Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 551066
Title The distributed simple dynamical systems (dS2) model
Author(s) Buitink, J.; Melsen, L.A.; Kirchner, J.W.; Teuling, A.J.
Source Wageningen : Wageningen University & Research
DOI https://doi.org/10.4121/uuid:cc8e0008-ab1f-43ee-b50d-24de01d2d0be
Department(s) Hydrology and Quantitative Water Management
WIMEK
WIMEK
Publication type Software program
Publication year 2019
Keyword(s) hydrological model - rainfall-runoff - simple dynamical systems approach
Abstract 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.
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