The distributed simple dynamical systems (dS2) model

We present a new numerically robust distributed rainfall runoff model for computationally efficiency simulation at high (hourly) temporal resolution: the distributed simple dynamical systems (dS2) model. The model is based on the simple dynamical systems approach as proposed by Kirchner (2009), and the distributed implementation allows for spatial heterogeneity in the parameters and/or model forcing fields for instance as derived from precipitation radar data. The concept is extended with snow and routing modules, where the latter transports water from each pixel to the catchment outlet. The sensitivity function, which links changes in storage to changes in discharge, is implemented by a new 3-parameter equation that is able to represent the widely observed downward curvature in log-log space. The simplicity of the underlying concept allows the model to calculate discharge in a computationally efficient manner, even at high temporal and spatial resolution, while maintaining proven model performance at high temporal and spatial resolution. The model code is written in Python in order to be easily readable and adjustable while maintaining computational efficiency. Since this model has short run times, it allows for extended sensitivity and uncertainty studies with relatively low computational costs

Data underlying the publication : "A review of drought indices: predominance of drivers over impacts and the importance of local context"

This dataset has been used to determine the country of application of different drought related studies, focusing on both categories of drought indicators and drought impacts. There is a readme file inside providing more details.</p

Meteorological forcing, and corresponding hydrological model input and output used in the paper: The impact of hydrological model structure on the simulation of extreme runoff events.

The data sets used in Van Kempen et al., 2021 are included in this repository. These files consist of the original meteorological forcing, hydrological model input and hydrological model output.The readme file provides a description of the set-up of these data sets. </div

Meteorological forcing, and corresponding hydrological model input and output used in the paper: The impact of hydrological model structure on the simulation of extreme runoff events.

The datasets used in Van Kempen et al., 2021 are included in this repository. These files consist of the original meteorological forcing, hydrological model input and hydrological model output.The README provides a description of the set-up of these datasets. </div