The Wageningen Lowland Runoff Simulator (WALRUS)

Application to the Hupsel Brook catchment and the Cabauw polder

Journal Article (2014)
Author(s)

C. C. Brauer (Wageningen University & Research)

P. J. J. F. Torfs (Wageningen University & Research)

A. J. Teuling (Wageningen University & Research)

Remko Uijlenhoet (Wageningen University & Research)

Affiliation
External organisation
DOI related publication
https://doi.org/10.5194/hess-18-4007-2014
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Publication Year
2014
Language
English
Affiliation
External organisation
Issue number
10
Volume number
18
Pages (from-to)
4007-4028

Abstract

The Wageningen Lowland Runoff Simulator (WALRUS) is a new parametric (conceptual) rainfall-runoff model which accounts explicitly for processes that are important in lowland areas, such as groundwater-unsaturated zone coupling, wetness-dependent flowroutes, groundwater-surface water feedbacks, and seepage and surface water supply (see companion paper by Brauer et al., 2014). Lowland catchments can be divided into slightly sloping, freely draining catchments and flat polders with controlled water levels. Here, we apply WALRUS to two contrasting Dutch catchments: the Hupsel Brook catchment and the Cabauw polder. In both catchments, WALRUS performs well: Nash-Sutcliffe efficiencies obtained after calibration on 1 year of discharge observations are 0.87 for the Hupsel Brook catchment and 0.83 for the Cabauw polder, with values of 0.74 and 0.76 for validation. The model also performs well during floods and droughts and can forecast the effect of control operations. Through the dynamic division between quick and slow flowroutes controlled by a wetness index, temporal and spatial variability in groundwater depths can be accounted for, which results in adequate simulation of discharge peaks as well as low flows. The performance of WALRUS is most sensitive to the parameter controlling the wetness index and the groundwater reservoir constant, and to a lesser extent to the quickflow reservoir constant. The effects of these three parameters can be identified in the discharge time series, which indicates that the model is not overparameterised (parsimonious). Forcing uncertainty was found to have a larger effect on modelled discharge than parameter uncertainty and uncertainty in initial conditions.

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