English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

. In this paper, we introduce a new numerically robust distributed rainfall–runoff model for computationally efficient simulation at high spatio-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 at high spatio-temporal resolution (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 three-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. The model code is written in Python in order to be easily readable and adjustable while maintaining computational efficiency. Since this model has short runtimes, it allows for extended sensitivity and uncertainty studies with relatively low computational costs. A test application shows good and consistent model performance across scales ranging from 3 to over 1700 km 2 .

A distributed simple dynamical systems approach (dS2 v1.0) for computationally efficient hydrological modelling at high spatio-temporal resolution