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Abstract Drought, a potent natural climatic phenomenon, significantly challenges hydropower systems, bearing adverse consequences for economies, societies, and the environment. This study delves into the profound impact of drought on hydropower generation (HG) in the United States, revealing a robust correlation between hydrologic drought and hydroelectricity generation. Our analysis of the period from 2003 to 2020 for the Contiguous United States (CONUS) indicates that drought events led to a considerable decline in hydroelectricity generation, amounting to approximately 300 million MWh, and resulting in an estimated loss of $28 billion to the sector. Moreover, our findings highlight the adverse environmental effect of drought-induced HG reductions, which are often compensated by increased reliance on natural gas usage, which led to substantial emissions of carbon dioxide (CO₂), sulfur dioxide (SO₂), and nitrogen oxide (NO_X), totaling 161 700 kilotons, 1199 tons, and 181 977 tons, respectively. In addition to these findings, we assess the state-level vulnerability of hydropower to drought, identifying Washington and California as the most vulnerable states, while Nevada exhibits the least vulnerability. Overall, this study enhances understanding of the multifaceted effects of drought on hydropower, which can assist in informing policies and practices related to drought management and energy production. 

Abstract Drought poses a major threat to agricultural production and food security. This study evaluates the changes in drought-induced crop yield loss risk for six crops (alfalfa, barley, corn, soybean, spring wheat, and winter wheat) between 1971–2000 and 1991–2020 across the contiguous US. Using a copula-based probabilistic framework, our results reveal a spatially heterogeneous change in yield risk to meteorological droughts, which varies with crop types. Regional analyses identify the largest temporal decline in yield risk in the Southeast and Upper Midwest, while the Northwest and South show an increase in risk. Among the considered anthropogenic and climatic drivers of crop productivity, changes in climatic variables such as high temperatures (e.g., killing degree days), vapor pressure deficit and precipitation show significantly stronger associations with changes in yield risk than irrigated area and nitrogen fertilizer application. Among the counties that observe drier drought events, only 55% exhibit an increase in crop yield loss risk due to drier droughts. The rest 45% show a decrease in yield loss risk due to mediation of favorable climatic and anthropogenic factors. Alarmingly, more than half (for barley and spring wheat), and one-third (for alfalfa, corn, soybean and winter wheat) of that the risk increasing regions have outsized influence on destabilizing national crop production. The findings provide valuable insights for policymakers, agricultural stakeholders, and decision-makers in terms of the potential ways and locations to be prioritized for enhancing local and national agricultural resilience and ensuring food security. 

Abstract In hydrology, a fundamental task involves enhancing the predictive power of a model in ungagged basins by transferring information on physical attributes and hydroclimate dynamics from gauged basins. Introducing an integrated nonlinear clustering framework, this study aims to develop a comprehensive framework that augments predictive performance in basins where direct measurements are sparse or absent. In this framework, uniform manifold approximation and projection (UMAP) is used as a nonlinear method to extract the essential features embedded in hydro‐climatological attributes and physical properties. Then, the Growing Neural Gas (GNG) clustering model is used to find the basins that potentially share similar hydro‐climatological behaviors. Besides UMAP‐GNG, the integration of Principal Component Analysis (PCA) as a linear method to reduce dimensionality with common clustering methods are also assessed to serve as benchmarks. The results reveal that the combination of clustering algorithms with the PCA method may lead to loss of information while the nonlinear method (UMAP) can extract more informative features. The efficacy of the proposed framework is assessed across the Contiguous United States (CONUS) by training a single Base Model using long short‐term memory (LSTM) for the centroids of all clusters and then, fine‐tuning the model on the centroids of each cluster separately to create a regional model. The results indicate that using the information extracted by the UMAP‐GNG method to guide a Base Model can significantly improve the accuracy in most of the clusters and enhance the median prediction accuracy within different clusters from 0.04 to 0.37 of KGE in ungauged basins. 

Abstract Accurate assessment of changes in water availability with changing climate is vital for effective mitigation and adaptation. In this research, we employ a parsimonious Budyko curve method to evaluate changes in water availability under low‐ (SSP126) and high‐emission (SSP585) scenarios for 331 river basins in the contiguous United States. We also assess the relative role of changes in precipitation (∆P) and potential evapotranspiration (∆PET) with changing climate on the increase in water availability vulnerability. Results highlight that around 43% (28%) of basins are projected to experience increased vulnerability to changing climate in high‐emission (low‐emission) scenarios. Sub‐humid basins are most often impacted, while arid and semi‐arid basins exhibit lower sensitivity to changes. Intriguingly, ∆PET emerges as the dominant control on vulnerability, surpassing ∆P, particularly under SSP585 scenario. The analysis prompts water managers to focus on long‐term mitigation planning and scientists to further constraint climate and water budget forecasts in affected basins. 

Abstract Evapotranspiration (ET) plays a critical role in water and energy budgets at regional to global scales. ET is composed of direct evaporation (E) and plant transpiration (T) where the latter is regulated via stomatal conductance (g_sc), which depends on a multitude of plant physiological processes and hydrometeorological forcings. In recent years, significant advances have been made toward estimatingg_scusing a variety of models, ranging from relatively simple empirical models to more complex and data‐intensive plant hydraulic models. Using machine learning (ML) and eddy covariance flux tower data of 642 site years across 84 sites distributed across 10 land covers globally, here we show that structural constraints inherent in current empirical and plant hydraulic models ofg_sclimit their effectiveness for predicting ET. These constraints also prevent the models from fully utilizing the available hydrometeorological data at eddy covariance sites. Even if theseg_scmodels are calibrated locally, structural simplifications inherent in them limit their capability to accurately captureg_scdynamics. In contrast, a ML approach, wherein the model structure is learned from the data, outperforms traditional models, thus highlighting that there still is significant room for improvement in the structure of traditional models for predicting ET. These results underscore the need to prioritize improvements ing_scmodels for more accurate ET estimation. This, in turn, will help reduce uncertainties in the assessments of plants' role in regulating the Earth's climate. 

Abstract Droughts are among the most devastating natural hazards, occurring in all regions with different climate conditions. The impacts of droughts result in significant damages annually around the world. While drought is generally described as a slow‐developing hazardous event, a rapidly developing type of drought, the so‐called flash drought has been revealed by recent studies. The rapid onset and strong intensity of flash droughts require accurate real‐time monitoring. Addressing this issue, a Generative Adversarial Network (GAN) is developed in this study to monitor flash droughts over the Contiguous United States (CONUS). GAN contains two models: (a) discriminator and (b) generator. The developed architecture in this study employs a Markovian discriminator, which emphasizes the spatial dependencies, with a modified U‐Net generator, tuned for optimal performance. To determine the best loss function for the generator, four different networks are developed with different loss functions, including Mean Absolute Error (MAE), adversarial loss, a combination of adversarial loss with Mean Square Error (MSE), and a combination of adversarial loss with MAE. Utilizing daily datasets collected from NLDAS‐2 and Standardized Soil Moisture Index (SSI) maps, the network is trained for real‐time daily SSI monitoring. Comparative assessments reveal the proposed GAN's superior ability to replicate SSI values over U‐Net and Naïve models. Evaluation metrics further underscore that the developed GAN successfully identifies both fine‐ and coarse‐scale spatial drought patterns and abrupt changes in the SSI temporal patterns that is important for flash drought identification. 

Abstract Irrigation expansion is often posed as a promising option to enhance food security. Here, we assess the influence of expansion of irrigation, primarily in rural areas of the contiguous United States (CONUS), on the intensification and spatial proliferation of freshwater scarcity. Results show rain-fed to irrigation-fed (RFtoIF) transition will result in an additional 169.6 million hectares or 22% of the total CONUS land area facing moderate or severe water scarcity. Analysis of just the 53 large urban clusters with 146 million residents shows that the transition will result in 97 million urban population facing water scarcity for at least one month per year on average versus 82 million before the irrigation expansion. Notably, none of the six large urban regions facing an increase in scarcity with RFtoIF transition are located in arid regions in part because the magnitude of impact is dependent on multiple factors including local water demand, abstractions in the river upstream, and the buffering capacity of ancillary water sources to cities. For these reasons, areas with higher population and industrialization also generally experience a relatively smaller change in scarcity than regions with lower water demand. While the exact magnitude of impacts are subject to simulation uncertainties despite efforts to exercise due diligence, the study unambiguously underscores the need for strategies aimed at boosting crop productivity to incorporate the effects on water availability throughout the entire extent of the flow networks, instead of solely focusing on the local level. The results further highlight that if irrigation expansion is poorly managed, it may increase urban water scarcity, thus also possibly increasing the likelihood of water conflict between urban and rural areas. 

Abstract In recent decades, irrigated agriculture has expanded dramatically over the Southeastern United States (SEUS). The trend is more likely to continue in future given the need to further improve crop productivity and its resilience against droughts, however, the impact of these SEUS land cover changes remains unknown. This study investigates how and to what extent rain-fed to irrigation-fed (RFtoIF) transition in the SEUS region modulates precipitation spatially and temporally under a severe drought meteorological condition. In this study, we perform three Weather Research Forecasting model simulations with varying degrees of irrigated crop areas with meteorological boundary conditions of a record-breaking 2007 drought in the SEUS region. Results show that the SEUS irrigation expansion reduces both the convective triggering potential and low-level humidity index through land-atmospheric interaction. This is accompanied by reduction in the height of atmospheric boundary layer (ABL)-lifting condensation level crossing and increase in the convective available potential energy. These modulations within the ABL provide a favorable condition for strong deep convection during the drought period. However, the impact on precipitation is heterogeneous, with crop areas undergoing RFtoIF transition experiencing an overall reduction in precipitation while other landcovers experiencing an increase. The reduction in precipitation over RFtoIF transitioned croplands is in part due to moisture redistribution aided by generation of an anomalous high-pressure system. The results highlight the complexity of response of precipitation to irrigation expansion in the SEUS, and underscore the need to perform spatially-explicit analysis for mitigating risks to water resources and food security.