of natural grassland in accordance with alternative scenarios of future climate change. Results show that increases in annual rainfall have important consequences for soilmore » C storage, but that the strength and even direction of these effects depend entirely on seasonal timing. Rainfall increases during the winter rainy season led to pronounced C loss from soil while rainfall increases after the typical rainy season increased soil C stocks. Analysis of mineral-OM associations reveals a powerful mechanism underlying this difference: increased winter rainfall vastly diminished the role of Fe and Al oxides in SOM stabilization. Dithionite extractable crystalline Fe oxides explained more than 35 percent of the variability in C storage in ambient control and spring-addition treatments, compared to less than 0.01 percent in the winter-addition treatment. Likewise, poorly crystalline Fe and Al oxides explained more than 25 and 40 percent of the variability in C storage, respectively, in the control and spring-addition treatments compared to less than 5 percent in the -winter-addition treatment. Increases in annual precipitation identical in amount but at three-month offsets produced opposite effects on soil C storage. These results highlight the complexity inherent in biospheric feedbacks to the climate system, and the way that careful experimentation can penetrate that complexity to improve predictions of ecosystem and climatic change.« less

Rainfall variability over the tropical Pacific from July 1987 through December 1991 as inferred via monthly estimates from SSM/I

NASA Technical Reports Server (NTRS)

Berg, Wesley; Avery, Susan K.

1994-01-01

Estimates of monthly rainfall have been computed over the tropical Pacific using passive microwave satellite observations from the Special Sensor Microwave/Imager (SSM/I) for the preiod from July 1987 through December 1991. The monthly estimates were calibrated using measurements from a network of Pacific atoll rain gauges and compared to other satellite-based rainfall estimation techniques. Based on these monthly estimates, an analysis of the variability of large-scale features over intraseasonal to interannual timescales has been performed. While the major precipitation features as well as the seasonal variability distributions show good agreement with expected values, the presence of a moderately intense El Nino during 1986-87 and an intense La Nina during 1988-89 highlights this time period.

Technical Note: An operational landslide early warning system at regional scale based on space-time-variable rainfall thresholds

NASA Astrophysics Data System (ADS)

Segoni, S.; Battistini, A.; Rossi, G.; Rosi, A.; Lagomarsino, D.; Catani, F.; Moretti, S.; Casagli, N.

2015-04-01

We set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity-duration rainfall thresholds (Segoni et al., 2014b) and makes use of LAMI (Limited Area Model Italy) rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult, and it provides different outputs. When switching among different views, the system is able to focus both on monitoring of real-time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a basic data view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain gauges can be displayed and constantly compared with rainfall thresholds. To better account for the variability of the geomorphological and meteorological settings encountered in Tuscany, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of more than 300 rain gauges, it allows for the monitoring of each alert zone separately so that warnings can be issued independently. An important feature of the warning system is that the visualization of the thresholds in the WebGIS interface may vary in time depending on when the starting time of the rainfall event is set. The starting time of the rainfall event is considered as a variable by the early warning system: whenever new rainfall data are available, a recursive algorithm identifies the starting time for which the rainfall path is closest to or overcomes the threshold. This is considered the most hazardous condition, and it is displayed by the WebGIS interface. The early warning system is used to forecast and monitor the landslide hazard in the whole region, providing specific alert levels for 25 distinct alert zones. In addition, the system can be used to gather, analyze, display, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

Water isotope variability across single rainfall events in the tropical Pacific

NASA Astrophysics Data System (ADS)

Cobb, K. M.; Moerman, J. W.; Ellis, S. A.; Bennett, L.; Bosma, C.; Hitt, N. T.

2017-12-01

Water isotopologues provide a powerful diagnostic tool for probing the dynamical processes involved in the initiation and evolution of tropical convective events, yet water isotope observations rarely meet the temporal resolution required to resolve such processes. Here we present timeseries of rainfall oxygen and hydrogen isotopologues across over 30 individual convective events sampled at 1- to 5-minute intervals at both terrestrial (Gunung Mulu National Park, 4N, 115W) and maritime (Kiritimati Island, 2N, 157W) sites located in the equatorial Pacific. The sites are the loci of significant paleoclimate research that employ water isotopologues to reconstruct a variety of climatic parameters of interest over the last century, in the case of coral d18O, to hundreds of thousands of years before present, in the case of stalagmite d18O. As such, there is significant scientific value in refining our understanding of water isotope controls at these particular sites. Our results illustrate large, short-term excursions in water isotope values that far exceed the signals recovered in daily timeseries of rainfall isotopologues from the sites, illustrating the fundamental contribution of mesoscale processes in driving rainfall isotope variability. That said, the cross-event profiles exhibit a broad range of trajectories, even for events collected at the same time of day on adjoining days. Profiles collected at different phases of the 2015-2017 strong El Nino-Southern Oscillation cycle also exhibit appreciable variability. We compare our observations to hypothetical profiles from a 1-dimensional model of each rainfall event, as well as to output from 4-dimensional isotope-equipped, ocean-atmosphere coupled models of rainfall isotope variability in the tropical Pacific. We discuss the implications of our findings for the interpretation of water isotope-based reconstructions of hydroclimate in the tropics.

Weather models as virtual sensors to data-driven rainfall predictions in urban watersheds

NASA Astrophysics Data System (ADS)

Cozzi, Lorenzo; Galelli, Stefano; Pascal, Samuel Jolivet De Marc; Castelletti, Andrea

2013-04-01

Weather and climate predictions are a key element of urban hydrology where they are used to inform water management and assist in flood warning delivering. Indeed, the modelling of the very fast dynamics of urbanized catchments can be substantially improved by the use of weather/rainfall predictions. For example, in Singapore Marina Reservoir catchment runoff processes have a very short time of concentration (roughly one hour) and observational data are thus nearly useless for runoff predictions and weather prediction are required. Unfortunately, radar nowcasting methods do not allow to carrying out long - term weather predictions, whereas numerical models are limited by their coarse spatial scale. Moreover, numerical models are usually poorly reliable because of the fast motion and limited spatial extension of rainfall events. In this study we investigate the combined use of data-driven modelling techniques and weather variables observed/simulated with a numerical model as a way to improve rainfall prediction accuracy and lead time in the Singapore metropolitan area. To explore the feasibility of the approach, we use a Weather Research and Forecast (WRF) model as a virtual sensor network for the input variables (the states of the WRF model) to a machine learning rainfall prediction model. More precisely, we combine an input variable selection method and a non-parametric tree-based model to characterize the empirical relation between the rainfall measured at the catchment level and all possible weather input variables provided by WRF model. We explore different lead time to evaluate the model reliability for different long - term predictions, as well as different time lags to see how past information could improve results. Results show that the proposed approach allow a significant improvement of the prediction accuracy of the WRF model on the Singapore urban area.

A 305 year monthly rainfall series for the Island of Ireland (1711-2016)

NASA Astrophysics Data System (ADS)

Murphy, Conor; Burt, Tim P.; Broderick, Ciaran; Duffy, Catriona; Macdonald, Neil; Matthews, Tom; McCarthy, Mark P.; Mullan, Donal; Noone, Simon; Ryan, Ciara; Thorne, Peter; Walsh, Seamus; Wilby, Robert L.

2017-04-01

This paper derives a continuous 305-year monthly rainfall series for the Island of Ireland (IoI) for the period 1711-2016. Two key data sources are employed: i) a previously unpublished UK Met Office Note which compiled annual rainfall anomalies and corresponding monthly per mille amounts from weather diaries and early observational records for the period 1711-1977; and ii) a long-term, homogenised monthly IoI rainfall series for the period 1850-2016. Using estimates of long-term average precipitation sampled from the quality assured series, the full record is reconstituted and insights drawn regarding notable periods and the range of climate variability and change experienced. Consistency with other long records for the region is examined, including: the England and Wales Precipitation series (EWP; 1766-2016); the early EWP Glasspoole series (1716-1765) and the Central England Temperature series (CET; 1711-2016). Strong correspondence between all records is noted from 1780 onwards. While disparities are evident between the early EWP and Ireland series, the latter shows strong decadal consistency with CET throughout the record. In addition, independent, early observations from Cork and Dublin, along with available documentary sources, corroborate the derived series and add confidence to our reconstruction. The new IoI rainfall record reveals that the wettest decades occurred in the early 18th Century, despite the fact that IoI has experienced a long-term winter wetting trend consistent with climate model projections. These exceptionally wet winters of the 1720s and 1730s were concurrent with almost unprecedented warmth in the CET, glacial advance throughout Scandinavia, and glacial retreat in West Greenland, consistent with a wintertime NAO-type forcing. Our study therefore demonstrates the value of long-term observational records for providing insight to the natural climate variability of the North Atlantic region.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Rainfall prediction with backpropagation method

NASA Astrophysics Data System (ADS)

Wahyuni, E. G.; Fauzan, L. M. F.; Abriyani, F.; Muchlis, N. F.; Ulfa, M.

2018-03-01

Rainfall is an important factor in many fields, such as aviation and agriculture. Although it has been assisted by technology but the accuracy can not reach 100% and there is still the possibility of error. Though current rainfall prediction information is needed in various fields, such as agriculture and aviation fields. In the field of agriculture, to obtain abundant and quality yields, farmers are very dependent on weather conditions, especially rainfall. Rainfall is one of the factors that affect the safety of aircraft. To overcome the problems above, then it’s required a system that can accurately predict rainfall. In predicting rainfall, artificial neural network modeling is applied in this research. The method used in modeling this artificial neural network is backpropagation method. Backpropagation methods can result in better performance in repetitive exercises. This means that the weight of the ANN interconnection can approach the weight it should be. Another advantage of this method is the ability in the learning process adaptively and multilayer owned on this method there is a process of weight changes so as to minimize error (fault tolerance). Therefore, this method can guarantee good system resilience and consistently work well. The network is designed using 4 input variables, namely air temperature, air humidity, wind speed, and sunshine duration and 3 output variables ie low rainfall, medium rainfall, and high rainfall. Based on the research that has been done, the network can be used properly, as evidenced by the results of the prediction of the system precipitation is the same as the results of manual calculations.

The Role of Rainfall Patterns in Seasonal Malaria Transmission

NASA Astrophysics Data System (ADS)

Bomblies, A.

2010-12-01

Seasonal total precipitation is well known to affect malaria transmission because Anopheles mosquitoes depend on standing water for breeding habitat. However, the within-season temporal pattern of the rainfall influences persistence of standing water and thus rainfall patterns also affect mosquito population dynamics. In this talk, I show that intraseasonal rainfall pattern describes 40% of the variance in simulated mosquito abundance in a Niger Sahel village where malaria is endemic but highly seasonal, demonstrating the necessity for detailed distributed hydrology modeling to explain the variance from this important effect. I apply a field validated, high spatial- and temporal-resolution hydrology model coupled with an entomology model. Using synthetic rainfall time series generated using a stationary first-order Markov Chain model, I hold all variables except hourly rainfall constant, thus isolating the contribution of rainfall pattern to variance in mosquito abundance. I further show the utility of hydrology modeling to assess precipitation effects by analyzing collected water. Time-integrated surface area of pools explains 70% of the variance in mosquito abundance, and time-integrated surface area of pools persisting longer than seven days explains 82% of the variance, showing an improved predictive ability when pool persistence is explicitly modeled at high spatio-temporal resolution. I extend this analysis to investigate the impacts of this effect on malaria vector mosquito populations under climate shift scenarios, holding all climate variables except precipitation constant. In these scenarios, rainfall mean and variance change with climatic change, and the modeling approach evaluates the impact of non-stationarity in rainfall and the associated rainfall patterns on expected mosquito activity.

Vegetation Interaction Enhances Interdecadal Climate Variability in the Sahel

NASA Technical Reports Server (NTRS)

Zeng, Ning; Neelin, J. David; Lau, William K.-M.

1999-01-01

The role of naturally varying vegetation in influencing the climate variability in the Sahel is explored in a coupled atmosphere-land-vegetation model. The Sahel rainfall variability is influenced by sea surface temperature (SST) variations in the oceans. Land-surface feedback is found to increase this variability both on interannual and interdecadal time scales. Interactive vegetation enhances the interdecadal variation significantly, but can reduce year to year variability due to a phase lag introduced by the relatively slow vegetation adjustment time. Variations in vegetation accompany the changes in rainfall, in particular, the multi-decadal drying trend from the 1950s to the 80s.

Forecasting Andean rainfall and crop yield from the influence of El Nino on Pleiades visibility

PubMed

Orlove; Chiang; Cane

2000-01-06

Farmers in drought-prone regions of Andean South America have historically made observations of changes in the apparent brightness of stars in the Pleiades around the time of the southern winter solstice in order to forecast interannual variations in summer rainfall and in autumn harvests. They moderate the effect of reduced rainfall by adjusting the planting dates of potatoes, their most important crop. Here we use data on cloud cover and water vapour from satellite imagery, agronomic data from the Andean altiplano and an index of El Nino variability to analyse this forecasting method. We find that poor visibility of the Pleiades in June-caused by an increase in subvisual high cirrus clouds-is indicative of an El Nino year, which is usually linked to reduced rainfall during the growing season several months later. Our results suggest that this centuries-old method of seasonal rainfall forecasting may be based on a simple indicator of El Nino variability.

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

NASA Astrophysics Data System (ADS)

Gabriele, Salvatore; Chiaravalloti, Francesco; Procopio, Antonio

2017-07-01

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

Models are likely to underestimate increase in heavy rainfall in regions with high rainfall intensity

NASA Astrophysics Data System (ADS)

Borodina, Aleksandra; Fischer, Erich M.; Knutti, Reto

2017-04-01

Model projections of heavy rainfall are uncertain. On timescales of few decades, internal variability plays an important role and therefore poses a challenge to detect robust model responses. We show that spatial aggregation across regions with intense heavy rainfall events, - defined as grid cells with high annual precipitation maxima (Rx1day), - allows to reduce the role of internal variability and thus to detect a robust signal during the historical period. This enables us to evaluate models with observational datasets and to constrain long-term projections of the intensification of heavy rainfall, i.e., to recalibrate full model ensemble consistent with observations resulting in narrower range of projections. In the regions of intense heavy rainfall, we found two present-day metrics that are related to a model's projection. The first metric is the observed relationship between the area-weighted mean of the annual precipitation maxima (Rx1day) and the global land temperatures. The second is the fraction of land exhibiting statistically significant relationships between local annual precipitation maxima (Rx1day) and global land temperatures over the historical period. The models that simulate high values in both metrics are those that are in better agreement with observations and show strong future intensification of heavy rainfall. This implies that changes in heavy rainfall are likely to be more intense than anticipated from the multi-model mean.

Assessment of future impacts of potential climate change scenarios on aquifer recharge in continental Spain

NASA Astrophysics Data System (ADS)

Pulido-Velazquez, David; Collados-Lara, Antonio-Juan; Alcalá, Francisco J.

2017-04-01

This research proposes and applies a method to assess potential impacts of future climatic scenarios on aquifer rainfall recharge in wide and varied regions. The continental Spain territory was selected to show the application. The method requires to generate future series of climatic variables (precipitation, temperature) in the system to simulate them within a previously calibrated hydrological model for the historical data. In a previous work, Alcalá and Custodio (2014) used the atmospheric chloride mass balance (CMB) method for the spatial evaluation of average aquifer recharge by rainfall over the whole of continental Spain, by assuming long-term steady conditions of the balance variables. The distributed average CMB variables necessary to calculate recharge were estimated from available variable-length data series of variable quality and spatial coverage. The CMB variables were regionalized by ordinary kriging at the same 4976 nodes of a 10 km x 10 km grid. Two main sources of uncertainty affecting recharge estimates (given by the coefficient of variation, CV), induced by the inherent natural variability of the variables and from mapping were segregated. Based on these stationary results we define a simple empirical rainfall-recharge model. We consider that spatiotemporal variability of rainfall and temperature are the most important climatic feature and variables influencing potential aquifer recharge in natural regime. Changes in these variables can be important in the assessment of future potential impacts of climatic scenarios over spatiotemporal renewable groundwater resource. For instance, if temperature increases, actual evapotranspitration (EA) will increases reducing the available water for others groundwater balance components, including the recharge. For this reason, instead of defining an infiltration rate coefficient that relates precipitation (P) and recharge we propose to define a transformation function that allows estimating the spatial distribution of recharge (both average value and its uncertainty) from the difference in P and EA in each area. A complete analysis of potential short-term (2016-2045) future climate scenarios in continental Spain has been performed by considering different sources of uncertainty. It is based on the historical climatic data for the period 1976-2005 and the climatic models simulations (for the control [1976-2005] and future scenarios [2016-2045]) performed in the frame of the CORDEX EU project. The most pessimistic emission scenario (RCP8.5) has been considered. For the RCP8.5 scenario we have analyzed the time series generated by simulating with 5 Regional Climatic models (CCLM4-8-17, RCA4, HIRHAM5, RACMO22E, and WRF331F) nested to 4 different General Circulation Models (GCMs). Two different conceptual approaches (bias correction and delta change techniques) have been applied to generate potential future climate scenarios from these data. Different ensembles of obtained time series have been proposed to obtain more representative scenarios by considering all the simulations or only those providing better approximations to the historical statistics based on a multicriteria analysis. This was a step to analyze future potential impacts on the aquifer recharge by simulating them within a rainfall-recharge model. This research has been supported by the CGL2013-48424-C2-2-R (MINECO) and the PMAFI/06/14 (UCAM) projects.

Improve projections of changes in southern African summer rainfall through comprehensive multi-timescale empirical statistical downscaling

NASA Astrophysics Data System (ADS)

Dieppois, B.; Pohl, B.; Eden, J.; Crétat, J.; Rouault, M.; Keenlyside, N.; New, M. G.

2017-12-01

The water management community has hitherto neglected or underestimated many of the uncertainties in climate impact scenarios, in particular, uncertainties associated with decadal climate variability. Uncertainty in the state-of-the-art global climate models (GCMs) is time-scale-dependant, e.g. stronger at decadal than at interannual timescales, in response to the different parameterizations and to internal climate variability. In addition, non-stationarity in statistical downscaling is widely recognized as a key problem, in which time-scale dependency of predictors plays an important role. As with global climate modelling, therefore, the selection of downscaling methods must proceed with caution to avoid unintended consequences of over-correcting the noise in GCMs (e.g. interpreting internal climate variability as a model bias). GCM outputs from the Coupled Model Intercomparison Project 5 (CMIP5) have therefore first been selected based on their ability to reproduce southern African summer rainfall variability and their teleconnections with Pacific sea-surface temperature across the dominant timescales. In observations, southern African summer rainfall has recently been shown to exhibit significant periodicities at the interannual timescale (2-8 years), quasi-decadal (8-13 years) and inter-decadal (15-28 years) timescales, which can be interpret as the signature of ENSO, the IPO, and the PDO over the region. Most of CMIP5 GCMs underestimate southern African summer rainfall variability and their teleconnections with Pacific SSTs at these three timescales. In addition, according to a more in-depth analysis of historical and pi-control runs, this bias is might result from internal climate variability in some of the CMIP5 GCMs, suggesting potential for bias-corrected prediction based empirical statistical downscaling. A multi-timescale regression based downscaling procedure, which determines the predictors across the different timescales, has thus been used to simulate southern African summer rainfall. This multi-timescale procedure shows much better skills in simulating decadal timescales of variability compared to commonly used statistical downscaling approaches.

The role of climate variability in extreme floods in Europe

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Aerts, Jeroen C. J. H.; Jongman, Brenden; Ward, Philip J.

2017-04-01

Between 1980 and 2015, Europe experienced 18% of worldwide weather-related loss events, which accounted for over US500 billion in damage. Consequently, it is urgent to further develop adaptation strategies to mitigate the consequences of weather-related disasters, such as floods. Europe's capability to prepare for such disasters is challenged by a large range of uncertainties and a limited understanding of the driving forces of hydrometeorological hazards. One of the major sources of uncertainty is the relationship between climate variability and weather-related losses. Previous studies show that climate variability drives temporal changes in hydrometereological variables in Europe. However, their influence on flood risk has received little attention. We investigated the influence of the positive and negative phases of El Niño Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Arctic Oscillation (AO), on the seasonal frequency and intensity of extreme rainfall, and anomalies in flood occurrence and damage compared to the neutral phases of the indices of climate variability. Using statistical methods to analyze relationships between the indices of climate variability and four indicators of flooding, we found that positive and negative phases of NAO and AO are associated with more (or less) frequent and intense seasonal extreme rainfall over large areas of Europe. The relationship between ENSO and both the occurrence of extreme rainfall and intensity of extreme rainfall in Europe is much smaller than the relationship with NAO or AO, but still significant in some regions. We observe that flood damage and flood occurrence have strong links with climate variability, especially in southern and eastern Europe. Therefore, when investigating flooding across Europe, all three indices of climate variability should be considered. Seasonal forecasting of flooding could be enhanced by the inclusion of climate variability indicators .

Coherent variability between seasonal temperatures and rainfalls in the Iberian Peninsula, 1951-2016

NASA Astrophysics Data System (ADS)

Rodrigo, F. S.

2018-02-01

In this work trends of seasonal mean of daily minimum (TN), maximum (TX), mean (TM) temperatures, daily range of temperature (DTR), and total seasonal rainfall (R) in 35 Iberian stations since mid-twentieth century are studied. The interest is focused on the relationships between temperature variables and rainfall, taking into account the correlation coefficients between R and the temperature variables. The negative link between rainfall and temperatures is detected in the four seasons of the year, except in western stations in winter for TN and TM, and in autumn for TN (for this variable a certain annual cycle is detected, with predominance of positive correlation in winter, negative in spring and summer, and the autumn as transition season). The role of cloud cover is confirmed in those stations with total cloud cover data. Using an average peninsular series, the relationship between nighttime temperature and rainfall related to long wave radiation is confirmed for the four seasons of the year, although in spring and summer has minor importance than in the cold half year. The relationships between R, TN, and TX are in general terms stable after a moving correlation analysis, although the negative correlation between TX and R seems be weakened in spring and autumn and reinforced in summer. The role of convective precipitation in autumn is discussed. The analysis of combined extreme indices in four representative stations shows an increase of warm and dry days, and a decrease of cold and wet days.

Multi-year encoding of daily rainfall and streamflow via the fractal-multifractal method

NASA Astrophysics Data System (ADS)

Puente, C. E.; Maskey, M.; Sivakumar, B.

2017-12-01

A deterministic geometric approach, the fractal-multifractal (FM) method, which has been proven to be faithful in encoding daily geophysical sets over a year, is used to describe records over multiple years at a time. Looking for FM parameter trends over longer periods, the present study shows FM descriptions of daily rainfall and streamflow gathered over five consecutive years optimizing deviations on accumulated sets. The results for 100 and 60 sets of five years for rainfall streamflow, respectively, near Sacramento, California illustrate that: (a) encoding of both types of data sets may be accomplished with relatively small errors; and (b) predicting the geometry of both variables appears to be possible, even five years ahead, training neural networks on the respective FM parameters. It is emphasized that the FM approach not only captures the accumulated sets over successive pentades but also preserves other statistical attributes including the overall "texture" of the records.

Soil property changes during loblolly pine production

Treesearch

R. Wayne Skaggs; Devendra M. Amatya; G.M. Chescheir; Christine D. Blanton

2006-01-01

Three watersheds, each approximately 25 ha, were instrumented to measure and record drainage rate, water table depth, rainfall and meteorological data. Data continuously collected on the site since 1988 include response of hydrologic and water quality variables for nearly all growth stages of a Loblolly pine plantation. Data for drainage outflow rates and water table...

Effects of ambient air temperature, humidity and rainfall on annual survival of adult little penguins Eudyptula minor in southeastern Australia

NASA Astrophysics Data System (ADS)

Ganendran, L. B.; Sidhu, L. A.; Catchpole, E. A.; Chambers, L. E.; Dann, P.

2016-08-01

Seabirds are subject to the influences of local climate variables during periods of land-based activities such as breeding and, for some species, moult; particularly if they undergo a catastrophic moult (complete simultaneous moult) as do penguins. We investigated potential relationships between adult penguin survival and land-based climate variables (ambient air temperature, humidity and rainfall) using 46 years of mark-recapture data of little penguins Eudyptula minor gathered at a breeding colony on Phillip Island in southeastern Australia. Our results showed that adult penguin survival had a stronger association with land-based climate variables during the moult period, when birds were unable to go to sea for up to 3 weeks, than during the breeding period, when birds could sacrifice breeding success in favour of survival. Annual adult survival probability was positively associated with humidity during moult and negatively associated with rainfall during moult. Prolonged heat during breeding and moult had a negative association with annual adult survival. Local climate projections suggest increasing days of high temperatures, fewer days of rainfall which will result in more droughts (and by implication, lower humidity) and more extreme rainfall events. All of these predicted climate changes are expected to have a negative impact on adult penguin survival.

Modeling the temporal variability of zinc concentrations in zinc roof runoff-experimental study and uncertainty analysis.

PubMed

Sage, Jérémie; El Oreibi, Elissar; Saad, Mohamed; Gromaire, Marie-Christine

2016-08-01

This study investigates the temporal variability of zinc concentrations from zinc roof runoff. The influence of rainfall characteristics and dry period duration is evaluated by combining laboratory experiment on small zinc sheets and in situ measurements under real weather conditions from a 1.6-m(2) zinc panel. A reformulation of a commonly used conceptual runoff quality model is introduced and its ability to simulate the evolution of zinc concentrations is evaluated. A systematic and sharp decrease from initially high to relatively low and stable zinc concentrations after 0.5 to 2 mm of rainfall is observed for both experiments, suggesting that highly soluble corrosion products are removed at early stages of runoff. A moderate dependence between antecedent dry period duration and the magnitude of zinc concentrations at the beginning of a rain event is evidenced. Contrariwise, results indicate that concentrations are not significantly influenced by rainfall intensities. Simulated rainfall experiment nonetheless suggests that a slight effect of rainfall intensities may be expected after the initial decrease of concentrations. Finally, this study shows that relatively simple conceptual runoff quality models may be adopted to simulate the variability of zinc concentrations during a rain event and from a rain event to another.

Termites promote resistance of decomposition to spatiotemporal variability in rainfall.

PubMed

Veldhuis, Michiel P; Laso, Francisco J; Olff, Han; Berg, Matty P

2017-02-01

The ecological impact of rapid environmental change will depend on the resistance of key ecosystems processes, which may be promoted by species that exert strong control over local environmental conditions. Recent theoretical work suggests that macrodetritivores increase the resistance of African savanna ecosystems to changing climatic conditions, but experimental evidence is lacking. We examined the effect of large fungus-growing termites and other non-fungus-growing macrodetritivores on decomposition rates empirically with strong spatiotemporal variability in rainfall and temperature. Non-fungus-growing larger macrodetritivores (earthworms, woodlice, millipedes) promoted decomposition rates relative to microbes and small soil fauna (+34%) but both groups reduced their activities with decreasing rainfall. However, fungus-growing termites increased decomposition rates strongest (+123%) under the most water-limited conditions, making overall decomposition rates mostly independent from rainfall. We conclude that fungus-growing termites are of special importance in decoupling decomposition rates from spatiotemporal variability in rainfall due to the buffered environment they create within their extended phenotype (mounds), that allows decomposition to continue when abiotic conditions outside are less favorable. This points at a wider class of possibly important ecological processes, where soil-plant-animal interactions decouple ecosystem processes from large-scale climatic gradients. This may strongly alter predictions from current climate change models. © 2016 by the Ecological Society of America.

Effects of ambient air temperature, humidity and rainfall on annual survival of adult little penguins Eudyptula minor in southeastern Australia.

PubMed

Ganendran, L B; Sidhu, L A; Catchpole, E A; Chambers, L E; Dann, P

2016-08-01

Seabirds are subject to the influences of local climate variables during periods of land-based activities such as breeding and, for some species, moult; particularly if they undergo a catastrophic moult (complete simultaneous moult) as do penguins. We investigated potential relationships between adult penguin survival and land-based climate variables (ambient air temperature, humidity and rainfall) using 46 years of mark-recapture data of little penguins Eudyptula minor gathered at a breeding colony on Phillip Island in southeastern Australia. Our results showed that adult penguin survival had a stronger association with land-based climate variables during the moult period, when birds were unable to go to sea for up to 3 weeks, than during the breeding period, when birds could sacrifice breeding success in favour of survival. Annual adult survival probability was positively associated with humidity during moult and negatively associated with rainfall during moult. Prolonged heat during breeding and moult had a negative association with annual adult survival. Local climate projections suggest increasing days of high temperatures, fewer days of rainfall which will result in more droughts (and by implication, lower humidity) and more extreme rainfall events. All of these predicted climate changes are expected to have a negative impact on adult penguin survival.

A role of high impact weather events in waterborne disease outbreaks in Canada, 1975 - 2001.

PubMed

Thomas, Kate M; Charron, Dominique F; Waltner-Toews, David; Schuster, Corinne; Maarouf, Abdel R; Holt, John D

2006-06-01

Recent outbreaks of Escherichia coli O157:H7, Campylobacter, and Cryptosporidium have heightened awareness of risks associated with contaminated water supply. The objectives of this research were to describe the incidence and distribution of waterborne disease outbreaks in Canada in relation to preceding weather conditions and to test the association between high impact weather events and waterborne disease outbreaks. We examined extreme rainfall and spring snowmelt in association with 92 Canadian waterborne disease outbreaks between 1975 and 2001, using case-crossover methodology. Explanatory variables including accumulated rainfall, air temperature, and peak stream flow were used to determine the relationship between high impact weather events and the occurrence of waterborne disease outbreaks. Total maximum degree-days above 0 degrees C and accumulated rainfall percentile were associated with outbreak risk. For each degree-day above 0 degrees C the relative odds of an outbreak increased by a factor of 1.007 (95% confidence interval [CI] = 1.002 - 1.012). Accumulated rainfall percentile was dichotomized at the 93rd percentile. For rainfall events greater than the 93rd percentile the relative odds of an outbreak increased by a factor of 2.283 (95% [CI] = 1.216 - 4.285). These results suggest that warmer temperatures and extreme rainfall are contributing factors to waterborne disease outbreaks in Canada. This could have implications for water management and public health initiatives.

Integrated Assessments of the Impact of Climate Change on Agriculture: An Overview of AgMIP Regional Research in South Asia

NASA Technical Reports Server (NTRS)

McDermid, Sonali P.; Dileepkumar, Guntuku; Murthy, K. M. Dakshina; Nedumaran, S.; Singh, Piara; Srinivasa, Chukka; Gangwar, B.; Subash, N.; Ahmad, Ashfaq; Zubair, Lareef;

High resolution modeling in urban hydrology: comparison between two modeling approaches and their sensitivity to high rainfall variability

NASA Astrophysics Data System (ADS)

Ichiba, Abdellah; Gires, Auguste; Tchiguirinskaia, Ioulia; Bompard, Philippe; Schertzer, Daniel

2015-04-01

Urban water management is becoming increasingly complex, due to the rapid increase of impervious areas, and the potential effects of climate change. The large amount of water generated in a very short period of time and the limited capacity of sewer systems increase the vulnerability of urban environments to flooding risk and make it necessary to implement specific devices in order to handle the volume of water generated. This complex situation in urban environments makes the use of hydrological models as well as the implementation of more accurate and reliable tools for flow and rainfall measurements essential for a good pluvial network management, the use of decision support tools such as real-time radar forecasting system, the developpement of general public communication and warning systems, and the implementation of management strategy participate on limiting the flood damages. The very high spatial variability characteristic of urban environments makes it necessary to integrate the variability of physical properties and precipitation at fine scales in modeling processes, suggesting a high resolution modeling approach. In this paper we suggest a comparison between two modeling approaches and their sensitivity to small-scale rainfall variability on a 2.15 km2 urban area located in the County of Val-de-Marne (South-East of Paris, France). The first model used in this study is CANOE, which is a semi-distributed model widely used in France by practitioners for urban hydrology and urban water management. Two configurations of this model are be used in this study, the first one integrate 9 sub-catchments with sizes range from (1ha to 76ha), in the second configuration, the spatial resolution of this model has been improved with 45 sub-catchments with sizes range from (1ha to 14ha), the aim is to see how the semi-distributed model resolution affects it sensitivity to rainfall variability. The second model is Multi-Hydro fully distributed model developed at the Ecole des Ponts ParisTech. It is an interacting core between open source software packages, each of them representing a portion of the water cycle in urban environment. Multi-Hydro has been set up at two resolutions, 10m and 5m. The validation of these two models is performed using 5 rainfall events that occurred between 2010 and 2013. Radar data comes from the Météo-France radar mosaic and the resolution is 1 km in space and 5 min in time. Raingauge and flow measurements data comes from the General Council of Val-de-Marne County. In this validation part, the hydrological responses given by two models and the different configurations are compared to flow measurements. It appears that CANOE gives better results than Multi-Hydro model, especially when using raingauge data. For some events, we noticed that model responses given when using raingauge and radar data are different, suggesting a sign of sensitivity to the spatial variability of rainfall. 10 high-resolution rainfall events are used in the second part to study the sensitivity of each modeling approach to high rainfall variability. Radar data was available at four spatial resolutions (100, 200, 500 and 1000m) and two temporal resolutions (1min and 5min), for each event, two rainfall directions (parallel and perpendicular) are used, meaning that 16 hydrological responses are simulated for each event and the variability within it analyzed. First results suggest that the fully distributed model is more sensitive to high rainfall variability than the semi-distributed one, the increase of both hydrological model spatial resolution improves their sensitivity to rainfall variability. This study highlights some technical challenges facing the high-resolution modeling, especially the difficulty to obtain reliable input data at an acceptable resolution and also the high computation time noticed particularly for the semi-distributed model making it difficult to use it in real time. The authors greatly acknowledge partial financial support from the project RainGain (http://www.raingain.eu) of the EU Interreg program.

Decadal features of heavy rainfall events in eastern China

NASA Astrophysics Data System (ADS)

Chen, Huopo; Sun, Jianqi; Fan, Ke

2012-06-01

Based on daily precipitation data, the spatial-temporal features of heavy rainfall events (HREs) during 1960-2009 are investigated. The results indicate that the HREs experienced strong decadal variability in the past 50 years, and the decadal features varied across regions. More HRE days are observed in the 1960s, 1980s, and 1990s over Northeast China (NEC); in the 1960s, 1970s, and 1990s over North China (NC); in the early 1960s, 1980s, and 2000s over the Huaihe River basin (HR); in the 1970s-1990s over the mid-lower reaches of the Yangtze River valley (YR); and in the 1970s and 1990s over South China (SC). These decadal changes of HRE days in eastern China are closely associated with the decadal variations of water content and stratification stability of the local atmosphere. The intensity of HREs in each sub-region is also characterized by strong decadal variability. The HRE intensity and frequency co-vary on the long-term trend, and show consistent variability over NEC, NC, and YR, but inconsistent variability over SC and HR. Further analysis of the relationships between the annual rainfall and HRE frequency as well as intensity indicates that the HRE frequency is the major contributor to the total rainfall variability in eastern China, while the HRE intensity shows only relative weak contribution.

Impact of Satellite Remote Sensing Data on Simulations of ...

EPA Pesticide Factsheets

We estimated surface salinity flux and solar penetration from satellite data, and performed model simulations to examine the impact of including the satellite estimates on temperature, salinity, and dissolved oxygen distributions on the Louisiana continental shelf (LCS) near the annual hypoxic zone. Rainfall data from the Tropical Rainfall Measurement Mission (TRMM) were used for the salinity flux, and the diffuse attenuation coefficient (Kd) from Moderate Resolution Imaging Spectroradiometer (MODIS) were used for solar penetration. Improvements in the model results in comparison with in situ observations occurred when the two types of satellite data were included. Without inclusion of the satellite-derived surface salinity flux, realistic monthly variability in the model salinity fields was observed, but important inter-annual variability wasmissed. Without inclusion of the satellite-derived light attenuation, model bottom water temperatures were too high nearshore due to excessive penetration of solar irradiance. In general, these salinity and temperature errors led to model stratification that was too weak, and the model failed to capture observed spatial and temporal variability in water-column vertical stratification. Inclusion of the satellite data improved temperature and salinity predictions and the vertical stratification was strengthened, which improved prediction of bottom-water dissolved oxygen. The model-predicted area of bottom-water hypoxia on the

Coherent response of the Indo-African boreal summer monsoon to Pacific SST captured in Ethiopian rain δ18O

NASA Astrophysics Data System (ADS)

Madhavan, M.; Palliyil, L. R.; Ramesh, R.

2017-12-01

Pacific Sea Surface Temperature (SST) plays an important role in the inter-annual to inter-decadal variability of boreal monsoons. We identified a common mode of inter annual variability in the Indian and African boreal summer monsoon (June to September) rainfalls, which is linked to Pacific SSTs, using Empirical Orthogonal Function (EOF) analysis. Temporal coefficients (Principle component: PC1) of the leading mode of variability (EOF-1) is well correlated with the Indian summer monsoon rainfall and Sahel rainfall. About forty year long monthly observations of δ18O (and δD) at Addis Ababa, Ethiopia show a strong association with PC1 (r=0.69 for δ18O and r=0.75 for δD). Analysis of SST, sea level pressure and lower tropospheric winds suggest that 18O depletion in Ethiopian rainfall (and wet phases of PC1) is associated with cooler eastern tropical Pacific and warmer western Pacific and strengthening of Pacific subtropical high in both the hemispheres. Associated changes in the trade winds cause enhanced westerly moisture transport into the Indian subcontinent and northern Africa and cause enhanced rainfall. The intrusion of Atlantic westerly component of moisture transport at Addis Ababa during wet phases of PC1 is clearly recorded in δ18O of rain. We also observe the same common mode of variability (EOF1) of Indo-African boreal summer monsoon rain on decadal time scales. A 100 year long δ18O record of actively growing speleothem from the Mechara cave, Ethiopia, matches very well with the PC1 on the decadal time scale. This highlights the potential of speleothem δ18O and leaf wax δD from Ethiopia to investigate the natural variability and teleconnections of Indo-African boreal monsoon.

Classification and Space-Time Analysis of Precipitation Events in Manizales, Caldas, Colombia.

NASA Astrophysics Data System (ADS)

Suarez Hincapie, J. N.; Vélez, J.; Romo Melo, L.; Chang, P.

2015-12-01

Manizales is a mid-mountain Andean city located near the Nevado del Ruiz volcano in west-central Colombia, this location exposes it to earthquakes, floods, landslides and volcanic eruptions. It is located in the intertropical convergence zone (ITCZ) and presents a climate with a bimodal rainfall regime (Cortés, 2010). Its mean annual rainfall is 2000 mm, one may observe precipitation 70% of the days over a year. This rain which favors the formation of large masses of clouds and the presence of macroclimatic phenomenon as "El Niño South Oscillation", has historically caused great impacts in the region (Vélez et al, 2012). For example the geographical location coupled with rain events results in a high risk of landslides in the city. Manizales has a hydrometeorological network of 40 stations that measure and transmit data of up to eight climate variables. Some of these stations keep 10 years of historical data. However, until now this information has not been used for space-time classification of precipitation events, nor has the meteorological variables that influence them been thoroughly researched. The purpose of this study was to classify historical events of rain in an urban area of Manizales and investigate patterns of atmospheric behavior that influence or trigger such events. Classification of events was performed by calculating the "n" index of the heavy rainfall, describing the behavior of precipitation as a function of time throughout the event (Monjo, 2009). The analysis of meteorological variables was performed using statistical quantification over variable time periods before each event. The proposed classification allowed for an analysis of the evolution of rainfall events. Specially, it helped to look for the influence of different meteorological variables triggering rainfall events in hazardous areas as the city of Manizales.

Calibrating a Rainfall-Runoff and Routing Model for the Continental United States

NASA Astrophysics Data System (ADS)

Jankowfsky, S.; Li, S.; Assteerawatt, A.; Tillmanns, S.; Hilberts, A.

2014-12-01

Catastrophe risk models are widely used in the insurance industry to estimate the cost of risk. The models consist of hazard models linked to vulnerability and financial loss models. In flood risk models, the hazard model generates inundation maps. In order to develop country wide inundation maps for different return periods a rainfall-runoff and routing model is run using stochastic rainfall data. The simulated discharge and runoff is then input to a two dimensional inundation model, which produces the flood maps. In order to get realistic flood maps, the rainfall-runoff and routing models have to be calibrated with observed discharge data. The rainfall-runoff model applied here is a semi-distributed model based on the Topmodel (Beven and Kirkby, 1979) approach which includes additional snowmelt and evapotranspiration models. The routing model is based on the Muskingum-Cunge (Cunge, 1969) approach and includes the simulation of lakes and reservoirs using the linear reservoir approach. Both models were calibrated using the multiobjective NSGA-II (Deb et al., 2002) genetic algorithm with NLDAS forcing data and around 4500 USGS discharge gauges for the period from 1979-2013. Additional gauges having no data after 1979 were calibrated using CPC rainfall data. The model performed well in wetter regions and shows the difficulty of simulating areas with sinks such as karstic areas or dry areas. Beven, K., Kirkby, M., 1979. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. Bull. 24 (1), 43-69. Cunge, J.A., 1969. On the subject of a flood propagation computation method (Muskingum method), J. Hydr. Research, 7(2), 205-230. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on evolutionary computation, 6(2), 182-197.

Soil erodibility variability in laboratory and field rainfall simulations

NASA Astrophysics Data System (ADS)

Szabó, Boglárka; Szabó, Judit; Jakab, Gergely; Centeri, Csaba; Szalai, Zoltán

2017-04-01

Rainfall simulation experiments are the most common way to observe and to model the soil erosion processes in in situ and ex situ circumstances. During modelling soil erosion, one of the most important factors are the annual soil loss and the soil erodibility which represent the effect of soil properties on soil loss and the soil resistance against water erosion. The amount of runoff and soil loss can differ in case of the same soil type, while it's characteristics determine the soil erodibility factor. This leads to uncertainties regarding soil erodibility. Soil loss and soil erodibility were examined with the investigation of the same soil under laboratory and field conditions with rainfall simulators. The comparative measurement was carried out in a laboratory on 0,5 m2, and in the field (Shower Power-02) on 6 m2 plot size where the applied slope angles were 5% and 12% with 30 and 90 mm/h rainfall intensity. The main idea was to examine and compare the soil erodibility and its variability coming from the same soil, but different rainfall simulator type. The applied model was the USLE, nomograph and other equations which concern single rainfall events. The given results show differences between the field and laboratory experiments and between the different calculations. Concerning for the whole rainfall events runoff and soil loss, were significantly higher at the laboratory experiments, which affected the soil erodibility values too. The given differences can originate from the plot size. The main research questions are that: How should we handle the soil erodibility factors and its significant variability? What is the best solution for soil erodibility determination?

An assessment of the ability of Bartlett-Lewis type of rainfall models to reproduce drought statistics

NASA Astrophysics Data System (ADS)

Pham, M. T.; Vanhaute, W. J.; Vandenberghe, S.; De Baets, B.; Verhoest, N. E. C.

2013-12-01

Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett-Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett-Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as a test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett-Lewis model types studied fail to preserve extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

A copula-based assessment of Bartlett-Lewis type of rainfall models for preserving drought statistics

NASA Astrophysics Data System (ADS)

Pham, M. T.; Vanhaute, W. J.; Vandenberghe, S.; De Baets, B.; Verhoest, N. E. C.

2013-06-01

Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett-Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett-Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett-Lewis type of models studied fail in preserving extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

Tree-Ring Reconstruction of Wet Season Rainfall Totals in the Amazon

NASA Astrophysics Data System (ADS)

Stahle, D. W.; Lopez, L.; Granato-Souza, D.; Barbosa, A. C. M. C.; Torbenson, M.; Villalba, R.; Pereira, G. D. A.; Feng, S.; Schongart, J.; Cook, E. R.

2017-12-01

The Amazon Basin is a globally important center of deep atmospheric convection, energy balance, and biodiversity, but only a handful of weather stations in this vast Basin have recorded rainfall measurements for at least 50 years. The available rainfall and river level observations suggest that the hydrologic cycle in the Amazon may have become amplified in the last 40-years, with more extreme rainfall and streamflow seasonality, deeper droughts, and more severe flooding. These changes in the largest hydrological system on earth may be early evidence of the expected consequences of anthropogenic climate change and deforestation in the coming century. Placing these observed and simulated changes in the context of natural climate variability during the late Holocene is a significant challenge for high-resolution paleoclimatology. We have developed exactly dated and well-replicated annual tree-ring chronologies from two native Amazonian tree species (Cedrela sp and Centrolobium microchaete). These moisture sensitive chronologies have been used to compute two reconstructions of wet season rainfall totals, one in the southern Amazon based on Centrolobium and another in the eastern equatorial Amazon using Cedrela. Both reconstructions are over 200-years long and extend the available instrumental observations in each region by over 150-years. These reconstructions are well correlated with the same regional and large-scale climate dynamics that govern the inter-annual variability of the instrumental wet season rainfall totals. Increased multi-decadal variability is reconstructed after 1950 with the Centrolobium chronologies in the southern Amazon. The Cedrela reconstruction from the eastern Amazon exhibits changes in the spatial pattern of correlation with regional rainfall stations and the large-scale sea surface temperature field after 1990 that may be consistent with recent changes in the mean position of the Inter-Tropical Convergence Zone in March over the western Atlantic and South American sector.

Application of Archimedean copulas to the impact assessment of hydro-climatic variables in semi-arid aquifers of western India

NASA Astrophysics Data System (ADS)

Wable, Pawan S.; Jha, Madan K.

2018-02-01

The effects of rainfall and the El Niño Southern Oscillation (ENSO) on groundwater in a semi-arid basin of India were analyzed using Archimedean copulas considering 17 years of data for monsoon rainfall, post-monsoon groundwater level (PMGL) and ENSO Index. The evaluated dependence among these hydro-climatic variables revealed that PMGL-Rainfall and PMGL-ENSO Index pairs have significant dependence. Hence, these pairs were used for modeling dependence by employing four types of Archimedean copulas: Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard, and Frank. For the copula modeling, the results of probability distributions fitting to these hydro-climatic variables indicated that the PMGL and rainfall time series are best represented by Weibull and lognormal distributions, respectively, while the non-parametric kernel-based normal distribution is the most suitable for the ENSO Index. Further, the PMGL-Rainfall pair is best modeled by the Clayton copula, and the PMGL-ENSO Index pair is best modeled by the Frank copula. The Clayton copula-based conditional probability of PMGL being less than or equal to its average value at a given mean rainfall is above 70% for 33% of the study area. In contrast, the spatial variation of the Frank copula-based probability of PMGL being less than or equal to its average value is 35-40% in 23% of the study area during El Niño phase, while it is below 15% in 35% of the area during the La Niña phase. This copula-based methodology can be applied under data-scarce conditions for exploring the impacts of rainfall and ENSO on groundwater at basin scales.

Understanding litter decomposition in semiarid ecosystems: linking leaf traits, UV exposure and rainfall variability

PubMed Central

Gaxiola, Aurora; Armesto, Juan J.

2015-01-01

Differences in litter quality, microbial activity or abiotic conditions cannot fully account for the variability in decomposition rates observed in semiarid ecosystems. Here we tested the role of variation in litter quality, water supply, and UV radiation as drivers of litter decomposition in arid lands. And show that carry-over effects of litter photodegradation during dry periods can regulate decomposition during subsequent wet periods. We present data from a two-phase experiment, where we first exposed litter from a drought-deciduous and an evergreen shrub to natural UV levels during five, rainless summer months and, subsequently, in the laboratory, we assessed the carry-over effects of photodegradation on biomass loss under different irrigation treatments representing the observed range of local rainfall variation among years (15–240 mm). Photodegradation of litter in the field produced average carbon losses of 12%, but deciduous Proustia pungens lost >25%, while evergreen Porlieria chilensis less than 5%. Natural exposure to UV significantly reduced carbon-to-nitrogen and lignin:N ratios in Proustia litter but not in Porlieria. During the subsequent wet phase, remaining litter biomass was lower in Proustia than in Porlieria. Indeed UV exposure increased litter decomposition of Proustia under low and medium rainfall treatments, whereas no carry-over effects were detected under high rainfall treatment. Consequently, for deciduous Proustia carry-over effects of UV exposure were negligible under high irrigation. Litter decomposition of the evergreen Porlieria depended solely on levels of rainfall that promote microbial decomposers. Our two-phase experiment revealed that both the carry-over effects of photodegradation and litter quality, modulated by inter-annual variability in rainfall, can explain the marked differences in decomposition rates and the frequent decoupling between rainfall and litter decomposition observed in semiarid ecosystems. PMID:25852705

Quasi-continuous stochastic simulation framework for flood modelling

NASA Astrophysics Data System (ADS)

Moustakis, Yiannis; Kossieris, Panagiotis; Tsoukalas, Ioannis; Efstratiadis, Andreas

2017-04-01

Typically, flood modelling in the context of everyday engineering practices is addressed through event-based deterministic tools, e.g., the well-known SCS-CN method. A major shortcoming of such approaches is the ignorance of uncertainty, which is associated with the variability of soil moisture conditions and the variability of rainfall during the storm event.In event-based modeling, the sole expression of uncertainty is the return period of the design storm, which is assumed to represent the acceptable risk of all output quantities (flood volume, peak discharge, etc.). On the other hand, the varying antecedent soil moisture conditions across the basin are represented by means of scenarios (e.g., the three AMC types by SCS),while the temporal distribution of rainfall is represented through standard deterministic patterns (e.g., the alternative blocks method). In order to address these major inconsistencies,simultaneously preserving the simplicity and parsimony of the SCS-CN method, we have developed a quasi-continuous stochastic simulation approach, comprising the following steps: (1) generation of synthetic daily rainfall time series; (2) update of potential maximum soil moisture retention, on the basis of accumulated five-day rainfall; (3) estimation of daily runoff through the SCS-CN formula, using as inputs the daily rainfall and the updated value of soil moisture retention;(4) selection of extreme events and application of the standard SCS-CN procedure for each specific event, on the basis of synthetic rainfall.This scheme requires the use of two stochastic modelling components, namely the CastaliaR model, for the generation of synthetic daily data, and the HyetosMinute model, for the disaggregation of daily rainfall to finer temporal scales. Outcomes of this approach are a large number of synthetic flood events, allowing for expressing the design variables in statistical terms and thus properly evaluating the flood risk.

Understanding litter decomposition in semiarid ecosystems: linking leaf traits, UV exposure and rainfall variability.

PubMed

Gaxiola, Aurora; Armesto, Juan J

2015-01-01

Differences in litter quality, microbial activity or abiotic conditions cannot fully account for the variability in decomposition rates observed in semiarid ecosystems. Here we tested the role of variation in litter quality, water supply, and UV radiation as drivers of litter decomposition in arid lands. And show that carry-over effects of litter photodegradation during dry periods can regulate decomposition during subsequent wet periods. We present data from a two-phase experiment, where we first exposed litter from a drought-deciduous and an evergreen shrub to natural UV levels during five, rainless summer months and, subsequently, in the laboratory, we assessed the carry-over effects of photodegradation on biomass loss under different irrigation treatments representing the observed range of local rainfall variation among years (15-240 mm). Photodegradation of litter in the field produced average carbon losses of 12%, but deciduous Proustia pungens lost >25%, while evergreen Porlieria chilensis less than 5%. Natural exposure to UV significantly reduced carbon-to-nitrogen and lignin:N ratios in Proustia litter but not in Porlieria. During the subsequent wet phase, remaining litter biomass was lower in Proustia than in Porlieria. Indeed UV exposure increased litter decomposition of Proustia under low and medium rainfall treatments, whereas no carry-over effects were detected under high rainfall treatment. Consequently, for deciduous Proustia carry-over effects of UV exposure were negligible under high irrigation. Litter decomposition of the evergreen Porlieria depended solely on levels of rainfall that promote microbial decomposers. Our two-phase experiment revealed that both the carry-over effects of photodegradation and litter quality, modulated by inter-annual variability in rainfall, can explain the marked differences in decomposition rates and the frequent decoupling between rainfall and litter decomposition observed in semiarid ecosystems.

Rainfall Erosivity Database on the European Scale (REDES): A product of a high temporal resolution rainfall data collection in Europe

NASA Astrophysics Data System (ADS)

Panagos, Panos; Ballabio, Cristiano; Borrelli, Pasquale; Meusburger, Katrin; Alewell, Christine

2016-04-01

The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the (R)USLE model. The R-factor is calculated from a series of single storm events by multiplying the total storm kinetic energy with the measured maximum 30-minutes rainfall intensity. This estimation requests high temporal resolution (e.g. 30 minutes) rainfall data for sufficiently long time periods (i.e. 20 years) which are not readily available at European scale. The European Commission's Joint Research Centre(JRC) in collaboration with national/regional meteorological services and Environmental Institutions made an extensive data collection of high resolution rainfall data in the 28 Member States of the European Union plus Switzerland in order to estimate rainfall erosivity in Europe. This resulted in the Rainfall Erosivity Database on the European Scale (REDES) which included 1,541 rainfall stations in 2014 and has been updated with 134 additional stations in 2015. The interpolation of those point R-factor values with a Gaussian Process Regression (GPR) model has resulted in the first Rainfall Erosivity map of Europe (Science of the Total Environment, 511, 801-815). The intra-annual variability of rainfall erosivity is crucial for modelling soil erosion on a monthly and seasonal basis. The monthly feature of rainfall erosivity has been added in 2015 as an advancement of REDES and the respective mean annual R-factor map. Almost 19,000 monthly R-factor values of REDES contributed to the seasonal and monthly assessments of rainfall erosivity in Europe. According to the first results, more than 50% of the total rainfall erosivity in Europe takes place in the period from June to September. The spatial patterns of rainfall erosivity have significant differences between Northern and Southern Europe as summer is the most erosive period in Central and Northern Europe and autumn in the Mediterranean area. This spatio-temporal analysis of rainfall erosivity at European scale is very important for policy makers and farmers for soil conservation, optimization of agricultural land use and natural hazards prediction. REDES is also used in combination with future rainfall data from WorldClim to run climate change scenarios. The projection of REDES combined with climate change scenarios (HADGEM2, RCP4.5) and using a robust geo-statistical model resulted in a 10-20% increase of the R-factor in Europe till 2050.

Atmospheric Teleconnection and Climate Variability: Affecting Rice Productivity of Bihar, India

NASA Astrophysics Data System (ADS)

Saini, A.

2017-12-01

Climate variability brought various negative results to the environment around us and area under rice crop in Bihar has also faced a lot of negative impacts due to variability in temperature and rainfall. Location of Bihar in Northern Plain of India automatically makes it prime location for agriculture and therefore variability in climatic variables brings highly sensitive results to the agricultural production (especially rice). In this study, rainfall and temperature variables are taken into consideration to investigate the impact on rice cultivated area. Change in climate variable with the passage of time is prevailing since the start of geological time scale, how the variability in climate variables has affected the major crops. Climate index of Pacific Ocean and Indian Ocean influences the seasonal weather in Bihar and therefore role of ENSO and IOD is an interesting point of inquiry. Does there exists direct relation between climate variability and area under agricultural crops? How many important variables directly signals towards the change in area under agriculture production? These entire questions are answered with respect to change in area under rice cultivation of Bihar State of India. Temperature, rainfall and ENSO are a good indicator with respect to rice cultivation in Indian subcontinent. Impact on the area under rice has been signaled through ONI, Niño3 and DMI. Increasing range of temperature in the rice productivity declining years is observed since 1990.

Short-term effects of weather and air pollution on atopic dermatitis symptoms in children: A panel study in Korea.

PubMed

Kim, Young-Min; Kim, Jihyun; Han, Youngshin; Jeon, Byoung-Hak; Cheong, Hae-Kwan; Ahn, Kangmo

2017-01-01

The effects of weather and air pollution on the severity and persistence of atopic dermatitis (AD) are important issues that have not been investigated in detail. The objective of our study was to determine the short-term effects of meteorological variables and air pollution on AD symptoms in children. We enrolled 177 AD patients with 5 years or younger from the Seoul Metropolitan Area, Korea, and followed for 17 months between August 2013 and December 2014. Symptoms records of 35,158 person-days, including itching, sleep disturbance, erythema, dry skin, oozing, and edema, were obtained. We estimated the effect of meteorological variables including daily mean temperature, relative humidity (RH), diurnal temperature range (DTR), rainfall and air pollutants including particulate matter with an aerodynamic diameter ≤10 μm (PM10), nitrogen dioxide (NO2), and tropospheric ozone (O3) on AD symptoms using a generalized linear mixed model with adjustment for related confounding factors. A 5°C increase in outdoor temperature and a 5% increase in outdoor RH was associated with 12.8% (95% confidence intervals (CI): 10.5, 15.2) and 3.3% (95% CI: 1.7, 4.7) decrease in AD symptoms, respectively, on the same day. An increase of rainfall by 5 mm increased AD symptoms by 7.3% (95% CI: 3.6, 11.1) for the days with <40 mm rainfall. The risk of AD symptoms increased by 284.9% (95% CI: 67.6, 784.2) according to a 5°C increase in DTR when it was >14°C. An increase in PM10, NO2, and O3 by 10 units increased the risk of AD symptoms on the same day by 3.2% (95% CI: 1.5, 4.9), 5.0% (95% CI: 1.4, 8.8), and 6.1% (95% CI: 3.2, 9.0), respectively. Exposure to meteorological variables and air pollutants are associated with AD symptoms in young children.

Recent climate variability and its impacts on soybean yields in Southern Brazil

NASA Astrophysics Data System (ADS)

Ferreira, Danielle Barros; Rao, V. Brahmananda

2011-08-01

Recent climate variability in rainfall, temperatures (maximum and minimum), and the diurnal temperature range is studied with emphasis on its influence over soybean yields in southern Brazil, during 1969 to 2002. The results showed that the soybean ( Glycine max L. Merril) yields are more affected by changes in temperature during summer, while changes in rainfall are more important during the beginning of plantation and at its peak of development. Furthermore, soybean yields in Paraná are more sensitive to rainfall variations, while soybean yields in the Rio Grande do Sul are more sensitive to variations in temperature. Effects of interannual climatic variability on soybean yields are evaluated through three agro-meteorological models: additive Stewart, multiplicative Rao, and multiplicative Jensen. The Jensen model is able to reproduce the interannual behavior of soybean yield reasonably well.

Hydrometeorology as an Inversion Problem: Can River Discharge Observations Improve the Atmosphere by Ensemble Data Assimilation?

NASA Astrophysics Data System (ADS)

Sawada, Yohei; Nakaegawa, Tosiyuki; Miyoshi, Takemasa

2018-01-01

We examine the potential of assimilating river discharge observations into the atmosphere by strongly coupled river-atmosphere ensemble data assimilation. The Japan Meteorological Agency's Non-Hydrostatic atmospheric Model (JMA-NHM) is first coupled with a simple rainfall-runoff model. Next, the local ensemble transform Kalman filter is used for this coupled model to assimilate the observations of the rainfall-runoff model variables into the JMA-NHM model variables. This system makes it possible to do hydrometeorology backward, i.e., to inversely estimate atmospheric conditions from the information of river flows or a flood on land surfaces. We perform a proof-of-concept Observing System Simulation Experiment, which reveals that the assimilation of river discharge observations into the atmospheric model variables can improve the skill of the short-term severe rainfall forecast.

Detecting the hydrological impacts of forest cover change in tropical mountain areas: need for detrending time series of rainfall and streamflow data.

NASA Astrophysics Data System (ADS)

Molina, A.; Vanacker, V.; Brisson, E.; Balthazar, V.

2012-04-01

Interactions between human activities and the physical environment have increasingly transformed the hydrological functioning of Andean ecosystems. In these human-modified landscapes, land use/-cover change may have a profound effect on riverine water and sediment fluxes. The hydrological impacts of land use/-cover change are diverse, as changes in vegetation affect the various components of the hydrological cycle including evapotranspiration, infiltration and surface runoff. Quantitative data for tropical mountain regions are scarce, as few long time series on rainfall, water discharge and land use are available. Furthermore, time series of rainfall and streamflow data in tropical mountains are often highly influenced by large inter- and intra-annual variability. In this paper, we analyse the hydrological response to complex forest cover change for a catchment of 280 km2 located in the Ecuadorian Andes. Forest cover change in the Pangor catchment was reconstructed based on airphotos (1963, 1977), LANDSAT TM (1991) and ETM+ data (2001, 2009). From 1963, natural vegetation was converted to agricultural land and pine plantations: forests decreased by a factor 2, and paramo decreased by 20 km2 between 1963 and 2009. For this catchment, there exists an exceptionally long record of rainfall and streamflow data that dates back from the '70s till now, but large variability in hydrometeorological data exists that is partly related to ENSO events. Given the nonstationary and nonlinear character of the ENSO-related changes in rainfall, we used the Hilbert-Huang transformation to detrend the time series of the river flow data from inter- and intra-annual fluctuations in rainfall. After applying adaptive data analysis based on empirical model decomposition techniques, it becomes apparent that the long-term trend in streamflow is different from the long-term trend in rainfall data. While the streamflow data show a long-term decrease in monthly flow, the rainfall data have a trend of increasing and then decreasing precipitation amounts. These results suggest that the land use changes had an important impact on the total water yield of the catchment. Interestingly, the effect of reforestation in the upper part of the catchment with its associated decrease in water yield seems to be dominant over the effect of deforestation in the lower part of the basin.

WegenerNet 1km-scale sub-daily rainfall data and their application: a hydrological modeling study on the sensitivity of small-catchment runoff to spatial rainfall variability

NASA Astrophysics Data System (ADS)

Oh, Sungmin; Hohmann, Clara; Foelsche, Ulrich; Fuchsberger, Jürgen; Rieger, Wolfgang; Kirchengast, Gottfried

2017-04-01

WegenerNet Feldbach region (WEGN), a pioneering experiment for weather and climate observations, has recently completed its first 10-year precipitation measurement cycle. The WEGN has measured precipitation, temperature, humidity, and other parameters since the beginning of 2007, supporting local-level monitoring and modeling studies, over an area of about 20 km x 15 km centered near the City of Feldbach (46.93 ˚ N, 15.90 ˚ E) in the Alpine forelands of southeast Austria. All the 151 stations in the network are now equipped with high-quality Meteoservis sensors as of August 2016, following an equipment with Friedrichs sensors at most stations before, and continue to provide high-resolution (2 km2/5-min) gauge based precipitation measurements for interested users in hydro-meteorological communities. Here we will present overall characteristics of the WEGN, with a focus on sub-daily precipitation measurements, from the data processing (data quality control, gridded data products generation, etc.) to data applications (e.g., ground validation of satellite estimates). The latter includes our recent study on the propagation of uncertainty from rainfall to runoff. The study assesses responses of small-catchment runoff to spatial rainfall variability in the WEGN region over the Raab valley, using a physics-based distributed hydrological model; Water Flow and Balance Simulation Model (WaSiM), developed at ETH Zurich (Schulla, ETH Zurich, 1997). Given that uncertainty due to resolution of rainfall measurements is believed to be a significant source of error in hydrologic modeling especially for convective rainfall that dominates in the region during summer, the high-resolution of WEGN data furnishes a great opportunity to analyze effects of rainfall events on the runoff at different spatial resolutions. Furthermore, the assessment can be conducted not only for the lower Raab catchment (area of about 500 km2) but also for its sub-catchments (areas of about 30-70 km2). Beside the question how many stations are necessary for reliable hydrological modeling, different interpolation methods like Inverse Distance Interpolation, Elevation Dependent Regression, and combinations will be tested. This presentation will show the first results from a scale-depending analysis of spatial and temporal structures of heavy rainfall events and responses of simulated runoff at the event scale in the WEGN region.

Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

NASA Astrophysics Data System (ADS)

Garrigues, S.; Olioso, A.; Carrer, D.; Decharme, B.; Calvet, J.-C.; Martin, E.; Moulin, S.; Marloie, O.

2015-10-01

Generic land surface models are generally driven by large-scale data sets to describe the climate, the soil properties, the vegetation dynamic and the cropland management (irrigation). This paper investigates the uncertainties in these drivers and their impacts on the evapotranspiration (ET) simulated from the Interactions between Soil, Biosphere, and Atmosphere (ISBA-A-gs) land surface model over a 12-year Mediterranean crop succession. We evaluate the forcing data sets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) high spatial resolution atmospheric reanalysis, the leaf area index (LAI) time courses derived from the ECOCLIMAP-II land surface parameter database and the soil texture derived from the French soil database. For climate, we focus on the radiations and rainfall variables and we test additional data sets which include the ERA-Interim (ERA-I) low spatial resolution reanalysis, the Global Precipitation Climatology Centre data set (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The evaluation of the drivers indicates very low bias in daily downwelling shortwave radiation for ERA-I (2.5 W m-2) compared to the negative biases found for SAFRAN (-10 W m-2) and the MSG satellite (-12 W m-2). Both SAFRAN and ERA-I underestimate downwelling longwave radiations by -12 and -16 W m-2, respectively. The SAFRAN and ERA-I/GPCC rainfall are slightly biased at daily and longer timescales (1 and 0.5 % of the mean rainfall measurement). The SAFRAN rainfall is more precise than the ERA-I/GPCC estimate which shows larger inter-annual variability in yearly rainfall error (up to 100 mm). The ECOCLIMAP-II LAI climatology does not properly resolve Mediterranean crop phenology and underestimates the bare soil period which leads to an overall overestimation of LAI over the crop succession. The simulation of irrigation by the model provides an accurate irrigation amount over the crop cycle but the timing of irrigation occurrences is frequently unrealistic. Errors in the soil hydrodynamic parameters and the lack of irrigation in the simulation have the largest influence on ET compared to uncertainties in the large-scale climate reanalysis and the LAI climatology. Among climate variables, the errors in yearly ET are mainly related to the errors in yearly rainfall. The underestimation of the available water capacity and the soil hydraulic diffusivity induce a large underestimation of ET over 12 years. The underestimation of radiations by the reanalyses and the absence of irrigation in the simulation lead to the underestimation of ET while the overall overestimation of LAI by the ECOCLIMAP-II climatology induces an overestimation of ET over 12 years. This work shows that the key challenges to monitor the water balance of cropland at regional scale concern the representation of the spatial distribution of the soil hydrodynamic parameters, the variability of the irrigation practices, the seasonal and inter-annual dynamics of vegetation and the spatiotemporal heterogeneity of rainfall.

SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds. The two-CN system approach

NASA Astrophysics Data System (ADS)

Soulis, K. X.; Valiantzas, J. D.

2011-10-01

The Soil Conservation Service Curve Number (SCS-CN) approach is widely used as a simple method for predicting direct runoff volume for a given rainfall event. The CN values can be estimated by being selected from tables. However, it is more accurate to estimate the CN value from measured rainfall-runoff data (assumed available) in a watershed. Previous researchers indicated that the CN values calculated from measured rainfall-runoff data vary systematically with the rainfall depth. They suggested the determination of a single asymptotic CN value observed for very high rainfall depths to characterize the watersheds' runoff response. In this paper, the novel hypothesis that the observed correlation between the calculated CN value and the rainfall depth in a watershed reflects the effect of the inevitable presence of soil-cover complex spatial variability along watersheds is being tested. Based on this hypothesis, the simplified concept of a two-CN heterogeneous system is introduced to model the observed CN-rainfall variation by reducing the CN spatial variability into two classes. The behavior of the CN-rainfall function produced by the proposed two-CN system concept is approached theoretically, it is analyzed systematically, and it is found to be similar to the variation observed in natural watersheds. Synthetic data tests, natural watersheds examples, and detailed study of two natural experimental watersheds with known spatial heterogeneity characteristics were used to evaluate the method. The results indicate that the determination of CN values from rainfall runoff data using the proposed two-CN system approach provides reasonable accuracy and it over performs the previous original method based on the determination of a single asymptotic CN value. Although the suggested method increases the number of unknown parameters to three (instead of one), a clear physical reasoning for them is presented.

Seasonal Evolution and Variability Associated with the West African Monsoon System

NASA Technical Reports Server (NTRS)

Gu, Guojun; Adler, Robert F.

2003-01-01

In this study, we investigate the seasonal variations in surface rainfall and associated large-scale processes in the tropical eastern Atlantic and West African region. The 5-yr (1998-2002) high-quality TRMM rainfall, sea surface temperature (SST), water vapor and cloud liquid water observations are applied along with the NCEP/NCAR reanalysis wind components and a 3-yr (2000-2002) Quickscat satellite-observed surface wind product. Major mean rainfall over West Africa tends to be concentrated in two regions and is observed in two different seasons, manifesting an abrupt shift of the mean rainfall zone during June-July. (i) Near the Gulf of Guinea (about 5 degN), intense convection and rainfall are seen during April-June and roughly follow the seasonality of SST in the tropical eastern Atlantic. (ii) Along the latitudes of about 10 deg. N over the interior West African continent, a second intense rain belt begins to develop from July and remains there during the later summer season. This belt co-exists with a northwardmoved African Easterly Jet (AEJ) and its accompanying horizonal and vertical shear zones, the appearance and intensification of an upper tropospheric Tropical Easterly Jet (TEJ), and a strong low-level westerly flow. Westward-propagating wave signals [ i e . , African easterly waves (AEWs)] dominate the synoptic-scale variability during July-September, in contrast to the evident eastward-propagating wave signals during May- June. The abrupt shift of mean rainfall zone thus turns out to be a combination of two different physical processes: (i) Evident seasonal cycles in the tropical eastern Atlantic ocean which modulate convection and rainfall in the Gulf of Guinea by means of SST thermal forcing and SST-related meridional gradient; (ii) The interaction among the AEJ, TEJ, low-level westerly flow, moist convection and AEWs during July-September which modulates rainfall variability in the interior West Africa, primarily within the ITCZ rain band. Evident seasonality in synoptic-scale wave signals is shown to be a good evidence for this seasonal evolution.

Downscaling of global climate change estimates to regional scales: An application to Iberian rainfall in wintertime

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

von Storch, H.; Zorita, E.; Cubasch, U.

A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique. The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It ismore » shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM). The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with the proposed statistical model. In an instantaneous [open quotes]2 CO[sub 2][close quotes] doubling experiment, using the simulated change of the mean North Atlantic SLP field to predict Iberian rainfall yields, there is an insignificant increase of area-averaged rainfall of I mm/month, with maximum values of 4 mm/month in the northwest of the peninsula. In contrast, for the four GCM grid points representing the lberian Peninsula, the change is - 10 mm/month, with a minimum of - 19 mm/month in the southwest. In the second experiment, with the IPCC scenario A ([open quotes]business as usual[close quotes]) increase of CO[sub 2], the statistical-model results partially differ from the directly simulated rainfall changes: in the experimental range of 100 years, the area-averaged rainfall decreases by 7 mm/month (statistical model), and by 9 mm/month (GCM); at the same time the amplitude of the interdecadal variability is quite different. 17 refs., 10 figs.« less

Skill of ENSEMBLES seasonal re-forecasts for malaria prediction in West Africa

NASA Astrophysics Data System (ADS)

Jones, A. E.; Morse, A. P.

2012-12-01

This study examines the performance of malaria-relevant climate variables from the ENSEMBLES seasonal ensemble re-forecasts for sub-Saharan West Africa, using a dynamic malaria model to transform temperature and rainfall forecasts into simulated malaria incidence and verifying these forecasts against simulations obtained by driving the malaria model with General Circulation Model-derived reanalysis. Two subregions of forecast skill are identified: the highlands of Cameroon, where low temperatures limit simulated malaria during the forecast period and interannual variability in simulated malaria is closely linked to variability in temperature, and northern Nigeria/southern Niger, where simulated malaria variability is strongly associated with rainfall variability during the peak rain months.

Disparity in rainfall trend and patterns among different regions: analysis of 158 years' time series of rainfall dataset across India

NASA Astrophysics Data System (ADS)

Saha, Saurav; Chakraborty, Debasish; Paul, Ranjit Kumar; Samanta, Sandipan; Singh, S. B.

2017-10-01

Rainfall anomaly during crop-growing season can have large impact on the agricultural output of a country, especially like India, where two-thirds of the crop land is rain-fed. In such situation, decreased agricultural production not only challenges food security of the country but directly and immediately hits the livelihood of its farming community. In a vast country like India, rainfall or its anomalies hardly follow a specific pattern, rather it is having high variability in spatial domain. This study focused on the trends of national and regional rainfall anomalies (wetness/dryness) along with their interrelationship using time series data of past 158 years. The significant reducing wetness trend (p < 0.05) over north mountainous India was prominent with an increasing trend over southern peninsular India (p < 0.10). However, long-term annual wetness was increasing over entire peninsular India. The results of change point tests indicate that major abrupt changes occurred between early to mid-twentieth century having regional variations. The regional interrelationship was studied using principal component, hierarchical clustering, and pair-wise difference test, which clearly indicated a significantly different pattern in rainfall anomalies for north east India (p = 0.022), north central India (p = 0.022), and north mountainous India (p = 0.011) from that of the all India. Result of this study affirmed high spatial variability in rainfall anomaly and most importantly established the unalike pattern in trends of regional rainfall vis-à-vis national level, ushering towards paradigm shift in rainfall forecast from country scale to regional scale for pragmatic planning.

Vegetation-rainfall feedbacks across the Sahel: a combined observational and modeling study

NASA Astrophysics Data System (ADS)

Yu, Y.; Notaro, M.; Wang, F.; Mao, J.; Shi, X.; Wei, Y.

2016-12-01

The Sahel rainfall is characterized by large interannual variability. Past modeling studies have concluded that the Sahel rainfall variability is primarily driven by oceanic forcings and amplified by land-atmosphere interactions. However, the relative importance of oceanic versus terrestrial drivers has never been assessed from observations. The current understanding of vegetation's impacts on climate, i.e. positive vegetation-rainfall feedback through the albedo, moisture, and momentum mechanisms, comes from untested models. Neither the positive vegetation-rainfall feedback, nor the underlying mechanisms, has been fully resolved in observations. The current study fills the knowledge gap about the observed vegetation-rainfall feedbacks, through the application of the multivariate statistical method Generalized Equilibrium Feedback Assessment (GEFA) to observational data. According to GEFA, the observed oceanic impacts dominate over terrestrial impacts on Sahel rainfall, except in the post-monsoon period. Positive leaf area index (LAI) anomalies favor an extended, wetter monsoon across the Sahel, largely due to moisture recycling. The albedo mechanism is not responsible for this positive vegetation feedback on the seasonal-interannual time scale, which is too short for a grass-desert transition. A low-level stabilization and subsidence is observed in response to increased LAI - potentially responsible for a negative vegetation-rainfall feedback. However, the positive moisture feedback overwhelms the negative momentum feedback, resulting in an observed positive vegetation-rainfall feedback. We further applied GEFA to a fully-coupled Community Earth System Model (CESM) control run, as an example of evaluating climate models against the GEFA-based observational benchmark. In contrast to the observed positive vegetation-rainfall feedbacks, CESM simulates a negative vegetation-rainfall feedback across Sahel, peaking in the pre-monsoon season. The simulated negative feedback is largely due to the low-level stabilization caused by increased LAI. Positive moisture feedback is present in the CESM simulation, but an order weaker than the observed and weaker than the negative momentum feedback, thereby leading to the simulated negative vegetation-rainfall feedbacks.

Climate downscaling over South America for 1971-2000: application in SMAP rainfall-runoff model for Grande River Basin

NASA Astrophysics Data System (ADS)

da Silva, Felipe das Neves Roque; Alves, José Luis Drummond; Cataldi, Marcio

2018-03-01

This paper aims to validate inflow simulations concerning the present-day climate at Água Vermelha Hydroelectric Plant (AVHP—located on the Grande River Basin) based on the Soil Moisture Accounting Procedure (SMAP) hydrological model. In order to provide rainfall data to the SMAP model, the RegCM regional climate model was also used working with boundary conditions from the MIROC model. Initially, present-day climate simulation performed by RegCM model was analyzed. It was found that, in terms of rainfall, the model was able to simulate the main patterns observed over South America. A bias correction technique was also used and it was essential to reduce mistakes related to rainfall simulation. Comparison between rainfall simulations from RegCM and MIROC showed improvements when the dynamical downscaling was performed. Then, SMAP, a rainfall-runoff hydrological model, was used to simulate inflows at Água Vermelha Hydroelectric Plant. After calibration with observed rainfall, SMAP simulations were evaluated in two different periods from the one used in calibration. During calibration, SMAP captures the inflow variability observed at AVHP. During validation periods, the hydrological model obtained better results and statistics with observed rainfall. However, in spite of some discrepancies, the use of simulated rainfall without bias correction captured the interannual flow variability. However, the use of bias removal in the simulated rainfall performed by RegCM brought significant improvements to the simulation of natural inflows performed by SMAP. Not only the curve of simulated inflow became more similar to the observed inflow, but also the statistics improved their values. Improvements were also noticed in the inflow simulation when the rainfall was provided by the regional climate model compared to the global model. In general, results obtained so far prove that there was an added value in rainfall when regional climate model was compared to global climate model and that data from regional models must be bias-corrected so as to improve their results.

Multidecadal oscillations in rainfall and hydrological extremes

NASA Astrophysics Data System (ADS)

Willems, Patrick

2013-04-01

Many studies have anticipated a worldwide increase in the frequency and intensity of precipitation extremes and floods since the last decade(s). Natural variability by climate oscillations partly determines the observed evolution of precipitation extremes. Based on a technique for the identification and analysis of changes in extreme quantiles, it is shown that hydrological extremes have oscillatory behaviour at multidecadal time scales. Results are based on nearly independent extremes extracted from long-term historical time series of precipitation intensities and river flows. Study regions include Belgium - The Netherlands (Meuse basin), Ethiopia (Blue Nile basin) and Ecuador (Paute basin). For Belgium - The Netherlands, the past 100 years showed larger and more hydrological extremes around the 1910s, 1950-1960s, and more recently during the 1990-2000s. Interestingly, the oscillations for southwestern Europe are anti-correlated with these of northwestern Europe, thus with oscillation highs in the 1930-1940s and 1970s. The precipitation oscillation peaks are explained by persistence in atmospheric circulation patterns over the North Atlantic during periods of 10 to 15 years. References: Ntegeka V., Willems P. (2008), 'Trends and multidecadal oscillations in rainfall extremes, based on a more than 100 years time series of 10 minutes rainfall intensities at Uccle, Belgium', Water Resources Research, 44, W07402, doi:10.1029/2007WR006471 Mora, D., Willems, P. (2012), 'Decadal oscillations in rainfall and air temperature in the Paute River Basin - Southern Andes of Ecuador', Theoretical and Applied Climatology, 108(1), 267-282, doi:0.1007/s00704-011-0527-4 Taye, M.T., Willems, P. (2011). 'Influence of climate variability on representative QDF predictions of the upper Blue Nile Basin', Journal of Hydrology, 411, 355-365, doi:10.1016/j.jhydrol.2011.10.019 Taye, M.T., Willems, P. (2012). 'Temporal variability of hydro-climatic extremes in the Blue Nile basin', Water Resources Research, 48, W03513, 13p. Willems, P., Olsson, J., Arnbjerg-Nielsen, K., Beecham, S., Pathirana, A., Bülow Gregersen, I., Madsen, H., Nguyen, V-T-V. (2012), 'Impacts of climate change on rainfall extremes and urban drainage', IWA Publishing, 252p., Paperback Print ISBN 9781780401256; Ebook ISBN 9781780401263

Dynamic factor analysis of groundwater quality trends in an agricultural area adjacent to Everglades National Park.

PubMed

Muñoz-Carpena, R; Ritter, A; Li, Y C

2005-11-01

The extensive eastern boundary of Everglades National Park (ENP) in south Florida (USA) is subject to one of the most expensive and ambitious environmental restoration projects in history. Understanding and predicting the water quality interactions between the shallow aquifer and surface water is a key component in meeting current environmental regulations and fine-tuning ENP wetland restoration while still maintaining flood protection for the adjacent developed areas. Dynamic factor analysis (DFA), a recent technique for the study of multivariate non-stationary time-series, was applied to study fluctuations in groundwater quality in the area. More than two years of hydrological and water quality time series (rainfall; water table depth; and soil, ground and surface water concentrations of N-NO3-, N-NH4+, P-PO4(3-), Total P, F-and Cl-) from a small agricultural watershed adjacent to the ENP were selected for the study. The unexplained variability required for determining the concentration of each chemical in the 16 wells was greatly reduced by including in the analysis some of the observed time series as explanatory variables (rainfall, water table depth, and soil and canal water chemical concentration). DFA results showed that groundwater concentration of three of the agrochemical species studied (N-NO3-, P-PO4(3-)and Total P) were affected by the same explanatory variables (water table depth, enriched topsoil, and occurrence of a leaching rainfall event, in order of decreasing relative importance). This indicates that leaching by rainfall is the main mechanism explaining concentration peaks in groundwater. In the case of N-NH4+, in addition to leaching, groundwater concentration is governed by lateral exchange with canals. F-and Cl- are mainly affected by periods of dilution by rainfall recharge, and by exchange with the canals. The unstructured nature of the common trends found suggests that these are related to the complex spatially and temporally varying land use patterns in the watershed. The results indicate that peak concentrations of agrochemicals in groundwater could be reduced by improving fertilization practices (by splitting and modifying timing of applications) and by operating the regional canal system to maintain the water table low, especially during the rainy periods.

Dynamic factor analysis of groundwater quality trends in an agricultural area adjacent to Everglades National Park

NASA Astrophysics Data System (ADS)

Muñoz-Carpena, R.; Ritter, A.; Li, Y. C.

2005-11-01

The extensive eastern boundary of Everglades National Park (ENP) in south Florida (USA) is subject to one of the most expensive and ambitious environmental restoration projects in history. Understanding and predicting the water quality interactions between the shallow aquifer and surface water is a key component in meeting current environmental regulations and fine-tuning ENP wetland restoration while still maintaining flood protection for the adjacent developed areas. Dynamic factor analysis (DFA), a recent technique for the study of multivariate non-stationary time-series, was applied to study fluctuations in groundwater quality in the area. More than two years of hydrological and water quality time series (rainfall; water table depth; and soil, ground and surface water concentrations of N-NO 3-, N-NH 4+, P-PO 43-, Total P, F -and Cl -) from a small agricultural watershed adjacent to the ENP were selected for the study. The unexplained variability required for determining the concentration of each chemical in the 16 wells was greatly reduced by including in the analysis some of the observed time series as explanatory variables (rainfall, water table depth, and soil and canal water chemical concentration). DFA results showed that groundwater concentration of three of the agrochemical species studied (N-NO 3-, P-PO 43-and Total P) were affected by the same explanatory variables (water table depth, enriched topsoil, and occurrence of a leaching rainfall event, in order of decreasing relative importance). This indicates that leaching by rainfall is the main mechanism explaining concentration peaks in groundwater. In the case of N-NH 4+, in addition to leaching, groundwater concentration is governed by lateral exchange with canals. F -and Cl - are mainly affected by periods of dilution by rainfall recharge, and by exchange with the canals. The unstructured nature of the common trends found suggests that these are related to the complex spatially and temporally varying land use patterns in the watershed. The results indicate that peak concentrations of agrochemicals in groundwater could be reduced by improving fertilization practices (by splitting and modifying timing of applications) and by operating the regional canal system to maintain the water table low, especially during the rainy periods.

Interannual variability of Indian monsoon rainfall

NASA Technical Reports Server (NTRS)

Paolino, D. A.; Shukla, J.

1984-01-01

The interannual variability of the Indian summer monsoon and its relationships with other atmospheric fluctuations were studied in hopes of gaining some insight into the predicability of the rainfall. Rainfall data for 31 meteorological subdivisions over India were provided by the India Meteorological Department (IMD). Fifty-three years of seasonal mean anomaly sea-level pressure (SLP) fields were used to determine if any relationships could be detected between fluctuations in Northern Hemisphere surface pressure and Indian monsoon rainfall. Three month running mean sea-level pressure anomalies at Darwin (close to one of the centers of the Southern Oscillation) were compiled for months preceding and following extreme years for rainfall averaged over all of India. Anomalies are small before the monsoon, but are quite large in months following the summer season. However, there is a large decrease in Darwin pressure for months preceding a heavy monsoon, while a deficient monsoon is preceded by a sharp increase in Darwin pressure. If a time series is constructed of the tendency of Darwin SLP between the Northern Hemisphere winter (DJF) and spring (MAM) and a correlation coefficient is computed between it and 81 years of rainfall average over all of India, one gets a C. C. of -.46, which is higher than any other previously computed predictor of the monsoon rainfall. This relationship can also be used to make a qualitative forecast for rainfall over the whole of India by considering the sign of the tendency in extreme monsoon years.

Prediction of early summer rainfall over South China by a physical-empirical model

NASA Astrophysics Data System (ADS)

Yim, So-Young; Wang, Bin; Xing, Wen

2014-10-01

In early summer (May-June, MJ) the strongest rainfall belt of the northern hemisphere occurs over the East Asian (EA) subtropical front. During this period the South China (SC) rainfall reaches its annual peak and represents the maximum rainfall variability over EA. Hence we establish an SC rainfall index, which is the MJ mean precipitation averaged over 72 stations over SC (south of 28°N and east of 110°E) and represents superbly the leading empirical orthogonal function mode of MJ precipitation variability over EA. In order to predict SC rainfall, we established a physical-empirical model. Analysis of 34-year observations (1979-2012) reveals three physically consequential predictors. A plentiful SC rainfall is preceded in the previous winter by (a) a dipole sea surface temperature (SST) tendency in the Indo-Pacific warm pool, (b) a tripolar SST tendency in North Atlantic Ocean, and (c) a warming tendency in northern Asia. These precursors foreshadow enhanced Philippine Sea subtropical High and Okhotsk High in early summer, which are controlling factors for enhanced subtropical frontal rainfall. The physical empirical model built on these predictors achieves a cross-validated forecast correlation skill of 0.75 for 1979-2012. Surprisingly, this skill is substantially higher than four-dynamical models' ensemble prediction for 1979-2010 period (0.15). The results here suggest that the low prediction skill of current dynamical models is largely due to models' deficiency and the dynamical prediction has large room to improve.

An Investigation of the Hydroclimate Variability of Eastern Africa

NASA Astrophysics Data System (ADS)

Smith, K. A.; Semazzi, F. H. M.

2015-12-01

The flow of the Victoria Nile, and the productivity of the dams along it, is determined by the level of Lake Victoria, which is primarily dictated by the rainfall and temperature variability over the Lake Victoria Basin. Notwithstanding the indisputable decline of water resources over the lake basin during the Long Rains of March - May, there is a strong indication based on IPCC climate projections that this trend, which has persisted for several decades, will reverse in the next few decades. This phenomenon has come to be known as the Eastern-Central African climate change paradox and could have profound implications on sustainable development for the next few decades in Lake Victoria Basin. The purpose of this study is to investigate the climate variability associated with the East African Climate Change Paradox for the recent decades. This research analyzes observations to understand the sources of variability and potential physical mechanisms related to the decline in precipitation over Eastern Africa. We then investigate the hydrological factors involved in the decline of Lake Victoria levels in the context of the decline in rainfall. While East Africa has been experiencing persistent decline of the Long Rains for multiple decades, this same decline is not seen in annual rainfall. The remaining seasons show an increase in rainfall which is compensating for the decline of the Long Rains. It is possible that the Long Rains season is shifting in such a way that the season starts earlier, in February, and ending sooner. The corresponding annual Lake Victoria levels modeled using observed rainfall do not decline in the recent decades, except when the Long Rains seasonal variability is considered without variability from other seasons. This shift could impact hydroelectric power planning on a monthly or seasonal time scale, and could potentially have a large impact on agriculture, since it would shift the growing season in the region.

Influence of preonset land atmospheric conditions on the Indian summer monsoon rainfall variability

NASA Astrophysics Data System (ADS)

Rai, Archana; Saha, Subodh K.; Pokhrel, Samir; Sujith, K.; Halder, Subhadeep

2015-05-01

A possible link between preonset land atmospheric conditions and the Indian summer monsoon rainfall (ISMR) is explored. It is shown that, the preonset positive (negative) rainfall anomaly over northwest India, Pakistan, Afghanistan, and Iran is associated with decrease (increase) in ISMR, primarily in the months of June and July, which in turn affects the seasonal mean. ISMR in the months of June and July is also strongly linked with the preonset 2 m air temperature over the same regions. The preonset rainfall/2 m air temperature variability is linked with stationary Rossby wave response, which is clearly evident in the wave activity flux diagnostics. As the predictability of Indian summer monsoon relies mainly on the El Niño-Southern Oscillation (ENSO), the found link may further enhance our ability to predict the monsoon, particularly during a non-ENSO year.

Rainfall Climatology over Asir Region, Saudi Arabia

NASA Astrophysics Data System (ADS)

Sharif, H.; Furl, C.; Al-Zahrani, M.

2012-04-01

Arid and semi-arid lands occupy about one-third of the land surface of the earth and support about one-fifth of the world population. The Asir area in Saudi Arabia is an example of these areas faced with the problem of maintaining sustainable water resources. This problem is exacerbated by the high levels of population growth, land use changes, increasing water demand, and climate variability. In this study, the characteristics of decade-scale variations in precipitation are examined in more detail for Asir region. The spatio-temporal distributions of rainfall over the region are analyzed. The objectives are to identify the sensitivity, magnitude, and range of changes in annual and seasonal evapotranspiration resulting from observed decade-scale precipitation variations. An additional objective is to characterize orographic controls on the space-time variability of rainfall. The rainfall data is obtained from more than 30 rain gauges spread over the region.

Convective rainfall estimation from digital GOES-1 infrared data

NASA Technical Reports Server (NTRS)

Sickler, G. L.; Thompson, A. H.

1979-01-01

An investigation was conducted to determine the feasibility of developing and objective technique for estimating convective rainfall from digital GOES-1 infrared data. The study area was a 240 km by 240 km box centered on College Station, Texas (Texas A and M University). The Scofield and Oliver (1977) rainfall estimation scheme was adapted and used with the digital geostationary satellite data. The concept of enhancement curves with respect to rainfall approximation is discussed. Raingage rainfall analyses and satellite-derived rainfall estimation analyses were compared. The correlation for the station data pairs (observed versus estimated rainfall amounts) for the convective portion of the storm was 0.92. It was demonstrated that a fairly accurate objective rainfall technique using digital geostationary infrared satellite data is feasible. The rawinsonde and some synoptic data that were used in this investigation came from NASA's Atmospheric Variability Experiment, AVE 7.

Vegetation Response to Rainfall and Soil Moisture Variability in Botswana

DTIC Science & Technology

1991-01-01

Effects of Varying Soil Type on the NDVI /Rainfall and NDVI /Soil Moisture...examine the effects of different soil types on the vegetation growth/rainfall relationship. The goals are to determine whether differences in the water-use...34first step" in removing the soil effect (Huete et al., 1985). Indeed, no large-scale soil corrections have been attempted as yet on NDVI data.

The influence of ENSO, PDO and PNA on secular rainfall variations in Hawai‘i

Treesearch

Abby G. Frazier; Oliver Elison Timm; Thomas W. Giambelluca; Henry F. Diaz

2017-01-01

Over the last century, significant declines in rainfall across the state of Hawai‘i have been observed, and it is unknown whether these declines are due to natural variations in climate, or manifestations of human-induced climate change. Here, a statistical analysis of the observed rainfall variability was applied as first step towards better understanding causes for...

Temporal and spatial changes of rainfall and streamflow in the Upper Tekezē-Atbara river basin, Ethiopia

NASA Astrophysics Data System (ADS)

Gebremicael, Tesfay G.; Mohamed, Yasir A.; Zaag, Pieter v.; Hagos, Eyasu Y.

2017-04-01

The Upper Tekezē-Atbara river sub-basin, part of the Nile Basin, is characterized by high temporal and spatial variability of rainfall and streamflow. In spite of its importance for sustainable water use and food security, the changing patterns of streamflow and its association with climate change is not well understood. This study aims to improve the understanding of the linkages between rainfall and streamflow trends and identify possible drivers of streamflow variabilities in the basin. Trend analyses and change-point detections of rainfall and streamflow were analysed using Mann-Kendall and Pettitt tests, respectively, using data records for 21 rainfall and 9 streamflow stations. The nature of changes and linkages between rainfall and streamflow were carefully examined for monthly, seasonal and annual flows, as well as indicators of hydrologic alteration (IHA). The trend and change-point analyses found that 19 of the tested 21 rainfall stations did not show statistically significant changes. In contrast, trend analyses on the streamflow showed both significant increasing and decreasing patterns. A decreasing trend in the dry season (October to February), short season (March to May), main rainy season (June to September) and annual totals is dominant in six out of the nine stations. Only one out of nine gauging stations experienced significant increasing flow in the dry and short rainy seasons, attributed to the construction of Tekezē hydropower dam upstream this station in 2009. Overall, streamflow trends and change-point timings were found to be inconsistent among the stations. Changes in streamflow without significant change in rainfall suggests factors other than rainfall drive the change. Most likely the observed changes in streamflow regimes could be due to changes in catchment characteristics of the basin. Further studies are needed to verify and quantify the hydrological changes shown in statistical tests by identifying the physical mechanisms behind those changes. The findings from this study are useful as a prerequisite for studying the effects of catchment management dynamics on the hydrological variabilities in the basin.

Trends in rainfall and rainfall-related extremes in the east coast of peninsular Malaysia

NASA Astrophysics Data System (ADS)

Mayowa, Olaniya Olusegun; Pour, Sahar Hadi; Shahid, Shamsuddin; Mohsenipour, Morteza; Harun, Sobri Bin; Heryansyah, Arien; Ismail, Tarmizi

2015-12-01

The coastlines have been identified as the most vulnerable regions with respect to hydrological hazards as a result of climate change and variability. The east of peninsular Malaysia is not an exception for this, considering the evidence of heavy rainfall resulting in floods as an annual phenomenon and also water scarcity due to long dry spells in the region. This study examines recent trends in rainfall and rainfall- related extremes such as, maximum daily rainfall, number of rainy days, average rainfall intensity, heavy rainfall days, extreme rainfall days, and precipitation concentration index in the east coast of peninsular Malaysia. Recent 40 years (1971-2010) rainfall records from 54 stations along the east coast of peninsular Malaysia have been analyzed using the non-parametric Mann-Kendall test and the Sen's slope method. The Monte Carlo simulation technique has been used to determine the field significance of the regional trends. The results showed that there was a substantial increase in the annual rainfall as well as the rainfall during the monsoon period. Also, there was an increase in the number of heavy rainfall days during the past four decades.

Trend and variability in western and central Africa streamflow, and their relation to climate variability between 1950 and 2010

NASA Astrophysics Data System (ADS)

Sidibe, Moussa; Dieppois, Bastien; Mahé, Gil; Paturel, Jean-Emmanuel; Rouché, Nathalie; Amoussou, Ernest; Anifowose, Babatunde; Lawler, Damian

2017-04-01

Unprecedented drought episodes that struck western and central Africa between the late 1960s and 1980s. This triggered many studies investigating rainfall variability and its impacts on food production systems. However, most studies were focused at the catchment scale. In this study, we examine how rainfall variability has impacted on river flow at the subcontinental scale between 1950 and 2010, as well as the key large-scale controls on this relationship. For the first time, we establish a complete, gap-filled, monthly streamflow data set, which extends from 1950 to 2010, over the western and central African region. To achieve this, we used linear regression modelling across and between 600 flow gauging stations (see initial database information at http://www.hydrosciences.fr/sierem/index_en.htm). Streamflow trend and variability are then seasonally assessed at this subcontinental scale and compared to those observed in three different rainfall data sets (i.e. CRU TS3.24, GPCC V7, IRD-HSM). Long-term trends and variability in streamflow are mainly consistent with trends in rainfall. However, these relationships may have been moderated by: i) changes in land use; and ii) contributions from groundwater resources. In particular, we note that the recent post 1990s partial recovery in Sahel rainfall could have, at least partially, positively impacted river flows (e.g. the Senegal and Niger rivers). Using multi-temporal trend and continuous wavelet analysis, the time-evolution of western and central African river flows are analysed, and are all characterized by very strong decadal fluctuations, which can be interpreted as modulations in the baseflow. These decadal fluctuations, which are also significantly detected in rainfall, are likely related to large-scale sea-surface temperature (SST) anomaly patterns, such as the tropical Atlantic SST variability, the Atlantic Multidecadal Oscillation, the Interdecadal Pacific Oscillation and/or the Pacific Decadal Oscillation. Furthermore, hitherto-poorly understood hydroclimatic processes related to these teleconnections at decadal timescales will be examined in this study. Influences of the catchment properties (e.g. size, shape, vegetation and landuse cover, soil type, ground-water level, direction of stream flow across climate zones) on these decadal fluctuations in river flows will also be assessed. This study therefore aims to improve the ability of current regional and global climate models to simulate such ranges of variability, to significantly improve regional hydroclimate understanding, as a means for improving the development of future scenarios for water resources in western and central Africa.

Radar-raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology.

PubMed

Wang, Li-Pen; Ochoa-Rodríguez, Susana; Simões, Nuno Eduardo; Onof, Christian; Maksimović, Cedo

2013-01-01

The applicability of the operational radar and raingauge networks for urban hydrology is insufficient. Radar rainfall estimates provide a good description of the spatiotemporal variability of rainfall; however, their accuracy is in general insufficient. It is therefore necessary to adjust radar measurements using raingauge data, which provide accurate point rainfall information. Several gauge-based radar rainfall adjustment techniques have been developed and mainly applied at coarser spatial and temporal scales; however, their suitability for small-scale urban hydrology is seldom explored. In this paper a review of gauge-based adjustment techniques is first provided. After that, two techniques, respectively based upon the ideas of mean bias reduction and error variance minimisation, were selected and tested using as case study an urban catchment (∼8.65 km(2)) in North-East London. The radar rainfall estimates of four historical events (2010-2012) were adjusted using in situ raingauge estimates and the adjusted rainfall fields were applied to the hydraulic model of the study area. The results show that both techniques can effectively reduce mean bias; however, the technique based upon error variance minimisation can in general better reproduce the spatial and temporal variability of rainfall, which proved to have a significant impact on the subsequent hydraulic outputs. This suggests that error variance minimisation based methods may be more appropriate for urban-scale hydrological applications.

Variability and Predictability of West African Droughts. A Review in the Role of Sea Surface Temperature Anomalies

NASA Technical Reports Server (NTRS)

Rodriguez-Fonseca, Belen; Mohino, Elsa; Mechoso, Carlos R.; Caminade, Cyril; Biasutti, Michela; Gaetani, Marco; Garcia-Serrano, J.; Vizy, Edward K.; Cook, Kerry; Xue, Yongkang;

Difference infiltrometer: a method to measure temporally variable infiltration rates during rainstorms

USGS Publications Warehouse

Moody, John A.; Ebel, Brian A.

2012-01-01

We developed a difference infiltrometer to measure time series of non-steady infiltration rates during rainstorms at the point scale. The infiltrometer uses two, tipping bucket rain gages. One gage measures rainfall onto, and the other measures runoff from, a small circular plot about 0.5-m in diameter. The small size allows the infiltration rate to be computed as the difference of the cumulative rainfall and cumulative runoff without having to route water through a large plot. Difference infiltrometers were deployed in an area burned by the 2010 Fourmile Canyon Fire near Boulder, Colorado, USA, and data were collected during the summer of 2011. The difference infiltrometer demonstrated the capability to capture different magnitudes of infiltration rates and temporal variability associated with convective (high intensity, short duration) and cyclonic (low intensity, long duration) rainstorms. Data from the difference infiltrometer were used to estimate saturated hydraulic conductivity of soil affected by the heat from a wildfire. The difference infiltrometer is portable and can be deployed in rugged, steep terrain and does not require the transport of water, as many rainfall simulators require, because it uses natural rainfall. It can be used to assess infiltration models, determine runoff coefficients, identify rainfall depth or rainfall intensity thresholds to initiate runoff, estimate parameters for infiltration models, and compare remediation treatments on disturbed landscapes. The difference infiltrometer can be linked with other types of soil monitoring equipment in long-term studies for detecting temporal and spatial variability at multiple time scales and in nested designs where it can be linked to hillslope and basin-scale runoff responses.

Evaluation of rainfall structure on hydrograph simulation: Comparison of radar and interpolated methods, a study case in a tropical catchment

NASA Astrophysics Data System (ADS)

Velasquez, N.; Ochoa, A.; Castillo, S.; Hoyos Ortiz, C. D.

2017-12-01

The skill of river discharge simulation using hydrological models strongly depends on the quality and spatio-temporal representativeness of precipitation during storm events. All precipitation measurement strategies have their own strengths and weaknesses that translate into discharge simulation uncertainties. Distributed hydrological models are based on evolving rainfall fields in the same time scale as the hydrological simulation. In general, rainfall measurements from a dense and well maintained rain gauge network provide a very good estimation of the total volume for each rainfall event, however, the spatial structure relies on interpolation strategies introducing considerable uncertainty in the simulation process. On the other hand, rainfall retrievals from radar reflectivity achieve a better spatial structure representation but with higher uncertainty in the surface precipitation intensity and volume depending on the vertical rainfall characteristics and radar scan strategy. To assess the impact of both rainfall measurement methodologies on hydrological simulations, and in particular the effects of the rainfall spatio-temporal variability, a numerical modeling experiment is proposed including the use of a novel QPE (Quantitative Precipitation Estimation) method based on disdrometer data in order to estimate surface rainfall from radar reflectivity. The experiment is based on the simulation of 84 storms, the hydrological simulations are carried out using radar QPE and two different interpolation methods (IDW and TIN), and the assessment of simulated peak flow. Results show significant rainfall differences between radar QPE and the interpolated fields, evidencing a poor representation of storms in the interpolated fields, which tend to miss the precise location of the intense precipitation cores, and to artificially generate rainfall in some areas of the catchment. Regarding streamflow modelling, the potential improvement achieved by using radar QPE depends on the density of the rain gauge network and its distribution relative to the precipitation events. The results for the 84 storms show a better model skill using radar QPE than the interpolated fields. Results using interpolated fields are highly affected by the dominant rainfall type and the basin scale.

The influence of intra- and inter-annual meteorological variability on dengue transmission: a multi-level modeling analysis

NASA Astrophysics Data System (ADS)

Wen, Tzai-Hung; Chen, Tzu-Hsin

2017-04-01

Dengue fever is one of potentially life-threatening mosquito-borne diseases and IPCC Fifth Assessment Report (AR5) has confirmed that dengue incidence is sensitive to the critical weather conditions, such as effects of temperature. However, previous literature focused on the effects of monthly or weekly average temperature or accumulative precipitation on dengue incidence. The influence of intra- and inter-annual meteorological variability on dengue outbreak is under investigated. The purpose of the study focuses on measuring the effect of the intra- and inter-annual variations of temperature and precipitation on dengue outbreaks. We developed the indices of intra-annual temperature variability are maximum continuity, intermittent, and accumulation of most suitable temperature (MST) for dengue vectors; and also the indices of intra-annual precipitation variability, including the measure of continuity of wetness or dryness during a pre-epidemic period; and rainfall intensity during an epidemic period. We used multi-level modeling to investigate the intra- and inter-annual meteorological variations on dengue outbreaks in southern Taiwan from 1998-2015. Our results indicate that accumulation and maximum continuity of MST are more significant than average temperature on dengue outbreaks. The effect of continuity of wetness during the pre-epidemic period is significantly more positive on promoting dengue outbreaks than the rainfall effect during the epidemic period. Meanwhile, extremely high or low rainfall density during an epidemic period do not promote the spread of dengue epidemics. Our study differentiates the effects of intra- and inter-annual meteorological variations on dengue outbreaks and also provides policy implications for further dengue control under the threats of climate change. Keywords: dengue fever, meteorological variations, multi-level model

Effect of climatic variability on malaria trends in Baringo County, Kenya.

PubMed

Kipruto, Edwin K; Ochieng, Alfred O; Anyona, Douglas N; Mbalanya, Macrae; Mutua, Edna N; Onguru, Daniel; Nyamongo, Isaac K; Estambale, Benson B A

2017-05-25

Malaria transmission in arid and semi-arid regions of Kenya such as Baringo County, is seasonal and often influenced by climatic factors. Unravelling the relationship between climate variables and malaria transmission dynamics is therefore instrumental in developing effective malaria control strategies. The main aim of this study was to describe the effects of variability of rainfall, maximum temperature and vegetation indices on seasonal trends of malaria in selected health facilities within Baringo County, Kenya. Climate variables sourced from the International Research Institute (IRI)/Lamont-Doherty Earth Observatory (LDEO) climate database and malaria cases reported in 10 health facilities spread across four ecological zones (riverine, lowland, mid-altitude and highland) between 2004 and 2014 were subjected to a time series analysis. A negative binomial regression model with lagged climate variables was used to model long-term monthly malaria cases. The seasonal Mann-Kendall trend test was then used to detect overall monotonic trends in malaria cases. Malaria cases increased significantly in the highland and midland zones over the study period. Changes in malaria prevalence corresponded to variations in rainfall and maximum temperature. Rainfall at a time lag of 2 months resulted in an increase in malaria transmission across the four zones while an increase in temperature at time lags of 0 and 1 month resulted in an increase in malaria cases in the riverine and highland zones, respectively. Given the existence of a time lag between climatic variables more so rainfall and peak malaria transmission, appropriate control measures can be initiated at the onset of short and after long rains seasons.

Rainfall Variability, Adaptation through Irrigation, and Sustainable Management of Water Resources in India

NASA Astrophysics Data System (ADS)

Fishman, R.

2013-12-01

Most studies of the impact of climate change on agriculture account for shifts in temperature and total seasonal (or monthly) precipitation. However, climate change is also projected to increase intra-seasonal precipitation variability in many parts of the world. To provide first estimates of the potential impact, I paired daily rainfall and rice yield data during the period 1970-2004, from across India, where about a fifth of the world's rice is produced, and yields have always been highly dependent on the erratic monsoon rainfall. Multivariate regression models revealed that the number of rainless days during the wet season has a statistically robust negative impact on rice yields that exceeds that of total seasonal rainfall. Moreover, a simulation of climate change impacts found that the negative impact of the projected increase in the number of rainless days will trump the positive impact of the projected increase in total precipitation, and reverse the net precipitation effect on rice production from positive (+3%) to negative (-10%). The results also indicate that higher irrigation coverage is correlated with reduced sensitivity to rainfall variability, suggesting the expansion of irrigation can effectively adapt agriculture to these climate change impacts. However, taking into account limitations on water resource availability in India, I calculate that under current irrigation practices, sustainable use of water can mitigate less than a tenth of the impact.

Does the Madden-Julian Oscillation influence aerosol variability?

NASA Astrophysics Data System (ADS)

Tian, Baijun; Waliser, Duane E.; Kahn, Ralph A.; Li, Qinbin; Yung, Yuk L.; Tyranowski, Tomasz; Geogdzhayev, Igor V.; Mishchenko, Michael I.; Torres, Omar; Smirnov, Alexander

2008-06-01

We investigate the modulation of aerosols by the Madden-Julian Oscillation (MJO) using multiple, global satellite aerosol products: aerosol index (AI) from the Total Ozone Mapping Spectrometer (TOMS) on Nimbus-7, and aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Advanced Very High Resolution Radiometer (AVHRR) on NOAA satellites. A composite MJO analysis indicates that large variations in the TOMS AI and MODIS/AVHRR AOT are found over the equatorial Indian and western Pacific Oceans where MJO convection is active, as well as the tropical Africa and Atlantic Ocean where MJO convection is weak but the background aerosol level is high. A strong inverse linear relationship between the TOMS AI and rainfall anomalies, but a weaker, less coherent positive correlation between the MODIS/AVHRR AOT and rainfall anomalies, were found. The MODIS/AVHRR pattern is consistent with ground-based Aerosol Robotic Network data. These results indicate that the MJO and its associated cloudiness, rainfall, and circulation variability systematically influence the variability in remote sensing aerosol retrieval results. Several physical and retrieval algorithmic factors that may contribute to the observed aerosol-rainfall relationships are discussed. Preliminary analysis indicates that cloud contamination in the aerosol retrievals is likely to be a major contributor to the observed relationships, although we cannot exclude possible contributions from other physical mechanisms. Future research is needed to fully understand these complex aerosol-rainfall relationships.

Impacts of climate variability and extreme events on soil hydrological processes

NASA Astrophysics Data System (ADS)

Ramos, M. C.; Mulligan, M.

2003-04-01

The Mediterranean climate (dry subhumid), characterised by a high variability, produces in many situations an insufficient water supply to support stable agriculture. Not only is there insufficient rainfall, but its occurrence is also highly variable between years, during the year, and spatially, during a single rainfall event. One of the main climatic characteristics affecting the vulnerability of the Mediterranean region is the high intensity rainfalls which fall after a very dry summer and the high degree of climatic fluctuation in the short and long term, especially in rainfall quantity. In addition, the rainwater penetration and storage of water in the soil are conditioned by the soil characteristics, in some cases modified by changes in land use and with new management practices. The aim of this study was to evaluate the impact of this high variability, from year to year and through the year, on soil hydrological processes, in fields resulted of the mechanisation works in vineyards in a Mediterranean environment. The PATTERNlight model, a simplified two-dimensional version of the hydrological and growth PATTERN model (Mulligan, 1996) is used here to simulate the water balance for three situations: normal, wet and dry years. Ssignificant differences in soil moisture and recharge were observed under vine culture from year to year, giving rise very often, to critical situations for the development of the crops. The distribution of the rainfall through the year together with the intensity of the recorded rainfalls is much very significant for soil hydrology than the total annual rainfall. Very low soil moisture conditions are raised when spring rainfall is scarce, which contribute to exhaustion of profile soil water over the summer, especially if the antecedent soil moisture is low. This low soil moisture has a significant effect on the development of the vine crop. The simulations of leaf and root biomass carried out with the PATTERNLIGHT model indicate the differences in the development of the leaf biomass between wet and dry conditions, especially with dry springs. Wet conditions favour the development of root and leaf biomass in a significant way. Mulligan, M., 1996. Modelling the hydrology of vegetation competition in a degrade semiarid environment. PhD Theses. Department of Geography, King's College London, University of London.

A method to combine spaceborne radar and radiometric observations of precipitation

NASA Astrophysics Data System (ADS)

Munchak, Stephen Joseph

This dissertation describes the development and application of a combined radar-radiometer rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) satellite. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution (PSD), and cloud water path (cLWP) are retrieved for each radar profile. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, FL shows agreement within 2% which exceeds previous algorithms' ability to match rainfall at these two sites. The algorithm is then applied to two years of TRMM data over oceans to determine the sources of DSD variability. Three correlated sets of variables representing storm dynamics, background environment, and cloud microphysics are found to account for approximately 50% of the variability in the absolute and reflectivity-normalized median drop size. Structures of radar reflectivity are also identified and related to drop size, with these relationships being confirmed by ground-based polarimetric radar data from the North American Monsoon Experiment (NAME). Regional patterns of DSD and the sources of variability identified herein are also shown to be consistent with previous work documenting regional DSD properties. In particular, mid-latitude regions and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Intertropical Convergence Zone. Due to properties of the DSD and rain water/cloud water partitioning that change with column water vapor, it is shown that increases in water vapor in a global warming scenario could lead to slight (1%) underestimates of a rainfall trends by radar but larger overestimates (5%) by radiometer algorithms. Further analyses are performed to compare tropical oceanic mean rainfall rates between the combined algorithm and other sources. The combined algorithm is 15% higher than the version 6 of the 2A25 radar-only algorithm and 6.6% higher than the Global Precipitation Climatology Project (GPCP) estimate for the same time-space domain. Despite being higher than these two sources, the combined total is not inconsistent with estimates of the other components of the energy budget given their uncertainties.

Global Climatic Indices Influence on Rainfall Spatiotemporal Distribution : A Case Study from Morocco

NASA Astrophysics Data System (ADS)

Elkadiri, R.; Zemzami, M.; Phillips, J.

2017-12-01

The climate of Morocco is affected by the Mediterranean Sea, the Atlantic Ocean the Sahara and the Atlas mountains, creating a highly variable spatial and temporal distribution. In this study, we aim to decompose the rainfall in Morocco into global and local signals and understand the contribution of the climatic indices (CIs) on rainfall. These analyses will contribute in understanding the Moroccan climate that is typical of other Mediterranean and North African climatic zones. In addition, it will contribute in a long-term prediction of climate. The constructed database ranges from 1950 to 2013 and consists of monthly data from 147 rainfall stations and 37 CIs data provided mostly by the NOAA Climate Prediction Center. The next general steps were followed: (1) the study area was divided into 9 homogenous climatic regions and weighted precipitation was calculated for each region to reduce the local effects. (2) Each CI was decomposed into nine components of different frequencies (D1 to D9) using wavelet multiresolution analysis. The four lowest frequencies of each CI were selected. (3) Each of the original and resulting signals were shifted from one to six months to account for the effect of the global patterns. The application of steps two and three resulted in the creation of 1225 variables from the original 37 CIs. (4) The final 1225 variables were used to identify links between the global and regional CIs and precipitation in each of the nine homogenous regions using stepwise regression and decision tree. The preliminary analyses and results were focused on the north Atlantic zone and have shown that the North Atlantic Oscillation (PC-based) from NCAR (NAOPC), the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the Western Mediterranean Oscillation (WMO) and the Extreme Eastern Tropical Pacific Sea Surface Temperature (NINO12) have the highest correlation with rainfall (33%, 30%, 27%, 21% and -20%, respectively). In addition the 4-months lagged NINO12 and the 6-months lagged NAOPC and WMO have a collective contribution of more than 45% of the rainfall signal. Low frequencies are also represented in the rainfall; especially the 5th and 4th components of the decomposed CIs (48% and 42% of the frequencies, respectively) suggesting their potential contribution in the interannual rainfall variability.

New features of global climatology revealed by satellite-derived oceanic rainfall maps

NASA Technical Reports Server (NTRS)

Rao, M. S. V.; Theon, J. S.

1977-01-01

Quantitative rainfall maps over the oceanic areas of the globe were derived from the Nimbus 5 Electrically Scanning Microwave Radiometer (ESMR) data. Analysis of satellite derived oceanic rainfall maps reveal certain distinctive characteristics of global patterns for the years 1973-74. The main ones are (1) the forking of the Intertropical Convergence Zone in the Pacific, (2) a previously unrecognized rain area in the South Atlantic, (3) the bimodal behavior of rainbelts in the Indian Ocean and (4) the large interannual variability in oceanic rainfall. These features are discussed.

A Method for Partitioning Surface and Subsurface Flow Using Rainfall Simulaton and Two-Dimensional Surface Electrical Resistivity Imaging

NASA Astrophysics Data System (ADS)

Carey, A. M.; Paige, G. B.; Miller, S. N.; Carr, B. J.; Holbrook, W. S.

2014-12-01

In semi-arid rangeland environments understanding how surface and subsurface flow processes and their interactions are influenced by watershed and rainfall characteristics is critical. However, it is difficult to resolve the temporal variations between mechanisms controlling these processes and challenging to obtain field measurements that document their interactions. Better insight into how these complex systems respond hydrologically is necessary in order to refine hydrologic models and decision support tools. We are conducting field studies integrating high resolution, two-dimensional surface electrical resistivity imaging (ERI) with variable intensity rainfall simulation, to quantify real-time partitioning of rainfall into surface and subsurface response. These studies are being conducted at the hillslope scale on long-term runoff plots on four different ecological sites in the Upper Crow Creek Watershed in southeastern Wyoming. Variable intensity rainfall rates were applied using the Walnut Gulch Rainfall Simulator in which intensities were increased incrementally from 49 to 180 mm hr-1 and steady-state runoff rates for each intensity were measured. Two 13.5 m electrode arrays at 0.5 m spacing were positioned on the surface perpendicular to each plot and potentials were measured at given time intervals prior to, during and following simulations using a dipole-dipole array configuration. The configuration allows for a 2.47 m depth of investigation in which magnitude and direction of subsurface flux can be determined. We used the calculated steady state infiltration rates to quantify the variability in the partial area runoff response on the ecological sites. Coupling this information with time-lapse difference inversions of ERI data, we are able to track areas of increasing and decreasing resistivity in the subsurface related to localized areas of infiltration during and following rainfall events. We anticipate implementing this method across a variety of ecological sites in the Upper Crow Creek in order to characterize the variable hydrologic response of this complex rangeland watershed. This information is being used to refine current physically based hydrologic models and watershed assessment tools.

Climate forcing and desert malaria: the effect of irrigation.

PubMed

Baeza, Andres; Bouma, Menno J; Dobson, Andy P; Dhiman, Ramesh; Srivastava, Harish C; Pascual, Mercedes

2011-07-14

Rainfall variability and associated remote sensing indices for vegetation are central to the development of early warning systems for epidemic malaria in arid regions. The considerable change in land-use practices resulting from increasing irrigation in recent decades raises important questions on concomitant change in malaria dynamics and its coupling to climate forcing. Here, the consequences of irrigation level for malaria epidemics are addressed with extensive time series data for confirmed Plasmodium falciparum monthly cases, spanning over two decades for five districts in north-west India. The work specifically focuses on the response of malaria epidemics to rainfall forcing and how this response is affected by increasing irrigation. Remote sensing data for the Normalized Difference Vegetation Index (NDVI) are used as an integrated measure of rainfall to examine correlation maps within the districts and at regional scales. The analyses specifically address whether irrigation has decreased the coupling between malaria incidence and climate variability, and whether this reflects (1) a breakdown of NDVI as a useful indicator of risk, (2) a weakening of rainfall forcing and a concomitant decrease in epidemic risk, or (3) an increase in the control of malaria transmission. The predictive power of NDVI is compared against that of rainfall, using simple linear models and wavelet analysis to study the association of NDVI and malaria variability in the time and in the frequency domain respectively. The results show that irrigation dampens the influence of climate forcing on the magnitude and frequency of malaria epidemics and, therefore, reduces their predictability. At low irrigation levels, this decoupling reflects a breakdown of local but not regional NDVI as an indicator of rainfall forcing. At higher levels of irrigation, the weakened role of climate variability may be compounded by increased levels of control; nevertheless this leads to no significant decrease in the actual risk of disease. This implies that irrigation can lead to more endemic conditions for malaria, creating the potential for unexpectedly large epidemics in response to excess rainfall if these climatic events coincide with a relaxation of control over time. The implications of our findings for control policies of epidemic malaria in arid regions are discussed.

Rainfall continuous time stochastic simulation for a wet climate in the Cantabric Coast

NASA Astrophysics Data System (ADS)

Rebole, Juan P.; Lopez, Jose J.; Garcia-Guzman, Adela

2010-05-01

Rain is the result of a series of complex atmospheric processes which are influenced by numerous factors. This complexity makes its simulation practically unfeasible from a physical basis, advising the use of stochastic diagrams. These diagrams, which are based on observed characteristics (Todorovic and Woolhiser, 1975), allow the introduction of renewal alternating processes, that account for the occurrence of rainfall at different time lapses (Markov chains are a particular case, where lapses can be described by exponential distributions). Thus, a sequential rainfall process can be defined as a temporal series in which rainfall events (periods in which rainfall is recorded) alternate with non rain events (periods in which no rainfall is recorded). The variables of a temporal rain sequence have been characterized (duration of the rainfall event, duration of the non rainfall event, average intensity of the rain in the rain event, and a temporal distribution of the amount of rain in the rain event) in a wet climate such as that of the coastal area of Guipúzcoa. The study has been performed from two series recorded at the meteorological stations of Igueldo-San Sebastián and Fuenterrabia / Airport (data every ten minutes and for its hourly aggregation). As a result of this work, the variables satisfactorily fitted the following distribution functions: the duration of the rain event to a exponential function; the duration of the dry event to a truncated exponential mixed distribution; the average intensity to a Weibull distribution; and the distribution of the rain fallen to the Beta distribution. The characterization was made for an hourly aggregation of the recorded interval of ten minutes. The parameters of the fitting functions were better obtained by means of the maximum likelihood method than the moment method. The parameters obtained from the characterization were used to develop a stochastic rainfall process simulation model by means of a three states Markov chain (Hutchinson, 1990), performed in an hourly basis by García-Guzmán (1993) and Castro et al. (1997, 2005 ). Simulation process results were valid in the hourly case for all the four described variables, with a slightly better response in Fuenterrabia than in Igueldo. In summary, all the variables were better simulated in Fuenterrabia than in Igueldo. Fuenterrabia data series is shorter and with longer sequences without missing data, compared to Igueldo. The latter shows higher number of missing data events, whereas its mean duration is longer in Fuenterrabia.

Seasonal variation and climate change impact in Rainfall Erosivity across Europe

NASA Astrophysics Data System (ADS)

Panagos, Panos; Borrelli, Pasquale; Meusburger, Katrin; Alewell, Christine; Ballabio, Cristiano

2017-04-01

Rainfall erosivity quantifies the climatic effect on water erosion and is of high importance for soil scientists, land use planners, agronomists, hydrologists and environmental scientists in general. The rainfall erosivity combines the influence of rainfall duration, magnitude, frequency and intensity. Rainfall erosivity is calculated from a series of single storm events by multiplying the total storm kinetic energy with the measured maximum 30-minute rainfall intensity. This estimation requests high temporal resolution (e.g. 30 minutes) rainfall data for sufficiently long time periods (i.e. 20 years). The European Commission's Joint Research Centr(JRC) in collaboration with national/regional meteorological services and Environmental Institutions made an extensive data collection of high resolution rainfall data in the 28 Member States of the European Union plus Switzerland to estimate rainfall erosivity in Europe. This resulted in the Rainfall Erosivity Database on the European Scale (REDES) which included 1,675 stations. The interpolation of those point erosivity values with a Gaussian Process Regression (GPR) model has resulted in the first Rainfall Erosivity map of Europe (Science of the Total Environment, 511: 801-815). In 2016, REDES extended with a monthly component, which allowed developing monthly and seasonal erosivity maps and assessing rainfall erosivity both spatially and temporally for European Union and Switzerland. The monthly erosivity maps have been used to develop composite indicators that map both intra-annual variability and concentration of erosive events (Science of the Total Environment, 579: 1298-1315). Consequently, spatio-temporal mapping of rainfall erosivity permits to identify the months and the areas with highest risk of soil loss where conservation measures should be applied in different seasons of the year. Finally, the identification of the most erosive month allows recommending certain agricultural management practices (crop residues, reduced tillage) in regions with high erosivity. Besides soil erosion mapping, the intra-annual analysis of rainfall erosivity is an important step towards flood prevention, hazard mitigation, ecosystem services, land use change and agricultural production. The application of REDES in combination with moderate climate change scenarios scenario (HadGEM RCP 4.5) resulted in predictions of erosivity in 2050. The overall increase of rainfall erosivity in Europe by 18% until 2050 are in line with projected increases of 17% for the U.S.A. The predicted mean rise of erosivity is also expected to increase the threat of soil erosion in Europe. The most noticeable increase of erosivity is projected for North-Central Europe, the English Channel, The Netherlands and Northern France. On the contrary, the Mediterranean basin show mixed trends. The success story with the compilation of REDES and first rainfall erosivity map of Europe was a driver to implement a Global Rainfall Erosivity Database (GloREDa). During the last 3 years, JRC was leading an effort to collect high temporal resolution rainfall data worldwide. In collaboration with 50 scientists worldwide and 100+ Meteorological and environmental Organisations, we have developed a Global Erosivity Database. In this database, we managed to include calculated erosivity values for 3,625 stations covering 63 countries worldwide.

Assessing operative natural and anthropogenic forcing factors from long-term climate time series of Uttarakhand (India) in the backdrop of recurring extreme rainfall events over northwest Himalaya

NASA Astrophysics Data System (ADS)

Agnihotri, Rajesh; Dimri, A. P.; Joshi, H. M.; Verma, N. K.; Sharma, C.; Singh, J.; Sundriyal, Y. P.

2017-05-01

The entire Indo-Himalayan region from northwest (Kashmir) to northeast (Assam) is facing prevalence of floods and landslides in recent years causing massive loss of property, human and animal lives, infrastructure, and eventually threatening tourist activities substantially. Extremely intense rainfall event of 2013 C.E. (between 15 and 17 June) kicked off mammoth flash floods in the Kedarnath area of Uttarakhand state, resulting in huge socioeconomic losses to the state and country. Uttarakhand is an important hilly region attracting thousands of tourists every year owing to numerous shrines and forested mountainous tourist spots. Though recent studies indicate a plausible weakening of Indian summer monsoon rainfall overall, recurrent anomalous high rainfall events over northwest Himalaya (e.g. -2010, 2013, and 2016) point out the need for a thorough reassessment of long-term time series data of regional rainfall and ambient temperatures in order to trace signatures of a shifting pattern in regional meteorology, if any. Accordingly, here we investigate 100-year-long monthly rainfall and air temperature time series data for a selected grid (28.5°N, 31.25°N; 78.75°E, 81.25°E) covering most parts of Uttarakhand state. We also examined temporal variance in interrelationships among regional meteorological data (temperature and precipitation) and key global climate variability indices using advance statistical methods. Major findings are (i) significant increase in pre-monsoon air temperature over Uttarakhand after 1997, (ii) increasing upward trend in June-July rainfall and its relationship with regional May temperatures (iii) monsoonal rainfall (June, July, August, and September; JJAS) showing covariance with interannual variability in Eurasian snow cover (ESC) extent during the month of March, and (iv) enhancing tendency of anomalous high rainfall events during negative phases of Arctic Oscillation. Obtained results indicate that under warming scenario, JJ rainfall (over AS) may further increase with occasional extreme rainfall spells when AO index (March) is negative.

A hydro-mechanical framework for early warning of rainfall-induced landslides (Invited)

NASA Astrophysics Data System (ADS)

Godt, J.; Lu, N.; Baum, R. L.

2013-12-01

Landslide early warning requires an estimate of the location, timing, and magnitude of initial movement, and the change in volume and momentum of material as it travels down a slope or channel. In many locations advance assessment of landslide location, volume, and momentum is possible, but prediction of landslide timing entails understanding the evolution of rainfall and soil-water conditions, and consequent effects on slope stability in real time. Existing schemes for landslide prediction generally rely on empirical relations between landslide occurrence and rainfall amount and duration, however, these relations do not account for temporally variable rainfall nor the variably saturated processes that control the hydro-mechanical response of hillside materials to rainfall. Although limited by the resolution and accuracy of rainfall forecasts and now-casts in complex terrain and by the inherent difficulty in adequately characterizing subsurface materials, physics-based models provide a general means to quantitatively link rainfall and landslide occurrence. To obtain quantitative estimates of landslide potential from physics-based models using observed or forecasted rainfall requires explicit consideration of the changes in effective stress that result from changes in soil moisture and pore-water pressures. The physics that control soil-water conditions are transient, nonlinear, hysteretic, and dependent on material composition and history. In order to examine the physical processes that control infiltration and effective stress in variably saturated materials, we present field and laboratory results describing intrinsic relations among soil water and mechanical properties of hillside materials. At the REV (representative elementary volume) scale, the interaction between pore fluids and solid grains can be effectively described by the relation between soil suction, soil water content, hydraulic conductivity, and suction stress. We show that these relations can be obtained independently from outflow, shear strength, and deformation tests for a wide range of earth materials. We then compare laboratory results with measurements of pore pressure and moisture content from landslide-prone settings and demonstrate that laboratory results obtained for hillside materials are representative of field conditions. These fundamental relations provide a basis to combine observed or forecasted rainfall with in-situ measurements of soil water conditions using hydro-mechanical models that simulate transient variably saturated flow and slope stability. We conclude that early warning using an approach in which in-situ observations are used to establish initial conditions for hydro-mechanical models is feasible in areas of high landslide risk where laboratory characterization of materials is practical and accurate rainfall information can be obtained. Analogous to weather and climate forecasting, such models could then be applied in an ensemble fashion to obtain quantitative estimates of landslide probability and error. Application to broader regions likely awaits breakthroughs in the development of remotely sensed proxies of soil properties and subsurface moisture conditions.

Moisture status during a strong El Niño explains a tropical montane cloud forest's upper limit.

PubMed

Crausbay, Shelley D; Frazier, Abby G; Giambelluca, Thomas W; Longman, Ryan J; Hotchkiss, Sara C

2014-05-01

Growing evidence suggests short-duration climate events may drive community structure and composition more directly than long-term climate means, particularly at ecotones where taxa are close to their physiological limits. Here we use an empirical habitat model to evaluate the role of microclimate during a strong El Niño in structuring a tropical montane cloud forest's upper limit and composition in Hawai'i. We interpolate climate surfaces, derived from a high-density network of climate stations, to permanent vegetation plots. Climatic predictor variables include (1) total rainfall, (2) mean relative humidity, and (3) mean temperature representing non-El Niño periods and a strong El Niño drought. Habitat models explained species composition within the cloud forest with non-El Niño rainfall; however, the ecotone at the cloud forest's upper limit was modeled with relative humidity during a strong El Niño drought and secondarily with non-El Niño rainfall. This forest ecotone may be particularly responsive to strong, short-duration climate variability because taxa here, particularly the isohydric dominant Metrosideros polymorpha, are near their physiological limits. Overall, this study demonstrates moisture's overarching influence on a tropical montane ecosystem, and suggests that short-term climate events affecting moisture status are particularly relevant at tropical ecotones. This study further suggests that predicting the consequences of climate change here, and perhaps in other tropical montane settings, will rely on the skill and certainty around future climate models of regional rainfall, relative humidity, and El Niño.

Forecasting Global Point Rainfall using ECMWF's Ensemble Forecasting System

NASA Astrophysics Data System (ADS)

Pillosu, Fatima; Hewson, Timothy; Zsoter, Ervin; Baugh, Calum

2017-04-01

ECMWF (the European Centre for Medium range Weather Forecasts), in collaboration with the EFAS (European Flood Awareness System) and GLOFAS (GLObal Flood Awareness System) teams, has developed a new operational system that post-processes grid box rainfall forecasts from its ensemble forecasting system to provide global probabilistic point-rainfall predictions. The project attains a higher forecasting skill by applying an understanding of how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals. In turn this approach facilitates identification of cases in which very localized extreme totals are much more likely. This approach aims also to improve the rainfall input required in different hydro-meteorological applications. Flash flood forecasting, in particular in urban areas, is a good example. In flash flood scenarios precipitation is typically characterised by high spatial variability and response times are short. In this case, to move beyond radar based now casting, the classical approach has been to use very high resolution hydro-meteorological models. Of course these models are valuable but they can represent only very limited areas, may not be spatially accurate and may give reasonable results only for limited lead times. On the other hand, our method aims to use a very cost-effective approach to downscale global rainfall forecasts to a point scale. It needs only rainfall totals from standard global reporting stations and forecasts over a relatively short period to train it, and it can give good results even up to day 5. For these reasons we believe that this approach better satisfies user needs around the world. This presentation aims to describe two phases of the project: The first phase, already completed, is the implementation of this new system to provide 6 and 12 hourly point-rainfall accumulation probabilities. To do this we use a limited number of physically relevant global model parameters (i.e. convective precipitation ratio, speed of steering winds, CAPE - Convective Available Potential Energy - and solar radiation), alongside the rainfall forecasts themselves, to define the "weather types" that in turn define the expected sub-grid variability. The calibration and computational strategy intrinsic to the system will be illustrated. The quality of the global point rainfall forecasts is also illustrated by analysing recent case studies in which extreme totals and a greatly elevated flash flood risk could be foreseen some days in advance but especially by a longer-term verification that arises out of retrospective global point rainfall forecasting for 2016. The second phase, currently in development, is focussing on the relationships with other relevant geographical aspects, for instance, orography and coastlines. Preliminary results will be presented. These are promising but need further study to fully understand their impact on the spatial distribution of point rainfall totals.

Use of a scenario-neutral approach to identify the key hydro-meteorological attributes that impact runoff from a natural catchment

NASA Astrophysics Data System (ADS)

Guo, Danlu; Westra, Seth; Maier, Holger R.

2017-11-01

Scenario-neutral approaches are being used increasingly for assessing the potential impact of climate change on water resource systems, as these approaches allow the performance of these systems to be evaluated independently of climate change projections. However, practical implementations of these approaches are still scarce, with a key limitation being the difficulty of generating a range of plausible future time series of hydro-meteorological data. In this study we apply a recently developed inverse stochastic generation approach to support the scenario-neutral analysis, and thus identify the key hydro-meteorological variables to which the system is most sensitive. The stochastic generator simulates synthetic hydro-meteorological time series that represent plausible future changes in (1) the average, extremes and seasonal patterns of rainfall; and (2) the average values of temperature (Ta), relative humidity (RH) and wind speed (uz) as variables that drive PET. These hydro-meteorological time series are then fed through a conceptual rainfall-runoff model to simulate the potential changes in runoff as a function of changes in the hydro-meteorological variables, and runoff sensitivity is assessed with both correlation and Sobol' sensitivity analyses. The method was applied to a case study catchment in South Australia, and the results showed that the most important hydro-meteorological attributes for runoff were winter rainfall followed by the annual average rainfall, while the PET-related meteorological variables had comparatively little impact. The high importance of winter rainfall can be related to the winter-dominated nature of both the rainfall and runoff regimes in this catchment. The approach illustrated in this study can greatly enhance our understanding of the key hydro-meteorological attributes and processes that are likely to drive catchment runoff under a changing climate, thus enabling the design of tailored climate impact assessments to specific water resource systems.

Scaling Linguistic Characterization of Precipitation Variability

NASA Astrophysics Data System (ADS)

Primo, C.; Gutierrez, J. M.

2003-04-01

Rainfall variability is influenced by changes in the aggregation of daily rainfall. This problem is of great importance for hydrological, agricultural and ecological applications. Rainfall averages, or accumulations, are widely used as standard climatic parameters. However different aggregation schemes may lead to the same average or accumulated values. In this paper we present a fractal method to characterize different aggregation schemes. The method provides scaling exponents characterizing weekly or monthly rainfall patterns for a given station. To this aim, we establish an analogy with linguistic analysis, considering precipitation as a discrete variable (e.g., rain, no rain). Each weekly, or monthly, symbolic precipitation sequence of observed precipitation is then considered as a "word" (in this case, a binary word) which defines a specific weekly rainfall pattern. Thus, each site defines a "language" characterized by the words observed in that site during a period representative of the climatology. Then, the more variable the observed weekly precipitation sequences, the more complex the obtained language. To characterize these languages, we first applied the Zipf's method obtaining scaling histograms of rank ordered frequencies. However, to obtain significant exponents, the scaling must be maintained some orders of magnitude, requiring long sequences of daily precipitation which are not available at particular stations. Thus this analysis is not suitable for applications involving particular stations (such as regionalization). Then, we introduce an alternative fractal method applicable to data from local stations. The so-called Chaos-Game method uses Iterated Function Systems (IFS) for graphically representing rainfall languages, in a way that complex languages define complex graphical patterns. The box-counting dimension and the entropy of the resulting patterns are used as linguistic parameters to quantitatively characterize the complexity of the patterns. We illustrate the high climatological discrimination power of the linguistic parameters in the Iberian peninsula, when compared with other standard techniques (such as seasonal mean accumulated precipitation). As an example, standard and linguistic parameters are used as inputs for a clustering regionalization method, comparing the resulting clusters.

Validation Of TRMM For Hazard Assessment In The Remote Context Of Tropical Africa

NASA Astrophysics Data System (ADS)

Monsieurs, E.; Kirschbaum, D.; Tan, J.; Jacobs, L.; Kervyn, M.; Demoulin, A.; Dewitte, O.

2017-12-01

Accurate rainfall data is fundamental for understanding and mitigating the disastrous effects of many rainfall-triggered hazards, especially when one considers the challenges arising from climate change and rainfall variability. In tropical Africa in particular, the sparse operational rainfall gauging network hampers the ability to understand these hazards. Satellite rainfall estimates (SRE) can therefore be of great value. Yet, rigorous validation is required to identify the uncertainties when using SRE for hazard applications. We evaluated the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 Research Derived Daily Product from 1998 to 2017, at 0.25° x 0.25° spatial and 24 h temporal resolution. The validation was done over the western branch of the East African Rift, with the perspective of regional landslide hazard assessment in mind. Even though we collected an unprecedented dataset of 47 gauges with a minimum temporal resolution of 24 h, the sparse and heterogeneous temporal coverage in a region with high rainfall variability poses challenges for validation. In addition, the discrepancy between local-scale gauge data and spatially averaged ( 775 km²) TMPA data in the context of local convective storms and orographic rainfall is a crucial source of uncertainty. We adopted a flexible framework for SRE validation that fosters explorative research in a remote context. Results show that TMPA performs reasonably well during the rainy seasons for rainfall intensities <20 mm/day. TMPA systematically underestimates rainfall, but most problematic is the decreasing probability of detection of high intensity rainfalls. We suggest that landslide hazard might be efficiently assessed if we take account of the systematic biases in TMPA data and determine rainfall thresholds modulated by controls on, and uncertainties of, TMPA revealed in this study. Moreover, it is found relevant in mapping regional-scale rainfall-triggered hazards that are in any case poorly covered by the sparse available gauges. We anticipate validation of TMPA's successor (Integrated Multi-satellitE Retrievals for Global Precipitation Measurement; 10 km × 10 km, half-hourly) using the proposed framework, as soon as this product will be available in early 2018 for the 1998-present period.

Hydrologic system state at debris flow initiation in the Pitztal catchment, Austria

NASA Astrophysics Data System (ADS)

Mostbauer, Karin; Hrachowitz, Markus; Prenner, David; Kaitna, Roland

2017-04-01

Debris flows represent a severe hazard in mountain regions. Though significant effort has been made to forecast such events, the trigger conditions as well as the hydrologic disposition of a watershed at the time of debris flow occurrence are not well understood. To improve our knowledge on the connection between debris flow initiation and the hydrologic system, this study applies a semi-distributed conceptual rainfall-runoff model, linking different system state variables such as soil moisture, snowmelt, or runoff with documented debris flow events in the Pitztal watershed, western Austria. The hydrologic modelling was performed on a daily basis between 1953 and 2012. High-intensity rainfall could be identified as the dominant trigger (31 out of 43 debris flows), while triggering exclusively by low-intensity, long-lasting rainfall was only observed in one single case. The remaining events were related to snowmelt; whether all of these events where triggered by rain-on-snow, or whether some of these events were actually triggered by snowmelt only, remains unclear since the occurrence of un- resp. underrecorded rainfall was detected frequently. The usage of a conceptual hydrological model for investigating debris flow initiation constitutes a novel approach in debris flow research and was assessed as very valuable. For future studies, it is recommended to evaluate also sub-daily information. As antecedent snowmelt was found to be much more important to debris flow initiation than antecedent rainfall, it might prove beneficial to include snowmelt in the commonly used rainfall intensity-duration thresholds.

A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

NASA Astrophysics Data System (ADS)

Luk, K. C.; Ball, J. E.; Sharma, A.

2000-01-01

Artificial neural networks (ANNs), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems, such as function approximation and pattern recognition. Due to their powerful capability and functionality, ANNs provide an alternative approach for many engineering problems that are difficult to solve by conventional approaches. Rainfall forecasting has been a difficult subject in hydrology due to the complexity of the physical processes involved and the variability of rainfall in space and time. In this study, ANNs were adopted to forecast short-term rainfall for an urban catchment. The ANNs were trained to recognise historical rainfall patterns as recorded from a number of gauges in the study catchment for reproduction of relevant patterns for new rainstorm events. The primary objective of this paper is to investigate the effect of temporal and spatial information on short-term rainfall forecasting. To achieve this aim, a comparison test on the forecast accuracy was made among the ANNs configured with different orders of lag and different numbers of spatial inputs. In developing the ANNs with alternative configurations, the ANNs were trained to an optimal level to achieve good generalisation of data. It was found in this study that the ANNs provided the most accurate predictions when an optimum number of spatial inputs was included into the network, and that the network with lower lag consistently produced better performance.

a Empirical Modelation of Runoff in Small Watersheds Using LIDAR Data

NASA Astrophysics Data System (ADS)

Lopatin, J.; Hernández, J.; Galleguillos, M.; Mancilla, G.

2013-12-01

Hydrological models allow the simulation of water natural processes and also the quantification and prediction of the effects of human impacts in runoff behavior. However, obtaining the information that is need for applying these models can be costly in both time and resources, especially in large and difficult to access areas. The objective of this research was to integrate LiDAR data in the hydrological modeling of runoff in small watersheds, using derivated hydrologic, vegetation and topography variables. The study area includes 10 small head watersheds cover bay forest, between 2 and 16 ha, which are located in the south-central coastal range of Chile. In each of the former instantaneous rainfall and runoff flow of a total of 15 rainfall events were measured, between August 2012 and July 2013, yielding a total of 79 observations. In March 2011 a Harrier 54/G4 Dual System was used to obtain a LiDAR point cloud of discrete pulse with an average of 4.64 points per square meter. A Digital Terrain Model (DTM) of 1 meter resolution was obtained from the point cloud, and subsequently 55 topographic variables were derived, such as physical watershed parameters and morphometric features. At the same time, 30 vegetation descriptive variables were obtained directly from the point cloud and from a Digital Canopy Model (DCM). The classification and regression "Random Forest" (RF) algorithm was used to select the most important variables in predicting water height (liters), and the "Partial Least Squares Path Modeling" (PLS-PM) algorithm was used to fit a model using the selected set of variables. Four Latent variables were selected (outer model) related to: climate, topography, vegetation and runoff, where in each one was designated a group of the predictor variables selected by RF (inner model). The coefficient of determination (R2) and Goodnes-of-Fit (GoF) of the final model were obtained. The best results were found when modeling using only the upper 50th percentile of rainfall events. The best variables selected by the RF algorithm were three topographic variables and three vegetation related ones. We obtained an R2 of 0.82 and a GoF of 0.87 with a 95% of confidence interval. This study shows that it is possible to predict the water harvesting collected during a rainstorm event in forest environment using only LiDAR data. However, this type of methodology does not have good result in flow produced by low magnitude rainfall events, as these are more influenced by initial conditions of soil, vegetation and climate, which make their behavior slower and erratic.

An assessment of the feasibility of the use of satellite-only rainfall estimates for the hydrological monitoring in central Italy

NASA Astrophysics Data System (ADS)

Campo, Lorenzo; Caparrini, Francesca

2013-04-01

The need for accurate distributed hydrological modelling has constantly increased in last years for several purposes: agricultural applications, water resources management, hydrological balance at watershed scale, floods forecast. The main input for the hydrological numerical models is rainfall data that present, at the same time, a large availability of measures (in gauged regions, with respect to other micro-meteorological variables) and the most complex spatial patterns. While also in presence of densely gauged watersheds the spatial interpolation of the rainfall is a non-trivial problem, due to the spatial intermittence of the variable (especially at finer temporal scales), ungauged regions need an alternative source of rainfall data in order to perform the hydrological modelling. Such source can be constituted by the satellite-estimated rainfall fields, with reference to both geostationary and polar-orbit platforms. In this work the rainfall product obtained by the Aqua-AIRS sensor were used in order to assess the feasibility of the use of satellite-based rainfall as input for distributed hydrological modelling. The MOBIDIC (MOdello di BIlancio Distribuito e Continuo) model, developed at the Department of civil and Environmental Engineering of the University of Florence and operationally used by Tuscany Region and Umbria Region for flood prediction and management, was used for the experiments. In particular three experiments were carried on: a) hydrological simulation with the use of rain-gauges data, b) simulation with the use of satellite-only rainfall estimates, c) simulation with the combined use of the two sources of data in order to obtain an optimal estimate of the actual rainfall fields. The domain of the study was the central Italy. Several critical events occurred in the area were analyzed. A discussion of the results is provided.

Challenges in predicting and simulating summer rainfall in the eastern China

NASA Astrophysics Data System (ADS)

Liang, Ping; Hu, Zeng-Zhen; Liu, Yunyun; Yuan, Xing; Li, Xiaofan; Jiang, Xingwen

2018-05-01

To demonstrate the challenge of summer rainfall prediction and simulation in the eastern China, in this work, we examine the skill of the state-of-the-art climate models, evaluate the impact of sea surface temperature (SST) on forecast skill and estimate the predictability by using perfect model approach. The challenge is further demonstrated by assessing the ability of various reanalyses in capturing the observed summer rainfall variability in the eastern China and by examining the biases in reanalyses and in a climate model. Summer rainfall forecasts (hindcasts) initiated in May from eight seasonal forecast systems have low forecast skill with linear correlation of - 0.3 to 0.5 with observations. The low forecast skill is consistent with the low perfect model score ( 0.1-0.3) of atmospheric model forced by observed SST, due to the fact that external forcing (SST) may play a secondary role in the summer rainfall variation in the eastern China. This is a common feature for the climate variation over the middle and high latitude lands, where the internal dynamical processes dominate the rainfall variation in the eastern China and lead to low predictability, and external forcing (such as SST) plays a secondary role and is associated with predictable fraction. Even the reanalysis rainfall has some remarkable disagreements with the observation. Statistically, more than 20% of the observed variance is not captured by the mean of six reanalyses. Among the reanalyses, JRA55 stands out as the most reliable one. In addition, the reanalyses and climate model have pronounced biases in simulating the mean rainfall. These defaults mean an additional challenge in predicting the summer rainfall variability in the eastern China that has low predictability in nature.

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

PubMed

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

2018-03-02

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

Rainfall Results of the Florida Area Cumulus Experiment, 1970-76.

NASA Astrophysics Data System (ADS)

Woodley, William L.; Jordan, Jill; Barnston, Anthony; Simpson, Joanne; Biondini, Ron; Flueck, John

1982-02-01

The Florida Area Cumulus Experiment of 1970-76 (FACE-1) is a single-area, randomized, exploratory experiment to determine whether seeding cumuli for dynamic effects (dynamic seeding) can be used to augment convective rainfall over a substantial target area (1.3 × 104 km2) in south Florida. Rainfall is estimated using S-band radar observations after adjustment by raingages. The two primary response variables are rain volumes in the total target (TT) and in the floating target (FT), the most intensely treated portion of the target. The experimental unit is the day and the main observational period is the 6 h after initiation of treatment (silver iodide flares on seed days and either no flares or placebos on control days). Analyses without predictors suggest apparent increases in both the location (means and medians) and the dispersion (standard deviation and interquartile range) characteristics of rainfall due to seeding in the FT and TT variables with substantial statistical support for the FT results and lesser statistical support for the TT results. Analyses of covariance using meteorologically meaningful predictor variables suggest a somewhat larger effect of seeding with stronger statistical support. These results are interpreted in terms of the FACE conceptual model.

Malaria epidemics and the influence of the tropical South Atlantic on the Indian monsoon

NASA Astrophysics Data System (ADS)

Cash, B. A.; Rodó, X.; Ballester, J.; Bouma, M. J.; Baeza, A.; Dhiman, R.; Pascual, M.

2013-05-01

The existence of predictability in the climate system beyond the relatively short timescales of synoptic weather has provided significant impetus to investigate climate variability and its consequences for society. In particular, relationships between the relatively slow changes in sea surface temperature (SST) and climate variability at widely removed points across the globe provide a basis for statistical and dynamical efforts to predict numerous phenomena, from rainfall to disease incidence, at seasonal to decadal timescales. We describe here a remote influence, identified through observational analysis and supported through numerical experiments with a coupled atmosphere-ocean model, of the tropical South Atlantic (TSA) on both monsoon rainfall and malaria epidemics in arid northwest India. Moreover, SST in the TSA is shown to provide the basis for an early warning of anomalous hydrological conditions conducive to malaria epidemics four months later, therefore at longer lead times than those afforded by rainfall. We find that the TSA is not only significant as a modulator of the relationship between the monsoon and the El Niño/Southern Oscillation, as has been suggested by previous work, but for certain regions and temporal lags is in fact a dominant driver of rainfall variability and hence malaria outbreaks.

Rainfall and temperature distinguish between Karnal bunt positive and negative years in wheat fields in Texas.

PubMed

Workneh, F; Allen, T W; Nash, G H; Narasimhan, B; Srinivasan, R; Rush, C M

2008-01-01

Karnal bunt of wheat, caused by the fungus Tilletia indica, is an internationally regulated disease. Since its first detection in central Texas in 1997, regions in which the disease was detected have been under strict federal quarantine regulations resulting in significant economic losses. A study was conducted to determine the effect of weather factors on incidence of the disease since its first detection in Texas. Weather variables (temperature and rainfall amount and frequency) were collected and used as predictors in discriminant analysis for classifying bunt-positive and -negative fields using incidence data for 1997 and 2000 to 2003 in San Saba County. Rainfall amount and frequency were obtained from radar (Doppler radar) measurements. The three weather variables correctly classified 100% of the cases into bunt-positive or -negative fields during the specific period overlapping the stage of wheat susceptibility (boot to soft dough) in the region. A linear discriminant-function model then was developed for use in classification of new weather variables into the bunt occurrence groups (+ or -). The model was evaluated using weather data for 2004 to 2006 for San Saba area (central Texas), and data for 2001 and 2002 for Olney area (north-central Texas). The model correctly predicted bunt occurrence in all cases except for the year 2004. The model was also evaluated for site-specific prediction of the disease using radar rainfall data and in most cases provided similar results as the regional level evaluation. The humid thermal index (HTI) model (widely used for assessing risk of Karnal bunt) agreed with our model in all cases in the regional level evaluation, including the year 2004 for the San Saba area, except for the Olney area where it incorrectly predicted weather conditions in 2001 as unfavorable. The current model has a potential to be used in a spray advisory program in regulated wheat fields.

Factors affecting Escherichia coli concentrations at Lake Erie public bathing beaches

USGS Publications Warehouse

Francy, Donna S.; Darner, Robert A.

1998-01-01

The environmental and water-quality factors that affect concentrations of Escherichia coli (E. coli) in water and sediment were investigated at three public bathing beachesEdgewater Park, Villa Angela, and Sims Parkin the Cleveland, Ohio metropolitan area. This study was done to aid in the determination of safe recreational use and to help water- resource managers assess more quickly and accurately the degradation of recreational water quality. Water and lake-bottom sediments were collected and ancillary environmental data were compiled for 41 days from May through September 1997. Water samples were analyzed for E. coli concentrations, suspended sediment concentrations, and turbidity. Lake- bottom sediment samples from the beach area were analyzed for E. coli concentrations and percent dry weight. Concentrations of E. coli were higher and more variable at Sims Park than at Villa Angela or Edgewater Park; concentrations were lowest at Edgewater Park. Time-series plots showed that short-term storage (less than one week) of E. coli in lake-bottom sediments may have occurred, although no evidence for long-term storage was found during the sampling period. E. coli concentrations in water were found to increase with increasing wave height, but the resuspension of E. coli from lake-bottom sediments by wave action could not be adequately assessed; higherwave heights were often associated with the discharge of sewage containing E. coli during or after a rainfall and wastewater-treatment plant overflow. Multiple linear regression (MLR) was used to develop models to predict recreational water quality at the in water. The related variables included turbidity, antecedent rainfall, antecedent weighted rainfall, volumes of wastewater-treatment plant overflows and metered outfalls (composed of storm-water runoff and combined-sewer overflows), a resuspension index, and wave heights. For the beaches in this study, wind speed, wind direction, water temperature, and the prswimmers were not included in the model because they were shown to be statistically unrelated to E. coli concentrations. From the several models developed, one model was chosen that accounted for 58 percent of the variability in E. coli concentrations. The chosen MLR model contained weighted categorical rainfall, beach-specific turbidity, wave height, and terms to correct for the different magnitudes of E. coli concentrations among the three beaches. For 1997, the MLR model predicted the recreational water quality as well as, and in some cases better than, antecedent E. coli concentrations (the current method). The MLR model improved the sensitivity of the prediction and the percentage of correct predictions over the current method; however, the MLR model predictions still erred to a similar degree as the current method with regard to false negatives. A false negative would allow swimming when, in fact, the bathing water standard was exceeded. More work needs to be done to validate the MLR model with data collected during other recreational seasons, especially during a season with a greater frequency and intensity of summer rains. Studies could focus on adding to the MLR model other environmental and water-quality variables that improve the predictive ability of the model. These variables might include concentrations of E. coli in deeper sediments outside the bathing area, the direction of lake currents, site-specific-rainfall amounts, time-of-day information on overflows and metered outfalls, concentrations of E. coli in treated wastewater-treatment plant effluents, and occurrences of sewage-line breaks. Rapid biological or chemical methods for determination of recreational water quality could also be used as variables in model refinements. Possible methods include the use of experimental rapid assay methods for determination of E. coli concentrations or other fecal indicators and the use of chemical tracers for fecal contamination, such as coprostanol (a degradation

A 1000-Year Carbon Isotope Rainfall Proxy Record from South African Baobab Trees (Adansonia digitata L.)

PubMed Central

2015-01-01

A proxy rainfall record for northeastern South Africa based on carbon isotope analysis of four baobab (Adansonia digitata L.) trees shows centennial and decadal scale variability over the last 1,000 years. The record is in good agreement with a 200-year tree ring record from Zimbabwe, and it indicates the existence of a rainfall dipole between the summer and winter rainfall areas of South Africa. The wettest period was c. AD 1075 in the Medieval Warm Period, and the driest periods were c. AD 1635, c. AD 1695 and c. AD1805 during the Little Ice Age. Decadal-scale variability suggests that the rainfall forcing mechanisms are a complex interaction between proximal and distal factors. Periods of higher rainfall are significantly associated with lower sea-surface temperatures in the Agulhas Current core region and a negative Dipole Moment Index in the Indian Ocean. The correlation between rainfall and the El Niño/Southern Oscillation Index is non-static. Wetter conditions are associated with predominantly El Niño conditions over most of the record, but since about AD 1970 this relationship inverted and wet conditions are currently associated with la Nina conditions. The effect of both proximal and distal oceanic influences are insufficient to explain the rainfall regime shift between the Medieval Warm Period and the Little Ice Age, and the evidence suggests that this was the result of a northward shift of the subtropical westerlies rather than a southward shift of the Intertropical Convergence Zone. PMID:25970402

A 1000-Year Carbon Isotope Rainfall Proxy Record from South African Baobab Trees (Adansonia digitata L.).

PubMed

Woodborne, Stephan; Hall, Grant; Robertson, Iain; Patrut, Adrian; Rouault, Mathieu; Loader, Neil J; Hofmeyr, Michele

2015-01-01

A proxy rainfall record for northeastern South Africa based on carbon isotope analysis of four baobab (Adansonia digitata L.) trees shows centennial and decadal scale variability over the last 1,000 years. The record is in good agreement with a 200-year tree ring record from Zimbabwe, and it indicates the existence of a rainfall dipole between the summer and winter rainfall areas of South Africa. The wettest period was c. AD 1075 in the Medieval Warm Period, and the driest periods were c. AD 1635, c. AD 1695 and c. AD1805 during the Little Ice Age. Decadal-scale variability suggests that the rainfall forcing mechanisms are a complex interaction between proximal and distal factors. Periods of higher rainfall are significantly associated with lower sea-surface temperatures in the Agulhas Current core region and a negative Dipole Moment Index in the Indian Ocean. The correlation between rainfall and the El Niño/Southern Oscillation Index is non-static. Wetter conditions are associated with predominantly El Niño conditions over most of the record, but since about AD 1970 this relationship inverted and wet conditions are currently associated with la Nina conditions. The effect of both proximal and distal oceanic influences are insufficient to explain the rainfall regime shift between the Medieval Warm Period and the Little Ice Age, and the evidence suggests that this was the result of a northward shift of the subtropical westerlies rather than a southward shift of the Intertropical Convergence Zone.

Spatio-Temporal Variability of Summer Precipitation in Mexico under the Influence of the MJO, with Emphasis on the Bimodal Pattern

NASA Astrophysics Data System (ADS)

Perdigón, J.; Romero-Centeno, R.; Barrett, B.; Ordoñez-Perez, P.

2017-12-01

In many regions of Mexico, precipitation occurs in a very well defined annual cycle with peaks in May-June and September-October and a relative minimum in the middle of the rainy season known as the midsummer drought (MSD). The MJO is the most important mode of intraseasonal variability in the tropics, and, although some studies have shown its evident influence on summer precipitation in Mexico, its role in modulating the bimodal pattern of the summer precipitation cycle is still an open question. The spatio-temporal variability of summer precipitation in Mexico is analyzed through composite analysis according to the phases of the MJO, using the very high resolution CHIRPS precipitation data base and gridded data from the CFSR reanalysis to analyzing the MJO influence on the atmospheric circulation over Mexico and its adjacent basins. In general, during MJO phases 8-2 (4-6) rainfall is above-normal (below-normal), although, in some cases, the summer rainfall patterns during the same phase present considerable differences. The atmospheric circulation shows low (high) troposphere southwesterly (northeasterly) wind anomalies in southern Mexico under wetter conditions compared with climatological patterns, while the inverse pattern is observed under drier conditions. Composite anomalies of several variables also agreed well with those rainfall anomalies. Finally, a MJO complete cycle that reinforces (weakens) the bimodal pattern of summer rainfall in Mexico was found.

Analysis of rainfall and temperature time series to detect long-term climatic trends and variability over semi-arid Botswana

NASA Astrophysics Data System (ADS)

Byakatonda, Jimmy; Parida, B. P.; Kenabatho, Piet K.; Moalafhi, D. B.

2018-03-01

Arid and semi-arid environments have been identified with locations prone to impacts of climate variability and change. Investigating long-term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible inhomogeneities and years of intervention are analysed using four absolute homogeneity tests. Trends in the climatic variables were determined using Mann-Kendall and Sen's Slope estimator statistics. Association of El Niño Southern Oscillation (ENSO) with local climate is also investigated through multivariate analysis. Results from the study show that rainfall time series are fully homogeneous with 78.6 and 50% of the stations for maximum and minimum temperature, respectively, showing homogeneity. Trends also indicate a general decrease of 5.8, 7.4 and 18.1% in annual, summer and winter rainfall, respectively. Warming trends are observed in annual and winter temperature at 0.3 and 1.5% for maximum temperature and 1.7 and 6.5% for minimum temperature, respectively. Rainfall reported a positive correlation with Southern Oscillation Index (SOI) and at the same time negative association with Sea Surface Temperatures (SSTs). Strong relationships between SSTs and maximum temperature are observed during the El Niño and La Niña years. These study findings could facilitate planning and management of agricultural and water resources in Botswana.

NASA Tropical Rainfall Measurement Mission (TRMM): Effects of tropical rainfall on upper ocean dynamics, air-sea coupling and hydrologic cycle

NASA Technical Reports Server (NTRS)

Lagerloef, Gary; Busalacchi, Antonio J.; Liu, W. Timothy; Lukas, Roger B.; Niiler, Pern P.; Swift, Calvin T.

1995-01-01

This was a Tropical Rainfall Measurement Mission (TRMM) modeling, analysis and applications research project. Our broad scientific goals addressed three of the seven TRMM Priority Science Questions, specifically: What is the monthly average rainfall over the tropical ocean areas of about 10(exp 5) sq km, and how does this rain and its variability affect the structure and circulation of the tropical oceans? What is the relationship between precipitation and changes in the boundary conditions at the Earth's surface (e.g., sea surface temperature, soil properties, vegetation)? How can improved documentation of rainfall improve understanding of the hydrological cycle in the tropics?

Contrasting rainfall declines in northern and southern Tanzania: Potential differential impacts of west Pacific warming and east Pacific cooling

NASA Astrophysics Data System (ADS)

Harrison, L.; Funk, C. C.; Verdin, J. P.; Pedreros, D. H.; Shukla, S.; Husak, G. J.

2015-12-01

Here, we present analysis of a new 1900-2014 rainfall record for the Greater Horn of Africa with high station density (CenTrends), and evaluate potential climate change "hot spots" in Tanzania. We identify recent (1981-2014) downward trends in Tanzanian rainfall, use CenTrends to place these in a longer historical context, and relate rainfall in these regions to decadal changes in global sea surface temperatures (SSTs). To identify areas of concern, we consider the potential food security impacts of the recent rainfall declines and also rapid population growth. Looking forward, we consider what the links to SSTs might mean for rainfall in the next several decades based on SST projections. In addition to CenTrends, we use a variety of geographic data sets, including 1981-2014 rainfall from the Climate Hazards group InfraRed Precipitation with Stations (CHIRPSv2.0), simulated crop stress from the USGS Geospatial Water Requirement Satisfaction Index (GeoWRSI) model, NOAA Extended Reconstructed SSTs (ERSST v4), SST projections from the Coupled Model Intercomparison Project (CMIP5), and land cover and population maps from SERVIR, WorldPOP, and CIESIN's Gridded Population of the World. The long-term CenTrends record allows us to suggest an interesting dichotomy in decadal rainfall forcing. During the March to June season, SSTs in the west Pacific appear to be driving post-1980 rainfall reductions in northern Tanzania. In the 2000s, northern Tanzania's densely populated Pangani River, Internal Drainage, and Lake Victoria basins experienced the driest period in more than a century. During summer, negative trends in southern Tanzania appear linked to a negative SST trend in the Nino3.4 region. Since the SST trend in the west (east) Pacific appears strongly influenced by global warming (natural decadal variability), we suggest that water resources in northern Tanzania may face increasing challenges, but that this will be less the case in southern Tanzania.

On the uncertainties associated with using gridded rainfall data as a proxy for observed

NASA Astrophysics Data System (ADS)

Tozer, C. R.; Kiem, A. S.; Verdon-Kidd, D. C.

2011-09-01

Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods)? This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets - the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia (SA) initially using gridded data as the source of rainfall input and then gauged rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged or point data. Rather the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.

Drivers of Water Quality Variability in Northern Coastal Ecuador

PubMed Central

Hubbard, Alan E.; Nelson, Kara L.; Eisenberg, Joseph N.S.

2012-01-01

The microbiological safety of water is commonly measured using indicator organisms, but the spatiotemporal variability of these indicators can make interpretation of data difficult. Here we systematically explore variability in E.coli concentrations in surface source and household drinking water in a rural Ecuadorian village over one year. We observed more variability in water quality on an hourly basis (up to 2.4-log difference) than on a daily (2.2-log difference) or weekly basis (up to 1.8-log difference). E.coli counts were higher in the wet season than in the dry season for both source (0.42-log difference; p<0.0001) and household (0.11-log difference; p=0.077) samples. In the wet season, a one-cm increase in weekly rainfall was associated with a 3% decrease (p=0.006) in E.coli counts in source samples and a 6% decrease (p=0.012) in household samples. Each additional person in the river when source samples were collected was associated with a 4% increase (p=0.026) in E.coli counts in the wet season. Factors affecting household water quality included rainfall, water source, and covering the container. The variability can be understood as a combination of environmental (e.g., seasonal and soil processes) and other drivers (e.g., human river use, water practices and sanitation), each working at different timescales. PMID:19368173

Study of hydrological extremes - floods and droughts in global river basins using satellite data and model output

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Fayne, J.; Bolten, J. D.

2016-12-01

We will use satellite data from TRMM (Tropical Rainfall Measurement Mission), AMSR (Advanced Microwave Scanning Radiometer), GRACE (Gravity Recovery and Climate Experiment) and MODIS (Moderate Resolution Spectroradiometer) and model output from NASA GLDAS (Global Land Data Assimilation System) to understand the linkages between hydrological variables. These hydrological variables include precipitation soil moisture vegetation index surface temperature ET and total water. We will present results for major river basins such as Amazon, Colorado, Mississippi, California, Danube, Nile, Congo, Yangtze Mekong, Murray-Darling and Ganga-Brahmaputra.The major floods and droughts in these watersheds will be mapped in time and space using the satellite data and model outputs mentioned above. We will analyze the various hydrological variables and conduct a synergistic study during times of flood and droughts. In order to compare hydrological variables between river basins with vastly different climate and land use we construct an index that is scaled by the climatology. This allows us to compare across different climate, topography, soils and land use regimes. The analysis shows that the hydrological variables derived from satellite data and NASA models clearly reflect the hydrological extremes. This is especially true when data from different sensors are analyzed together - for example rainfall data from TRMM and total water data from GRACE. Such analyses will help to construct prediction tools for water resources applications.

Intra-Seasonal Rainfall Variations and Linkage with Kharif Crop Production: An Attempt to Evaluate Predictability of Sub-Seasonal Rainfall Events

NASA Astrophysics Data System (ADS)

Singh, Ankita; Ghosh, Kripan; Mohanty, U. C.

2018-03-01

The sub-seasonal variation of Indian summer monsoon rainfall highly impacts Kharif crop production in comparison with seasonal total rainfall. The rainfall frequency and intensity corresponding to various rainfall events are found to be highly related to crop production and therefore, the predictability of such events are considered to be diagnosed. Daily rainfall predictions are made available by one of the coupled dynamical model National Centers for Environmental Prediction Climate Forecast System (NCEPCFS). A large error in the simulation of daily rainfall sequence influences to take up a bias correction and for that reason, two approaches are used. The bias-corrected GCM is able to capture the inter-annual variability in rainfall events. Maximum prediction skill of frequency of less rainfall (LR) event is observed during the month of September and a similar result is also noticed for moderate rainfall event with maximum skill over the central parts of the country. On the other hand, the impact of rainfall weekly rainfall intensity is evaluated against the Kharif rice production. It is found that weekly rainfall intensity during July is having a significant impact on Kharif rice production, but the corresponding skill was found very low in GCM. The GCM are able to simulate the less and moderate rainfall frequency with significant skill.

Evaluation of different rainfall products over India for the summer monsoon

NASA Astrophysics Data System (ADS)

Prakash, Satya; Mitra, Ashis; Turner, Andrew; Collins, Mathew; AchutoRao, Krishna

2015-04-01

Summer rainfall over India forms an integral part of the Asian monsoon, which plays a key role in the global water cycle and climate system through coupled atmospheric and oceanic processes. Accurate prediction of Indian summer monsoon rainfall and its variability at various spatiotemporal scales are crucial for agriculture, water resources and hydroelectric-power sectors. Reliable rainfall observations are very important for verification of numerical model outputs and model development. However, high spatiotemporal variability of rainfall makes it difficult to measure adequately with ground-based instruments over a large region of various surface types from deserts to oceans. A number of multi-satellite rainfall products are available to users at different spatial and temporal scales. Each rainfall product has some advantages as well as limitations, hence it is essential to find a suitable region-specific data set among these rainfall products for a particular user application, such as water resources, agricultural modelling etc. In this study, we examine seasonal-mean and daily rainfall datasets for monsoon model validation. First, six multi-satellite and gauge-only rainfall products were evaluated over India at seasonal scale for 27 (JJAS 1979-2005) summer monsoon seasons against gridded 0.5-degree IMD gauge-based rainfall. Various skill metrics are computed to assess the potential of these data sets in representation of large-scale monsoon rainfall at all-India and sub-regional scales. Among the gauge-only data sets, APHRODITE and GPCC appear to outperform the others whereas GPCP is better than CMAP in the merged multi-satellite category. However, there are significant differences among these data sets indicating uncertainty in the observed rainfall over this region, with important implications for the evaluation of model simulations. At the daily scale, TRMM TMPA-3B42 is one of the best available products and is widely used for various hydro-meteorological applications. The existing version 6 (V6) products of TRMM underwent major changes and version 7 (V7) products were released in late 2012, and we compare these to the IMD daily gridded data over the 1998-2010 period. We show a clear improvement in V7 over V6 in the South Asian monsoon region using various skill metrics. Over typical monsoon rainfall zones, biases are improved by 5-10% in V7 over higher-rainfall regions. These results will help users to select appropriate rainfall product for their application. With the recent launch of the GPM Core Observatory, the release of a more advanced high-resolution multi-satellite rainfall product is expected soon.

Dependence of winter precipitation over Portugal on NAO and baroclinic wave activity

NASA Astrophysics Data System (ADS)

Ulbrich, U.; Christoph, M.; Pinto, J. G.; Corte-Real, J.

1999-03-01

The relationship between winter (DJF) rainfall over Portugal and the variable large scale circulation is addressed. It is shown that the poles of the sea level pressure (SLP) field variability associated with rainfall variability are shifted about 15° northward with respect to those used in standard definitions of the North Atlantic Oscillation (NAO). It is suggested that the influence of NAO on rainfall dominantly arises from the associated advection of humidity from the Atlantic Ocean. Rainfall is also related to different aspects of baroclinic wave activity, the variability of the latter quantity in turn being largely dependent on the NAO.A negative NAO index (leading to increased westerly surface geostrophic winds into Portugal) is associated with an increased number of deep (ps<980 hPa) surface lows over the central North Atlantic and of intermediate (980

Vulnerability of riparian obligate species to the interactive effect of fire, climate and hydrological change

Treesearch

Megan M. Friggens; Rachel Loehman; Lisa Holsinger; Deborah Finch

2014-01-01

Climate change is expected to have multiple direct and indirect impacts on ecosystems in the interior western U.S. (Christensen et al., 2007; IPCC 2013). Global climate predictions for the Southwest include higher temperatures, more variable rainfall, and more drought periods, which will likely exacerbate the ongoing issues relating to wildfire and water allocation in...

Statistical and dynamical assessment of land-ocean-atmosphere interactions across North Africa

NASA Astrophysics Data System (ADS)

Yu, Yan

North Africa is highly vulnerable to hydrologic variability and extremes, including impacts of climate change. The current understanding of oceanic versus terrestrial drivers of North African droughts and pluvials is largely model-based, with vast disagreement among models in terms of the simulated oceanic impacts and vegetation feedbacks. Regarding oceanic impacts, the relative importance of the tropical Pacific, tropical Indian, and tropical Atlantic Oceans in regulating the North African rainfall variability, as well as the underlying mechanism, remains debated among different modeling studies. Classic theory of land-atmosphere interactions across the Sahel ecotone, largely based on climate modeling experiments, has promoted positive vegetation-rainfall feedbacks associated with a dominant surface albedo mechanism. However, neither the proposed positive vegetation-rainfall feedback with its underlying albedo mechanism, nor its relative importance compared with oceanic drivers, has been convincingly demonstrated up to now using observational data. Here, the multivariate Generalized Equilibrium Feedback Assessment (GEFA) is applied in order to identify the observed oceanic and terrestrial drivers of North African climate and quantify their impacts. The reliability of the statistical GEFA method is first evaluated against dynamical experiments within the Community Earth System Model (CESM). In order to reduce the sampling error caused by short data records, the traditional GEFA approach is refined through stepwise GEFA, in which unimportant forcings are dropped through stepwise selection. In order to evaluate GEFA's reliability in capturing oceanic impacts, the atmospheric response to a sea-surface temperature (SST) forcing across the tropical Pacific, tropical Indian, and tropical Atlantic Ocean is estimated independently through ensembles of dynamical experiments and compared with GEFA-based assessments. Furthermore, GEFA's performance in capturing terrestrial impacts is evaluated through ensembles of fully coupled CESM dynamical experiments, with modified leaf area index (LAI) and soil moisture across the Sahel or West African Monsoon (WAM) region. The atmospheric responses to oceanic and terrestrial forcings are generally consistent between the dynamical experiments and statistical GEFA, confirming GEFA's capability of isolating the individual impacts of oceanic and terrestrial forcings on North African climate. Furthermore, with the incorporation of stepwise selection, GEFA can now provide reliable estimates of the oceanic and terrestrial impacts on the North African climate with the typical length of observational datasets, thereby enhancing the method's applicability. After the successful validation of GEFA, the key observed oceanic and terrestrial drivers of North African climate are identified through the application of GEFA to gridded observations, remote sensing products, and reanalyses. According to GEFA, oceanic drivers dominate over terrestrial drivers in terms of their observed impacts on North African climate in most seasons. Terrestrial impacts are comparable to, or more important than, oceanic impacts on rainfall during the post-monsoon across the Sahel and WAM region, and after the short rain across the Horn of Africa (HOA). The key ocean basins that regulate North African rainfall are typically located in the tropics. While the observed impacts of SST variability across the tropical Pacific and tropical Atlantic Oceans on the Sahel rainfall are largely consistent with previous model-based findings, minimal impacts from tropical Indian Ocean variability on Sahel rainfall are identified in observations, in contrast to previous modeling studies. The current observational analysis verifies model-hypothesized positive vegetation-rainfall feedback across the Sahel and HOA, which is confined to the post-monsoon and post-short rains season, respectively. However, the observed positive vegetation feedback to rainfall in the semi-arid Sahel and HOA is largely due to moisture recycling, rather than the classic albedo mechanism. Future projections of Sahel rainfall remain highly uncertain in terms of both sign and magnitude within phases three and five of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). The GEFA-based observational analyses will provide a benchmark for evaluating climate models, which will facilitate effective process-based model weighting for more reliable projections of regional climate, as well as model development.

Variability of extreme weather events over the equatorial East Africa, a case study of rainfall in Kenya and Uganda

NASA Astrophysics Data System (ADS)

Ongoma, Victor; Chen, Haishan; Omony, George William

2018-01-01

This study investigates the variability of extreme rainfall events over East Africa (EA), using indices from the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). The analysis was based on observed daily rainfall from 23 weather stations, with length varying within 1961 and 2010. The indices considered are: wet days ( R ≥1 mm), annual total precipitation in wet days (PRCPTOT), simple daily intensity index (SDII), heavy precipitation days ( R ≥ 10 mm), very heavy precipitation days ( R ≥ 20 mm), and severe precipitation ( R ≥ 50 mm). The non-parametric Mann-Kendall statistical analysis was carried out to identify trends in the data. Temporal precipitation distribution was different from station to station. Almost all indices considered are decreasing with time. The analysis shows that the PRCPTOT, very heavy precipitation, and severe precipitation are generally declining insignificantly at 5 % significant level. The PRCPTOT is evidently decreasing over Arid and Semi-Arid Land (ASAL) as compared to other parts of EA. The number of days that recorded heavy rainfall is generally decreasing but starts to rise in the last decade although the changes are insignificant. Both PRCPTOT and heavy precipitation show a recovery in trend starting in the 1990s. The SDII shows a reduction in most areas, especially the in ASAL. The changes give a possible indication of the ongoing climate variability and change which modify the rainfall regime of EA. The results form a basis for further research, utilizing longer datasets over the entire region to reduce the generalizations made herein. Continuous monitoring of extreme events in EA is critical, given that rainfall is projected to increase in the twenty-first century.

An assessment of Indian monsoon seasonal forecasts and mechanisms underlying monsoon interannual variability in the Met Office GloSea5-GC2 system

NASA Astrophysics Data System (ADS)

Johnson, Stephanie J.; Turner, Andrew; Woolnough, Steven; Martin, Gill; MacLachlan, Craig

2017-03-01

We assess Indian summer monsoon seasonal forecasts in GloSea5-GC2, the Met Office fully coupled subseasonal to seasonal ensemble forecasting system. Using several metrics, GloSea5-GC2 shows similar skill to other state-of-the-art seasonal forecast systems. The prediction skill of the large-scale South Asian monsoon circulation is higher than that of Indian monsoon rainfall. Using multiple linear regression analysis we evaluate relationships between Indian monsoon rainfall and five possible drivers of monsoon interannual variability. Over the time period studied (1992-2011), the El Niño-Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) are the most important of these drivers in both observations and GloSea5-GC2. Our analysis indicates that ENSO and its teleconnection with Indian rainfall are well represented in GloSea5-GC2. However, the relationship between the IOD and Indian rainfall anomalies is too weak in GloSea5-GC2, which may be limiting the prediction skill of the local monsoon circulation and Indian rainfall. We show that this weak relationship likely results from a coupled mean state bias that limits the impact of anomalous wind forcing on SST variability, resulting in erroneous IOD SST anomalies. Known difficulties in representing convective precipitation over India may also play a role. Since Indian rainfall responds weakly to the IOD, it responds more consistently to ENSO than in observations. Our assessment identifies specific coupled biases that are likely limiting GloSea5-GC2 Indian summer monsoon seasonal prediction skill, providing targets for model improvement.

Exploring the Role of Soil Moisture Conditions for Rainfall Triggered Landslides on Catchment Scale: the case of the Ialomita Sub Carpathians, Romania

NASA Astrophysics Data System (ADS)

Chitu, Zenaida; Bogaard, Thom; Adler, Mary-Jeanne; Steele-Dunne, Susan; Hrachowitz, Markus; Busuioc, Aristita; Sandric, Ionut; Istrate, Alexandru

2014-05-01

Like in many parts of the world, landslides represent in Romania recurrent phenomena that produce numerous damages to the infrastructure every few years. The high frequency of landslide events over the world has resulted to the development of many early warning systems that are based on the definition of rainfall thresholds triggering landslides. In Romania in particular, recent studies exploring the temporal occurrence of landslides have revealed that rainfall represents the most important triggering factor for landslides. The presence of low permeability soils and gentle slope degrees in the Ialomita Subcarpathians of Romania makes that cumulated precipitation over variable time interval and the hydraulic response of the soil plays a key role in landslides triggering. In order to identify the slope responses to rainfall events in this particular area we investigate the variability of soil moisture and its relationship to landslide events in three Subcarpathians catchments (Cricovul Dulce, Bizididel and Vulcana) by combining in situ measurements, satellite-based radiometry and hydrological modelling. For the current study, hourly soil moisture measurements from six soil moisture monitoring stations that are fitted with volumetric soil moisture sensors, temperature soil sensors and rain gauges sensors are used. Pedotransfer functions will be applied in order to infer hydraulic soil properties from soil texture sampled from 50 soil profiles. The information about spatial and temporal variability of soil moisture content will be completed with the Level 2 soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) mission. A time series analysis of soil moisture is planned to be integrated to landslide and rainfall time series in order to determine a preliminary rainfall threshold triggering landslides in Ialomita Subcarpathians.

A self-consistency approach to improve microwave rainfall rate estimation from space

NASA Technical Reports Server (NTRS)

Kummerow, Christian; Mack, Robert A.; Hakkarinen, Ida M.

1989-01-01

A multichannel statistical approach is used to retrieve rainfall rates from the brightness temperature T(B) observed by passive microwave radiometers flown on a high-altitude NASA aircraft. T(B) statistics are based upon data generated by a cloud radiative model. This model simulates variabilities in the underlying geophysical parameters of interest, and computes their associated T(B) in each of the available channels. By further imposing the requirement that the observed T(B) agree with the T(B) values corresponding to the retrieved parameters through the cloud radiative transfer model, the results can be made to agree quite well with coincident radar-derived rainfall rates. Some information regarding the cloud vertical structure is also obtained by such an added requirement. The applicability of this technique to satellite retrievals is also investigated. Data which might be observed by satellite-borne radiometers, including the effects of nonuniformly filled footprints, are simulated by the cloud radiative model for this purpose.

A framework for nowcasting and forecasting of rainfall-triggered landslide activity using remotely sensed data

NASA Astrophysics Data System (ADS)

Kirschbaum, Dalia; Stanley, Thomas

2016-04-01

Remote sensing data offers the unique perspective to provide situational awareness of hydrometeorological hazards over large areas in a way that is impossible to achieve with in situ data. Recent work has shown that rainfall-triggered landslides, while typically local hazards that occupy small spatial areas, can be approximated over regional or global scales in near real-time. This work presents a regional and global approach to approximating potential landslide activity using the landslide hazard assessment for situational awareness (LHASA) model. This system couples remote sensing data, including Global Precipitation Measurement rainfall data, Shuttle Radar Topography Mission and other surface variables to estimate where and when landslide activity may be likely. This system also evaluates the effectiveness of quantitative precipitation estimates from the Goddard Earth Observing System Model, Version 5 to provide a 24 forecast of potential landslide activity. Preliminary results of the LHASA model and implications for are presented for a regional version of this system in Central America as well as a prototype global approach.

A probabilistic approach of the Flash Flood Early Warning System (FF-EWS) in Catalonia based on radar ensemble generation

NASA Astrophysics Data System (ADS)

Velasco, David; Sempere-Torres, Daniel; Corral, Carles; Llort, Xavier; Velasco, Enrique

2010-05-01

Early Warning Systems (EWS) are commonly identified as the most efficient tools in order to improve the preparedness and risk management against heavy rains and Flash Floods (FF) with the objective of reducing economical losses and human casualties. In particular, flash floods affecting torrential Mediterranean catchments are a key element to be incorporated within operational EWSs. The characteristic high spatial and temporal variability of the storms requires high-resolution data and methods to monitor/forecast the evolution of rainfall and its hydrological impact in small and medium torrential basins. A first version of an operational FF-EWS has been implemented in Catalonia (NE Spain) under the name of EHIMI system (Integrated Tool for Hydrometeorological Forecasting) with the support of the Catalan Water Agency (ACA) and the Meteorological Service of Catalonia (SMC). Flash flood warnings are issued based on radar-rainfall estimates. Rainfall estimation is performed on radar observations with high spatial and temporal resolution (1km2 and 10 minutes) in order to adapt the warning scale to the 1-km grid of the EWS. The method is based on comparing observed accumulated rainfall against rainfall thresholds provided by the regional Intensity-Duration-Frequency (IDF) curves. The so-called "aggregated rainfall warning" at every river cell is obtained as the spatially averaged rainfall over its associated upstream draining area. Regarding the time aggregation of rainfall, the critical duration is thought to be an accumulation period similar to the concentration time of each cachtment. The warning is issued once the forecasted rainfall accumulation exceeds the rainfall thresholds mentioned above, which are associated to certain probability of occurrence. Finally, the hazard warning is provided and shown to the decision-maker in terms of exceeded return periods at every river cell covering the whole area of Catalonia. The objective of the present work includes the probabilistic component to the FF-EWS. As a first step, we have incorporated the uncertainty in rainfall estimates and forecasts based on an ensemble of equiprobable rainfall scenarios. The presented study has focused on a number of rainfall events and the performance of the FF-EWS evaluated in terms of its ability to produce probabilistic hazard warnings for decision-making support.

Tropical Rainfall Measuring Mission

NASA Technical Reports Server (NTRS)

1999-01-01

Tropical rainfall affects the lives and economics of a majority of the Earth's population. Tropical rain systems, such as hurricanes, typhoons, and monsoons, are crucial to sustaining the livelihoods of those living in the tropics. Excess rainfall can cause floods and great property and crop damage, whereas too little rainfall can cause drought and crop failure. The latent heat release during the process of precipitation is a major source of energy that drives the atmospheric circulation. This latent heat can intensify weather systems, affecting weather thousands of kilometers away, thus making tropical rainfall an important indicator of atmospheric circulation and short-term climate change. Tropical forests and the underlying soils are major sources of many of the atmosphere's trace constituents. Together, the forests and the atmosphere act as a water-energy regulating system. Most of the rainfall is returned to the atmosphere through evaporation and transpiration, and the atmospheric trace constituents take part in the recycling process. Hence, the hydrological cycle provides a direct link between tropical rainfall and the global cycles of carbon, nitrogen, and sulfur, all important trace materials for the Earth's system. Because rainfall is such an important component in the interactions between the ocean, atmosphere, land, and the biosphere, accurate measurements of rainfall are crucial to understanding the workings of the Earth-atmosphere system. The large spatial and temporal variability of rainfall systems, however, poses a major challenge to estimating global rainfall. So far, there has been a lack of rain gauge networks, especially over the oceans, which points to satellite measurement as the only means by which global observation of rainfall can be made. The Tropical Rainfall Measuring Mission (TRMM), jointly sponsored by the National Aeronautics and Space Administration (NASA) of the United States and the National Space Development Agency (NASDA) of Japan, provides visible, infrared, and microwave observations of tropical and subtropical rain systems.The satellite observations are complemented by ground radar and rain gauge measurements to validate satellite rain estimation techniques. Goddard Space Flight Center's involvement includes the observatory, four instruments, integration and testing of the observatory, data processing and distribution, and satellite operations. TRMM has a design lifetime of three years. Data generated from TRMM and archived at the GDAAC are useful not only for hydrologists, atmospheric scientists, and climatologists, but also for the health community studying infectious diseases, the ocean research community, and the agricultural community.

Evaluation of rainfall simulations over West Africa in dynamically downscaled CMIP5 global circulation models

NASA Astrophysics Data System (ADS)

Akinsanola, A. A.; Ajayi, V. O.; Adejare, A. T.; Adeyeri, O. E.; Gbode, I. E.; Ogunjobi, K. O.; Nikulin, G.; Abolude, A. T.

2018-04-01

This study presents evaluation of the ability of Rossby Centre Regional Climate Model (RCA4) driven by nine global circulation models (GCMs), to skilfully reproduce the key features of rainfall climatology over West Africa for the period of 1980-2005. The seasonal climatology and annual cycle of the RCA4 simulations were assessed over three homogenous subregions of West Africa (Guinea coast, Savannah, and Sahel) and evaluated using observed precipitation data from the Global Precipitation Climatology Project (GPCP). Furthermore, the model output was evaluated using a wide range of statistical measures. The interseasonal and interannual variability of the RCA4 were further assessed over the subregions and the whole of the West Africa domain. Results indicate that the RCA4 captures the spatial and interseasonal rainfall pattern adequately but exhibits a weak performance over the Guinea coast. Findings from the interannual rainfall variability indicate that the model performance is better over the larger West Africa domain than the subregions. The largest difference across the RCA4 simulated annual rainfall was found in the Sahel. Result from the Mann-Kendall test showed no significant trend for the 1980-2005 period in annual rainfall either in GPCP observation data or in the model simulations over West Africa. In many aspects, the RCA4 simulation driven by the HadGEM2-ES perform best over the region. The use of the multimodel ensemble mean has resulted to the improved representation of rainfall characteristics over the study domain.

Detecting potential anomalies in projections of rainfall trends and patterns using human observations

NASA Astrophysics Data System (ADS)

Kohfeld, K. E.; Savo, V.; Sillmann, J.; Morton, C.; Lepofsky, D.

2016-12-01

Shifting precipitation patterns are a well-documented consequence of climate change, but their spatial variability is particularly difficult to assess. While the accuracy of global models has increased, specific regional changes in precipitation regimes are not well captured by these models. Typically, researchers who wish to detect trends and patterns in climatic variables, such as precipitation, use instrumental observations. In our study, we combined observations of rainfall by subsistence-oriented communities with several metrics of rainfall estimated from global instrumental records for comparable time periods (1955 - 2005). This comparison was aimed at identifying: 1) which rainfall metrics best match human observations of changes in precipitation; 2) areas where local communities observe changes not detected by global models. The collated observations ( 3800) made by subsistence-oriented communities covered 129 countries ( 1830 localities). For comparable time periods, we saw a substantial correspondence between instrumental records and human observations (66-77%) at the same locations, regardless of whether we considered trends in general rainfall, drought, or extreme rainfall. We observed a clustering of mismatches in two specific regions, possibly indicating some climatic phenomena not completely captured by the currently available global models. Many human observations also indicated an increased unpredictability in the start, end, duration, and continuity of the rainy seasons, all of which may hamper the performance of subsistence activities. We suggest that future instrumental metrics should capture this unpredictability of rainfall. This information would be important for thousands of subsistence-oriented communities in planning, coping, and adapting to climate change.

Animal perception of seasonal thresholds: changes in elephant movement in relation to rainfall patterns.

PubMed

Birkett, Patricia J; Vanak, Abi T; Muggeo, Vito M R; Ferreira, Salamon M; Slotow, Rob

2012-01-01

The identification of temporal thresholds or shifts in animal movement informs ecologists of changes in an animal's behaviour, which contributes to an understanding of species' responses in different environments. In African savannas, rainfall, temperature and primary productivity influence the movements of large herbivores and drive changes at different scales. Here, we developed a novel approach to define seasonal shifts in movement behaviour by examining the movements of a highly mobile herbivore (elephant; Loxodonta africana), in relation to local and regional rainfall patterns. We used speed to determine movement changes of between 8 and 14 GPS-collared elephant cows, grouped into five spatial clusters, in Kruger National Park, South Africa. To detect broad-scale patterns of movement, we ran a three-year daily time-series model for each individual (2007-2009). Piecewise regression models provided the best fit for elephant movement, which exhibited a segmented, waveform pattern over time. Major breakpoints in speed occurred at the end of the dry and wet seasons of each year. During the dry season, female elephant are constrained by limited forage and thus the distances they cover are shorter and less variable. Despite the inter-annual variability of rainfall, speed breakpoints were strongly correlated with both local and regional rainfall breakpoints across all three years. Thus, at a multi-year scale, rainfall patterns significantly affect the movements of elephant. The variability of both speed and rainfall breakpoints across different years highlights the need for an objective definition of seasonal boundaries. By using objective criteria to determine behavioural shifts, we identified a biologically meaningful indicator of major changes in animal behaviour in different years. We recommend the use of such criteria, from an animal's perspective, for delineating seasons or other extrinsic shifts in ecological studies, rather than arbitrarily fixed definitions based on convention or common practice.

Vertical Motion Changes Related to North-East Brazil Rainfall Variability: a GCM Simulation

NASA Astrophysics Data System (ADS)

Roucou, Pascal; Oribe Rocha de Aragão, José; Harzallah, Ali; Fontaine, Bernard; Janicot, Serge

1996-08-01

The atmospheric structure over north-east Brazil during anomalous rainfall years is studied in the 11 levels of the outputs of the Laboratoire de Météorologie Dynamique atmospheric general circulation model (LMD AGCM). Seven 19-year simulations were performed using observed sea-surface temperature (SST) corresponding to the period 1970- 1988. The ensemble mean is calculated for each month of the period, leading to an ensemble-averaged simulation. The simulated March-April rainfall is in good agreement with observations. Correlations of simulated rainfall and three SST indices relative to the equatorial Pacific and northern and southern parts of the Atlantic Ocean exhibit stronger relationships in the simulation than in the observations. This is particularly true with the SST gradient in the Atlantic (Atlantic dipole). Analyses on 200 ;hPa velocity potential, vertical velocity, and vertical integral of the zonal component of mass flux are performed for years of abnormal rainfall and positive/negative SST anomalies in the Pacific and Atlantic oceans in March-April during the rainy season over the Nordeste region. The results at 200 hPa show a convergence anomaly over Nordeste and a divergence anomaly over the Pacific concomitant with dry seasons associated with warm SST anomalies in the Pacific and warm (cold) waters in the North (South) Atlantic. During drought years convection inside the ITCZ indicated by the vertical velocity exhibits a displacement of the convection zone corresponding to a northward migration of the ITCZ. The east-west circulation depicted by the zonal divergent mass flux shows subsiding motion over Nordeste and ascending motion over the Pacific in drought years, accompanied by warm waters in the eastern Pacific and warm/cold waters in northern/southern Atlantic. Rainfall variability of the Nordeste rainfall is linked mainly to vertical motion and SST variability through the migration of the ITCZ and the east-west circulation.

Improving accuracy in shallow-landslide susceptibility analyses at regional scale

NASA Astrophysics Data System (ADS)

Iovine, Giulio G. R.; Rago, Valeria; Frustaci, Francesco; Bruno, Claudia; Giordano, Stefania; Muto, Francesco; Gariano, Stefano L.; Pellegrino, Annamaria D.; Conforti, Massimo; Pascale, Stefania; Distilo, Daniela; Basile, Vincenzo; Soleri, Sergio; Terranova, Oreste G.

2015-04-01

Calabria (southern Italy) is particularly exposed to geo-hydrological risk. In the last decades, slope instabilities, mainly related to rainfall-induced landslides, repeatedly affected its territory. Among these, shallow landslides, characterized by abrupt onset and extremely rapid movements, are among the most destructive and dangerous phenomena for people and infrastructures. In this study, a susceptibility analysis to shallow landslides has been performed by refining a method recently applied in Costa Viola - central Calabria (Iovine et al., 2014), and only focusing on landslide source activations (regardless of their possible evolution as debris flows). A multivariate approach has been applied to estimating the presence/absence of sources, based on linear statistical relationships with a set of causal variables. The different classes of numeric causal variables have been determined by means of a data clustering method, designed to determine the best arrangement. A multi-temporal inventory map of sources, mainly obtained from interpretation of air photographs taken in 1954-1955, and in 2000, has been adopted to selecting the training and the validation sets. Due to the wide extend of the territory, the analysis has been iteratively performed by a step-by-step decreasing cell-size approach, by adopting greater spatial resolutions and thematic details (e.g. lithology, land-use, soil, morphometry, rainfall) for high-susceptible sectors. Through a sensitivity analysis, the weight of the considered factors in predisposing shallow landslides has been evaluated. The best set of variables has been identified by iteratively including one variable at a time, and comparing the results in terms of performance. Furthermore, susceptibility evaluations obtained through logistic regression have been compared to those obtained by applying neural networks. Obtained results may be useful to improve land utilization planning, and to select proper mitigation measures in shallow-landslide prone areas. The susceptibility map may also be included into a regional warning system, combined with suitable threshold evaluations (Vennari et al., 2014), to help Civil Protection Authorities to managing emergencies for events triggered by intense rainfalls. At this purpose, both hydrological (e.g. Capparelli et al., 2012) and geotechnical (e.g. Iovine et al., 2010) modelling approaches may also be profitably included. References Capparelli G., Iaquinta P., Iovine G., Terranova O.G. & Versace P. (2012) - Modelling the rainfall-induced mobilization of a large slope movement in northern Calabria. Natural Hazards, 61(1), pp.247-256. Iovine G., Lollino P., Gariano S.L. & Terranova O.G. (2010) - Coupling limit equilibrium analyses and real-time monitoring to refine a landslide surveillance system in Calabria (Southern Italy). Natural Hazards and Earth System Sciences, 10, 2341-2354. Iovine G., Greco R., Gariano S.L., Pellegrino A.D., Terranova O.G. (2014) - Shallow-landslide susceptibility in the Costa Viola mountain ridge (southern Calabria, Italy) with considerations on the role of causal factors. Natural Hazards, 73(1), pp.111-136. Vennari C., Gariano S.L., Antronico L., Brunetti M.T., Iovine G., Peruccacci S., Terranova O., Guzzetti F. (2014) - Rainfall thresholds for shallow landslide occurrence in Calabria, southern Italy. Natural Hazards and Earth Systems Sciences, 14(2), 317-330.

The Angola Low: relationship with southern African rainfall and ENSO

NASA Astrophysics Data System (ADS)

Crétat, Julien; Pohl, Benjamin; Dieppois, Bastien; Berthou, Ségolène; Pergaud, Julien

2018-05-01

The main states of the Angola Low (AL) are identified using clustering analysis applied to daily anomalous patterns of 700-hPa wind vorticity over Angola and adjacent countries from November to March for the 1980/81-2014/15 period. At the daily timescale, we examine the extent to which the main states of the AL modulate daily rainfall over southern Africa. At the interannual timescale, we assess both the relationship between the occurrence of these AL states and El Niño southern oscillation (ENSO) and the role of the AL in explaining ENSO's failure in driving southern African rainfall at times. Three reanalyses are considered to account for uncertainties induced by the scarcity of data available for assimilation over southern Africa. Three preferential states of the Angola Low are identified: AL state close to its seasonal climatology with slight zonal displacements, anomalously weak AL state and anomalously strong AL state with meridional displacements. These different states all significantly modulate daily southern African rainfall. Near-climatological AL state promotes wet rainfall anomalies over eastern subtropical southern Africa and dry rainfall anomalies over its western part. A slight westward shift in the near-climatological position of the AL leads to reversed zonal gradient in rainfall. The remaining regimes significantly modulate the meridional gradient in southern African rainfall. Anomalously weak and anomalously northward AL states promote wet rainfall anomalies over tropical southern Africa and dry rainfall anomalies over subtropical southern Africa. The reverse prevails for anomalously southward AL. At the interannual timescale, ENSO significantly modulates the seasonal occurrence of most AL states in the three reanalyses. Anomalously weak and southward AL states are more strongly correlated with regional rainfall than ENSO in all reanalyses, suggesting that accounting for AL variability may improve seasonal forecasts. Case study analysis of the major 1982/83 and 1997/98 El Niño events suggests that the weak rainfall anomalies and strong seasonal AL in 1997/98 may result from counteracting effects between ENSO and Indian Ocean coupled modes of variability.

Effect of meteorological variables on Plasmodium vivax and Plasmodium falciparum malaria in outbreak prone districts of Rajasthan, India.

PubMed

Lingala, Mercy A L

Malaria is a public health problem caused by Plasmodium parasite and transmitted by anopheline mosquitoes. Arid and semi-arid regions of western India are prone to malaria outbreaks. Malaria outbreak prone districts viz. Bikaner, Barmer and Jodhpur were selected to study the effect of meteorological variables on Plasmodium vivax and Plasmodium falciparum malaria outbreaks for the period of 2009-2012. The data of monthly malaria cases and meteorological variables was analysed using SPSS 20v. Spearman correlation analysis was conducted to examine the strength of the relationship between meteorological variables, P. vivax and P. falciparum malaria cases. Pearson's correlation analysis was carried out among the meteorological variables to observe the independent effect of each independent variable on the outcome. Results indicate that malaria outbreaks have occurred in Bikaner and Barmer due to continuous rains for more than two months. Rainfall has shown to be an important predictor of malaria outbreaks in Rajasthan. P. vivax is more significantly correlated with rainfall, minimum temperature (P<0.01) and less significantly with relative humidity (P<0.05); whereas P. falciparum is significantly correlated with rainfall, relative humidity (P<0.01) and less significantly with temperature (P<0.05). The determination of the lag period for P. vivax is relative humidity and for P. falciparum is temperature. The lag period between malaria cases and rainfall is shorter for P. vivax than P. falciparum. In conclusion, the knowledge generated is not only useful to take prompt malaria control interventions but also helpful to develop better forecasting model in outbreak prone regions. Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.

Impact of climate change on runoff in Lake Urmia basin, Iran

NASA Astrophysics Data System (ADS)

Sanikhani, Hadi; Kisi, Ozgur; Amirataee, Babak

2018-04-01

Investigation of the impact of climate change on water resources is very necessary in dry and arid regions. In the first part of this paper, the climate model Long Ashton Research Station Weather Generator (LARS-WG) was used for downscaling climate data including rainfall, solar radiation, and minimum and maximum temperatures. Two different case studies including Aji-Chay and Mahabad-Chay River basins as sub-basins of Lake Urmia in the northwest part of Iran were considered. The results indicated that the LARS-WG successfully downscaled the climatic variables. By application of different emission scenarios (i.e., A1B, A2, and B1), an increasing trend in rainfall and a decreasing trend in temperature were predicted for both the basins over future time periods. In the second part of this paper, gene expression programming (GEP) was applied for simulating runoff of the basins in the future time periods including 2020, 2055, and 2090. The input combination including rainfall, solar radiation, and minimum and maximum temperatures in current and prior time was selected as the best input combination with highest predictive power for runoff prediction. The results showed that the peak discharge will decrease by 50 and 55.9% in 2090 comparing with the baseline period for the Aji-Chay and Mahabad-Chay basins, respectively. The results indicated that the sustainable adaptation strategies are necessary for these basins for protection of water resources in future.

Reduced-complexity multi-site rainfall generation: one million years over night using the model TripleM

NASA Astrophysics Data System (ADS)

Breinl, Korbinian; Di Baldassarre, Giuliano; Girons Lopez, Marc

2017-04-01

We assess uncertainties of multi-site rainfall generation across spatial scales and different climatic conditions. Many research subjects in earth sciences such as floods, droughts or water balance simulations require the generation of long rainfall time series. In large study areas the simulation at multiple sites becomes indispensable to account for the spatial rainfall variability, but becomes more complex compared to a single site due to the intermittent nature of rainfall. Weather generators can be used for extrapolating rainfall time series, and various models have been presented in the literature. Even though the large majority of multi-site rainfall generators is based on similar methods, such as resampling techniques or Markovian processes, they often become too complex. We think that this complexity has been a limit for the application of such tools. Furthermore, the majority of multi-site rainfall generators found in the literature are either not publicly available or intended for being applied at small geographical scales, often only in temperate climates. Here we present a revised, and now publicly available, version of a multi-site rainfall generation code first applied in 2014 in Austria and France, which we call TripleM (Multisite Markov Model). We test this fast and robust code with daily rainfall observations from the United States, in a subtropical, tropical and temperate climate, using rain gauge networks with a maximum site distance above 1,000km, thereby generating one million years of synthetic time series. The modelling of these one million years takes one night on a recent desktop computer. In this research, we first start the simulations with a small station network of three sites and progressively increase the number of sites and the spatial extent, and analyze the changing uncertainties for multiple statistical metrics such as dry and wet spells, rainfall autocorrelation, lagged cross correlations and the inter-annual rainfall variability. Our study contributes to the scientific community of earth sciences and the ongoing debate on extreme precipitation in a changing climate by making a stable, and very easily applicable, multi-site rainfall generation code available to the research community and providing a better understanding of the performance of multi-site rainfall generation depending on spatial scales and climatic conditions.

The long-term variability of Changma in the East Asian summer monsoon system: A review and revisit

NASA Astrophysics Data System (ADS)

Lee, June-Yi; Kwon, MinHo; Yun, Kyung-Sook; Min, Seung-Ki; Park, In-Hong; Ham, Yoo-Geun; Jin, Emilia Kyung; Kim, Joo-Hong; Seo, Kyong-Hwan; Kim, WonMoo; Yim, So-Young; Yoon, Jin-Ho

2017-05-01

Changma, which is a vital part of East Asian summer monsoon (EASM) system, plays a critical role in modulating water and energy cycles in Korea. Better understanding of its long-term variability and change is therefore a matter of scientific and societal importance. It has been indicated that characteristics of Changma have undergone significant interdecadal changes in association with the mid-1970s global-scale climate shift and the mid-1990s EASM shift. This paper reviews and revisits the characteristics on the long-term changes of Changma focusing on the underlying mechanisms for the changes. The four important features are manifested mainly during the last few decades: 1) mean and extreme rainfalls during Changma period from June to September have been increased with the amplification of diurnal cycle of rainfall, 2) the dry spell between the first and second rainy periods has become shorter, 3) the rainfall amount as well as the number of rainy days during August have significantly increased, probably due to the increase in typhoon landfalls, and 4) the relationship between the Changma rainfall and Western Pacific Subtropical High on interannual time scale has been enhanced. The typhoon contribution to the increase in heavy rainfall is attributable to enhanced interaction between typhoons and midlatitude baroclinic environment. It is noted that the change in the relationship between Changma and the tropical sea surface temperature (SST) over the Indian, Pacific, and Atlantic Oceans is a key factor in the long-term changes of Changma and EASM. Possible sources for the recent mid-1990s change include 1) the tropical dipole-like SST pattern between the central Pacific and Indo-Pacific region (the global warming hiatus pattern), 2) the recent intensification of tropical SST gradients among the Indian Ocean, the western Pacific, and the eastern Pacific, and 3) the tropical Atlantic SST warming.

El Niño, Rainfall, and the Shifting Geography of Cholera in Africa

NASA Astrophysics Data System (ADS)

Moore, S.; Azman, A. S.; Zaitchik, B. F.; McKay, H.; Lessler, J.

2017-12-01

The El Niño Southern Oscillation (ENSO) and other climate patterns can have profound impacts on the occurrence of infectious diseases. Because of the key role of water supplies in cholera transmission, a relationship between El Niño events and cholera incidence is highly plausible, and previous research has shown a link between El Niño patterns and cholera in Bangladesh. However, there is little systematic evidence for this link in Africa where many cholera cases and deaths are reported. To understand how ENSO affects the geographic distribution of cholera incidence in Africa, we used a hierarchical Bayesian approach to integrate over 17,000 annual observations of cholera incidence from 2000-2014 in over 3,000 unique locations of varying spatial extent, ranging from entire countries to neighborhoods. The resulting maps reflect modeled cholera incidence at a fine spatial resolution using reported counts of cholera cases, key explanatory variables, and a spatially-dependent covariance term. We then examined the potential mechanistic association between ENSO-related changes in cholera incidence and several environmental variables including rainfall. El Niño profoundly changed the annual geographic distribution of cholera in Africa from 2000-2014, shifting the burden to continental East Africa, where almost 50,000 additional cases occur during El Niño years. Cholera incidence during El Niño years was higher in regions of East Africa with increased rainfall, but incidence was also higher in some areas with decreased rainfall suggesting a complex relationship between rainfall and cholera incidence. Here we show clear evidence for a shift in the distribution of cholera incidence throughout Africa in El Niño and non-El Niño years, likely mediated by El Niño's impact on local climatic factors. Knowledge of this relationship between cholera and climate patterns coupled with El Niño forecasting could be used to notify countries in Africa when they are likely to see a major shift in their cholera risk.

Tropical Rainfall Measuring Mission (TRMM). Phase B: Data capture facility definition study

NASA Technical Reports Server (NTRS)

1990-01-01

The National Aeronautics and Aerospace Administration (NASA) and the National Space Development Agency of Japan (NASDA) initiated the Tropical Rainfall Measuring Mission (TRMM) to obtain more accurate measurements of tropical rainfall then ever before. The measurements are to improve scientific understanding and knowledge of the mechanisms effecting the intra-annual and interannual variability of the Earth's climate. The TRMM is largely dependent upon the handling and processing of the data by the TRMM Ground System supporting the mission. The objective of the TRMM is to obtain three years of climatological determinations of rainfall in the tropics, culminating in data sets of 30-day average rainfall over 5-degree square areas, and associated estimates of vertical distribution of latent heat release. The scope of this study is limited to the functions performed by TRMM Data Capture Facility (TDCF). These functions include capturing the TRMM spacecraft return link data stream; processing the data in the real-time, quick-look, and routine production modes, as appropriate; and distributing real time, quick-look, and production data products to users. The following topics are addressed: (1) TRMM end-to-end system description; (2) TRMM mission operations concept; (3) baseline requirements; (4) assumptions related to mission requirements; (5) external interface; (6) TDCF architecture and design options; (7) critical issues and tradeoffs; and (8) recommendation for the final TDCF selection process.

Observed Oceanic and Terrestrial Drivers of North African Climate

NASA Astrophysics Data System (ADS)

Yu, Y.; Notaro, M.; Wang, F.; Mao, J.; Shi, X.; Wei, Y.

2015-12-01

Hydrologic variability can pose a serious threat to the poverty-stricken regions of North Africa. Yet, the current understanding of oceanic versus terrestrial drivers of North African droughts/pluvials is largely model-based, with vast disagreement among models. In order to identify the observed drivers of North African climate and develop a benchmark for model evaluations, the multivariate Generalized Equilibrium Feedback Assessment (GEFA) is applied to observations, remotely sensed data, and reanalysis products. The identified primary oceanic drivers of North African rainfall variability are the Atlantic, tropical Indian, and tropical Pacific Oceans and Mediterranean Sea. During the summer monsoon, positive tropical eastern Atlantic sea-surface temperature (SST) anomalies are associated with a southward shift of the Inter-Tropical Convergence Zone, enhanced ocean evaporation, and greater precipitable water across coastal West Africa, leading to increased West African monsoon (WAM) rainfall and decreased Sahel rainfall. During the short rains, positive SST anomalies in the western tropical Indian Ocean and negative anomalies in the eastern tropical Indian Ocean support greater easterly oceanic flow, evaporation over the western ocean, and moisture advection to East Africa, thereby enhancing rainfall. The sign, magnitude, and timing of observed vegetation forcing on rainfall vary across North Africa. The positive feedback of leaf area index (LAI) on rainfall is greatest during DJF for the Horn of Africa, while it peaks in autumn and is weakest during the summer monsoon for the Sahel. Across the WAM region, a positive LAI anomaly supports an earlier monsoon onset, increased rainfall during the pre-monsoon, and decreased rainfall during the wet season. Through unique mechanisms, positive LAI anomalies favor enhanced transpiration, precipitable water, and rainfall across the Sahel and Horn of Africa, and increased roughness, ascent, and rainfall across the WAM region. The current study represents the first attempt to separate the observed roles of oceanic and vegetation feedbacks across North Africa, and provides observational benchmark for model evaluation.

A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures

NASA Astrophysics Data System (ADS)

Kossieris, Panagiotis; Makropoulos, Christos; Onof, Christian; Koutsoyiannis, Demetris

2018-01-01

Many hydrological applications, such as flood studies, require the use of long rainfall data at fine time scales varying from daily down to 1 min time step. However, in the real world there is limited availability of data at sub-hourly scales. To cope with this issue, stochastic disaggregation techniques are typically employed to produce possible, statistically consistent, rainfall events that aggregate up to the field data collected at coarser scales. A methodology for the stochastic disaggregation of rainfall at fine time scales was recently introduced, combining the Bartlett-Lewis process to generate rainfall events along with adjusting procedures to modify the lower-level variables (i.e., hourly) so as to be consistent with the higher-level one (i.e., daily). In the present paper, we extend the aforementioned scheme, initially designed and tested for the disaggregation of daily rainfall into hourly depths, for any sub-hourly time scale. In addition, we take advantage of the recent developments in Poisson-cluster processes incorporating in the methodology a Bartlett-Lewis model variant that introduces dependence between cell intensity and duration in order to capture the variability of rainfall at sub-hourly time scales. The disaggregation scheme is implemented in an R package, named HyetosMinute, to support disaggregation from daily down to 1-min time scale. The applicability of the methodology was assessed on a 5-min rainfall records collected in Bochum, Germany, comparing the performance of the above mentioned model variant against the original Bartlett-Lewis process (non-random with 5 parameters). The analysis shows that the disaggregation process reproduces adequately the most important statistical characteristics of rainfall at wide range of time scales, while the introduction of the model with dependent intensity-duration results in a better performance in terms of skewness, rainfall extremes and dry proportions.

SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds - the two-CN system approach

NASA Astrophysics Data System (ADS)

Soulis, K. X.; Valiantzas, J. D.

2012-03-01

The Soil Conservation Service Curve Number (SCS-CN) approach is widely used as a simple method for predicting direct runoff volume for a given rainfall event. The CN parameter values corresponding to various soil, land cover, and land management conditions can be selected from tables, but it is preferable to estimate the CN value from measured rainfall-runoff data if available. However, previous researchers indicated that the CN values calculated from measured rainfall-runoff data vary systematically with the rainfall depth. Hence, they suggested the determination of a single asymptotic CN value observed for very high rainfall depths to characterize the watersheds' runoff response. In this paper, the hypothesis that the observed correlation between the calculated CN value and the rainfall depth in a watershed reflects the effect of soils and land cover spatial variability on its hydrologic response is being tested. Based on this hypothesis, the simplified concept of a two-CN heterogeneous system is introduced to model the observed CN-rainfall variation by reducing the CN spatial variability into two classes. The behaviour of the CN-rainfall function produced by the simplified two-CN system is approached theoretically, it is analysed systematically, and it is found to be similar to the variation observed in natural watersheds. Synthetic data tests, natural watersheds examples, and detailed study of two natural experimental watersheds with known spatial heterogeneity characteristics were used to evaluate the method. The results indicate that the determination of CN values from rainfall runoff data using the proposed two-CN system approach provides reasonable accuracy and it over performs the previous methods based on the determination of a single asymptotic CN value. Although the suggested method increases the number of unknown parameters to three (instead of one), a clear physical reasoning for them is presented.

Soil Carbon Recovery of Degraded Steppe Ecosystems of the Mongolian Plateau

NASA Astrophysics Data System (ADS)

Ojima, D. S.; Togtohyn, C.; Qi, J.

2013-12-01

Mongolian steppe grassland systems are critical source of ecosystem services to societal groups in temperate East Asia. These systems are characterized by their arid and semiarid environments where rainfall tends to be too variable or evaporative losses reduce water availability to reliably support cropping systems or substantial forest cover. These steppe ecosystems have supported land use practices to accommodate the variable rainfall patterns, and seasonal and spatial patterns of forage production displayed by the nomadic pastoral systems practiced across Asia. These pastoral systems are dependent on grassland ecosystem services, including forage production, wool, skins, meat and dairy products, and in many systems provide critical biodiversity and land and water protection services which serve to maintain pastoral livelihoods. Precipitation variability and associated drought conditions experienced frequently in these grassland systems are key drivers of these systems. However, during the past several decades climate change and grazing and land use conversion have resulted in degradation of ecosystem services and loss of soil organic matter. Recent efforts in China and Mongolia are investigating different grazing management practices to restore soil organic matter in these degraded systems. Simulation modeling is being applied to evaluate the long-term benefits of different grazing management regimes under various climate scenarios.

Classification of rainfall events for weather forecasting purposes in andean region of Colombia

NASA Astrophysics Data System (ADS)

Suárez Hincapié, Joan Nathalie; Romo Melo, Liliana; Vélez Upegui, Jorge Julian; Chang, Philippe

2016-04-01

This work presents a comparative analysis of the results of applying different methodologies for the identification and classification of rainfall events of different duration in meteorological records of the Colombian Andean region. In this study the work area is the urban and rural area of Manizales that counts with a monitoring hydro-meteorological network. This network is composed of forty-five (45) strategically located stations, this network is composed of forty-five (45) strategically located stations where automatic weather stations record seven climate variables: air temperature, relative humidity, wind speed and direction, rainfall, solar radiation and barometric pressure. All this information is sent wirelessly every five (5) minutes to a data warehouse located at the Institute of Environmental Studies-IDEA. With obtaining the series of rainfall recorded by the hydrometeorological station Palogrande operated by the National University of Colombia in Manizales (http://froac.manizales.unal.edu.co/bodegaIdea/); it is with this information that we proceed to perform behavior analysis of other meteorological variables, monitored at surface level and that influence the occurrence of such rainfall events. To classify rainfall events different methodologies were used: The first according to Monjo (2009) where the index n of the heavy rainfall was calculated through which various types of precipitation are defined according to the intensity variability. A second methodology that permitted to produce a classification in terms of a parameter β introduced by Rice and Holmberg (1973) and adapted by Llasat and Puigcerver, (1985, 1997) and the last one where a rainfall classification is performed according to the value of its intensity following the issues raised by Linsley (1977) where the rains can be considered light, moderate and strong fall rates to 2.5 mm / h; from 2.5 to 7.6 mm / h and above this value respectively for the previous classifications. The main contribution which is done with this research is the obtainment elements to optimize and to improve the spatial resolution of the results obtained with mesoscale models such as the Weather Research & Forecasting Model- WRF, used in Colombia for the purposes of weather forecasting and that in addition produces other tools used in current issues such as risk management.

Vegetative and reproductive phenology of a floodplain tree species Barringtonia acutangula from North East India.

PubMed

Nath, Shikhasmita; Nath, Arun Jyoti; Das, Ashesh Kumar

2016-03-01

Vegetative and reproductive phenology of Barringtonia acutangula, a floodplain tree species was studied at Chatla floodplain, Assam North East India with the aim to investigate vegetative and reproductive phenology under stressful environment of seasonal submergence and to assess the impact of environmental variables (temperature and precipitation) on tree phenophases. Quantitative assessment was made at 15 day interval for all the phenophases (leaf initiation, leaf-fall, flowering and fruiting) by tagging 40 (forty) trees over aperiod of two years (2012-14).To test seasonal influence on the phenology of Barringtonia acutangula different phenophases were correlated with environmental variables and statistical spearman's rank correlation coefficient was employed. Aridity index was computed that delineate influence of rainfall and temperature together on any phenophases. Leaf initiation showed positively significant correlation with temperature (r(s) = 0.601, p = < .05) during the year 2012-2013 whereas it was significantly correlated with rainfall (r(s) = 0.583, p = < .05) and aridity index (r(s) = 0.583, p = < .05) during the year 2013-2014. Leaf-fall was significant negatively correlated with temperature (r(s) = -0.623, p = < .05), rainfall (r(s) = -0.730, p = < .01) and aridity index (r(s) = -0.730, p = < .01) for both the studied years. Flowering was significantly influenced by temperature (r(s) = 0.639, p = < .05), rainfall (r(s) = 0.890, p = < .01) and aridity index (r(s) = 0.890, p = < .01) while in one month lag flowering was significantly correlated with rainfall (r(s) = 0.678, p = < .01) in 2012-13. Fruiting was also positively significant with temperature (r(s) = 0.795, P < .05), rainfall (r(s) = 0.835, P < .01) and aridity index (r(s) = 0.835, P < .01) for both the years. During one month lag period fruiting was positively correlated with temperature, rainfall and aridity index in both the years. Temperature, rainfall and aridity index were major determinants of the various vegetative and reproductive phenology of B. acutangula and any changes in these variables in future due to climate change, might have profound effect on phenophases of this tree species.

Multiscaling properties of tropical rainfall: Analysis of rain gauge datasets in Lesser Antilles island environment

NASA Astrophysics Data System (ADS)

Bernard, Didier C.; Pasquier, Raphaël; Cécé, Raphaël; Dorville, Jean-François

2014-05-01

Changes in rainfall seem to be the main impact of climate change in the Caribbean area. The last conclusions of IPCC (2013), indicate that the end of this century will be marked by a rise of extreme rainfalls in tropical areas, linked with increase of the mean surface temperature. Moreover, most of the Lesser Antilles islands are characterized by a complex topography which tends to enhance the rainfall from synoptic disturbances by orographic effects. In the past five years, out of hurricanes passage, several extreme rainy events (approx. 16 mm in 6 minutes), including fatal cases, occurred in the Lesser Antilles Arc: in Guadeloupe (January 2011, May 2012 and 2013), in Martinique (May 2009, April 2011 and 2013), in Saint-Lucia (December 2013). These phenomena inducing floods, loss of life and material damages (agriculture sector and public infrastructures), inhibit the development of the islands. At this time, numerical weather prediction models as WRF, which are based on the equations of the atmospheric physics, do not show great results in the focused area (Bernard et al., 2013). Statistical methods may be used to examine explicitly local rainy updrafts, thermally and orographically induced at micro-scale. The main goal of the present insular tropical study is to characterize the multifractal symmetries occurring in the 6-min rainfall time series, registered since 2006 by the French Met. Office network weather stations. The universal multifractal model (Schertzer and Lovejoy, 1991) is used to define the statistical properties of measured rainfalls at meso-scale and micro-scale. This model is parametrized by a fundamental exponents set (H,a,C1,q) which are determined and compared with values found in the literature. The first three parameters characterize the mean pattern and the last parameter q, the extreme pattern. The occurrence ranges of multifractal regime are examined. The suggested links between the internal variability of the tropical rainy events and the multifractal properties found, are preliminary discussed. References Bernard, D., R. Cécé and J.-F. Dorville (2013). High resolution numerical simulation (WRF V3) of an extrem rainy event over the Guadeloupe archipelago: Case of 3-5 January 2011. EGU General Assembly 2013, Geophysical Research Abstracts, Vol. 15, EGU2013-9988, Vienna, April 2013. Schertzer, D., S. Lovejoy (1991). Nonlinear geodynamical variability: Multiple singularities, universality and observables. Scaling, fractals and non-linear variability in geophysics, D. Schertzer, S. Lovejoy eds.,41-82, Kluwer.

Using damage data to estimate the risk from summer convective precipitation extremes

NASA Astrophysics Data System (ADS)

Schroeer, Katharina; Tye, Mari

2017-04-01

This study explores the potential added value from including loss and damage data to understand the risks from high-intensity short-duration convective precipitation events. Projected increases in these events are expected even in regions that are likely to become more arid. Such high intensity precipitation events can trigger hazardous flash floods, debris flows, and landslides that put people and local assets at risk. However, the assessment of local scale precipitation extremes is hampered by its high spatial and temporal variability. In addition to this, not only are extreme events rare, but such small-scale events are likely to be underreported where they do not coincide with the observation network. Reports of private loss and damage on a local administrative unit scale (LAU 2 level) are used to explore the relationship between observed rainfall events and damages reportedly related to hydro-meteorological processes. With 480 Austrian municipalities located within our south-eastern Alpine study region, the damage data are available on a much smaller scale than the available rainfall data. Precipitation is recorded daily at 185 gauges and 52% of these stations additionally deliver sub-hourly rainfall information. To obtain physically plausible information, damage and rainfall data are grouped and analyzed on a catchment scale. The data indicate that rainfall intensities are higher on days that coincide with a damage claim than on days for which no damage was reported. However, approximately one third of the damages related to hydro-meteorological hazards were claimed on days for which no rainfall was recorded at any gauge in the respective catchment. Our goal is to assess whether these events indicate potential extreme events missing in the observations. Damage always is a consequence of an asset being exposed and susceptible to a hazardous process, and naturally, many factors influence whether an extreme rainfall event causes damage. We set up a statistical model to test whether the relationship between extreme rainfall events and damages is robust enough to estimate a potential underrepresentation of high intensity rainfall events in ungauged areas. Risk-relevant factors of socio-economic vulnerability, land cover, streamflow data, and weather type information are included to improve and sharpen the analysis. Within this study, we first aim to identify which rainfall events are most damaging and which factors affect the damages - seen as a proxy for the vulnerability - related to summer convective rainfall extremes in different catchment types. Secondly, we aim to detect potentially unreported damaging rainfall events and estimate the likelihood of such cases. We anticipate this damage perspective on summertime extreme convective precipitation to be beneficial for risk assessment, uncertainty management, and decision making with respect to weather and climate extremes on the regional-to-local level.

Interannual Rainfall Variability in North-East Brazil: Observation and Model Simulation

NASA Astrophysics Data System (ADS)

Harzallah, A.; Rocha de Aragão, J. O.; Sadourny, R.

1996-08-01

The relationship between interannual variability of rainfall in north-east Brazil and tropical sea-surface temperature is studied using observations and model simulations. The simulated precipitation is the average of seven independent realizations performed using the Laboratoire de Météorologie Dynamique atmospheric general model forced by the 1970-1988 observed sea-surface temperature. The model reproduces very well the rainfall anomalies (correlation of 091 between observed and modelled anomalies). The study confirms that precipitation in north-east Brazil is highly correlated to the sea-surface temperature in the tropical Atlantic and Pacific oceans. Using the singular value decomposition method, we find that Nordeste rainfall is modulated by two independent oscillations, both governed by the Atlantic dipole, but one involving only the Pacific, the other one having a period of about 10 years. Correlations between precipitation in north-east Brazil during February-May and the sea-surface temperature 6 months earlier indicate that both modes are essential to estimate the quality of the rainy season.

Inter-annual rainfall variability in the eastern Antilles and coupling with the regional and intra-seasonal circulation

NASA Astrophysics Data System (ADS)

Jury, Mark R.

2016-11-01

Climate variability in the eastern Antilles island chain is analyzed via principal component analysis of high-resolution monthly rainfall in the period 1981-2013. The second mode reflecting higher rainfall in July-October season between Martinique and Grenada is the focus of this study. Higher rainfall corresponds with a weakened trade wind and boundary current along the southern edge of the Caribbean. This quells the coastal upwelling off Venezuela and builds the freshwater plume east of Trinidad. There is corresponding upper easterly wind flow that intensifies passing tropical waves. During a storm event over the Antilles on 4-5 October 2010, there was inflow from east of Guyana where low salinity and high sea temperatures enable surplus latent heat fluxes. A N-S convective rain band forms ˜500 km east of the cyclonic vortex. Many features at the weather timescale reflect the seasonal correlation and composite difference maps and El Nino Southern Oscillation (ENSO) modulation of oceanic inter-basin transfers.

Modeling rainfall-runoff process using soft computing techniques

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Tombul, Mustafa

2013-02-01

Rainfall-runoff process was modeled for a small catchment in Turkey, using 4 years (1987-1991) of measurements of independent variables of rainfall and runoff values. The models used in the study were Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP) which are Artificial Intelligence (AI) approaches. The applied models were trained and tested using various combinations of the independent variables. The goodness of fit for the model was evaluated in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and scatter index (SI). A comparison was also made between these models and traditional Multi Linear Regression (MLR) model. The study provides evidence that GEP (with RMSE=17.82 l/s, MAE=6.61 l/s, CE=0.72 and R2=0.978) is capable of modeling rainfall-runoff process and is a viable alternative to other applied artificial intelligence and MLR time-series methods.

Impacts of Climatic Variability on Vibrio parahaemolyticus Outbreaks in Taiwan

PubMed Central

Hsiao, Hsin-I; Jan, Man-Ser; Chi, Hui-Ju

2016-01-01

This study aimed to investigate and quantify the relationship between climate variation and incidence of Vibrio parahaemolyticus in Taiwan. Specifically, seasonal autoregressive integrated moving average (ARIMA) models (including autoregression, seasonality, and a lag-time effect) were employed to predict the role of climatic factors (including temperature, rainfall, relative humidity, ocean temperature and ocean salinity) on the incidence of V. parahaemolyticus in Taiwan between 2000 and 2011. The results indicated that average temperature (+), ocean temperature (+), ocean salinity of 6 months ago (+), maximum daily rainfall (current (−) and one month ago (−)), and average relative humidity (current and 9 months ago (−)) had significant impacts on the incidence of V. parahaemolyticus. Our findings offer a novel view of the quantitative relationship between climate change and food poisoning by V. parahaemolyticus in Taiwan. An early warning system based on climate change information for the disease control management is required in future. PMID:26848675

Impacts of Climatic Variability on Vibrio parahaemolyticus Outbreaks in Taiwan.

PubMed

Hsiao, Hsin-I; Jan, Man-Ser; Chi, Hui-Ju

2016-02-03

This study aimed to investigate and quantify the relationship between climate variation and incidence of Vibrio parahaemolyticus in Taiwan. Specifically, seasonal autoregressive integrated moving average (ARIMA) models (including autoregression, seasonality, and a lag-time effect) were employed to predict the role of climatic factors (including temperature, rainfall, relative humidity, ocean temperature and ocean salinity) on the incidence of V. parahaemolyticus in Taiwan between 2000 and 2011. The results indicated that average temperature (+), ocean temperature (+), ocean salinity of 6 months ago (+), maximum daily rainfall (current (-) and one month ago (-)), and average relative humidity (current and 9 months ago (-)) had significant impacts on the incidence of V. parahaemolyticus. Our findings offer a novel view of the quantitative relationship between climate change and food poisoning by V. parahaemolyticus in Taiwan. An early warning system based on climate change information for the disease control management is required in future.

Understanding recent eastern Horn of Africa rainfall variability and change

USGS Publications Warehouse

Liebmann, Brant; Hoerling, Martin P.; Funk, Christopher C.; Blade, Ileana; Dole, Randall M.; Allured, Dave; Quan, Xiaowei; Eischeid, Jon K.

2014-01-01

The recent upward trend in the October–December wet season is rather weak, however, and its statistical significance is compromised by strong year-to-year fluctuations. October–December eastern Horn rain variability is strongly associated with El Niño–Southern Oscillation and Indian Ocean dipole phenomena on interannual scales, in both model and observations. The interannual October–December correlation between the ensemble-average and observed Horn rainfall 0.87. By comparison, interannual March–May Horn precipitation is only weakly constrained by SST anomalies.

Application of Wavelet Analysis on Variability, Teleconnectivity, and Predictability November-January Taiwan rainfall

NASA Astrophysics Data System (ADS)

T.; Gan, Y.

2009-04-01

First the wavelet analysis was used to analyze the variability of winter (November-January) rainfall (1974-2006) of Taiwan and seasonal sea surface temperature (SST) in selected domains of the Pacific Ocean. From the scale average wavelet power (SAWP) computed for the seasonal rainfall and seasonal SST, it seems that these data exhibit interannual oscillations at 2-4-year period. Correlations between rainfall and SST SAWP were further estimated. Next the SST in selected sectors of the western Pacific Ocean (around 5°N-30°N, 120°E-150°E) was used as predictors to predict the winter rainfall of Taiwan at one season lead time using an Artificial Neural Network calibrated by Genetic Algorithm (ANN-GA). The ANN-GA was first calibrated using the 1974-1998 data and independently validated using 1999-2005 data. In terms of summary statistics such as the correlation coefficient, root-mean-square errors (RMSE), and Hansen-Kuipers (HK) scores, the seasonal prediction for northern and western Taiwan are generally good for both calibration and validation stages, but not so in some stations located in southeast Taiwan and Central Mountain.

How East Asian westerly jet's meridional position affects the summer rainfall in Yangtze-Huaihe River Valley?

NASA Astrophysics Data System (ADS)

Wang, Shixin; Zuo, Hongchao; Zhao, Shuman; Zhang, Jiankai; Lu, Sha

2017-03-01

Existing studies show that the change in the meridional position of East Asian westerly jet (EAWJ) is associated with rainfall anomalies in Yangtze-Huaihe River Valley (YHRV) in summer. However, the dynamic mechanism has not been resolved yet. The present study reveals underlying mechanisms for this impact for early summer and midsummer, separately. Mechanism1: associated with EAWJ's anomalously southward displacement, the 500-hPa westerly wind over YHRV is strengthened through midtropospheric horizontal circulation anomalies; the westerly anomalies are related to the formation of warm advection anomalies over YHRV, which cause increased rainfall through adiabatic ascent motion and convective activities; the major difference in these processes between early summer and midsummer is the midtropospheric circulation anomaly pattern. Mechanism 2: associated with EAWJ's anomalously southward displacement, the large day-to-day variability of midtropospheric temperature advection in midlatitudes is displaced southward by the jet's trapping transient eddies; this change enhances the day-to-day variability of temperature advection over YHRV, which in turn causes the increased rainfall in most part of YHRV through "lower-bound effect" (rainfall amount can not become negative); there is not much difference in these processes between early summer and midsummer.

Use of microwave satellite data to study variations in rainfall over the Indian Ocean

NASA Technical Reports Server (NTRS)

Hinton, Barry B.; Martin, David W.; Auvine, Brian; Olson, William S.

1990-01-01

The University of Wisconsin Space Science and Engineering Center mapped rainfall over the Indian Ocean using a newly developed Scanning Multichannel Microwave Radiometer (SMMR) rain-retrieval algorithm. The short-range objective was to characterize the distribution and variability of Indian Ocean rainfall on seasonal and annual scales. In the long-range, the objective is to clarify differences between land and marine regimes of monsoon rain. Researchers developed a semi-empirical algorithm for retrieving Indian Ocean rainfall. Tools for this development have come from radiative transfer and cloud liquid water models. Where possible, ground truth information from available radars was used in development and testing. SMMR rainfalls were also compared with Indian Ocean gauge rainfalls. Final Indian Ocean maps were produced for months, seasons, and years and interpreted in terms of historical analysis over the sub-continent.

Detecting Climate Variability in Tropical Rainfall

NASA Astrophysics Data System (ADS)

Berg, W.

2004-05-01

A number of satellite and merged satellite/in-situ rainfall products have been developed extending as far back as 1979. While the availability of global rainfall data covering over two decades and encompassing two major El Niño events is a valuable resource for a variety of climate studies, significant differences exist between many of these products. Unfortunately, issues such as availability often determine the use of a product for a given application instead of an understanding of the strengths and weaknesses of the various products. Significant efforts have been made to address the impact of sparse sampling by satellite sensors of variable rainfall processes by merging various satellite and in-situ rainfall products. These combine high spatial and temporal frequency satellite infrared data with higher quality passive microwave observations and rain gauge observations. Combining such an approach with spatial and temporal averaging of the data can reduce the large random errors inherent in satellite rainfall estimates to very small levels. Unfortunately, systematic biases can and do result in artificial climate signals due to the underconstrained nature of the rainfall retrieval problem. Because all satellite retrieval algorithms make assumptions regarding the cloud structure and microphysical properties, systematic changes in these assumed parameters between regions and/or times results in regional and/or temporal biases in the rainfall estimates. These biases tend to be relatively small compared to random errors in the retrieval, however, when random errors are reduced through spatial and temporal averaging for climate applications, they become the dominant source of error. Whether or not such biases impact the results for climate studies is very much dependent on the application. For example, all of the existing satellite rainfall products capture the increased rainfall in the east Pacific associated with El Niño, however, the resulting tropical response to El Niño is substantially smaller due to decreased rainfall in the west Pacific partially canceling increases in the central and east Pacific. These differences are not limited to the long-term merged rainfall products using infrared data, but are also exist in state-of-the-art rainfall retrievals from the active and passive microwave sensors on board the Tropical Rainfall Measuring Mission (TRMM). For example, large differences exist in the response of tropical mean rainfall retrieved from the TRMM microwave imager (TMI) 2A12 algorithm and the precipitation radar (PR) 2A25 algorithm to the 1997/98 El Niño. To assist scientists attempting to wade through the vast array of climate rainfall products currently available, and to help them determine whether systematic biases in these rainfall products impact the conclusions of a given study, we have developed a Climate Rainfall Data Center (CRDC). The CRDC web site (rain.atmos.colostate.edu/CRDC) provides climate researchers information on the various rainfall datasets available as well as access to experts in the field of satellite rainfall retrievals to assist them in the appropriate selection and use of climate rainfall products.

Relative importance of impervious area, drainage density, width function, and subsurface storm drainage on flood runoff from an urbanized catchment

NASA Astrophysics Data System (ADS)

Ogden, Fred L.; Raj Pradhan, Nawa; Downer, Charles W.; Zahner, Jon A.

2011-12-01

The literature contains contradictory conclusions regarding the relative effects of urbanization on peak flood flows due to increases in impervious area, drainage density and width function, and the addition of subsurface storm drains. We used data from an urbanized catchment, the 14.3 km2 Dead Run watershed near Baltimore, Maryland, USA, and the physics-based gridded surface/subsurface hydrologic analysis (GSSHA) model to examine the relative effect of each of these factors on flood peaks, runoff volumes, and runoff production efficiencies. GSSHA was used because the model explicitly includes the spatial variability of land-surface and hydrodynamic parameters, including subsurface storm drains. Results indicate that increases in drainage density, particularly increases in density from low values, produce significant increases in the flood peaks. For a fixed land-use and rainfall input, the flood magnitude approaches an upper limit regardless of the increase in the channel drainage density. Changes in imperviousness can have a significant effect on flood peaks for both moderately extreme and extreme storms. For an extreme rainfall event with a recurrence interval in excess of 100 years, imperviousness is relatively unimportant in terms of runoff efficiency and volume, but can affect the peak flow depending on rainfall rate. Changes to the width function affect flood peaks much more than runoff efficiency, primarily in the case of lower density drainage networks with less impermeable area. Storm drains increase flood peaks, but are overwhelmed during extreme rainfall events when they have a negligible effect. Runoff in urbanized watersheds with considerable impervious area shows a marked sensitivity to rainfall rate. This sensitivity explains some of the contradictory findings in the literature.

Rainfall height stochastic modelling as a support tool for landslides early warning

NASA Astrophysics Data System (ADS)

Capparelli, G.; Giorgio, M.; Greco, R.; Versace, P.

2009-04-01

Occurrence of landslides is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Although heavy landslides frequently occurred in Campania, southern Italy, during the last decade, no complete data sets are available for natural slopes where landslides occurred. As a consequence, landslide risk assessment procedures and early warning systems in Campania still rely on simple empirical models based on correlation between daily rainfall records and observed landslides, like FLAIR model [Versace et al., 2003]. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction. In mountainous areas, rainfall spatial and temporal variability are very pronounced due to orographic effects, making predictions even more complicated. Existing rain gauge networks are not dense enough to resolve the small scale spatial variability, and the same limitation of spatial resolution affects rainfall height maps provided by radar sensors as well as by meteorological physically based models. Therefore, analysis of on-site recorded rainfall height time series still represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR and ARMA [Box and Jenkins, 1976]. Sometimes exogenous information coming from additional series of observations is also taken into account, and the models are called ARX and ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted in conjunction with FLAIR model to calculate the probability of flowslides occurrence. The final aim of the study is in fact to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil protection agency meteorological warning network. So far, the model has been applied only to data series recorded at a single rain gauge. Future extension will deal with spatial correlation between time series recorded at different gauges. ACKNOWLEDGEMENTS The research was co-financed by the Italian Ministry of University, by means of the PRIN 2006 PRIN program, within the research project entitled ‘Definition of critical rainfall thresholds for destructive landslides for civil protection purposes'. REFERENCES Box, G.E.P. and Jenkins, G.M., 1976. Time Series Analysis Forecasting and Control, Holden-Day, San Francisco. Cowpertwait, P.S.P., Kilsby, C.G. and O'Connell, P.E., 2002. A space-time Neyman-Scott model of rainfall: Empirical analysis of extremes, Water Resources Research, 38(8):1-14. Salas, J.D., 1992. Analysis and modeling of hydrological time series, in D.R. Maidment, ed., Handbook of Hydrology, McGraw-Hill, New York. Heneker, T.M., Lambert, M.F. and Kuczera G., 2001. A point rainfall model for risk-based design, Journal of Hydrology, 247(1-2):54-71. Versace, P., Sirangelo. B. and Capparelli, G., 2003. Forewarning model of landslides triggered by rainfall. Proc. 3rd International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment, Davos.

Monthly variations of diurnal rainfall in north coast of West Java Indonesia during boreal winter periods

NASA Astrophysics Data System (ADS)

Yulihastin, E.; Trismidianto

2018-05-01

Diurnal rainfall during the active monsoon period is usually associated with the highest convective activity that often triggers extreme rainfall. Investigating diurnal rainfall behavior in the north coast of West Java is important to recognize the behavioral trends of data leading to such extreme events in strategic West Java because the city of Jakarta is located in this region. Variability of diurnal rainfall during the period of active monsoon on December-January-February (DJF) composite during the 2000-2016 period was investigated using hourly rainfall data from Tropical Rainfall Measuring Mission (TRMM) 3B41RT dataset. Through the Empirical Mode Decomposition method was appears that the diurnal rain cycle during February has increased significantly in its amplitude and frequency. It is simultaneously shows that the indication of extreme rainfall events is related to diurnal rain divergences during February shown through phase shifts. The diurnal, semidiurnal, and terdiurnal cycles appear on the characteristics of the DJF composite rainfall data during the 2000-2016 period.The significant increases in amplitude occurred during February are the diurnal (IMF 3) and terdiurnal (IMF 1) of rainfall cycles.

The Impact of Climate Projection Method on the Analysis of Climate Change in Semi-arid Basins

NASA Astrophysics Data System (ADS)

Halper, E.; Shamir, E.

2016-12-01

In small basins with arid climates, rainfall characteristics are highly variable and stream flow is tightly coupled with the nuances of rainfall events (e.g. hourly precipitation patterns Climate change assessments in these basins typically employ CMIP5 projections downscaled with Bias Corrected Statistical Downscaling and Bias Correction/Constructed Analogs (BCSD-BCCA) methods, but these products have drawbacks. Specifically, BCSD-BCCA these projections do not explicitly account for localized physical precipitation mechanisms (e.g. monsoon and snowfall) that are essential to many hydrological systems in the U. S. Southwest. An investigation of the impact of different types of precipitation projections for two kinds of hydrologic studies is being conducted under the U.S. Bureau of Reclamation's Science and Technology Grant Program. An innovative modeling framework consisting of a weather generator of likely hourly precipitation scenarios, coupled with rainfall-runoff, river routing and groundwater models, has been developed in the Nogales, Arizona area. This framework can simulate the impact of future climate on municipal water operations. This framework allows the rigorous comparison of the BCSD-BCCA methods with alternative approaches including rainfall output from dynamical downscaled Regional Climate Models (RCM), a stochastic rainfall generator forced by either Global Climate Models (GCM) or RCM, and projections using historical records conditioned on either GCM or RCM. The results will provide guide for the use of climate change projections into hydrologic studies of semi-arid areas. The project extends this comparison to analyses of flood control. Large flows on the Bill Williams River are a concern for the operation of dams along the Lower Colorado River. After adapting the weather generator for this region, we will evaluate the model performance for rainfall and stream flow, with emphasis on statistical features important to the specific needs of flood management. The end product of the research is to develop a test to guide selection of a precipitation projection method (including downscaling procedure) for a given region and objective.

Prediction of Meiyu rainfall in Taiwan by multi-lead physical-empirical models

NASA Astrophysics Data System (ADS)

Yim, So-Young; Wang, Bin; Xing, Wen; Lu, Mong-Ming

2015-06-01

Taiwan is located at the dividing point of the tropical and subtropical monsoons over East Asia. Taiwan has double rainy seasons, the Meiyu in May-June and the Typhoon rains in August-September. To predict the amount of Meiyu rainfall is of profound importance to disaster preparedness and water resource management. The seasonal forecast of May-June Meiyu rainfall has been a challenge to current dynamical models and the factors controlling Taiwan Meiyu variability has eluded climate scientists for decades. Here we investigate the physical processes that are possibly important for leading to significant fluctuation of the Taiwan Meiyu rainfall. Based on this understanding, we develop a physical-empirical model to predict Taiwan Meiyu rainfall at a lead time of 0- (end of April), 1-, and 2-month, respectively. Three physically consequential and complementary predictors are used: (1) a contrasting sea surface temperature (SST) tendency in the Indo-Pacific warm pool, (2) the tripolar SST tendency in North Atlantic that is associated with North Atlantic Oscillation, and (3) a surface warming tendency in northeast Asia. These precursors foreshadow an enhanced Philippine Sea anticyclonic anomalies and the anomalous cyclone near the southeastern China in the ensuing summer, which together favor increasing Taiwan Meiyu rainfall. Note that the identified precursors at various lead-times represent essentially the same physical processes, suggesting the robustness of the predictors. The physical empirical model made by these predictors is capable of capturing the Taiwan rainfall variability with a significant cross-validated temporal correlation coefficient skill of 0.75, 0.64, and 0.61 for 1979-2012 at the 0-, 1-, and 2-month lead time, respectively. The physical-empirical model concept used here can be extended to summer monsoon rainfall prediction over the Southeast Asia and other regions.

Exploring the Variability of Short-term Precipitation and Hydrological Response of Small Czech Watersheds

NASA Astrophysics Data System (ADS)

Kavka, Petr; Strouhal, Ludek; Weyskrabova, Lenka; Müller, Miloslav; Kozant, Petr

2017-04-01

The short-term rainfall temporal distribution is known to have a significant effect on the small watersheds' hydrological response. In Czech Republic there are limited publicly available data on rainfall patterns of short-term precipitation. On one side there are catalogues of very short-term synthetic rainfalls used in urban drainage planning and on the other side hourly distribution of daily totals of rainfalls with long return period for larger catchments analyses. This contribution introduces the preliminary outcomes of a running three years' project, which should bridge this gap and provide such data and methodology to the community of scientists, state administration as well as design planners. Six generalized 6-hours hyetographs with 1 minute resolution were derived from 10 years of radar and gauging stations data. These hyetographs are accompanied with information concerning the region of occurrence as well as their frequency related to the rainfall amount. In the next step these hyetographs are used in a complex sensitivity analysis focused on a rainfall-runoff response of small watersheds. This analysis takes into account the uncertainty related to type of the hydrological model, watershed characteristics and main model routines parameterization. Five models with different methods and structure are considered and each model is applied on 5 characteristic watersheds selected from a classification of 7700 small Czech watersheds. For each combination of model and watershed 30, rainfall scenarios were simulated and other scenarios will be used to address the parameters uncertainty. In the last step the variability of outputs will be assessed in the context of economic impacts on design of landscape water structures or mitigation measures. The research is supported by the grant QJ1520265 of the Czech Ministry of Agriculture, rainfall data were provided by the Czech Hydrometeorological Institute.

Evaluate Hydrologic Response on Spatiotemporal Characteristics of Rainfall Using High Resolution Radar Rainfall Data and WRF-Hydro Model

NASA Astrophysics Data System (ADS)

Gao, S.; Fang, N. Z.

2017-12-01

A previously developed Dynamic Moving Storm (DMS) generator is a multivariate rainfall model simulating the complex nature of precipitation field: spatial variability, temporal variability, and storm movement. Previous effort by the authors has investigated the sensitivity of DMS parameters on corresponding hydrologic responses by using synthetic storms. In this study, the DMS generator has been upgraded to generate more realistic precipitation field. The dependence of hydrologic responses on rainfall features was investigated by dissecting the precipitation field into rain cells and modifying their spatio-temporal specification individually. To retrieve DMS parameters from radar rainfall data, rain cell segmentation and tracking algorithms were respectively developed and applied on high resolution radar rainfall data (1) to spatially determine the rain cells within individual radar image and (2) to temporally analyze their dynamic behavior. Statistics of DMS parameters were established by processing a long record of rainfall data (10 years) to keep the modification on real storms within the limit of regional climatology. Empirical distributions of the DMS parameters were calculated to reveal any preferential pattern and seasonality. Subsequently, the WRF-Hydro model forced by the remodeled and modified precipitation was used for hydrologic simulation. The study area was the Upper Trinity River Basin (UTRB) watershed, Texas; and two kinds of high resolution radar data i.e. the Next-Generation Radar (NEXRAD) level III Digital Hybrid Reflectivity (DHR) product and Multi-Radar Multi-Sensor (MRMS) precipitation rate product, were utilized to establish parameter statistics and to recreate/remodel historical events respectively. The results demonstrated that rainfall duration is a significant linkage between DMS parameters and their hydrologic impacts—any combination of spatiotemporal characteristics that keep rain cells longer over the catchment will produce higher peak discharge.

Critical rainfall conditions for the initiation of torrential flows. Results from the Rebaixader catchment (Central Pyrenees)

NASA Astrophysics Data System (ADS)

Abancó, Clàudia; Hürlimann, Marcel; Moya, José; Berenguer, Marc

2016-10-01

Torrential flows like debris flows or debris floods are fast movements formed by a mix of water and different amounts of unsorted solid material. They generally occur in steep torrents and pose high risk in mountainous areas. Rainfall is their most common triggering factor and the analysis of the critical rainfall conditions is a fundamental research task. Due to their wide use in warning systems, rainfall thresholds for the triggering of torrential flows are an important outcome of such analysis and are empirically derived using data from past events. In 2009, a monitoring system was installed in the Rebaixader catchment, Central Pyrenees (Spain). Since then, rainfall data of 25 torrential flows (;TRIG rainfalls;) were recorded, with a 5-min sampling frequency. Other 142 rainfalls that did not trigger torrential flows (;NonTRIG rainfalls;) were also collected and analyzed. The goal of this work was threefold: (i) characterize rainfall episodes in the Rebaixader catchment and compare rainfall data that triggered torrential flows and others that did not; (ii) define and test Intensity-Duration (ID) thresholds using rainfall data measured inside the catchment by with different techniques; (iii) analyze how the criterion used for defining the rainfall duration and the spatial variability of rainfall influences the value obtained for the thresholds. The statistical analysis of the rainfall characteristics showed that the parameters that discriminate better the TRIG and NonTRIG rainfalls are the rainfall intensities, the mean rainfall and the total rainfall amount. The antecedent rainfall was not significantly different between TRIG and NonTRIG rainfalls, as it can be expected when the source material is very pervious (a sandy glacial soil in the study site). Thresholds were derived from data collected at one rain gauge located inside the catchment. Two different methods were applied to calculate the duration and intensity of rainfall: (i) using total duration, Dtot, and mean intensity, Imean, of the rainfall event, and (ii) using floating durations, D, and intensities, Ifl, based on the maximum values over floating periods of different duration. The resulting thresholds are considerably different (Imean = 6.20 Dtot-0.36 and Ifl_90% = 5.49 D-0.75, respectively) showing a strong dependence on the applied methodology. On the other hand, the definition of the thresholds is affected by several types of uncertainties. Data from both rain gauges and weather radar were used to analyze the uncertainty associated with the spatial variability of the triggering rainfalls. The analysis indicates that the precipitation recorded by the nearby rain gauges can introduce major uncertainties, especially for convective summer storms. Thus, incorporating radar rainfall can significantly improve the accuracy of the measured triggering rainfall. Finally, thresholds were also derived according to three different criteria for the definition of the duration of the triggering rainfall: (i) the duration until the peak intensity, (ii) the duration until the end of the rainfall; and, (iii) the duration until the trigger of the torrential flow. An important contribution of this work is the assessment of the threshold relationships obtained using the third definition of duration. Moreover, important differences are observed in the obtained thresholds, showing that ID relationships are significantly dependent on the applied methodology.

Rainfall over the African continent from the 19th through the 21st century

NASA Astrophysics Data System (ADS)

Nicholson, Sharon E.; Funk, Chris; Fink, Andreas H.

2018-06-01

Most of the African continent is semi-arid and hence prone to extreme variations in rainfall from year to year. The extreme droughts that have plagued the Sahel and eastern Africa are particularly well known. This article uses a markedly expanded and updated rainfall data set to examine rainfall variability in 13 sectors that cover most of the continent. Annual rainfall is presented for each sector; the March-to-May and October-November seasons are also examined for equatorial sectors. In each case, the article includes the longest and most comprehensive precipitation gauge series ever published. All time series cover at least a century and most cover roughly one and one-half centuries or more. Although towards the end of the 20th century there was a widespread trend towards more arid conditions, few significant trends are evident over the entire period of record. The largest were downward trends in the Sahel and western sectors of North Africa. In those regions, an abrupt reduction in rainfall occurred around 1968, but a synchronous change occurred many other parts of Africa. A recovery did occur in the Sahel, but to varying degrees across the east-west expanse of the region. Noteworthy is that the west-to-east rainfall gradient across the region appears to have weakened in recent decades. For the continent as a whole, another change began in the 1980s decade, with more arid conditions persisting at the continental scale until early in the twenty-first century. No other such period of dry conditions occurred within the roughly one and one-half centuries evaluated here. A notable change also occurred at the seasonal level. During the period 1980 to 1998 rainfall during March-to-May was well below the long-term mean throughout most of the area from 20° N to 35° S. At the same time rainfall was above the long-term mean in most of eastern sectors within this latitude span, indicating a change in the seasonality of rainfall of a large part of Africa.

Effects of Raindrop Shape Parameter on the Simulation of Plum Rains

NASA Astrophysics Data System (ADS)

Mei, H.; Zhou, L.; Li, X.; Huang, X.; Guo, W.

2017-12-01

The raindrop shape parameter of particle distribution is generally set as constant in a Double-moment Bulk Microphysics Scheme (DBMS) using Gama distribution function though which suggest huge differences in time and space according to observations. Based on Milbrandt 2-mon(MY) DBMS, four cases during Plum Rains season are simulated coupled with four empirical relationships between shape parameter (μr) and slope parameter of raindrop which have been concluded from observations of raindrop distributions. The analysis of model results suggest that μr have some influences on rainfall. Introducing the diagnostic formulas of μr may have some improvement on systematic biases of 24h accumulated rainfall and show some correction ability on local characteristics of rainfall distribution. Besides,the tendency to improve strong rainfall could be sensitive to μr. With the improvement of the diagnosis of μr using the empirically diagnostic formulas, μr increases generally in the middle- and lower-troposphere and decreases with the stronger rainfall. Its conclued that, the decline in raindrop water content and the increased raindrop mass-weighted average terminal velocity directly related to μr are the direct reasons of variations in the precipitation.On the other side, the environmental conditions including relative humidity and dynamical parameters are the key indirectly causes which has close relationships with the changes in cloud particles and rainfall distributions.Furthermore,the differences in the scale of improvement between the weak and heavy rainfall mainly come from the distinctions of response features about their variable fields respectively. The extent of variation in the features of cloud particles in warm clouds of heavy rainfall differs greatly from that of weak rainfall, though they share the same trend of variation. On the conditions of weak rainfall, the response of physical characteristics to μr performed consistent trends and some linear features. However, environmental conditions of relative humidity and dynamical parameters perform strong and vertically deep adjustments in the heavy precipitation with vigorous cloud systems. In this case, the microphysical processes and environmental conditions experience complex interactions with each other and no significant laws could be concluded.

Ensembles modeling approach to study Climate Change impacts on Wheat

NASA Astrophysics Data System (ADS)

Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart

2017-04-01

Simulations of crop yield under climate variability are subject to uncertainties, and quantification of such uncertainties is essential for effective use of projected results in adaptation and mitigation strategies. In this study we evaluated the uncertainties related to crop-climate models using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and 14 general circulation models (GCMs) for 2 representative concentration pathways (RCP) of atmospheric CO2 (4.5 and 8.5 W m-2) in the Pacific Northwest (PNW), USA. The aim was to assess how different process-based crop models could be used accurately for estimation of winter wheat growth, development and yield. Firstly, all models were calibrated for high rainfall, medium rainfall, low rainfall and irrigated sites in the PNW using 1979-2010 as the baseline period. Response variables were related to farm management and soil properties, and included crop phenology, leaf area index (LAI), biomass and grain yield of winter wheat. All five models were run from 2000 to 2100 using the 14 GCMs and 2 RCPs to evaluate the effect of future climate (rainfall, temperature and CO2) on winter wheat phenology, LAI, biomass, grain yield and harvest index. Simulated time to flowering and maturity was reduced in all models except EPIC with some level of uncertainty. All models generally predicted an increase in biomass and grain yield under elevated CO2 but this effect was more prominent under rainfed conditions than irrigation. However, there was uncertainty in the simulation of crop phenology, biomass and grain yield under 14 GCMs during three prediction periods (2030, 2050 and 2070). We concluded that to improve accuracy and consistency in simulating wheat growth dynamics and yield under a changing climate, a multimodel ensemble approach should be used.

Role of Oceanic and Terrestrial Atmospheric Moisture Sources in Intraseasonal Variability of Indian Summer Monsoon Rainfall.

PubMed

Pathak, Amey; Ghosh, Subimal; Kumar, Praveen; Murtugudde, Raghu

2017-10-06

Summer Monsoon Rainfall over the Indian subcontinent displays a prominent variability at intraseasonal timescales with 10-60 day periods of high and low rainfall, known as active and break periods, respectively. Here, we study moisture transport from the oceanic and terrestrial sources to the Indian landmass at intraseasonal timescales using a dynamic recycling model, based on a Lagrangian trajectory approach applied to the ECMWF-ERA-interim reanalysis data. Intraseasonal variation of monsoon rainfall is associated with both a north-south pattern from the Indian landmass to the Indian Ocean and an east-west pattern from the Core Monsoon Zone (CMZ) to eastern India. We find that the oceanic sources of moisture, namely western and central Indian Oceans (WIO and CIO) contribute to the former, while the major terrestrial source, Ganga basin (GB) contributes to the latter. The formation of the monsoon trough over Indo-Gangetic plain during the active periods results in a high moisture transport from the Bay of Bengal and GB into the CMZ in addition to the existing southwesterly jet from WIO and CIO. Our results indicate the need for the correct representation of both oceanic and terrestrial sources of moisture in models for simulating the intraseasonal variability of the monsoon.

SMMR-SSM/I derived Greenland Sea ice variability: links with Indian and Korean Monsoons

NASA Astrophysics Data System (ADS)

Prabhu, Amita; Oh, Jaiho; Kim, In-won; Kripalani, R. H.; Pandithurai, G.

2018-02-01

Greenland Sea ice area (GRESIA) in boreal autumn and its association with the subsequent summer monsoon rainfall over India and South Korea is assessed for the period 1983-2013. It is found that GRESIA in the month of October has a significant positive relation (correlation coefficient (cc) = 0.45) with the subsequent Indian monsoon rainfall (IMR) while having a significant negative relation (cc = -0.40) with the ensuing Korean monsoon rainfall (KMR). GRESIA episodes in the preceding autumn impact the ensuing summer monsoon rainfall over India (South Korea) adversely (favourably). While central Pacific sea surface temperatures (SSTs) play a mediating role in transmitting the GRESIA signal towards the Indian subcontinent, snow over eastern Eurasia, just north of the Korea-Japan peninsula, plays a mediating role in transmitting the GRESIA signal towards the Korean peninsula. Although, the anomalies of equatorial central Pacific SSTs and eastern Eurasian snow play a crucial role in modulating IMR and KMR respectively, the GRESIA variability also plays a dominant role in modulating the monsoon variability over both the regions. Thus, a combination of autumn GRESIA along with SSTs over the central Pacific and snow over the eastern Eurasia, may possibly serve as a unique precursor to presage Asia's two diverse regional subsystems.

A High Space-Time Resolution Dataset Linking Meteorological Forcing and Hydro-Sedimentary Response in a Mesoscale Mediterranean Catchment (Auzon) of the Ardèche Region, France

NASA Astrophysics Data System (ADS)

Nord, G.; Braud, I.; Boudevillain, B.; Gérard, S.; Molinié, G.; Vandervaere, J. P.; Huza, J.; Le Coz, J.; Dramais, G.; Legout, C.; Berne, A.; Grazioli, J.; Raupach, T.; Van Baelen, J.; Wijbrans, A.; Delrieu, G.; Andrieu, J.; Caliano, M.; Aubert, C.; Teuling, R.; Le Boursicaud, R.; Branger, F.; Vincendon, B.; Horner, I.

2014-12-01

A comprehensive hydrometeorological dataset is presented spanning the period 1 Jan 2011-31 Dec 2014 to improve the understanding and simulation of the hydrological processes leading to flash floods in a mesoscale catchment (Auzon, 116 km2) of the Mediterranean region. The specificity of the dataset is its high space-time resolution, especially concerning rainfall and the hydrological response which is particularly adapted to the highly spatially variable rainfall events that may occur in this region. This type of dataset is rare in scientific literature because of the quantity and type of sensors for meteorology and surface hydrology. Rainfall data include continuous precipitation measured by rain-gages (5 min time step for the research network of 21 rain-gages and 1h time step for the operational network of 9 rain-gages), S-band Doppler dual-polarization radar (1 km2, 5 min resolution), and disdrometers (11 sensors working at 1 min time step). During the special observation period (SOP-1) and enhanced observation period (Sep-Dec 2012, Sep-Dec 2013) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). Meteorological data are taken from the operational surface weather observation stations of Meteo France at the hourly time resolution (6 stations in the region of interest). The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations measure water discharge and additional physico-chemical variables at a 2-10 min time resolution. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 gauges continuously measures water level and temperature in headwater subcatchments at a time resolution of 2-5 min. A network of soil moisture sensors enable the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, opportunistic observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. The data are appropriate for understanding rainfall variability, improving areal rainfall estimations and progress in distributed hydrological modelling.

A High Space-Time Resolution Dataset Linking Meteorological Forcing and Hydro-Sedimentary Response in a Mesoscale Mediterranean Catchment (Auzon) of the Ardèche Region, France

NASA Astrophysics Data System (ADS)

Nord, G.; Braud, I.; Boudevillain, B.; Gérard, S.; Molinié, G.; Vandervaere, J. P.; Huza, J.; Le Coz, J.; Dramais, G.; Legout, C.; Berne, A.; Grazioli, J.; Raupach, T.; Van Baelen, J.; Wijbrans, A.; Delrieu, G.; Andrieu, J.; Caliano, M.; Aubert, C.; Teuling, R.; Le Boursicaud, R.; Branger, F.; Vincendon, B.; Horner, I.

2015-12-01

A comprehensive hydrometeorological dataset is presented spanning the period 1 Jan 2011-31 Dec 2014 to improve the understanding and simulation of the hydrological processes leading to flash floods in a mesoscale catchment (Auzon, 116 km2) of the Mediterranean region. The specificity of the dataset is its high space-time resolution, especially concerning rainfall and the hydrological response which is particularly adapted to the highly spatially variable rainfall events that may occur in this region. This type of dataset is rare in scientific literature because of the quantity and type of sensors for meteorology and surface hydrology. Rainfall data include continuous precipitation measured by rain-gages (5 min time step for the research network of 21 rain-gages and 1h time step for the operational network of 9 rain-gages), S-band Doppler dual-polarization radar (1 km2, 5 min resolution), and disdrometers (11 sensors working at 1 min time step). During the special observation period (SOP-1) and enhanced observation period (Sep-Dec 2012, Sep-Dec 2013) of the HyMeX (Hydrological Cycle in the Mediterranean Experiment) project, two X-band radars provided precipitation measurements at very fine spatial and temporal scales (1 ha, 5 min). Meteorological data are taken from the operational surface weather observation stations of Meteo France at the hourly time resolution (6 stations in the region of interest). The monitoring of surface hydrology and suspended sediment is multi-scale and based on nested catchments. Three hydrometric stations measure water discharge and additional physico-chemical variables at a 2-10 min time resolution. Two experimental plots monitor overland flow and erosion at 1 min time resolution on a hillslope with vineyard. A network of 11 gauges continuously measures water level and temperature in headwater subcatchments at a time resolution of 2-5 min. A network of soil moisture sensors enable the continuous measurement of soil volumetric water content at 20 min time resolution at 9 sites. Additionally, opportunistic observations (soil moisture measurements and stream gauging) were performed during floods between 2012 and 2014. The data are appropriate for understanding rainfall variability, improving areal rainfall estimations and progress in distributed hydrological modelling.

Post-processing of global model output to forecast point rainfall

NASA Astrophysics Data System (ADS)

Hewson, Tim; Pillosu, Fatima

2016-04-01

ECMWF (the European Centre for Medium range Weather Forecasts) has recently embarked upon a new project to post-process gridbox rainfall forecasts from its ensemble prediction system, to provide probabilistic forecasts of point rainfall. The new post-processing strategy relies on understanding how different rainfall generation mechanisms lead to different degrees of sub-grid variability in rainfall totals. We use a number of simple global model parameters, such as the convective rainfall fraction, to anticipate the sub-grid variability, and then post-process each ensemble forecast into a pdf (probability density function) for a point-rainfall total. The final forecast will comprise the sum of the different pdfs from all ensemble members. The post-processing is essentially a re-calibration exercise, which needs only rainfall totals from standard global reporting stations (and forecasts) to train it. High density observations are not needed. This presentation will describe results from the initial 'proof of concept' study, which has been remarkably successful. Reference will also be made to other useful outcomes of the work, such as gaining insights into systematic model biases in different synoptic settings. The special case of orographic rainfall will also be discussed. Work ongoing this year will also be described. This involves further investigations of which model parameters can provide predictive skill, and will then move on to development of an operational system for predicting point rainfall across the globe. The main practical benefit of this system will be a greatly improved capacity to predict extreme point rainfall, and thereby provide early warnings, for the whole world, of flash flood potential for lead times that extend beyond day 5. This will be incorporated into the suite of products output by GLOFAS (the GLObal Flood Awareness System) which is hosted at ECMWF. As such this work offers a very cost-effective approach to satisfying user needs right around the world. This field has hitherto relied on using very expensive high-resolution ensembles; by their very nature these can only run over small regions, and only for lead times up to about 2 days.

The preconditioning role of Tropical Atlantic Variability in the development of the ENSO teleconnection: implications for the prediction of Nordeste rainfall

NASA Astrophysics Data System (ADS)

Giannini, A.; Saravanan, R.; Chang, P.

A comparison of rainfall variability in the semi-arid Brazilian Nordeste in observations and in two sets of model simulations leads to the conclusion that the evolving interaction between Tropical Atlantic Variability (TAV) and the El Niño-Southern Oscillation (ENSO) phenomenon can explain two puzzling features of ENSO's impact on the Nordeste: (1) the event-to-event unpredictability of ENSO's impact; (2) the greater impact of cold rather than warm ENSO events during the past 50 years. The explanation is in the `preconditioning' role of Tropical Atlantic Variability. When, in seasons prior to the mature phase of ENSO, the tropical Atlantic happens to be evolving consistently with the development expected of the ENSO teleconnection, ENSO and TAV add up to force large anomalies in Nordeste rainfall. When it happens to be evolving in opposition to the canonical development of ENSO, then the net outcome is less obvious, but also less anomalous. The more frequent occurrence of tropical Atlantic conditions consistent with those that develop during a cold ENSO event, i.e. of a negative meridional sea surface temperature gradient, explains the weaker warm ENSO and stronger cold ENSO anomalies in Nordeste rainfall of the latter part of the twentieth century. Close monitoring of the evolution of the tropical Atlantic in seasons prior to the mature phase of ENSO should lead to an enhanced forecast potential.

Reconstructing pre-instrumental streamflow in Eastern Australia using a water balance approach

NASA Astrophysics Data System (ADS)

Tozer, C. R.; Kiem, A. S.; Vance, T. R.; Roberts, J. L.; Curran, M. A. J.; Moy, A. D.

2018-03-01

Streamflow reconstructions based on paleoclimate proxies provide much longer records than the short instrumental period records on which water resource management plans are currently based. In Australia there is a lack of in-situ high resolution paleoclimate proxy records, but remote proxies with teleconnections to Australian climate have utility in producing streamflow reconstructions. Here we investigate, via a case study for a catchment in eastern Australia, the novel use of an Antarctic ice-core based rainfall reconstruction within a Budyko-framework to reconstruct ∼1000 years of annual streamflow. The resulting streamflow reconstruction captures interannual to decadal variability in the instrumental streamflow, validating both the use of the ice core rainfall proxy record and the Budyko-framework method. In the preinstrumental era the streamflow reconstruction shows longer wet and dry epochs and periods of streamflow variability that are higher than observed in the instrumental era. Importantly, for both the instrumental record and preinstrumental reconstructions, the wet (dry) epochs in the rainfall record are shorter (longer) in the streamflow record and this non-linearity must be considered when inferring hydroclimatic risk or historical water availability directly from rainfall proxy records alone. These insights provide a better understanding of present infrastructure vulnerability in the context of past climate variability for eastern Australia. The streamflow reconstruction presented here also provides a better understanding of the range of hydroclimatic variability possible, and therefore represents a more realistic baseline on which to quantify the potential impacts of anthropogenic climate change on water security.

Statistical Analysis of 30 Years Rainfall Data: A Case Study

NASA Astrophysics Data System (ADS)

Arvind, G.; Ashok Kumar, P.; Girish Karthi, S.; Suribabu, C. R.

2017-07-01

Rainfall is a prime input for various engineering design such as hydraulic structures, bridges and culverts, canals, storm water sewer and road drainage system. The detailed statistical analysis of each region is essential to estimate the relevant input value for design and analysis of engineering structures and also for crop planning. A rain gauge station located closely in Trichy district is selected for statistical analysis where agriculture is the prime occupation. The daily rainfall data for a period of 30 years is used to understand normal rainfall, deficit rainfall, Excess rainfall and Seasonal rainfall of the selected circle headquarters. Further various plotting position formulae available is used to evaluate return period of monthly, seasonally and annual rainfall. This analysis will provide useful information for water resources planner, farmers and urban engineers to assess the availability of water and create the storage accordingly. The mean, standard deviation and coefficient of variation of monthly and annual rainfall was calculated to check the rainfall variability. From the calculated results, the rainfall pattern is found to be erratic. The best fit probability distribution was identified based on the minimum deviation between actual and estimated values. The scientific results and the analysis paved the way to determine the proper onset and withdrawal of monsoon results which were used for land preparation and sowing.

Soil Texture Mediates the Response of Tree Cover to Rainfall Intensity in African Savannas

NASA Astrophysics Data System (ADS)

Case, M. F.; Staver, A. C.

2017-12-01

Global circulation models predict widespread shifts in the frequency and intensity of rainfall, even where mean annual rainfall does not change. Resulting changes in soil moisture dynamics could have major consequences for plant communities and ecosystems, but the direction of potential vegetation responses can be challenging to predict. In tropical savannas, where tree and grasses coexist, contradictory lines of evidence have suggested that tree cover could respond either positively or negatively to less frequent, more intense rainfall. Here, we analyzed remote sensing data and continental-scale soils maps to examine whether soil texture or fire could explain heterogeneous responses of savanna tree cover to intra-annual rainfall variability across sub-Saharan Africa. We find that tree cover generally increases with mean wet-season rainfall, decreases with mean wet-season rainfall intensity, and decreases with fire frequency. However, soil sand content mediates these relationships: the response to rainfall intensity switches qualitatively depending on soil texture, such that tree cover decreases dramatically with less frequent, more intense rainfall on clay soils but increases with rainfall intensity on sandy soils in semi-arid savannas. We propose potential ecohydrological mechanisms for this heterogeneous response, and emphasize that predictions of savanna vegetation responses to global change should account for interactions between soil texture and changing rainfall patterns.

The role of soil moisture in land surface-atmosphere coupling: climate model sensitivity experiments over India

NASA Astrophysics Data System (ADS)

Williams, Charles; Turner, Andrew

2015-04-01

It is generally acknowledged that anthropogenic land use changes, such as a shift from forested land into irrigated agriculture, may have an impact on regional climate and, in particular, rainfall patterns in both time and space. India provides an excellent example of a country in which widespread land use change has occurred during the last century, as the country tries to meet its growing demand for food. Of primary concern for agriculture is the Indian summer monsoon (ISM), which displays considerable seasonal and subseasonal variability. Although it is evident that changing rainfall variability will have a direct impact on land surface processes (such as soil moisture variability), the reverse impact is less well understood. However, the role of soil moisture in the coupling between the land surface and atmosphere needs to be properly explored before any potential impact of changing soil moisture variability on ISM rainfall can be understood. This paper attempts to address this issue, by conducting a number of sensitivity experiments using a state-of-the-art climate model from the UK Meteorological Office Hadley Centre: HadGEM2. Several experiments are undertaken, with the only difference between them being the extent to which soil moisture is coupled to the atmosphere. Firstly, the land surface is fully coupled to the atmosphere, globally (as in standard model configurations); secondly, the land surface is entirely uncoupled from the atmosphere, again globally, with soil moisture values being prescribed on a daily basis; thirdly, the land surface is uncoupled from the atmosphere over India but fully coupled elsewhere; and lastly, vice versa (i.e. the land surface is coupled to the atmosphere over India but uncoupled elsewhere). Early results from this study suggest certain 'hotspot' regions where the impact of soil moisture coupling/uncoupling may be important, and many of these regions coincide with previous studies. Focusing on the third experiment, i.e. uncoupled over India and coupled elsewhere, preliminary results suggest an increase in rainfall, surface temperature and pressure over northern India and the Himalayas, as well as a decrease in rainfall over the Bay of Bengal and the Maritime Continent. Other metrics, such as the northward propagation of intraseasonal rainfall variability and sensible and latent heat fluxes, are also discussed.

Subsurface drainage processes and management impacts

Treesearch

Elizabeth T. Keppeler; David Brown

1998-01-01

Storm-induced streamflow in forested upland watersheds is linked to rainfall by transient, variably saturated flow through several different flow paths. In the absence of exposed bedrock, shallow flow-restrictive layers, or compacted soil surfaces, virtually all of the infiltrated rainfall reaches the stream as subsurface flow. Subsurface runoff can occur within...

Automated canopy estimator (ACE): Enhancing crop modelling and decision making in agriculture

USDA-ARS?s Scientific Manuscript database

The Caribbean agriculture sector is dominated by small holdings, which are overly reliant on rainfall and highly dependent on manual means of optimization. The sector is therefore very vulnerable to the vagaries of climate variability and change, with rainfall variations being of particular concern...

Meteorological influences on algal bloom potential in a nutrient-rich blackwater river

EPA Science Inventory

The effect of variability in rainfall on the potential for algal blooms was examined for the St. Johns River in northeast Florida. Water chemistry and phytoplankton data were collected at selected sites monthly from 1993 through 2003. Information on rainfall and estimates ofw at...

Rainfall-runoff properties of tephra: Simulated effects of grain-size and antecedent rainfall

NASA Astrophysics Data System (ADS)

Jones, Robbie; Thomas, Robert E.; Peakall, Jeff; Manville, Vern

2017-04-01

Rain-triggered lahars (RTLs) are a significant and often persistent secondary volcanic hazard at many volcanoes around the world. Rainfall on unconsolidated volcaniclastic material is the primary initiation mechanism of RTLs: the resultant flows have the potential for large runout distances (> 100 km) and present a substantial hazard to downstream infrastructure and communities. RTLs are frequently anticipated in the aftermath of eruptions, but the pattern, timing and scale of lahars varies on an eruption-by-eruption and even catchment-by-catchment basis. This variability is driven by a set of local factors including the grain size distribution, thickness, stratigraphy and spatial distribution of source material in addition to topography, vegetation coverage and rainfall conditions. These factors are often qualitatively discussed in RTL studies based on post-eruption lahar observations or instrumental detections. Conversely, this study aims to move towards a quantitative assessment of RTL hazard in order to facilitate RTL predictions and forecasts based on constrained rainfall, grain size distribution and isopach data. Calibrated simulated rainfall and laboratory-constructed tephra beds are used within a repeatable experimental set-up to isolate the effects of individual parameters and to examine runoff and infiltration processes from analogous RTL source conditions. Laboratory experiments show that increased antecedent rainfall and finer-grained surface tephra individually increase runoff rates and decrease runoff lag times, while a combination of these factors produces a compound effect. These impacts are driven by increased residual moisture content and decreased permeability due to surface sealing, and have previously been inferred from downstream observations of lahars but not identified at source. Water and sediment transport mechanisms differ based on surface grain size distribution: a fine-grained surface layer displayed airborne remobilisation, accretionary pellet formation, rapid surface sealing and infiltration-excess overland flow generation whilst a coarse surface layer demonstrated exclusively rainsplash-driven particle detachment throughout the rainfall simulations. This experimental protocol has the potential to quantitatively examine the effects of a variety of individual parameters in RTL initiation under controlled conditions.

Hydrological Responses to Changes in the Rainfall Regime are Less Pronounced in Forested Basins: an Analysis of Southern Brazil, 1975-2010

NASA Astrophysics Data System (ADS)

Chagas, V. B. P.; Chaffe, P. L. B.

2017-12-01

It is unknown to what extent the hydrological responses to changes in the rainfall regime vary across forested and non-forested landscapes. Southern Brazil is approximately 570000 km² and was naturally covered mostly by tropical and subtropical forests. In the last century, a large proportion of forests were replaced by agricultural activities. The rainfall regime has also changed substantially in the last decades. The annual rainfall, number and magnitude of extreme events, and number of non-rainy days have increased in most of the area. In this study, we investigated the changes in the regime of 142 streamflow gauges and 674 rainfall gauges in Southern Brazil, from 1975 to 2010. The changes in the regime were analyzed for forested basins (i.e., with more than 50% forest coverage) and non-forested basins (i.e., with less than 20% forest coverage). The area of the river basins ranged from 100 to 60000 km². We analyzed a total of six signatures that represent the regime, including annual averages, seasonality, floods, and droughts. The statistical trends of the signatures were calculated using the Mann-Kendall test and the Sen's slope. The results showed that the majority of basins with opposing signal trends for mean annual streamflow and rainfall are non-forested basins (i.e., basins with higher anthropogenic impacts). Forested basins had a lower correlation between trends in the streamflow and rainfall trends for the seasonality and the average duration of drought events. There was a lower variability in the annual maximum 1-day streamflow trends in the forested basins. Additionally, despite a decrease in the 31-day rainfall minima and an increase in the seasonality, in forested basins the 7-day streamflow minima increases were substantially larger than in non-forested basins. In summary, the forested basins were less responsive to the changes in the precipitation 1-day maxima, seasonality, number of dry days, and 31-day minima.

Climate Change Impact on Variability of Rainfall Intensity in Upper Blue Nile Basin, Ethiopia

NASA Astrophysics Data System (ADS)

Worku, L. Y.

2015-12-01

Extreme rainfall events are major problems in Ethiopia with the resulting floods that usually could cause significant damage to agriculture, ecology, infrastructure, disruption to human activities, loss of property, loss of lives and disease outbreak. The aim of this study was to explore the likely changes of precipitation extreme changes due to future climate change. The study specifically focuses to understand the future climate change impact on variability of rainfall intensity-duration-frequency in Upper Blue Nile basin. Precipitations data from two Global Climate Models (GCMs) have been used in the study are HadCM3 and CGCM3. Rainfall frequency analysis was carried out to estimate quantile with different return periods. Probability Weighted Method (PWM) selected estimation of parameter distribution and L-Moment Ratio Diagrams (LMRDs) used to find the best parent distribution for each station. Therefore, parent distributions for derived from frequency analysis are Generalized Logistic (GLOG), Generalized Extreme Value (GEV), and Gamma & Pearson III (P3) parent distribution. After analyzing estimated quantile simple disaggregation model was applied in order to find sub daily rainfall data. Finally the disaggregated rainfall is fitted to find IDF curve and the result shows in most parts of the basin rainfall intensity expected to increase in the future. As a result of the two GCM outputs, the study indicates there will be likely increase of precipitation extremes over the Blue Nile basin due to the changing climate. This study should be interpreted with caution as the GCM model outputs in this part of the world have huge uncertainty.

A theoretically consistent stochastic cascade for temporal disaggregation of intermittent rainfall

NASA Astrophysics Data System (ADS)

Lombardo, F.; Volpi, E.; Koutsoyiannis, D.; Serinaldi, F.

2017-06-01

Generating fine-scale time series of intermittent rainfall that are fully consistent with any given coarse-scale totals is a key and open issue in many hydrological problems. We propose a stationary disaggregation method that simulates rainfall time series with given dependence structure, wet/dry probability, and marginal distribution at a target finer (lower-level) time scale, preserving full consistency with variables at a parent coarser (higher-level) time scale. We account for the intermittent character of rainfall at fine time scales by merging a discrete stochastic representation of intermittency and a continuous one of rainfall depths. This approach yields a unique and parsimonious mathematical framework providing general analytical formulations of mean, variance, and autocorrelation function (ACF) for a mixed-type stochastic process in terms of mean, variance, and ACFs of both continuous and discrete components, respectively. To achieve the full consistency between variables at finer and coarser time scales in terms of marginal distribution and coarse-scale totals, the generated lower-level series are adjusted according to a procedure that does not affect the stochastic structure implied by the original model. To assess model performance, we study rainfall process as intermittent with both independent and dependent occurrences, where dependence is quantified by the probability that two consecutive time intervals are dry. In either case, we provide analytical formulations of main statistics of our mixed-type disaggregation model and show their clear accordance with Monte Carlo simulations. An application to rainfall time series from real world is shown as a proof of concept.

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.

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.

Congo Basin rainfall climatology: can we believe the climate models?

PubMed

Washington, Richard; James, Rachel; Pearce, Helen; Pokam, Wilfried M; Moufouma-Okia, Wilfran

2013-01-01

The Congo Basin is one of three key convective regions on the planet which, during the transition seasons, dominates global tropical rainfall. There is little agreement as to the distribution and quantity of rainfall across the basin with datasets differing by an order of magnitude in some seasons. The location of maximum rainfall is in the far eastern sector of the basin in some datasets but the far western edge of the basin in others during March to May. There is no consistent pattern to this rainfall distribution in satellite or model datasets. Resolving these differences is difficult without ground-based data. Moisture flux nevertheless emerges as a useful variable with which to study these differences. Climate models with weak (strong) or even divergent moisture flux over the basin are dry (wet). The paper suggests an approach, via a targeted field campaign, for generating useful climate information with which to confront rainfall products and climate models.

Assessment of rainfall thresholds for landslide triggering in the Pacific Northwest: extreme short-term rainfall and long-term trends

NASA Astrophysics Data System (ADS)

Stanley, T.; Kirschbaum, D.; Sobieszczyk, S.; Jasinski, M. F.; Borak, J.; Yatheendradas, S.

2017-12-01

Landslides occur every year in the U.S. Pacific Northwest due to extreme rainfall, snow cover, and rugged topography. Data for 15,000 landslide events in Washington and Oregon were assembled from State Surveys, Departments of Transportation, a Global Landslide Catalog compiled by NASA, and other sources. This new inventory was evaluated against rainfall data from the National Climate Assessment (NCA) Land Data Assimilation System to characterize the regional rainfall conditions that trigger landslides. Analysis of these data sets indicates clear differences in triggering thresholds between extreme weather systems such as a Pineapple Express and the more typical peak seasonal rainfall between November and February. The study also leverages over 30 years of precipitation and land surface information to inform variability of landslide triggering over multiple decades and landslide trends within the region.

Interpretation of the impact of different managements and the rainfall variability on the soil erosion in a Mediterranean olive orchard microcatchment

NASA Astrophysics Data System (ADS)

Taguas, E. V.; Burguet, M.; Pérez, R.; Ayuso, J. L.; Gómez, J. A.

2012-04-01

The microcatchment is a spatial scale which allows to evaluate and to quantify the erosive processes under conditions close to those perceived by farmers. In this work, soil erosion and runoff over six hydrological years (2005 and 2011) were monitored in an olive orchard microcatchment of 6.4 ha, where different management types were applied. The aim was to evaluate the impact of the management and the rainfall regime variability. Non-tillage was applied during the years 2005-2007, tillage operations were carried in April in the period 2007-2010 while in the year 2010-2011, the tillage was applied in January and mulches (olives leaves and branches) were established for reducing the soil losses, mainly generated from rills. At the annual scale, the variation ranges of the cumulative rainfall depth and of the erosivity were between 600 and 1000 mm and between 600 and 1500 MJ mm ha-1 h-1, respectively. Although there are some gaps in the data series, the annual runoff coefficients calculated were smaller than 5% and the total sediment load range was between less than 1 t ha-1 year-1and more than 20 t ha-1 year-1. During these years olive yield also showed a high degree of variability, between 5000 kg ha-1 year-1and 10000 kg ha-1 year-1, typical of the alternate bearing of this crop, without correlation with annual rainfall. The annual rainfall depth explained significantly the sediment load and the runoff in spite of the different managements applied. At the event scale, rainfall depth was correlated with runoff, however, sediment load was very sensible to management. The high variability of the hydrological regime (inter and intra-annual) and the importance of the precedent hydrological years determine complex interpretations of the impact of the management on the soil losses and the olive yield by the farmers, so the continuity of the data analysis is essential for supporting the suitable taking decisions about the overall farm management.

The concurrent multiplicative-additive approach for gauge-radar/satellite multisensor precipitation estimates

NASA Astrophysics Data System (ADS)

Garcia-Pintado, J.; Barberá, G. G.; Erena Arrabal, M.; Castillo, V. M.

2010-12-01

Objective analysis schemes (OAS), also called ``succesive correction methods'' or ``observation nudging'', have been proposed for multisensor precipitation estimation combining remote sensing data (meteorological radar or satellite) with data from ground-based raingauge networks. However, opposite to the more complex geostatistical approaches, the OAS techniques for this use are not optimized. On the other hand, geostatistical techniques ideally require, at the least, modelling the covariance from the rain gauge data at every time step evaluated, which commonly cannot be soundly done. Here, we propose a new procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) for operational rainfall estimation using rain gauges and meteorological radar, which does not require explicit modelling of spatial covariances. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on the OAS, whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The approach considers radar estimates as background a priori information (first guess), so that nudging to observations (gauges) may be relaxed smoothly to the first guess, and the relaxation shape is obtained from the sequential optimization. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, an OAS spatially variable adjustment with multiplicative factors, ordinary cokriging, and kriging with external drift. In theory, it could be equally applicable to gauge-satellite estimates and other hydrometeorological variables.

A dependence modelling study of extreme rainfall in Madeira Island

NASA Astrophysics Data System (ADS)

Gouveia-Reis, Délia; Guerreiro Lopes, Luiz; Mendonça, Sandra

2016-08-01

The dependence between variables plays a central role in multivariate extremes. In this paper, spatial dependence of Madeira Island's rainfall data is addressed within an extreme value copula approach through an analysis of maximum annual data. The impact of altitude, slope orientation, distance between rain gauge stations and distance from the stations to the sea are investigated for two different periods of time. The results obtained highlight the influence of the island's complex topography on the spatial distribution of extreme rainfall in Madeira Island.

Diagnostics of Rainfall Anomalies in the Nordeste During the Global Weather Experiment

NASA Technical Reports Server (NTRS)

Sikdar, D. M.

1984-01-01

The relationship of the daily variability of large-scale pressure, cloudiness and upper level wind patterns over the Brazil-Atlantic sector during March/April 1979 to rainfall anomalies in northern Nordeste was investigated. The experiment divides the rainy season (March/April) of 1979 into wet and dry days, then composites bright cloudiness, sea level pressure, and upper level wind fields with respect to persistent rainfall episodes. Wet and dry anomalies are analyzed along with seasonal mean conditions.

The Tropical Rainfall Measuring Mission: An Overview

NASA Technical Reports Server (NTRS)

Kummerow. Christian; Hong, Ye

1999-01-01

The importance of quantitative knowledge of tropical rainfall, its associated latent heating and variability is summarized in the context of the global hydrologic cycle. Much of the tropics is covered by oceans. What land exists, is covered largely by rainforests that are only thinly populated. The only way to adequately measure the global tropical rainfall for climate and general circulation models is from space. To address these issues, the TRMM satellite was launched in Nov. 1997. It has been operating successfully ever since.

A Two-year Record of Daily Rainfall Isotopes from Fiji: Implications for Reconstructing Precipitation from Speleothem δ18O

NASA Astrophysics Data System (ADS)

Brett, M.; Mattey, D.; Stephens, M.

2015-12-01

Oxygen isotopes in speleothem provide opportunities to construct precisely dated records of palaeoclimate variability, underpinned by an understanding of both the regional climate and local controls on isotopes in rainfall and groundwater. For tropical islands, a potential means to reconstruct past rainfall variability is to exploit the generally high correlation between rainfall amount and δ18O: the 'amount effect'. The GNIP program provides δ18O data at monthly resolution for several tropical Pacific islands but there are few data for precipitation isotopes at daily resolution, for investigating the amount effect over different timescales in a tropical maritime setting. Timescales are important since meteoric water feeding a speleothem has undergone storage and mixing in the aquifer system and understanding how the isotope amount effect is preserved in aquifer recharge has fundamental implications on the interpretation of speleothem δ18O in terms of palaeo-precipitation. The islands of Fiji host speleothem caves. Seasonal precipitation is related to the movement of the South Pacific Convergence Zone, and interannual variations in rainfall are coupled to ENSO behaviour. Individual rainfall events are stratiform or convective, with proximal moisture sources. We have daily resolution isotope data for rainfall collected at the University of the South Pacific in Suva, covering every rain event in 2012 and 2013. δ18O varies between -18‰ and +3‰ with the annual weighted averages at -7.6‰ and -6.8‰ respectively, while total recorded rainfall amount is similar in both years. We shall present analysis of our data compared with GNIP, meteorological data and back trajectory analyses to demonstrate the nature of the relationship between rainfall amount and isotopic signatures over this short timescale. Comparison with GNIP data for 2012-13 will shed light on the origin of the amount effect at monthly and seasonal timescales in convective, maritime, tropical climates.

Soil erosion predictions from a landscape evolution model - An assessment of a post-mining landform using spatial climate change analogues.

PubMed

Hancock, G R; Verdon-Kidd, D; Lowry, J B C

2017-12-01

Landscape Evolution Modelling (LEM) technologies provide a means by which it is possible to simulate the long-term geomorphic stability of a conceptual rehabilitated landform. However, simulations rarely consider the potential effects of anthropogenic climate change and consequently risk not accounting for the range of rainfall variability that might be expected in both the near and far future. One issue is that high resolution (both spatial and temporal) rainfall projections incorporating the potential effects of greenhouse forcing are required as input. However, projections of rainfall change are still highly uncertain for many regions, particularly at sub annual/seasonal scales. This is the case for northern Australia, where a decrease or an increase in rainfall post 2030 is considered equally likely based on climate model simulations. The aim of this study is therefore to investigate a spatial analogue approach to develop point scale hourly rainfall scenarios to be used as input to the CAESAR - Lisflood LEM to test the sensitivity of the geomorphic stability of a conceptual rehabilitated landform to potential changes in climate. Importantly, the scenarios incorporate the range of projected potential increase/decrease in rainfall for northern Australia and capture the expected envelope of erosion rates and erosion patterns (i.e. where erosion and deposition occurs) over a 100year modelled period. We show that all rainfall scenarios produce sediment output and gullying greater than that of the surrounding natural system, however a 'wetter' future climate produces the highest output. Importantly, incorporating analogue rainfall scenarios into LEM has the capacity to both improve landform design and enhance the modelling software. Further, the method can be easily transferred to other sites (both nationally and internationally) where rainfall variability is significant and climate change impacts are uncertain. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Mato Grosso do Sul State, Brazil

NASA Astrophysics Data System (ADS)

Teodoro, Paulo Eduardo; de Oliveira-Júnior, José Francisco; da Cunha, Elias Rodrigues; Correa, Caio Cezar Guedes; Torres, Francisco Eduardo; Bacani, Vitor Matheus; Gois, Givanildo; Ribeiro, Larissa Pereira

2016-04-01

The State of Mato Grosso do Sul (MS) located in Brazil Midwest is devoid of climatological studies, mainly in the characterization of rainfall regime and producers' meteorological systems and rain inhibitors. This state has different soil and climatic characteristics distributed among three biomes: Cerrado, Atlantic Forest and Pantanal. This study aimed to apply the cluster analysis using Ward's algorithm and identify those meteorological systems that affect the rainfall regime in the biomes. The rainfall data of 32 stations (sites) of the MS State were obtained from the Agência Nacional de Águas (ANA) database, collected from 1954 to 2013. In each of the 384 monthly rainfall temporal series was calculated the average and applied the Ward's algorithm to identify spatial and temporal variability of rainfall. Bartlett's test revealed only in January homogeneous variance at all sites. Run test showed that there was no increase or decrease in trend of monthly rainfall. Cluster analysis identified five rainfall homogeneous regions in the MS State, followed by three seasons (rainy, transitional and dry). The rainy season occurs during the months of November, December, January, February and March. The transitional season ranges between the months of April and May, September and October. The dry season occurs in June, July and August. The groups G1, G4 and G5 are influenced by South Atlantic Subtropical Anticyclone (SASA), Chaco's Low (CL), Bolivia's High (BH), Low Levels Jet (LLJ) and South Atlantic Convergence Zone (SACZ) and Maden-Julian Oscillation (MJO). Group G2 is influenced by Upper Tropospheric Cyclonic Vortex (UTCV) and Front Systems (FS). The group G3 is affected by UTCV, FS and SACZ. The meteorological systems' interaction that operates in each biome and the altitude causes the rainfall spatial and temporal diversity in MS State.

Prolonged dry periods between rainfall events shorten the growth period of the resurrection plant Reaumuria soongorica.

PubMed

Zhang, Zhengzhong; Shan, Lishan; Li, Yi

2018-01-01

The resurrection plant Reaumuria soongorica is widespread across Asia, southern Europe, and North Africa and is considered to be a constructive keystone species in desert ecosystems, but the impacts of climate change on this species in desert ecosystems are unclear. Here, the morphological responses of R. soongorica to changes in rainfall quantity (30% reduction and 30% increase in rainfall quantity) and interval (50% longer drought interval between rainfall events) were tested. Stage-specific changes in growth were monitored by sampling at the beginning, middle, and end of the growing season. Reduced rainfall decreased the aboveground and total biomass, while additional precipitation generally advanced R. soongorica growth and biomass accumulation. An increased interval between rainfall events resulted in an increase in root biomass in the middle of the growing season, followed by a decrease toward the end. The response to the combination of increased rainfall quantity and interval was similar to the response to increased interval alone, suggesting that the effects of changes in rainfall patterns exert a greater influence than increased rainfall quantity. Thus, despite the short duration of this experiment, consequences of changes in rainfall regime on seedling growth were observed. In particular, a prolonged rainfall interval shortened the growth period, suggesting that climate change-induced rainfall variability may have significant effects on the structure and functioning of desert ecosystems.

Applications of high resolution rainfall radar data to quantify water temperature dynamics in urban catchments

NASA Astrophysics Data System (ADS)

Croghan, Danny; Van Loon, Anne; Bradley, Chris; Sadler, Jon; Hannnah, David

2017-04-01

Studies relating rainfall events to river water quality are frequently hindered by the lack of high resolution rainfall data. Local studies are particularly vulnerable due to the spatial variability of precipitation, whilst studies in urban environments require precipitation data at high spatial and temporal resolutions. The use of point-source data makes identifying causal effects of storms on water quality problematic and can lead to erroneous interpretations. High spatial and temporal resolution rainfall radar data offers great potential to address these issues. Here we use rainfall radar data with a 1km spatial resolution and 5 minute temporal resolution sourced from the UK Met Office Nimrod system to study the effects of storm events on water temperature (WTemp) in Birmingham, UK. 28 WTemp loggers were placed over 3 catchments on a rural-urban land use gradient to identify trends in WTemp during extreme events within urban environments. Using GIS, the catchment associated with each logger was estimated, and 5 min. rainfall totals and intensities were produced for each sub-catchment. Comparisons of rainfall radar data to meteorological stations in the same grid cell revealed the high accuracy of rainfall radar data in our catchments (<5% difference for studied months). The rainfall radar data revealed substantial differences in rainfall quantity between the three adjacent catchments. The most urban catchment generally received more rainfall, with this effect greatest in the highest intensity storms, suggesting the possibility of urban heat island effects on precipitation dynamics within the catchment. Rainfall radar data provided more accurate sub-catchment rainfall totals allowing better modelled estimates of storm flow, whilst spatial fluctuations in both discharge and WTemp can be simply related to precipitation intensity. Storm flow inputs for each sub-catchment were estimated and linked to changes in WTemp. WTemp showed substantial fluctuations (>1 °C) over short durations (<30 minutes) during storm events in urbanised sub-catchments, however WTemp recovery times were more prolonged. Use of the rainfall radar data allowed increased accuracy in estimates of storm flow timings and rainfall quantities at each sub-catchment, from which the impact of storm flow on WTemp could be quantified. We are currently using the radar data to derive thresholds for rainfall amount and intensity at which these storm deviations occur for each logger, from which the relative effects of land use and other catchment characteristics in each sub-catchment can be assessed. Our use of the rainfall radar data calls into question the validity of using station based data for small scale studies, particularly in urban areas, with high variation apparent in rainfall intensity both spatially and temporally. Variation was particularly high within the heavily urbanised catchment. For water quality studies, high resolution rainfall radar can be implemented to increase the reliability of interpretations of the response of water quality variables to storm water inputs in urban catchments.

Convective Cloud and Rainfall Processes Over the Maritime Continent: Simulation and Analysis of the Diurnal Cycle

NASA Astrophysics Data System (ADS)

Gianotti, Rebecca L.

The Maritime Continent experiences strong moist convection, which produces significant rainfall and drives large fluxes of heat and moisture to the upper troposphere. Despite the importance of these processes to global circulations, current predictions of climate change over this region are still highly uncertain, largely due to inadequate representation of the diurnally-varying processes related to convection. In this work, a coupled numerical model of the land-atmosphere system (RegCM3-IBIS) is used to investigate how more physically-realistic representations of these processes can be incorporated into large-scale climate models. In particular, this work improves simulations of convective-radiative feedbacks and the role of cumulus clouds in mediating the diurnal cycle of rainfall. Three key contributions are made to the development of RegCM3-IBIS. Two pieces of work relate directly to the formation and dissipation of convective clouds: a new representation of convective cloud cover, and a new parameterization of convective rainfall production. These formulations only contain parameters that can be directly quantified from observational data, are independent of model user choices such as domain size or resolution, and explicitly account for subgrid variability in cloud water content and nonlinearities in rainfall production. The third key piece of work introduces a new method for representation of cloud formation within the boundary layer. A comprehensive evaluation of the improved model was undertaken using a range of satellite-derived and ground-based datasets, including a new dataset from Singapore's Changi airport that documents diurnal variation of the local boundary layer height. The performance of RegCM3-IBIS with the new formulations is greatly improved across all evaluation metrics, including cloud cover, cloud liquid water, radiative fluxes and rainfall, indicating consistent improvement in physical realism throughout the simulation. This work demonstrates that: (1) moist convection strongly influences the near surface environment by mediating the incoming solar radiation and net radiation at the surface; (2) dissipation of convective cloud via rainfall plays an equally important role in the convectiveradiative feedback as the formation of that cloud; and (3) over parts of the Maritime Continent, rainfall is a product of diurnally-varying convective processes that operate at small spatial scales, on the order of 1 km. (Copies available exclusively from MIT Libraries, libraries.mit.edu/docs - docs@mit.edu)

Socio-economic, Biophysical, and Perceptional Factors Associated with Agricultural Adaptation of Smallholder Farmers in Gujarat, Northwest India

NASA Astrophysics Data System (ADS)

Jain, M.; DeFries, R. S.

2012-12-01

Climate change is predicted to negatively impact many agricultural communities across the globe, particularly smallholder farmers who often do not have access to appropriate technologies to reduce their vulnerability. To better predict which farmers will be most impacted by future climate change at a regional scale, we use remote sensing and agricultural census data to examine how cropping intensity and crop type have shifted based on rainfall variability across Gujarat, India from 1990 to 2010. Using household-level interviews, we then identify the socio-economic, biophysical, perceptional, and psychological factors associated with smallholder farmers who are the most impacted and the least able to adapt to contemporaneous rainfall variability. We interviewed 750 farmers in 2011 and 2012 that span a rainfall, irrigation, socio-economic, and caste gradient across central Gujarat. Our results show that farmers shift cropping practices in several ways based on monsoon onset, which farmers state is the main observable rainfall signal influencing cropping decisions during the monsoon season. When monsoon onset is delayed, farmers opt to plant more drought-tolerant crops, push back the date of sowing, and increase the number of irrigations used. Comparing self-reported income and yields, we find that switching crops does not improve agricultural income, shifting planting date does not influence crop yield, yet increasing the number of irrigations significantly increases yield. Future work will identify which social (e.g. social networks), psychological (e.g. risk preference), and knowledge (e.g. information sources) factors are associated with farmers who are best able to adapt to rainfall variability.

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.

Application of bayesian networks to real-time flood risk estimation

NASA Astrophysics Data System (ADS)

Garrote, L.; Molina, M.; Blasco, G.

2003-04-01

This paper presents the application of a computational paradigm taken from the field of artificial intelligence - the bayesian network - to model the behaviour of hydrologic basins during floods. The final goal of this research is to develop representation techniques for hydrologic simulation models in order to define, develop and validate a mechanism, supported by a software environment, oriented to build decision models for the prediction and management of river floods in real time. The emphasis is placed on providing decision makers with tools to incorporate their knowledge of basin behaviour, usually formulated in terms of rainfall-runoff models, in the process of real-time decision making during floods. A rainfall-runoff model is only a step in the process of decision making. If a reliable rainfall forecast is available and the rainfall-runoff model is well calibrated, decisions can be based mainly on model results. However, in most practical situations, uncertainties in rainfall forecasts or model performance have to be incorporated in the decision process. The computation paradigm adopted for the simulation of hydrologic processes is the bayesian network. A bayesian network is a directed acyclic graph that represents causal influences between linked variables. Under this representation, uncertain qualitative variables are related through causal relations quantified with conditional probabilities. The solution algorithm allows the computation of the expected probability distribution of unknown variables conditioned to the observations. An approach to represent hydrologic processes by bayesian networks with temporal and spatial extensions is presented in this paper, together with a methodology for the development of bayesian models using results produced by deterministic hydrologic simulation models

Seasonal and spatial variability of rainfall redistribution under Scots pine and Downy oak forests in Mediterranean conditions

NASA Astrophysics Data System (ADS)

Garcia-Estringana, Pablo; Latron, Jérôme; Molina, Antonio J.; Llorens, Pilar

2013-04-01

The large degree of temporal and spatial variability of throughfall input patterns may lead to significant changes in the volume of water that reach the soil in each location, and beyond in the hydrological response of forested hillslopes. To explore the role of vegetation in the temporal and spatial redistribution of rainfall in Mediterranean climatic conditions two contrasted stands were monitored. One is a Downy oak forest (Quercus pubescens) and the other is a Scots pine forest (Pinus sylvestris), both are located in the Vallcebre research catchments (NE Spain, 42° 12'N, 1° 49'E). These plots are representative of Mediterranean mountain areas with spontaneous afforestation by Scots pine as a consequence of the abandonment of agricultural terraces, formerly covered by Downy oaks. The monitoring design of each plot consists of a set of 20 automatic rain recorders and 40 automatic soil moisture probes located below the canopy. 100 hemispheric photographs of the canopy were used to place the instruments at representative locations (in terms of canopy cover) within the plot. Bulk rainfall, stemflow and meteorological conditions above the forest cover are also automatically recorded. Canopy cover as well as biometric characteristics of the plots are also regularly measured. This work presents the first results describing the variability of throughfall beneath each forest stand and compares the persistence of temporal patterns among stands, and for the oaks stand among the leafed and the leafless period. Furthermore, canopy structure, rainfall characteristics and meteorological conditions of rainfall events are evaluated as main drivers of throughfall redistribution.

Hydrologic response in karstic-ridge wetlands to rainfall and evapotranspiration, central Florida, 2001-2003

USGS Publications Warehouse

Knowles, Leel; Phelps, G.G.; Kinnaman, Sandra L.; German, Edward R.

2005-01-01

Two internally drained karstic wetlands in central Florida-Boggy Marsh at the Hilochee Wildlife Management Area and a large unnamed wetland at the Lyonia Preserve-were studied during 2001-03 to gain a better understanding of the net-recharge function that these wetlands provide, the significance of exchanges with ground water with regard to wetland water budgets, and the variability in wetland hydrologic response to a range of climate conditions. These natural, relatively remote and unaltered wetlands were selected to provide a baseline of natural wetland hydrologic variability to which anthropogenic influences on wetland hydrology could be compared. Large departures from normal rainfall during the study were fortuitous, and allowed monitoring of hydrologic processes over a wide range of climate conditions. Wetland responses varied greatly as a result of climate conditions that ranged from moderate drought to extremely moist. Anthropogenic activities influenced water levels at both study sites; however, because these activities were brief relative to the duration of the study, sufficient data were collected during unimpacted periods to allow for the following conclusions to be made. Water budgets developed for Boggy Marsh and the Lyonia large wetland showed strong similarity between the flux terms of rainfall, evaporation, net change in storage, and the net ground-water exchange residual. Runoff was assumed to be negligible. Of the total annual flux at Boggy Marsh, rainfall accounted for 45 percent; evaporation accounted for 25 percent; net change in storage accounted for 25 percent; and the net residual accounted for 5 percent. At the Lyonia large wetland, rainfall accounted for 44 percent; evaporation accounted for 29 percent; net change in storage accounted for 21 percent; and the net residual accounted for 6 percent of the total annual flux. Wetland storage and ground-water exchange were important when compared to the total water budget at both wetlands. Even though rainfall was far above average during the study, wetland evaporation volumetrically exceeded rainfall. Ground-water inflow was effective in partially offsetting the negative residual between rainfall and evaporation, thus adding to wetland storage. Ground-water inflow was most common at both wetlands when rainfall continued for days or weeks, or during a week with more than about 2.5 inches of rainfall. Large decreases in wetland storage were associated with large negative fluxes of evaporation and ground-water exchange. The response of wetland water levels to rainfall showed a strong and similar relation at both study sites; however, the greater variability in the relation of wetland water-level change to rainfall at higher rainfall rates indicated that hydrologic processes other than rainfall became more important in the response of the wetland. Changes in wetland water levels seemed to be related more to vertical gradients than to lateral gradients. The largest wetland water-level rises were associated mostly with lower vertical gradients, when vertical head differences were below the 18-month average; however, at the Lyonia large wetland, extremely large lateral gradients toward the wetland during late June 2002 may have contributed to substantial gains in wetland water. During the remainder of the study, wetland water-level rises were associated mostly with decreasing vertical gradients and highly variable lateral gradients. Conversely, wetland water-level decreases were associated mostly with increasing vertical gradients and lateral gradients away from the wetland, particularly during the dry season. The potential for lateral ground-water exchange with the wetlands varied substantially more than that for vertical exchange. Potential for vertical losses of wetland water to ground water was highest during a dry period from December 2001 to June 2002, during the wet season of 2002, and for several months into the following dry season. Lateral he

Climate and soil attributes determine plant species turnover in global drylands.

PubMed

Ulrich, Werner; Soliveres, Santiago; Maestre, Fernando T; Gotelli, Nicholas J; Quero, José L; Delgado-Baquerizo, Manuel; Bowker, Matthew A; Eldridge, David J; Ochoa, Victoria; Gozalo, Beatriz; Valencia, Enrique; Berdugo, Miguel; Escolar, Cristina; García-Gómez, Miguel; Escudero, Adrián; Prina, Aníbal; Alfonso, Graciela; Arredondo, Tulio; Bran, Donaldo; Cabrera, Omar; Cea, Alex; Chaieb, Mohamed; Contreras, Jorge; Derak, Mchich; Espinosa, Carlos I; Florentino, Adriana; Gaitán, Juan; Muro, Victoria García; Ghiloufi, Wahida; Gómez-González, Susana; Gutiérrez, Julio R; Hernández, Rosa M; Huber-Sannwald, Elisabeth; Jankju, Mohammad; Mau, Rebecca L; Hughes, Frederic Mendes; Miriti, Maria; Monerris, Jorge; Muchane, Muchai; Naseri, Kamal; Pucheta, Eduardo; Ramírez-Collantes, David A; Raveh, Eran; Romão, Roberto L; Torres-Díaz, Cristian; Val, James; Veiga, José Pablo; Wang, Deli; Yuan, Xia; Zaady, Eli

2014-12-01

Geographic, climatic, and soil factors are major drivers of plant beta diversity, but their importance for dryland plant communities is poorly known. This study aims to: i) characterize patterns of beta diversity in global drylands, ii) detect common environmental drivers of beta diversity, and iii) test for thresholds in environmental conditions driving potential shifts in plant species composition. 224 sites in diverse dryland plant communities from 22 geographical regions in six continents. Beta diversity was quantified with four complementary measures: the percentage of singletons (species occurring at only one site), Whittake's beta diversity (β(W)), a directional beta diversity metric based on the correlation in species occurrences among spatially contiguous sites (β(R 2 )), and a multivariate abundance-based metric (β(MV)). We used linear modelling to quantify the relationships between these metrics of beta diversity and geographic, climatic, and soil variables. Soil fertility and variability in temperature and rainfall, and to a lesser extent latitude, were the most important environmental predictors of beta diversity. Metrics related to species identity (percentage of singletons and β(W)) were most sensitive to soil fertility, whereas those metrics related to environmental gradients and abundance ((β(R 2 )) and β(MV)) were more associated with climate variability. Interactions among soil variables, climatic factors, and plant cover were not important determinants of beta diversity. Sites receiving less than 178 mm of annual rainfall differed sharply in species composition from more mesic sites (> 200 mm). Soil fertility and variability in temperature and rainfall are the most important environmental predictors of variation in plant beta diversity in global drylands. Our results suggest that those sites annually receiving ~ 178 mm of rainfall will be especially sensitive to future climate changes. These findings may help to define appropriate conservation strategies for mitigating effects of climate change on dryland vegetation.

Earthshots: Satellite images of environmental change – Lake Turkana, Kenya and Ethiopia

USGS Publications Warehouse

,

2013-01-01

Ethiopia is constructing a series of dams on the Omo River. The Gibe I and Gibe II dams are completed, and the Gibe III dam began filling its reservoir in 2015. Studies are ongoing to understand the interactions between regulated flows as a result of the dams and rainfall on the water levels of Lake Turkana. Scientists use many years’ worth of data to get a better understanding of the lake’s natural variability and how that variability might be affected by dams, irrigation, and rainfall.