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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. Key Points A new deep learning‐based model using a generative adversarial network (GAN) is developed for real‐time flash drought detection and monitoring Remote sensing maps are used as inputs to encompass the entire regions within the CONUS The proposed GAN is able to capture abrupt changes in drought patterns

Snow plays a crucial role in water resource management, acting as a natural reservoir that sustains agricultural, domestic, and ecological needs. However, declining snowpack poses significant challenges to water availability, particularly in snow-dominated regions. This study explores the relationship between Snow Water Equivalent (SWE) and streamflow in snow-dominated watersheds using the Long Short-Term Memory (LSTM) model and probabilistic analysis. While LSTM model is typically used for prediction, we employed it primarily to understand how snow affects streamflow. Our analysis yielded several key findings: (1) By analyzing multiple SWE products, we found a strong relationship between SWE and streamflow, particularly with a lookback of 60–90 days. (2) The University of Arizona (UAZ) dataset consistently provided the most reliable results, showing that SWE during winter significantly influences streamflow in spring and summer. (3) Our spatial analysis revealed that basins in the western United States consistently exhibited strong model performance, underscoring the robust relationship between SWE and streamflow in these snow-dominated regions. (4) Our probabilistic analysis revealed a systematic progression from snow drought to hydrologic drought, with the likelihood of hydrologic drought increasing from 0.32 in early phases (0–14 days) to over 0.8 in later phases (60–90 days). This progression provides an early warning indicator for hydrologic drought, improving our ability to anticipate and prepare for drought conditions in snow-dominated regions.

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 (CO 2 ), sulfur dioxide (SO 2 ), 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.

This paper presents an acoustic leak detection system for distribution water mains using machine learning methods. The problem is formulated as a binary classifier to identify leak and no-leak cases using acoustic signals. A supervised learning methodology has been employed using several detection features extracted from acoustic signals, such as power spectral density and time-series data. The training and validation data sets have been collected over several months from multiple cities across North America. The proposed solution includes a multi-strategy ensemble learning (MEL) using a gradient boosting tree (GBT) classification model, which has performed better in maximizing detection rate and minimizing false positives as compared with other classification models such as KNN, ANN, and rule-based techniques. Further improvements have been achieved using a multitude of GBT classifiers combined in a parallel ensemble method called bagging algorithm. The proposed MEL approach demonstrates a significant improvement in performance, resulting in a reduction of false positives reports by an order of magnitude.

Knowledge of the temporal dynamics and spatial variability of soil moisture is crucial in understanding many environmental processes and their impacts on plant fertility, crop yields, droughts, or exposure to flood hazards. The Soil Moisture Active Passive (SMAP) satellite was launched on 31 January 2015 by the National Aeronautics and Space Administration (NASA) to provide SSM using brightness temperature through its active (radar, 3 km) and passive (radiometer, 36 km) sensors at an intermediate resolution of 9 km. [...]we utilized these advancements to further postprocess the downscaled soil moisture dataset at 1-km spatial resolution and provide a more accurate and reliable product. According to the U.S. Department of Agriculture National Agricultural Statistical Service (USDA NASS), the rice farmlands are flooded and seeded each year from late April through May.

Soil moisture (SM) and evapotranspiration (ET) are key variables of the terrestrial water cycle with a strong relationship. This study examines remotely sensed soil moisture and evapotranspiration data assimilation (DA) with the aim of improving drought monitoring. Although numerous efforts have gone into assimilating satellite soil moisture observations into land surface models to improve their predictive skills, little attention has been given to the combined use of soil moisture and evapotranspiration to better characterize hydrologic fluxes. In this study, we assimilate two remotely sensed datasets, namely, Soil Moisture Operational Product System (SMOPS) and MODIS evapotranspiration (MODIS16 ET), at 1-km spatial resolution, into the VIC land surface model by means of an evolutionary particle filter method. To achieve this, a fully parallelized framework based on model and domain decomposition using a parallel divide-and-conquer algorithm was implemented. The findings show improvement in soil moisture predictions by multivariate assimilation of both ET and SM as compared to univariate scenarios. In addition, monthly and weekly drought maps are produced using the updated root-zone soil moisture percentiles over the Apalachicola–Chattahoochee–Flint basin in the southeastern United States. The model-based estimates are then compared against the corresponding U.S. Drought Monitor (USDM) archive maps. The results are consistent with the USDM maps during the winter and spring season considering the drought extents; however, the drought severity was found to be slightly higher according to DA method. Comparing different assimilation scenarios showed that ET assimilation results in wetter conditions comparing to open-loop and univariate SM DA. The multivariate DA then combines the effects of the two variables and provides an in-between condition.

The Hybrid Ensemble and Variational Data Assimilation framework for Environmental Systems (HEAVEN) is a method developed to enhance hydrologic model predictions while accounting for different sources of uncertainties involved in various layers of model simulations. While the effectiveness of this data assimilation in forecasting streamflow has been proven in previous studies, its potential to improve flood forecasting during extreme events remains unexplored. This study aims to demonstrate this potential by employing HEAVEN to assimilate streamflow data from United States Geological Survey (USGS) stations into a conceptual hydrologic model to enhance its capability to forecast hurricane-induced floods across multiple locations within three watersheds in the southeastern United States. The Sacramento Soil Moisture Accounting (SAC-SMA) hydrologic model is driven by two variables: precipitation and potential evapotranspiration (PET), collected from North American Land Data Assimilation System phase 2 (NLDAS-2) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, respectively. We validated the probabilistic streamflow predictions during five instances of hurricane-induced flooding across three regions. The results show that this data assimilation approach significantly improves the hydrologic model's ability to forecast extreme river flows. By accounting for different sources of uncertainty in model predictions – in particular model structural uncertainty (in addition to model parameter uncertainty) and atmospheric forcing data uncertainty – HEAVEN emerges as a powerful tool for enhancing flood prediction accuracy. The study found that data assimilation improved streamflow forecasting during Hurricane Harvey, enhancing the SAC-SMA model's accuracy across most USGS stations on the peak flow day. However, data assimilation had little effect on streamflow forecasting for Hurricane Rita. In Rita, the streamflow surged dramatically in a single day (from 28 to 566 m3 s−1), causing the model to miss the high-flow event despite accurate initialization the day before. For hurricanes Ivan and Matthew, data assimilation improved peak flow forecasts by 21 % to 46 % in Mobile and 5 % to 46 % in Savanah, with improvements varying by station location.

Soil moisture (SM) and evapotranspiration (ET) are among those key environmental variables that greatly affect hydroclimate extremes (e.g., floods and droughts), agricultural production, and irrigation management which all collectively control the land and atmospheric system. Land surface models most often do not provide accurate and reliable estimates of these prognostic variables as they suffer either from inadequate conceptualization of underlying physics or non-uniqueness of model parameters or inaccurate initialization. During the past two decades, Data Assimilation (DA) has received increased prominence among researchers and practitioners as an effective and reliable method to integrate the hydrometeorological observations from remotely sensed sensors into land surface models for enhancing their forecasting skills while taking into account all sources of uncertainties. Although numerous efforts have gone into assimilating satellite soil moisture observations into land surface models, little attention has been given to the combined use of soil moisture and evapotranspiration to better characterize drought conditions. Hence, will examine the multivariate and univariate assimilation of SM and ET observations to understand how they contribute to the improvement of drought monitoring and forecasting skills.While accounting for uncertainties in model outputs such as SM and ET, the significance of uncertainties stemming from the forcing data cannot be underestimated, especially precipitation, which is the most erroneous meteorological forcing in land surface modeling and soil moisture estimation. More accurate precipitation estimations at fine spatial and temporal resolutions have proven to improve our land surface hydrological simulations and provide us with a more accurate representation of extreme events such as floods and droughts. In this study, the influence of uncertainties stemming from forcing precipitation data in driving Land Surface Models (LSMs) and characterizing drought conditions was investigated. Furthermore, a deep learning-based data fusion technique is proposed here to improve quantitative precipitation estimation which can consequently help to better characterize hydroclimate extremes such as floods and droughts.

Boukan Dam reservoir is the largest infrastructure constructed on the Zarineh-Roud River regulating streamflow for different uses including supplying water to Lake Urmia, the second largest salt lake in the world. This paper presents a forecast-based adaptive real-time optimal operation model (ARTOM) for Boukan reservoir with the aim of maximizing releases feeding the lake while meeting other needs such as irrigation, industrial, and domestic uses. Adaptive neuro-fuzzy system-based inflow-to-reservoir forecasts are used in the ARTOM to determine optimal releases from the reservoir for future months up to the end of a year, but only the current period's release is applied. At the beginning of the next period, the forecasts are updated, and the procedure is repeated until the last period of the year. Additionally, the optimal terminal end-of-year reservoir storage volume is a dynamic updating input to the ARTOM, which is estimated from the results of a long-term reservoir operation optimization model. The ARTOM performance is tested against the last nine-year monthly data not utilized for training the forecast module. Results demonstrate that the ARTOM attains an objective function value very close to the best possible value that can ever be reached by utilizing an ideal operation model, benefiting from perfect foresight on future streamflows.