In accordance with the rapid proliferation of machine learning (ML) and data management,
ML applications have evolved to encompass all engineering disciplines. Owing to the …

Water resources, including groundwater and prominent rivers worldwide, are under duress
because of excessive contaminant and nutrient loads. To help mitigate this problem, the …

Wildfires naturally occur in many landscapes, however they are undergoing rapid regime
shifts. Despite the emphasis in the literature on the most severe hydrological responses to …

In this study, we have developed a comprehensive machine learning (ML) framework for
long-term groundwater contamination monitoring as the Python package PyLEnM (Python …

Measuring soil and snow temperature with high vertical and lateral resolution is critical for
advancing the predictive understanding of thermal and hydro-biogeochemical processes …

Understanding the partitioning of snow and rain contributing to either catchment streamflow
or evapotranspiration (ET) is of critical relevance for water management in response to …

Climate change and other disturbances significantly impact hydrogeochemical exports from
mountainous headwater catchments such as the Upper Colorado River Basin. Developing a …

Different sub-regions of Zhengzhou city have various levels of sensitivity to flood due to the
impact of urbanization. Thus, an accurate flood sensitivities assessment is a key tool for …

Each year, the National Ecological Observatory Network's (NEON) Airborne Observation
Platform (AOP) collects high‐resolution hyperspectral imagery, discrete and waveform lidar …

One of the persistent challenges of Land Surface Models (LSMs) is to determine a realistic
yet efficient sub‐grid representation of heterogeneous landscapes. This is particularly …