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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. HIGHLIGHTS State-of-the-art machine learning (ML) algorithms are used for solving the leak detection problem in water mains.; A large number of acoustic signals and data are collected and used along with dimensionality reduction techniques as input features to ML algorithms.; A novel multi-strategy ensemble-based algorithm is applied to improve further the performance of the investigated leak detection classification problem.;

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. [ABSTRACT FROM AUTHOR]