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

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine
learning) methods to harness and integrate a broad variety of predictions from dynamical …

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 …

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 …

Regional groundwater level forecasting is critical to water resource management, especially
for arid regions which require effective management of groundwater resources to meet …

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

Recently, rainfall‐runoff simulations in small headwater basins have been improved by
methodological advances such as deep neural networks (NNs) and hybrid physics‐NN …

The distribution of flowmeter data and basin characteristic information exhibits substantial
disparities, with most flow observations being recorded at a limited number of well …

Deep learning (DL) models trained on hydrologic observations can perform extraordinarily
well, but they can inherit deficiencies of the training data, such as limited coverage of in situ …

Streamflow prediction in ungauged basins (PUB) is challenging, and Long Short‐Term
Memory (LSTM) is widely used to for such predictions, owing to its excellent migration …