The distributed simple dynamical systems (dS2) model
datasetposted on 27.05.2019 by J. (Joost) Buitink, L.A. (Lieke) Melsen, J.W. (James) Kirchner, A.J. (Adriaan) Teuling
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
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.