Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …

Many of the scientific and societal challenges in understanding and preparing for global
environmental change rest upon our ability to understand and predict the water cycle …

Abstract Earth System Models (ESMs) are essential tools for understanding and predicting
global change, but they cannot explicitly resolve hillslope‐scale terrain structures that …

Depth to bedrock serves as the lower boundary of land surface models, which controls
hydrologic and biogeochemical processes. This paper presents a framework for global …

Map** the spatial distribution of soil taxonomic classes is important for informing soil use
and management decisions. Digital soil map** (DSM) can quantitatively predict the spatial …

Hydrology is an integrative discipline linking the broad array of water‐related research with
physical, ecological, and social sciences. The increasing breadth of hydrological research …

All hydrological models need to be calibrated to obtain satisfactory streamflow simulations.
Here we present a novel parameter regionalization approach that involves the optimization …

Many catchment hydrologic and ecologic processes are impacted by the storage capacity of
soil water, which is dictated by the profile thickness and water retention properties of soil …

Enhanced understanding of subsurface water storage will improve prediction of future
impacts of climate change, including drought, forest mortality, wildland fire, and strained …

This study proposes a probabilistic analysis method to assess shallow landslide
susceptibility over an extensive area by integrating an infinite slope model with GIS …