Predictions of floods, droughts, and fast drought‐flood transitions are required at different
time scales to develop management strategies targeted at minimizing negative societal and …

The theory that forms the basis of Topmodel was first outlined by Mike Kirkby some 45 years
ago. This paper recalls some of the early developments; the rejection of the first journal …

The purpose of the study was to use hierarchical clustering and Thiessen polygon
algorithms to identify the significant rain gauge stations for flood forecasting at Sardar …

Long short-term memory (LSTM) models are recurrent neural networks from the field of deep
learning (DL) which have shown promise for time series modelling, especially in conditions …

The choice of hydrological model structure, that is, a model's selection of states and fluxes
and the equations used to describe them, strongly controls model performance and realism …

Despite showing great success of applications in many commercial fields, machine learning
and data science models generally show limited success in many scientific fields, including …

Deep neural network (DNN) models have become increasingly popular in the hydrology
community. However, most studies are related to (rainfall-) runoff simulation and …

Rainfall‐runoff models are used across academia and industry, and the number and type
have proliferated over time. In this primer we briefly introduce the key features of these …

Evidence suggests that catchment state variables such as groundwater can exhibit multiyear
trends. This means that their state may reflect not only recent climatic conditions but also …

Hydrology plays a central role in applied and fundamental environmental sciences, but it is
well known to suffer from an overwhelming diversity of models, particularly to simulate …