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Highlights • We outline a water-energy balance approach for multi-scalar and multi-category drought analysis. • The proposed approach is able to capture meteorological, hydrological, and agricultural droughts. • The new drought index outperforms previous drought indices (SPEI and scPDSI), especially in non-humid regions. Abstract Different categories of droughts (e.g., meteorological, agricultural, hydrological), and their multi-scalar features often make description of drought onset, persistence, and termination challenging and often subjective. Here we show that a water-energy balance based indicator, named Standardized Moisture Anomaly Index (SZI), better captures multiple categories of droughts and their multi-scalar features. We globally evaluate and compare the performance of SZI with existing drought indicators that use potential evapotranspiration (PET) as a measure of atmospheric water demand including the Standardized Precipitation Evapotranspiration Index (SPEI) and self-calibrated Palmer Drought Severity Index (scPDSI). We show that while PET is a good indicator for characterizing the climate aridity, using it as a measure of atmospheric water demand for drought analysis leads to misrepresentation of droughts, especially over water-limited (non-humid) regions where the actual evapotranspiration is primarily dominated by water availability rather than energy (or PET). The main advantage of SZI is that, instead of PET, it uses a variable termed climatically appropriate precipitation for existing conditions (P ˆ) as the atmospheric water demand metric. Investigating droughts over 32 large basins across the globe, we show that the SZI can better represent meteorological, hydrological, and agricultural droughts compared to SPEI (especially in non-humid basins; 18 out of 32 basins) and scPDSI at multiple time scales. Given that SZI is physically more reasonable in reflecting surface water-energy balance over both humid and non-humid regions, it enables better characterization of different types of droughts in different climatic regions. [ABSTRACT FROM AUTHOR]

Precipitation variability significantly influences the heavily populated West Coast of the United States, raising the need for reliable predictions. We investigate the region's short- to extended-range precipitation prediction skill using the hindcast database of the Subseasonal-to-Seasonal Prediction Project (S2S). The prediction skill–lead time relationship is evaluated, using both deterministic and probabilistic skill scores. Results show that the S2S models display advantageous deterministic skill at week 1. For week 2, prediction is useful for the best-performing model, with a Pearson correlation coefficient larger than 0.6. Beyond week 2, predictions generally provide little useful deterministic skill. Sources of extended-range predictability are investigated, focusing on El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO). We found that periods of heavy precipitation associated with ENSO are more predictable at the extended range period. During El Niño years, Southern California tends to receive more precipitation in late winter, and most models show better extended-range prediction skill. On the contrary, during La Niña years Oregon tends to receive more precipitation in winter, with most models showing better extended-range skill. We believe the excessive precipitation and improved extended-range prediction skill are caused by the meridional shift of baroclinic systems as modulated by ENSO. Through examining precipitation anomalies conditioned on the MJO, we verified that active MJO events systematically modulate the area's precipitation distribution. Our results show that most models do not represent the MJO or its associated teleconnections, especially at phases 3–4. However, some models exhibit enhanced extended-range prediction skills under active MJO conditions. [ABSTRACT FROM AUTHOR]

Over the past 2 decades, a wide range of studies have incorporated Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and temporal scales, namely PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. Secondly, we offer an evaluation of the available operational products over the contiguous US (CONUS) at different spatial and temporal scales using Climate Prediction Center (CPC) unified gauge-based analysis as a benchmark. Due to limitations of the baseline dataset (CPC), daily scale is the finest temporal scale used for the evaluation over CONUS. Additionally, we provide a comparison of the available products at a quasi-global scale. Finally, we highlight the strengths and limitations of the PERSIANN products and briefly discuss expected future developments. [ABSTRACT FROM AUTHOR]

Hydroclimatic variables such as precipitation and temperature are often measured or simulated by climate models at coarser spatiotemporal scales than those needed for operational purposes. This has motivated more than half a century of research in developing disaggregation methods that break down coarse‐scale time series into finer scales, with two primary objectives: (a) reproducing the statistical properties of the fine‐scale process and (b) preserving the original coarse‐scale data. Existing methods either preserve a limited number of statistical moments at the fine scale, which is often insufficient and can lead to an unrepresentative approximation of the actual marginal distribution, or are based on a limited number of a priori distributional assumptions, for example, lognormal. Additionally, they are not able to account for potential nonstationarity in the underlying fine‐scale process. Here we introduce a novel disaggregation method, named Disaggregation Preserving Marginals and Correlations (DiPMaC), that is able to disaggregate a coarse‐scale time series to any finer scale, while reproducing the probability distribution and the linear correlation structure of the fine‐scale process. DiPMaC is also generalized for arbitrary nonstationary scenarios to reproduce time varying marginals. Additionally, we introduce a computationally efficient algorithm, based on Bernoulli trials, to optimize the disaggregation procedure and guarantee preservation of the coarse‐scale values. We focus on temporal disaggregation and demonstrate the method by disaggregating monthly precipitation to hourly, and time series with trends (e.g., climate model projections), while we show its potential to disaggregate based on general nonstationary scenarios. The example applications demonstrate the performance and robustness of DiPMaC. Key Points: Novel model enables disaggregation at any time scale by reproducing the marginal distribution and the correlation structure of the fine‐scale processThe model involves optimized algorithmic efficiency based on a novel Bernoulli‐trials frameworkThe model is applicable to both stationary and nonstationary processes [ABSTRACT FROM AUTHOR]

Hourly precipitation extremes are crucial in hydrological design. Their frequency and magnitude is encapsulated in the probability distribution tail. Traditional extreme‐analysis methods rely on theorems, like the Pickands‐Balkema‐de Haan, indicating specific type of tails assuming asymptotic convergence—a questionable assumption for real‐world samples. Moreover, popular stochastic models for hourly precipitation presume light‐tailed distributions to facilitate their mathematical formulation. In practice, limited information on hourly precipitation extremes makes identifying and quantifying their tail highly uncertain, especially on a station‐by‐station basis. Yet no comprehensive regional analysis of tails has been undertaken to quantify a climatology of tails for diagnostic and prognostic purposes. Here we undertake such an analysis for the conterminous United States. We introduce a novel Bayesian‐adjustment approach to assess the best model between power‐type and stretched‐exponential tails showing that the latter performs better. We present climatology of the tail and quantify its heaviness in over 4,000 hourly precipitation records across the United States and present three main conclusions. First, we show that hourly precipitation tails are heavier than those commonly used with important implications including underestimation of extremes. Second, we provide spatial maps of the tail behavior which reveal some strikingly coherent spatial patterns that can be used for inference in the absence of local observations. Third, we find a nonlinear increase in the tail heaviness with elevation and we formulate parametric functions to describe this law. These results can improve the accuracy of frequency analysis, probabilistic prediction, rainfall‐runoff modeling, and downscaling of historical observations and climate model projections. Key Points: Climatology of tails of hourly precipitation extremes over the United States shows heavier tails than those commonly used in practiceA striking coherent spatial pattern of tail's heaviness is revealed with mountainous regions showing heavier tailsSimple parametric law found to describe the nonlinear increase in the heaviness of the tail with elevation [ABSTRACT FROM AUTHOR]

Little dispute surrounds the observed global temperature changes over the past decades. As a result, there is widespread agreement that a corresponding response in the global hydrologic cycle must exist. However, exactly how such a response manifests remains unsettled. Here we use a unique recently developed long-term satellite-based record to assess changes in precipitation across spatial scales. We show that warm climate regions exhibit decreasing precipitation trends, while arid and polar climate regions show increasing trends. At the country scale, precipitation seems to have increased in 96 countries, and decreased in 104. We also explore precipitation changes over 237 global major basins. Our results show opposing trends at different scales, highlighting the importance of spatial scale in trend analysis. Furthermore, while the increasing global temperature trend is apparent in observations, the same cannot be said for the global precipitation trend according to the high-resolution dataset, PERSIANN-CDR, used in this study. [ABSTRACT FROM AUTHOR]

Abstract: During the last century, we have observed a warming climate with more intense precipitation extremes in some regions, likely due to increases in the atmosphere's water holding capacity. Traditionally, infrastructure design and rainfall‐triggered landslide models rely on the notion of stationarity, which assumes that the statistics of extremes do not change significantly over time. However, in a warming climate, infrastructures and natural slopes will likely face more severe climatic conditions, with potential human and socioeconomical consequences. Here we outline a framework for quantifying climate change impacts based on the magnitude and frequency of extreme rainfall events using bias corrected historical and multimodel projected precipitation extremes. The approach evaluates changes in rainfall Intensity‐Duration‐Frequency (IDF) curves and their uncertainty bounds using a nonstationary model based on Bayesian inference. We show that highly populated areas across the United States may experience extreme precipitation events up to 20% more intense and twice as frequent, relative to historical records, despite the expectation of unchanged annual mean precipitation. Since IDF curves are widely used for infrastructure design and risk assessment, the proposed framework offers an avenue for assessing resilience of infrastructure and landslide hazard in a warming climate. [ABSTRACT FROM AUTHOR]

An object-based verification approach is employed to assess the performance of the commonly used high-resolution satellite precipitation products: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Climate Prediction center MORPHing technique (CMORPH), and Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42RT. The evaluation of the satellite precipitation products focuses on the skill of depicting the geometric features of the localized precipitation areas. Seasonal variability of the performances of these products against the ground observations is investigated through the examples of warm and cold seasons. It is found that PERSIANN is capable of depicting the orientation of the localized precipitation areas in both seasons. CMORPH has the ability to capture the sizes of the localized precipitation areas and performs the best in the overall assessment for both seasons. 3B42RT is capable of depicting the location of the precipitation areas for both seasons. In addition, all of the products perform better on capturing the sizes and centroids of precipitation areas in the warm season than in the cold season, while they perform better on depicting the intersection area and orientation in the cold season than in the warm season. These products are more skillful on correctly detecting the localized precipitation areas against the observations in the warm season than in the cold season. [ABSTRACT FROM AUTHOR]

Summary The 2011 East Africa drought caused dire situations across several countries and led to a widespread and costly famine in the region. Numerous dynamic and statistical drought prediction models have been used for providing drought information and/or early warning. The concept of Ensemble Streamflow Prediction (ESP) has been successfully applied to univariate drought indicators (e.g., the Standardized Precipitation Index) for seasonal drought prediction. In this study, we outline a framework for using the ESP concept for multivariate, multi-index drought prediction. We employ the recently developed Multivariate Standardized Drought Index (MSDI), which integrates precipitation and soil moisture for describing drought. In this approach, the ESP concept is first used to predict the seasonal changes to precipitation and soil moisture. Then, the MSDI is estimated based on the joint probability of the predicted accumulated precipitation and soil moisture as composite (multi-index) drought information. Given its probabilistic nature, the presented model offers both a measure of drought severity and probability of drought occurrence. The suggested model is tested for part of the 2011 East Africa drought using monthly precipitation and soil moisture data obtained from the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA-Land). The results indicate that the suggested multi-index predictions are consistent with the observation. Furthermore, the results emphasize the potential application of the model for probabilistic drought early warning in East Africa. [ABSTRACT FROM AUTHOR]

Floods are among the most devastating natural hazards in society. Flood forecasting is crucially important in order to provide warnings in time to protect people and properties from such disasters. This research applied the high-resolution coupled hydrologic-hydraulic model from the University of California, Irvine, named HiResFlood-UCI, to simulate the historical 2008 Iowa flood. HiResFlood-UCI was forced with the near-real-time Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) and NEXRAD Stage 2 precipitation data. The model was run using the a priori hydrologic parameters and hydraulic Manning n values from lookup tables. The model results were evaluated in two aspects: point comparison using USGS streamflow and areal validation of inundation maps using USDA's flood extent maps derived from Advanced Wide Field Sensor (AWiFS) 56-m resolution imagery. The results show that the PERSIANN-CCS simulation tends to capture the observed hydrograph shape better than Stage 2 (minimum correlation of 0.86 for PERSIANN-CCS and 0.72 for Stage 2); however, at most of the stream gauges, Stage 2 simulation provides more accurate estimates of flood peaks compared to PERSIANN-CCS (49%-90% bias reduction from PERSIANN-CCS to Stage 2). The simulation in both cases shows a good agreement (0.67 and 0.73 critical success index for Stage 2 and PERSIANN-CCS simulations, respectively) with the AWiFS flood extent. Since the PERSIANN-CCS simulation slightly underestimated the discharge, the probability of detection (0.93) is slightly lower than that of the Stage 2 simulation (0.97). As a trade-off, the false alarm rate for the PERSIANN-CCS simulation (0.23) is better than that of the Stage 2 simulation (0.31). [ABSTRACT FROM AUTHOR]

Summary Multimodel ensembles are widely used to quantify uncertainties of climate model simulations. Previous studies have confirmed that a multimodel ensemble approach increases the skill of model simulations. However, one may need to know which ensemble member is more likely to be true, particularly when the ensemble is spread out over a wide area. Typically, ensemble response (climate response) is derived by taking the mean or median of ensemble members. However, strong similarities exist between models (members of an ensemble) which may cause biased climate response toward models with strong similarities. In this study, a model is proposed for deriving the climate response (ensemble response) of multimodel climate model simulations. The approach is based on the concept of Expert Advice (EA) algorithm which has been successfully applied to the financial sector. The goal of this methodology is to derive an ensemble response that at every time step is equal or better (less error) than the best model. The methodology is tested using the CMIP5 historical temperature simulations (1951–2005) and Climatic Research Unit observations, and the results show that the EA algorithm leads to smaller error compared to the ensemble mean. [ABSTRACT FROM AUTHOR]

This paper introduces the Standardized Drought Analysis Toolbox (SDAT) that offers a generalized framework for deriving nonparametric univariate and multivariate standardized indices. Current indicators suffer from deficiencies including temporal inconsistency, and statistical incomparability. Different indicators have varying scales and ranges and their values cannot be compared with each other directly. Most drought indicators rely on a representative parametric probability distribution function that fits the data. However, a parametric distribution function may not fit the data, especially in continental/global scale studies. SDAT is based on a nonparametric framework that can be applied to different climatic variables including precipitation, soil moisture and relative humidity, without having to assume representative parametric distributions. The most attractive feature of the framework is that it leads to statistically consistent drought indicators based on different variables. [ABSTRACT FROM AUTHOR]

Summary Extreme precipitation events have attracted a great deal of attention among the scientific community because of their devastating consequences on human livelihood and socio-economic development. To assess changes in precipitation extremes in a given region, it is essential to analyze decadal oscillations in precipitation extremes. This study examines temporal oscillations in precipitation data in several sub-regions of Iran using a novel quantile perturbation method during 1980–2010. Precipitation data from NASA’s Modern-Era Retrospective Analysis for Research and Applications-Land (MERRA-Land) are used in this study. The results indicate significant anomalies in precipitation extremes in the northwest and southeast regions of Iran. Analysis of extreme precipitation perturbations reveals that perturbations for the monthly aggregation level are generally lower than the annual perturbations. Furthermore, high-oscillation and low-oscillation periods are found in extreme precipitation quantiles across different seasons. In all selected regions, a significant anomaly (i.e., extreme wet/dry conditions) in precipitation extremes is observed during spring. [ABSTRACT FROM AUTHOR]

This paper analyzes changes in areas under droughts over the past three decades and alters our understanding of how amplitude and frequency of droughts differ in the Southern Hemisphere (SH) and Northern Hemisphere (NH). Unlike most previous global-scale studies that have been based on climate models, this study is based on satellite gauge-adjusted precipitation observations. Here, we show that droughts in terms of both amplitude and frequency are more variable over land in the SH than in the NH. The results reveal no significant trend in the areas under drought over land in the past three decades. However, after investigating land in the NH and the SH separately, the results exhibit a significant positive trend in the area under drought over land in the SH, while no significant trend is observed over land in the NH. We investigate the spatial patterns of the wetness and dryness over the past three decades, and we show that several regions, such as the southwestern United States, Texas, parts of the Amazon, the Horn of Africa, northern India, and parts of the Mediterranean region, exhibit a significant drying trend. The global trend maps indicate that central Africa, parts of southwest Asia (e.g., Thailand, Taiwan), Central America, northern Australia, and parts of eastern Europe show a wetting trend during the same time span. The results of this satellite-based study disagree with several model-based studies which indicate that droughts have been increasing over land. On the other hand, our findings concur with some of the observation-based studies. [ABSTRACT FROM AUTHOR]

The importance of satellite datasets as alternative sources of precipitation information has been argued in numerous studies. Future developments in satellite precipitation algorithms as well as utilization of satellite data in operational applications rely on a more in-depth understanding of satellite errors and biases across different spatial and temporal scales. This paper investigates the capability of satellite precipitation data sets with respect to detecting heavy precipitation rates over different temporal accumulations. In this study, the performance of Tropical Rainfall Measuring Mission real time (TRMM-RT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks and CPC MORPHing (CMORPH) is compared against radar-based gauge-adjusted Stage IV data. The results show that none of the high temporal resolution (3-h) datasets are ideal for detecting heavy precipitation rates. In fact, the detection skill of all products drops as the precipitation thresholds (i.e. 75 and 90 percentiles) increase. At higher temporal accumulations (6, 12 and 24 h), the detection skill improves for all precipitation products, with CMORPH showing a better detection skill compared to all other products. On the other hand, all precipitation products exhibit high false alarm ratios above the heavy precipitation thresholds, although TRMM-RT lead to a relatively smaller level of false alarms. These results indicate that further efforts are necessary to improve the precipitation algorithms so that they can capture heavy precipitation rates more reliably. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Accurate and reliable drought monitoring is essential to drought mitigation efforts and reduction of social vulnerability. A variety of indices, such as the standardized precipitation index (SPI), are used for drought monitoring based on different indicator variables. Because of the complexity of drought phenomena in their causation and impact, drought monitoring based on a single variable may be insufficient for detecting drought conditions in a prompt and reliable manner. This study outlines a multivariate, multi-index drought monitoring framework, namely, the multivariate standardized drought index (MSDI), for describing droughts based on the states of precipitation and soil moisture. In this study, the MSDI is evaluated against U.S. Drought Monitor (USDM) data as well as the commonly used standardized indices for drought monitoring, including detecting drought onset, persistence, and spatial extent across the continental United States. The results indicate that MSDI includes attractive properties, such as higher probability of drought detection, compared to individual precipitation and soil moisture-based drought indices. This study shows that the MSDI leads to drought information generally consistent with the USDM and provides additional information and insights into drought monitoring. [ABSTRACT FROM AUTHOR]

Abstract: Defining droughts based on a single variable/index (e.g., precipitation, soil moisture, or runoff) may not be sufficient for reliable risk assessment and decision-making. In this paper, a multivariate, multi-index drought-modeling approach is proposed using the concept of copulas. The proposed model, named Multivariate Standardized Drought Index (MSDI), probabilistically combines the Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SSI) for drought characterization. In other words, MSDI incorporates the meteorological and agricultural drought conditions for overall characterization of drought. In this study, the proposed MSDI is utilized to characterize the drought conditions over several Climate Divisions in California and North Carolina. The MSDI-based drought analyses are then compared with SPI and SSI. The results reveal that MSDI indicates the drought onset and termination based on the combination of SPI and SSI, with onset being dominated by SPI and drought persistence being more similar to SSI behavior. Overall, the proposed MSDI is shown to be a reasonable model for combining multiple indices probabilistically. [Copyright &y& Elsevier]

Numerical weather prediction models play a major role in weather forecasting, especially in cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research and practical applications. Previous studies have highlighted the sensitivity of this model to microphysics and cumulus schemes. This study investigated the performance of the WRF in forecasting precipitation, hurricane track, and landfall time using various microphysics and cumulus schemes. A total of 20 combinations of microphysics and cumulus schemes were used, and the model outputs were validated against ground-based observations. While the choice of microphysics and cumulus schemes can significantly impact model output, it is not the case that any single combination can be considered 'ideal' for modeling all characteristics of a hurricane, including precipitation amount, areal extent, hurricane track, and the time of landfall. For example, the model's ability to simulate precipitation (with the least total bias) is best achieved using Betts-Miller-Janjić (BMJ) cumulus parameterization in combination with the WRF single-moment five-class microphysics scheme (WSM5). It was determined that the WSM5-BMJ, WSM3 (the three-class version of the WSM scheme)-BMJ, and Ferrier microphysics in combination with the Grell-Devenyi cumulus scheme were the best combinations for simulation of the landfall time. However, the hurricane track was best estimated using the Lin et al. and Kessler microphysics options with BMJ cumulus parameterization. Contrary to previous studies, these results indicated that the use of cumulus schemes improves model outputs when the grid size is smaller than 10 km. However, it was found that many of the differences between parameterization schemes may be well within the uncertainty of the measurements. [ABSTRACT FROM AUTHOR]

The article presents information about the Advanced Concepts Workshop on Remote Sensing of Precipitation at Multiple Scales that was held at the University of California, Irvine on March 15-17, 2010. Research priorities and recommendations from the participants are related to quantification of uncertainties about multisensor observations, improvements in precipitation retrieval algorithms, and development of metrics validation for research.

Satellite estimates and weather forecast models have made it possible to observe and predict precipitation over large spatial scales. Despite substantial progress in observing patterns of precipitation, characterization of spatial patterns is still a challenge. Quantitative assessment methods for spatial patterns are essential for future developments in prediction of the spatial extent and patterns of precipitation. In this study, precipitation patterns are characterized using three geometrical indices: (i) a connectivity index, (ii) a shape index, and (iii) a dispersiveness index. Using multiple examples, the application of the proposed indices is explored in pattern analysis of satellite precipitation images and validation of numerical atmospheric models with respect to geometrical properties. The results indicate that the presented indices can be reasonably employed for a relative comparison of different patterns (e.g., multiple fields against spatial observations) with respect to their connectivity, organization, and shape. [ABSTRACT FROM AUTHOR]

Abstract: In this paper three different stochastic ensemble generators are assessed based on their performance in ensemble generation of hourly and 15-min radar-based rainfall fields. The simulation methods include a Gaussian copula model, a t-copula-based approach and a non-Gaussian v-copula model. In the first and second models, two different elliptical copulas are employed to describe the dependence structures and to simulate multivariate rainfall error fields. In the third approach, a non-Gaussian v-transformed copula is applied for multivariate error simulations. Using this type of copula, one can describe asymmetrical dependencies through the copula parameters. In all three models, the outputs of the error simulators are used to perturb radar-based rainfall estimates to obtain an ensemble of precipitation estimates. In order to examine the reliability and performance of the presented models, several case studies are performed using radar reflectivity data (Level II) and Stage IV NEXRAD multi-sensor precipitation estimates as input to the models. Various statistical tests are employed to assess the models. The results indicate that the presented models are rather similar with respect to the dependence structure. However, using the t-copula model may have significant advantages over the other models, particularly, with respect to extremes. While all the presented models are able to simulate reasonable rainfall ensembles, the t-copula model seems to be more promising for practical applications. [Copyright &y& Elsevier]

Abstract: Quantification of rainfall and its spatial and temporal variability is extremely important for reliable hydrological and meteorological modeling. While rain gauge measurements do not provide reasonable areal representation of rainfall, remotely sensed precipitation estimates offer much higher spatial resolution. However, uncertainties associated with remotely sensed rainfall estimates are not well quantified. This issue is important considering the fact that uncertainties in input rainfall are the main sources of error in hydrologic processes. Using an ensemble of rainfall estimates that resembles multiple realizations of possible true rainfall, one can assess uncertainties associated with remotely sensed rainfall data. In this paper, ensembles are generated by imposing rainfall error fields over remotely sensed rainfall estimates. A non-Gaussian copula-based model is introduced for simulation of rainfall error fields. The v-transformed copula is employed to describe the dependence structure of rainfall error estimates without the influence of the marginal distribution. Simulations using this model can be performed unconditionally or conditioned on ground reference measurements such that rain gauge data are honored at their locations. The presented model is implemented for simulation of rainfall ensembles across the Little Washita watershed, Oklahoma. The results indicate that the model generates rainfall fields with similar spatio-temporal characteristics and stochastic properties to those of observed rainfall data. [Copyright &y& Elsevier]

Highlights • Possible future change in the streamflow regimes in a changing climate is examined. • Opposing trends were found in the two tails of the future discharge simulations. • In the future, California can face wetter rainy seasons and drier dry seasons. Abstract In this study, we investigate changes in future streamflows in California using bias-corrected and routed streamflows derived from global climate model (GCM) simulations under two representative concentration pathways (RCPs): RCP4.5 and RCP8.5. Unlike previous studies that have focused mainly on the mean streamflow, annual maxima or seasonality, we focus on projected changes across the distribution of streamflow and the underlying causes. We report opposing trends in the two tails of the future streamflow simulations: lower low flows and higher high flows with no change in the overall mean of future flows relative to the historical baseline (statistically significant at 0.05 level). Furthermore, results show that streamflow is projected to increase during most of the rainy season (December to March) while it is expected to decrease in the rest of the year (i.e., wetter rainy seasons, and drier dry seasons). We argue that the projected changes to streamflow in California are driven by the expected changes to snow patterns and precipitation extremes in a warming climate. Changes to future low flows and extreme high flows can have significant implications for water resource planning, drought management, and infrastructure design and risk assessment. [ABSTRACT FROM AUTHOR]

Each year, droughts cause significant economic and agricultural losses across the world. The early warning and onset detection of drought is of particular importance for effective agriculture and water resource management. Previous studies show that the Standard Precipitation Index (SPI), a measure of precipitation deficit, detects drought onset earlier than other indicators. Here we show that satellite-based near surface air relative humidity data can further improve drought onset detection and early warning. This paper introduces the Standardized Relative Humidity Index (SRHI) based on the NASA Atmospheric Infrared Sounder (AIRS) observations. The results indicate that the SRHI typically detects the drought onset earlier than the SPI. While the AIRS mission was not originally designed for drought monitoring, we show that its relative humidity data offers a new and unique avenue for drought monitoring and early warning. We conclude that the early warning aspects of SRHI may have merit for integration into current drought monitoring systems. [ABSTRACT FROM AUTHOR]

In this study, we investigate the extent to which physical water resource availability constraints can limit the deployment of solar thermal and geothermal-based energy resources under future climate scenarios in California. This is accomplished by (1) calculating the water unconstrained potential capacity for solar thermal and geothermal power plants, (2) estimating the available water supply for supporting the water needs of these plants using four climate model simulations under representative concentration pathway (RCP) 8.5, and (3) determining the supportable capacity from the available water supply based on power plant cooling type. We show that regional water availability can limit the installable capacity of solar thermal resources to a range of 10.9–52.6% of solar thermal potential and geothermal resources to between 17.9% and 100% of geothermal potential, depending on cooling system and regional water demand levels by the year 2050. The limiting factor for installable capacity was driven by whether the locations of solar thermal and geothermal resources were spatially aligned with precipitation patterns, with cooling system type acting as a secondary factor. In regions with high solar thermal and geothermal potential, reducing water demand from other sectors was important for alleviating the water constraints on solar thermal and geothermal capacity and increasing total resource potential. Water conservation policies can therefore support the deployment of renewable energy resources and should be considered in future water and energy resource planning. [ABSTRACT FROM AUTHOR]

Summary A comprehensive error characterization of satellite-based precipitation products is vital for advancement of precipitation algorithms, evaluation of numerical model outputs, and their integration in various hydro-meteorological applications. The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products are in high demand to users for numerous applications. The latest version 7 (V7) of TMPA products are assumed to be better than its previous versions due to substantial changes in input data sets and algorithm. The TMPA algorithm is also a benchmark algorithm for the Global Precipitation Measurement (GPM) mission multisatellite precipitation products. In this study, both near-real-time (TMPA-RT) and research-quality (TMPA-V7) TMPA data sets are extensively evaluated against gridded gauge-based data over India for pre-monsoon, monsoon, and post-monsoon seasons at daily scale for a 13-year (2001–2013) period. The systematic and random error components in TMPA data sets are decomposed for their wider usage in agricultural and hydrological applications. Although TMPA-V7 and RT data sets represent the mean seasonal rainfall characteristics reasonably well, both the satellite-based data sets show an overestimation of rainfall over most parts of the country except over the orographic regions. However, TMPA-V7 is better than TMPA-RT when compared with the reference observations. Both the TMPA products show larger systematic error over the mountainous regions of northeast India. TMPA-RT has larger random error than TMPA-V7 possibly due to difference in calibration methods for the development of these data sets. Both the satellite-based data sets show significant difference from the reference observations for light to moderate rainfall ranges especially in the monsoon season. Larger error associated with systematic component in both the data sets is found during the pre-monsoon season. Furthermore, bias and error in satellite-derived rainfall data show a considerable interannual variation. Overall results suggest that a suitable region and season dependent bias-correction is required in TMPA data sets before its integration in hydrological applications. [ABSTRACT FROM AUTHOR]

Abstract In this study, we use several methods including the minimum cross entropy, wavelet transform coherence method, and the Mann-Kendall test to study the changes in the frequency of precipitation extremes and their contributions to total precipitation at different extreme percentile thresholds (90th percentile, 95th percentile, and 99th percentile) in the Beijing metropolitan area. Overall, the spatial distribution of the extreme precipitation thresholds is consistent with that of total precipitation, with the highest distribution appearing in the northeast part of the study area and the lowest distribution appearing in the west. The extreme precipitation events with high-percentile thresholds have more spatial and temporal variability than those at the low-percentile thresholds. We consider two types of indices representing frequency of extremes and their contributions to total precipitation. The frequency indices show similar spatial and temporal trends relative to the total precipitation than the contribution ratio indices. Additionally, the possible impacts of global climate indices, urban expansion and topography on the change in precipitation extremes are also considered. The weakening Eastern Asian monsoon (EAM) since 1970s has significantly influenced the change in contribution from indices of extreme precipitation over Beijing. The changes in precipitation extremes are also influenced by rapid urban expansion and topography, especially in spatial changes, with the higher values appearing in the urban and plain areas. In the context of global climate change and rapid urbanization, greater challenges in urban flooding and safety are caused by the changes in precipitation extremes. Highlights • We investigate the changes in extreme precipitation over Beijing based on entropy. • Two types and three thresholds was used to represent the precipitation extremes. • Possible causes of the changes in the precipitation extremes were discussed. [ABSTRACT FROM AUTHOR]

Summary HiResFlood-UCI was developed by coupling the NWS’s hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters ( a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning. [ABSTRACT FROM AUTHOR]

In this study, we investigate the capabilities of the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) and the recently released Integrated Multi-satellitE Retrievals for GPM (IMERG) in detecting and estimating heavy rainfall across India. First, the study analyzes TMPA data products over a 17-year period (1998–2014). While TMPA and reference gauge-based observations show similar mean monthly variations of conditional heavy rainfall events, the multi-satellite product systematically overestimates its inter-annual variations. Categorical as well as volumetric skill scores reveal that TMPA over-detects heavy rainfall events (above 75th percentile of reference data), but it shows reasonable performance in capturing the volume of heavy rain across the country. An initial assessment of the GPM-based multi-satellite IMERG precipitation estimates for the southwest monsoon season shows notable improvements over TMPA in capturing heavy rainfall over India. The recently released IMERG shows promising results to help improve modeling of hydrological extremes (e.g., floods and landslides) using satellite observations. [ABSTRACT FROM AUTHOR]