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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 data, namely 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 US Drought Monitoring System 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.

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.

he amount (soil moisture) and state (freeze-thaw) of the water in soil plays a pivotal role in global water, energy, and carbon cycles. The water content of the top few centimeters (~5 cm) of soil is typically referred to as surface soil moisture (SSM), which defines how wet or dry the soil is in its top layer. SSM is a key component of the microclimate that governs the interaction of water and heat fluxes between the ground and the atmosphere, regulating air temperature and humidity, and thus, affecting climatic conditions and weather changes. 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.