Abstract Artificial Neural Networks (ANNs), sometimes also called models for deep learning,
are used extensively for the prediction of a range of environmental variables. While the …

Many fish species depend on migration for various parts of their life cycle. Well‐known
examples include diadromous fish such as salmon and eels that need both fresh water and …

Accurate reference crop evapotranspiration (ET 0) estimation is essential for agricultural
water management, crop productivity, and irrigation systems. As the standard ET 0 …

Accurate multistep forecasting of the long-term streamflow (Q flow) in poorly gauged basins
is pivotal for sustainable water resource management and decision-making. The purpose of …

We investigate the applicability of machine learning technologies to the development of
parsimonious, interpretable, catchment‐scale hydrologic models using directed‐graph …

As hydrological systems are pushed outside the envelope of historical experience, the ability
of current hydrological models to serve as a basis for credible prediction and decision …

The ease and efficiency with which conceptual rainfall runoff (CRR) models can be
calibrated, as well as issues related to the uniqueness of their parameters, has received …

Abstract Explainable Artificial Intelligence (XAI) offers the promise of being able to provide
additional insight into complex hydrological problems. As the “new kid on the block”, these …

Sap flow offers key insights about transpiration dynamics and forest‐climate interactions.
Accurately simulating sap flow remains challenging due to measurement uncertainties and …

A sudden surge of data has created new challenges in water management, spanning quality
control, assimilation, and analysis. Few approaches are available to integrate growing …