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Flood modelling and forecasting can enhance our understanding of flood mechanisms and facilitate effective management of flood risk. Conventional flood hazard and risk assessments usually consider one driver at a time, whether it is ocean, fluvial or pluvial, without considering the compound nature of flood events. In this paper, we developed a novel approach for modelling and forecasting compound coastal-fluvial floods using a two-step framework. In step one, a hydrodynamic model is used to simulate floodwater propagation; while in step two, machine learning (ML) models are used to generate flood forecasts. The architecture of hydrodynamic-ML forecasting system incorporates a hydrodynamic model covering a specific domain, with individual ML models trained for each pixel. In total 7 ML models including: Support Vector Regression (SVR), Support Vector Machine (SVM), Radial Basis Function (RBF), Linear Regression (LR), Gaussian Process Regression (GPR), Decision Tree (DT), and Artificial Neural Network (ANN) were applied in this study. Forecasting compound floods is achieved using two sets of inputs: timeseries of river discharges in the upstream fluvial section and downstream ocean water levels in the coastal areas. The accuracy of the flood forecasting system is demonstrated for Cork City, Ireland; and modelling performance was evaluated using several statistical tools. Results show that the proposed models can provide reliable estimates of flood inundation and associated water depths. Overall, the RBF model exhibits the best performance. Despite the complexity of compound multi-driver floods, this study shows that the coupled hydrodynamic-ML approach can forecast coastal-fluvial flood with limited hydraulic and hydrological input data. This system overcomes the limitations of traditional hydrodynamic model-based systems where trade-offs between the always competing numerical model accuracy and computational time prohibit the model to be used for short-term flood forecasting. Once trained, the ML component of the coupled system can perform flood forecasting in near real-time, potentially integrating into a flood early warning system. Accurate flood forecasting has a wide range of positive societal impacts, including improved flood preparedness, increased confidence, better resource allocation, reduced flood damage, and potentially even flood prevention. • Novel technique to improve compound coastal-fluvial flood modelling. • Flood modelling technique based on seven Machine Learning methods. • Approach is successfully implemented, and results are evaluated over Cork, Ireland. • The results demonstrate minimal quantitative errors for the proposed models. [ABSTRACT FROM AUTHOR]

Traditional, mainstream definitions of drought describe it as deficit in water‐related variables or water‐dependent activities (e.g., precipitation, soil moisture, surface and groundwater storage, and irrigation) due to natural variabilities that are out of the control of local decision‐makers. Here, we argue that within coupled human‐water systems, drought must be defined and understood as a process as opposed to a product to help better frame and describe the complex and interrelated dynamics of both natural and human‐induced changes that define anthropogenic drought as a compound multidimensional and multiscale phenomenon, governed by the combination of natural water variability, climate change, human decisions and activities, and altered micro‐climate conditions due to changes in land and water management. This definition considers the full spectrum of dynamic feedbacks and processes (e.g., land‐atmosphere interactions and water and energy balance) within human‐nature systems that drive the development of anthropogenic drought. This process magnifies the water supply demand gap and can lead to water bankruptcy, which will become more rampant around the globe in the coming decades due to continuously growing water demands under compounding effects of climate change and global environmental degradation. This challenge has de facto implications for both short‐term and long‐term water resources planning and management, water governance, and policymaking. Herein, after a brief overview of the anthropogenic drought concept and its examples, we discuss existing research gaps and opportunities for better understanding, modeling, and management of this phenomenon. Plain Language Summary: This article reviews research and progress on the notion of anthropogenic drought broadly defined as drought events caused or intensified by human activities. Most commonly used drought definitions are based on deficit in hydrologic/meteorologic drivers such as precipitation and runoff. Within coupled human‐water systems, however, drought must be defined and understood as the complex and interrelated dynamics of both natural and human‐induced changes. This anthropogenic drought definition considers the full spectrum of dynamic feedbacks and processes (e.g., land‐atmosphere interactions and water and energy balance) within human‐nature systems. Ideally, anthropogenic drought and the corresponding human interactions should be incorporated in models that include land‐atmosphere interactions, water balance, and energy balance. In this article, we review existing research gaps and opportunities for better understanding, modeling, and management of this phenomenon. Key Points: Anthropogenic drought is primarily governed by the joint impacts of natural renewable water variability, climate change, and human decisionsAnthropogenic drought and water bankruptcy will become more ubiquitous under current development and climate change trajectoriesIdeally, human interactions should be incorporated in models that include land‐atmosphere interactions, water balance and energy balance [ABSTRACT FROM AUTHOR]

Snow plays a fundamental role in global water resources, climate, and biogeochemical processes; however, no global snow drought assessments currently exist. Changes in the duration and intensity of droughts can significantly impact ecosystems, food and water security, agriculture, hydropower, and the socioeconomics of a region. We characterize the duration and intensity of snow droughts (snow water equivalent deficits) worldwide and differences in their distributions over 1980 to 2018. We find that snow droughts became more prevalent, intensified, and lengthened across the western United States (WUS). Eastern Russia, Europe, and the WUS emerged as hot spots for snow droughts, experiencing ∼2, 16, and 28% longer snow drought durations, respectively, in the latter half of 1980 to 2018. In this second half of the record, these regions exhibited a higher probability (relative to the first half of the record) of having a snow drought exceed the average intensity from the first period by 3, 4, and 15%. The Hindu Kush and Central Asia, extratropical Andes, greater Himalayas, and Patagonia, however, experienced decreases (percent changes) in the average snow drought duration (−4, −7, −8, and −16%, respectively). Although we do not attempt to separate natural and human influences with a detailed attribution analysis, we discuss some relevant physical processes (e.g., Arctic amplification and polar vortex movement) that likely contribute to observed changes in snow drought characteristics. We also demonstrate how our framework can facilitate drought monitoring and assessment by examining two snow deficits that posed large socioeconomic challenges in the WUS (2014/2015) and Afghanistan (2017/2018). [ABSTRACT FROM AUTHOR]

Climate extremes threaten human health, economic stability, and the well-being of natural and built environments (e.g., 2003 European heat wave). As the world continues to warm, climate hazards are expected to increase in frequency and intensity. The impacts of extreme events will also be more severe due to the increased exposure (growing population and development) and vulnerability (aging infrastructure) of human settlements. Climate models attribute part of the projected increases in the intensity and frequency of natural disasters to anthropogenic emissions and changes in land use and land cover. Here, we review the impacts, historical and projected changes,and theoretical research gaps of key extreme events (heat waves, droughts, wildfires, precipitation, and flooding). We also highlight the need to improve our understanding of the dependence between individual and interrelated climate extremes because anthropogenic-induced warming increases the risk of not only individual climate extremes but also compound (co-occurring) and cascading hazards. ▪ Climate hazards are expected to increase in frequency and intensity in a warming world. ▪ Anthropogenic-induced warming increases the risk of compound and cascading hazards. ▪ We need to improve our understanding of causes and drivers of compound and cascading hazards. [ABSTRACT FROM AUTHOR]

• We introduce a process-informed extreme value analysis framework, namely ProNEVA. • ProNEVA allows incorporating physical drivers into statistical analysis of extremes. • ProNEVA allows both stationary and nonstationary modeling of climatic extremes. • Process-informed nonstationary models better capture the changing extreme events. • ProNEVA was applied to urbanization-flooding, CO2-temperature, sea level rise data. Evolving climate conditions and anthropogenic factors, such as CO 2 emissions, urbanization and population growth, can cause changes in weather and climate extremes. Most current risk assessment models rely on the assumption of stationarity (i.e., no temporal change in statistics of extremes). Most nonstationary modeling studies focus primarily on changes in extremes over time. Here, we present Process-informed Nonstationary Extreme Value Analysis (ProNEVA) as a generalized tool for incorporating different types of physical drivers (i.e., underlying processes), stationary and nonstationary concepts, and extreme value analysis methods (i.e., annual maxima, peak-over-threshold). ProNEVA builds upon a newly-developed hybrid evolution Markov Chain Monte Carlo (MCMC) approach for numerical parameters estimation and uncertainty assessment. This offers more robust uncertainty estimates of return periods of climatic extremes under both stationary and nonstationary assumptions. ProNEVA is designed as a generalized tool allowing using different types of data and nonstationarity concepts physically-based or purely statistical) into account. In this paper, we show a wide range of applications describing changes in: annual maxima river discharge in response to urbanization, annual maxima sea levels over time, annual maxima temperatures in response to CO 2 emissions in the atmosphere, and precipitation with a peak-over-threshold approach. ProNEVA is freely available to the public and includes a user-friendly Graphical User Interface (GUI) to enhance its implementation. [ABSTRACT FROM AUTHOR]

By combining long-term ground-based data on water withdrawal with climate model projections, this study quantifies the compounding effects of human activities and climate change on surface water availability in Iran over the twenty-first century. Our findings show that increasing water withdrawal in Iran, due to population growth and increased agricultural activities, has been the main source of historical water stress. Increased levels of water stress across Iran are expected to continue or even worsen over the next decades due to projected variability and change in precipitation combined with heightened water withdrawals due to increasing population and socio-economic activities. The greatest rate of decreased water storage is expected in the Urmia Basin, northwest of Iran, (varying from ~ − 8.3 mm/year in 2010–2039 to ~ − 61.6 mm/year in 2070–2099 compared with an observed rate of 4 mm/year in 1976–2005). Human activities, however, strongly dominate the effects of precipitation variability and change. Major shifts toward sustainable land and water management are needed to reduce the impacts of water scarcity in the future, particularly in Iran's heavily stressed basins like Urmia Basin, which feeds the shrinking Lake Urmia. [ABSTRACT FROM AUTHOR]

Existing major reservoirs in California, with average age above 50 years, were built in the previous century with limited data records and flood hazard assessment. Changes in climate and land use are anticipated to alter statistical properties of inflow to these infrastructure systems and potentially increase their hydrological failure probability. Because of large socioeconomic repercussions of infrastructure incidents, revisiting dam failure risks associated with possible shifts in the streamflow regime is fundamental for societal resilience. Here we compute historical and projected flood return periods as a proxy for potential changes in the risk of hydrological failure of dams in a warming climate. Our results show that hydrological failure probability is likely to increase for most dams in California by 2100. Noticeably, the New Don Pedro, Shasta, Lewiston, and Trinity Dams are associated with highest potential changes in flood hazard. Plain Language Summary: Dams are critical manmade infrastructure that provide resilience against extremes (e.g., droughts and floods) and regulate water resources. In 2017, California experienced a series of flooding events, which triggered incidents such as structural failure of the Oroville Dam's spillway. Because of the large social and economic impacts of such incidents and given the major dams in California have an average age of above 50 years, it is important to evaluate the risk of failure of dams over a planning period in the future. In this study, we inspected the possible impacts of climate change on the future flooding hazard for several major dams in California. Here we show that in the warmer future climate, the risk of dam failure most likely increases for most of the major dams in California. The insights gained from this study highlight the important role of adaptation strategies for the operational management of aging dams in a changing climate, together with adequate and timely maintenance. Key Points: Flood hazard is projected to increase at the upstream of major dams in California due to changes in precipitation extremesFailure probability is projected to increase for most of the California dams in a warming climateHistorical 100‐year flood is 5 times more likely for some biggest California dams under RCP8.5 (high greenhouse gas concentration levels) [ABSTRACT FROM AUTHOR]

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]

Temperature variability impacts the distribution and persistence of the mountain snowpack, which critically provides snowmeltderived water resources to large populations worldwide. Warmer temperatures decrease the amount of montane snow water equivalent (SWE), forcing its center of mass to higher elevations. We use a unique multivariate probabilistic framework to quantify the response of the 1 April SWE volume and its centroid to a 1.0 to 2.0 °C increase in winter air temperature across the Sierra Nevada (United States). A 1.0 °C increase reduces the probability of exceeding the long-term (1985-2016) average rangewide SWE volume (15.7 km3) by 20.7%. It correspondingly is 60.6% more likely for the centroid to be higher than its long-term average (2,540 m). We further show that a 1.5 and 2.0 °C increase in the winter temperature reduces the probability of exceeding the long-term average SWE volume by 31.0% and 41.1%, respectively, whereas it becomes 79.3% and 89.8% more likely that the centroid will be higher than 2,540 m for those respective temperature changes. We also characterize regional variability across the Sierra Nevada and show that the northwestern and southeastern regions of the mountain range are 30.3%and 14.0% less likely to have 1 April SWE volumes exceed their long-term average for a 1.0 °C increase about their respective average winter temperatures. Overall, the SWE in the northern Sierra Nevada exhibits higher hydrologic vulnerability to warming than in the southern region. Given the expected increases in mountain temperatures, the observed rates of change in SWE are expected to intensify in the future. [ABSTRACT FROM AUTHOR]

Climate change may affect ocean-driven coastal flooding regimes by both raising the mean sea level (msl) and altering ocean-atmosphere interactions. For reliable projections of coastal flood risk, information provided by different climate models must be considered in addition to associated uncertainties. In this paper, we propose a framework to project future coastal water levels and quantify the resulting flooding hazard to infrastructure. We use Bayesian Model Averaging to generate a weighted ensemble of storm surge predictions from eight climate models for two coastal counties in California. The resulting ensembles combined with msl projections, and predicted astronomical tides are then used to quantify changes in the likelihood of road flooding under representative concentration pathways 4.5 and 8.5 in the near-future (1998-2063) and mid-future (2018-2083). The results show that road flooding rates will be significantly higher in the near-future and mid-future compared to the recent past (1950-2015) if adaptation measures are not implemented. [ABSTRACT FROM AUTHOR]

Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980-2012, including the Millennium Drought in Australia (2001-2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25-45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops. [ABSTRACT FROM AUTHOR]

Iran is currently experiencing serious water problems. Frequent droughts coupled with over-abstraction of surface and groundwater through a large network of hydraulic infrastructure and deep wells have escalated the nation's water situation to a critical level. This is evidenced by drying lakes, rivers and wetlands, declining groundwater levels, land subsidence, water quality degradation, soil erosion, desertification and more frequent dust storms. This paper overviews the major drivers of Iran's water problems. It is argued that while climatic changes and economic sanctions are commonly blamed as the main drivers of water problems, Iran is mainly suffering from a socio-economic drought—i.e. "water bankruptcy," where water demand exceeds the natural water supply. In theory, this problem can be resolved by re-establishing the balance between water supply and demand through developing additional sources of water supply and implementing aggressive water demand reduction plans. Nevertheless, the current structure of the water governance system in Iran and the absence of a comprehensive understanding of the root causes of the problem leave minimal hope of developing sustainable solutions to Iran's unprecedented water problems. [ABSTRACT FROM AUTHOR]

Here we translate the impacts of climate change on hydropower generation, and discuss implications on greenhouse gas (GHG) emissions and operation in California. We integrate a model of major surface-water reservoirs with an electric grid dispatch model, and perturb it by projected runoff based on representative concentration pathways (RCP4.5 and RCP8.5). Results show that climate change and variability is expected to decrease the average annual hydropower generation by 3.1% under RCP4.5, but have negligible impact under the RCP8.5. Model simulations indicate more inflow, caused by more future extremes, in the future that does not necessarily translate to more energy production because of reservoir spillage of water. While overall volume of future available water for energy production may be similar or higher, the delivery of this volume is expected to be significantly more variable in the future climate than the historical average, which has many implications for hydropower generation. Our results show that the expected changes in future climate leads to increases in grid GHG emissions, load-following capacity, fuel usage, and costs for the RCP4.5 due to generation shortfall, and very slight increases in the same metrics for the RCP8.5 case due to variability causing decreased efficiencies in load-following power plants. [ABSTRACT FROM AUTHOR]

A combination of climate events (e.g., low precipitation and high temperatures) may cause a significant impact on the ecosystem and society, although individual events involved may not be severe extremes themselves. Analyzing historical changes in concurrent climate extremes is critical to preparing for and mitigating the negative effects of climatic change and variability. This study focuses on the changes in concurrences of heatwaves and meteorological droughts from 1960 to 2010. Despite an apparent hiatus in rising temperature and no significant trend in droughts, we show a substantial increase in concurrent droughts and heatwaves across most parts of the United States, and a statistically significant shift in the distribution of concurrent extremes. Although commonly used trend analysis methods do not show any trend in concurrent droughts and heatwaves, a unique statistical approach discussed in this study exhibits a statistically significant change in the distribution of the data. [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]

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]

The entropy theory has been widely applied in hydrology for probability inference based on incomplete information and the principle of maximum entropy. Meanwhile, copulas have been extensively used for multivariate analysis and modeling the dependence structure between hydrologic and climatic variables. The underlying assumption of the principle of maximum entropy is that the entropy variables are mutually independent from each other. The principle of maximum entropy can be combined with the copula concept for describing the probability distribution function of multiple dependent variables and their dependence structure. Recently, efforts have been made to integrate the entropy and copula concepts (hereafter, entropy-copula) in various forms to take advantage of the strengths of both methods. Combining the two concepts provides new insight into the probability inference; however, limited studies have utilized the entropy-copula methods in hydrology and climatology. In this paper, the currently available entropy-copula models are reviewed and categorized into three main groups based on their model structures. Then, a simple numerical example is used to illustrate the formulation and implementation of each type of the entropy-copula model. The potential applications of entropy-copula models in hydrology and climatology are discussed. Finally, an example application to flood frequency analysis is presented. [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]

Extreme climatic events are growing more severe and frequent, calling into question how prepared our infrastructure is to deal with these changes. Current infrastructure design is primarily based on precipitation Intensity-Duration-Frequency (IDF) curves with the so-called stationary assumption, meaning extremes will not vary significantly over time. However, climate change is expected to alter climatic extremes, a concept termed nonstationarity. Here we show that given nonstationarity, current IDF curves can substantially underestimate precipitation extremes and thus, they may not be suitable for infrastructure design in a changing climate. We show that a stationary climate assumption may lead to underestimation of extreme precipitation by as much as 60%, which increases the flood risk and failure risk in infrastructure systems. We then present a generalized framework for estimating nonstationary IDF curves and their uncertainties using Bayesian inference. The methodology can potentially be integrated in future design concepts. [ABSTRACT FROM AUTHOR]

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels. [ABSTRACT FROM AUTHOR]

A study was conducted to compare the technical potential and effectiveness of different water supply options for securing water availability in a large-scale, interconnected water supply system under historical and climate-change augmented inflow and demand conditions. Part 2 of the study focused on determining the greenhouse gas and renewable energy utilization impacts of different pathways to stabilize major surface reservoir levels. Using a detailed electric grid model and taking into account impacts on the operation of the water supply infrastructure, the greenhouse gas emissions and effect on overall grid renewable penetration level was calculated for each water supply option portfolio that successfully secured water availability from Part 1. The effects on the energy signature of water supply infrastructure were found to be just as important as that of the fundamental processes for each option. Under historical (baseline) conditions, many option portfolios were capable of securing surface reservoir levels with a net neutral or negative effect on emissions and a benefit for renewable energy utilization. Under climate change augmented conditions, however, careful selection of the water supply option portfolio was required to prevent imposing major emissions increases for the system. Overall, this analysis provided quantitative insight into the tradeoffs associated with choosing different pathways for securing California's water supply. [ABSTRACT FROM AUTHOR]

The technical potential and effectiveness of different water supply options for securing water availability in a large-scale, interconnected water supply system under historical and climate-change augmented inflow and demand conditions were compared. Part 1 of the study focused on determining the scale of the options required to secure water availability and compared the effectiveness of different options. A spatially and temporally resolved model of California's major surface reservoirs was developed, and its sensitivity to urban water conservation, desalination, and water reuse was examined. Potential capacities of the different options were determined. Under historical (baseline) hydrology conditions, many individual options were found to be capable of securing water availability alone. Under climate change augment conditions, a portfolio approach was necessary. The water savings from many individual options other than desalination were insufficient in the latter, however, relying on seawater desalination alone requires extreme capacity installations which have energy, brine disposal, management, and cost implications. The importance of identifying and utilizing points of leverage in the system for choosing where to deploy different options is also demonstrated. [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]

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: Extreme rainfall events are of particular importance due to their severe impacts on the economy, the environment and the society. Characterization and quantification of extremes and their spatial dependence structure may lead to a better understanding of extreme events. An important concept in statistical modeling is the tail dependence coefficient (TDC) that describes the degree of association between concurrent rainfall extremes at different locations. Accurate knowledge of the spatial characteristics of the TDC can help improve on the existing models of the occurrence probability of extreme storms. In this study, efficient estimation of the TDC in rainfall is investigated using a dense network of rain gauges located in south Louisiana, USA. The inter-gauge distances in this network range from about 1km to 9km. Four different nonparametric TDC estimators are implemented on samples of the rain gauge data and their advantages and disadvantages are discussed. Three averaging time-scales are considered: 1h, 2h and 3h. The results indicate that a significant tail dependency may exist that cannot be ignored for realistic modeling of multivariate rainfall fields. Presence of a strong dependence among extremes contradicts with the assumption of joint normality, commonly used in hydrologic applications. [ABSTRACT FROM AUTHOR]

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]

The article discusses the impact of California's drought, which began in 2012, on its forests, wildlife, and economy. Topics include the impact of growth in the state's population and agriculture on water use, California's adaptation to droughts through innovations such as water conservation, aqueduct networks, and water markets, and California's need for a proactive, long-term drought management plan that considers economical and environmental factors such as reduced precipitation or snow pack.

Soil moisture plays a crucial role in determining the yield of winter wheat. The Huang-Huai-Hai (HHH) Plain is the main growing area of winter wheat in China, and frequent occurrence of drought seriously restricts regional agricultural development. Hence, a daily-scale Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI), based on winter wheat crop coefficient (Kc), was developed in the present study to evaluate the impact of drought characteristics on winter wheat in different growth stages. Results showed that the water demand for winter wheat decreased with the increase in latitude, and the water shortage was affected by effective precipitation, showing a decreasing trend from the middle to both sides in the HHH Plain. Water demand and water shortage showed an increasing trend at the jointing stage and heading stage, while other growth stages showed a decreasing trend. The spatial distributions of drought duration and intensity were consistent, which were higher in the northern region than in the southern region. Moreover, the water shortage and drought intensity at the jointing stage and heading stage showed an increasing trend. The drought had the greatest impact on winter wheat yield at the tillering stage, jointing stage, and heading stage, and the proportions of drought risk vulnerability in these three stages accounted for 0.25, 0.21, and 0.19, respectively. The high-value areas of winter wheat loss due to drought were mainly distributed in the northeast and south-central regions. [ABSTRACT FROM AUTHOR]

Studies have demonstrated students' resistance to active learning, despite evidence illustrating that their learning is improved relative to students in lectures. Specifically, while active learning and group work are effective at engaging students in their learning process, studies report that students' perceptions of active learning approaches are not always positive. What remains underexplored is whether students' perceptions of active learning improve with effective instructor facilitation and whether there exists differential perceptions between racially minoritized students and represented students. Here, we estimate students' perceptions of effective instructor facilitation as the mediator in the relationship between active learning and perceptions of learning and perceived utility for class activities (task value). Then, we examine differences by racial identification. We collected classroom observation data to empirically categorize courses as active learning or lecture-based and surveyed 4,257 college students across 25 STEM classrooms at a research-intensive university. We first examined the relationship between active learning on student perceptions and found a negative relationship between active learning and perceptions of learning and task value for both racially minoritized students and represented students. Next, we assessed whether students' perceptions of instructor effectiveness in facilitating group activities mediate these negative relationships. We found that, on average, students of all races were more likely to positively perceive instructor facilitation in active learning classes relative to lectures. In turn, the positive perceptions of instructor facilitation partially suppressed the negative relationship between active learning and perceptions of learning and task value. These results demonstrate that effective instructor facilitation can influence both students' self-assessment of learning and perceived utility of the learning activities, and underscores the importance of developing pedagogical competence among college instructors. [ABSTRACT FROM AUTHOR]

The frequency, severity, and spatial extent of destructive wildfires have increased in several regions globally over the past decades. While direct impacts from wildfires are devastating, the hazardous legacy of wildfires affects nearby communities long after the flames have been extinguished. Post‐wildfire soil conditions control the persistence, severity, and timing of cascading geohazards in burned landscapes. The interplay and feedback between geohazards and wildfire‐induced changes to soil properties, land cover conditions, and near‐surface and surface processes are still poorly understood. Here, we synthesize wildfire‐induced processes that can affect the critical attributes of burned soils and their conditioning of subsequent geohazards. More specifically, we discuss the state of knowledge pertaining to changes in mineralogical, hydraulic, mechanical, and thermal properties of soil due to wildfire with a focus on advances in the past decade. We identify how these changes in soil properties alter evapotranspiration, interception, sediment transport, infiltration, and runoff. We then link these alterations to the evolution of different geohazards, including dry raveling, erosion, rockfalls, landslides, debris flows, and land subsidence. Finally, we identify research gaps and future directions to advance knowledge on how wildfires control the evolution of various earth surface processes and geohazards over time. Key Points: Post‐wildfire soil conditions control the persistence, severity, and timing of cascading geohazards in burned landscapesWe synthesize the interplay between geohazards and wildfire‐induced changes to soil properties, land cover, and near‐surface processesFuture directions are identified to advance knowledge on how wildfires affect surface processes and geohazards over time [ABSTRACT FROM AUTHOR]

The wheat belt along the Indo‐Gangetic Plains (IGP) in India is an emergent hotspot of climate change‐driven crop loss threatening local food security. Using statistical Generalized Linear Models, observed temperature and wheat production data, we show an increase in magnitude, frequency and areal extent of heat stress episodes in the IGP region during 1967–2018. Such episodes comprise of temperatures exceeding a senescence‐inducing threshold. Further, using a copula‐based multivariate framework, we probabilistically assess wheat production conditioned on heat stress indicators, both in the historical period and also in the Coupled Model Intercomparison Project (CMIP6) future projections. Probabilistic estimates of below‐average wheat production under scenario‐averaged heat stress conditions are expected to rise by 8%–27% under the SSP5‐8.5 scenario, with Punjab showing the largest increase. Quantitative links between heat stress indicators and loss of crop yield highlight increased agricultural vulnerability in India under climate change. Plain Language Summary: The three states of Punjab, Haryana and Uttar Pradesh in the Indo‐Gangetic Plains are the largest wheat producers in India, playing a crucial role in ensuring food security of this densely populated country. Wheat, a winter crop, is reported to be sensitive to heat stress because of rising temperatures and climate change. However, in previous studies, the sensitivity of wheat yield has been mainly explored with respect to the magnitude of temperature. Here, based on statistical analysis of observed temperature and actual wheat production data, we show that the magnitude, frequency and areal extent of agricultural heat stress events are increasing in India's wheat belt, with frequency showing the most pronounced trend. Further, we establish a quantitative link between crop loss and these three heat stress indicators in a multivariate, probabilistic framework. This probabilistic approach is used to obtain likelihoods of reduced wheat yield in future, using state‐of‐art climate model projections. Under climate change, chances of below‐average wheat production rise by 8%–27% in the worst‐case scenario. Thus, our results quantify rising agricultural vulnerability in India's wheat‐belt under climate change and variability. Key Points: Increase in magnitude, frequency and areal extent of heat stress on wheat cultivation in India; frequency has the most pronounced trendQuantitative links established between yield and heat stress magnitude, frequency and areal extent in observed and projected climateProbabilistic estimates of below‐average wheat production rise by 8%–27% in the worst case, with largest impact in Punjab [ABSTRACT FROM AUTHOR]

The seasonal snowpack plays a key role in global water resources, providing large populations with water for domestic, agricultural, and municipal uses. Below-average or low snow water equivalent (SWE) accumulation can greatly stress a region's water supply, economy, etc., which highlights an existing need for a global snow drought assessment. We therefore explore frameworks for characterizing and quantifying snow drought conditions (e.g., severity, duration, intensity) in snow-dominated areas around the world. We examine a variety of topographic, climatic, and ecologic regimes to better understand the spatial and temporal variability of snow droughts across the globe and how they are changing. Identifying regions that are becoming more prone to snow drought conditions will become increasingly important in a warming world, particularly in places that derive large fractions of their water resources from snowmelt. [ABSTRACT FROM AUTHOR]

Using publicly-available average monthly groundwater level data in 478 sub-basins and 30 basins in Iran, we quantify country-wide groundwater depletion in Iran. Natural and anthropogenic elements affecting the dynamics of groundwater storage are taken into account and quantified during the period of 2002–2015. We estimate that the total groundwater depletion in Iran to be ~ 74 km3 during this period with highly localized and variable rates of change at basin and sub-basin scales. The impact of depletion in Iran's groundwater reserves is already manifested by extreme overdrafts in ~ 77% of Iran's land area, a growing soil salinity across the entire country, and increasing frequency and extent of land subsidence in Iran's planes. While meteorological/hydrological droughts act as triggers and intensify the rate of depletion in country-wide groundwater storage, basin-scale groundwater depletions in Iran are mainly caused by extensive human water withdrawals. We warn that continuation of unsustainable groundwater management in Iran can lead to potentially irreversible impacts on land and environment, threatening country's water, food, socio-economic security. [ABSTRACT FROM AUTHOR]

Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the United States. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3 million wildfires from 1992–2020 in the United States. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including development of machine learning models. [ABSTRACT FROM AUTHOR]

Predicting the changes in teleconnection patterns and related hydroclimate extremes can provide vital information necessary to adapt to the effects of the El Niño–Southern Oscillation (ENSO). This study uses the outputs of global climate models to assess the changes in ENSO-related dry/wet patterns and the frequency of severe dry/wet events. The results show anomalous precipitation responding asymmetrically to La Niña and El Niño, indicating the teleconnections may not simply be strengthened. A "dry to drier, wet to wetter" annual anomalous precipitation pattern was projected during La Niña phases in some regions, with drier conditions over southern North America, southern South America, and southern central Asia, and wetter conditions in Southeast Asia and Australia. These results are robust, with agreement from the 26 models and from a subset of 8 models selected for their good performance in capturing observed patterns. However, we did not observe a similar strengthening of anomalous precipitation during future El Niño phases, for which the uncertainties in the projected influences are large. Under the RCP4.5 emissions scenario, 45 river basins under El Niño conditions and 39 river basins under La Niña conditions were predicted to experience an increase in the frequency of severe dry events; similarly, 59 river basins under El Niño conditions and 61 river basins under La Niña conditions were predicted to have an increase in the frequency of severe wet events, suggesting a likely increase in the risk of floods. Our results highlight the implications of changes in ENSO patterns for natural hazards, disaster management, and engineering infrastructure. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented concerning factors behind flooding in southern Louisiana on August 11, 2016 including the marginal condition of the majority of earthen levees in the United States and anomalously high temperatures which contributed to record-high moisture int he atmosphere.

The article discusses drought in California as of March 28, 2014, suggesting that lessons can be learned from the Millennium Drought in Australia from 1997-2009. Topics include the economic impact of the Millennium Drought, the role of water reforms including water markets, irrigation infrastructure, and water entitlements to critical habitats and populations, and public reaction to drought.

• Compound flood hazard levels computed for tidal channels or estuaries. • Bivariate statistical and hydrodynamic modeling is linked. • Spatially distributed flood hazard levels computed for return period T. • Method accounts for statistical and physical compounding effects. A method to link bivariate statistical analysis and hydrodynamic modeling for flood hazard estimation in tidal channels and estuaries is presented and discussed for the general case where flood hazards are linked to upstream riverine discharge Q and downstream ocean level, H. Using a bivariate approach, there are many possible combinations of Q and H that jointly reflect a specific return period, T , raising questions about the best choice as boundary forcing in a hydrodynamic model. We show, first of all, how possible Q and H values depend on whether the definition of T corresponds to the probability of exceedance of " H OR Q " or " H AND Q ". We also show that flood hazards defined by "OR" return periods are more conservative than "AND" return periods. Finally, we introduce a new composite water surface profile to represent the spatially distributed hazard for return period T. The composite profile synthesizes hydrodynamic model results from the "AND" hazard scenario and two scenarios based on traditional univariate analysis, a "Marginal Q " scenario and a "Marginal H " scenario. [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]

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]

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]

Climate change and increased electrification of space and water heating in buildings can significantly affect future electricity demand and hourly demand profiles, which has implications for electric grid greenhouse gas emissions and capacity requirements. We use EnergyPlus to quantify building energy demand under historical and under several climate change projections of 32 kinds of building prototypes in 16 different climate zones of California and imposed these impacts on a year 2050 electric grid configuration by simulation in the Holistic Grid Resource Integration and Deployment (HIGRID) model. We find that climate change only prompted modest increases in grid resource capacity and negligible difference in greenhouse gas emissions since the additional electric load generally occurred during times with available renewable generation. Heating electrification, however, prompted a 30–40% reduction in greenhouse gas emissions but required significant grid resource capacity increases, due to the higher magnitude of load increases and lack of readily available renewable generation during the times when electrified heating loads occurred. Overall, this study translates climate change and electrification impacts to system-wide endpoint impacts on future electric grid configurations and highlights the complexities associated with translating building-level impacts to electric system-wide impacts. [ABSTRACT FROM AUTHOR]