Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …

Understanding and predicting the quality of inland waters are challenging, particularly in the
context of intensifying climate extremes expected in the future. These challenges arise partly …

Predictions of hydrologic variables across the entire water cycle have significant value for
water resources management as well as downstream applications such as ecosystem and …

Dissolved oxygen (DO) reflects river metabolic pulses and is an essential water quality
measure. Our capabilities of forecasting DO however remain elusive. Water quality data …

As a genre of physics-informed machine learning, differentiable process-based hydrologic
models (abbreviated as δ or delta models) with regionalized deep-network-based …

It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate
and reliable groundwater level forecasts, which are an important tool for sustainable …

The concentration of dissolved oxygen (DO), an important measure of water quality and river
metabolism, varies tremendously in time and space. Riverine DO is commonly perceived as …

Abstract Study Region **aoqing River Basin, Shandong Province, China Study Focus
Identifying the driving factors of temporal and spatial variation in runoff is key to water …

The behaviors and skills of models in many geosciences (eg, hydrology and ecosystem
sciences) strongly depend on spatially-varying parameters that need calibration. A well …

This study examines whether deep learning models can produce reliable future projections
of streamflow under warming. We train a regional long short‐term memory network (LSTM) …