Abstract. We present the Wageningen Lowland Runoff Simulator (WALRUS), a novel rainfall-runoff model to fill the gap between complex, spatially distributed models which are often used in lowland catchments and simple, parametric (conceptual) models which have mostly been developed for sloping catchments. WALRUS explicitly accounts for processes that are important in lowland areas, notably (1) groundwater-unsaturated zone coupling, (2) wetnessdependent flow routes, (3) groundwater-surface water feedbacks and (4) seepage and surface water supply. WALRUS consists of a coupled groundwater-vadose zone reservoir, a quickflow reservoir and a surface water reservoir. WALRUS is suitable for operational use because it is computationally efficient and numerically stable (achieved with a flexible time step approach). In the open source model code default relations have been implemented, leaving only four parameters which require calibration. For research purposes, these defaults can easily be changed. Numerical experiments show that the implemented feedbacks have the desired effect on the system variables.

Radar rainfall nowcasting, the process of statistically extrapolating the most recent rainfall observation, is increasingly used for very short range rainfall forecasting (less than 6 hr ahead). We performed a large‐sample analysis of 1,533 events, systematically selected for 4 event durations and 12 lowland catchments (6.5–957 km2), to determine the predictive skill of nowcasting. Four algorithms are tested and compared with Eulerian Persistence: Rainymotion Sparse, Rainymotion DenseRotation, Pysteps deterministic, and Pysteps probabilistic with 20 ensemble members. We focus on the dependency of nowcast skill on event duration, season, catchment size, and location. Maximum skillful lead times increase for longer event durations, due to the more persistent character of these events. For all four event durations, Pysteps deterministic attains the longest average decorrelation times, with 25 min for 1‐hr durations, 40 min for 3 hr, 56 min for 6 hr, and 116 min for 24 hr. During winter, with more persistent stratiform precipitation, we find three times lower mean absolute errors than for convective summer precipitation. Higher skill is also found after spatially upscaling the forecast. Catchment location matters too: Given the prevailing storm movement, two times higher skillful lead times are found downwind than upwind toward the edge of the domain. In most cases, Pysteps algorithms outperform the Rainymotion benchmark algorithms. We speculate that most errors originate from growth and dissipation processes which are not or only partially (stochastically) accounted for.

Abstract. On 26 August 2010 the eastern part of The Netherlands and the bordering part of Germany were struck by a series of rainfall events lasting for more than a day. Over an area of 740 km 2 more than 120 mm of rainfall were observed in 24 h. This extreme event resulted in local flooding of city centres, highways and agricultural fields, and considerable financial loss.In this paper we report on the unprecedented flash flood triggered by this exceptionally heavy rainfall event in the 6.5 km 2 Hupsel Brook catchment, which has been the experimental watershed employed by Wageningen University since the 1960s. This study aims to improve our understanding of the dynamics of such lowland flash floods. We present a detailed hydrometeorological analysis of this extreme event, focusing on its synoptic meteorological characteristics, its space-time rainfall dynamics as observed with rain gauges, weather radar and a microwave link, as well as the measured soil moisture, groundwater and discharge response of the catchment.At the Hupsel Brook catchment 160 mm of rainfall was observed in 24 h, corresponding to an estimated return period of well over 1000 years. As a result, discharge at the catchment outlet increased from 4.4 × 10 −3 to nearly 5 m 3 s −1 . Within 7 h discharge rose from 5 × 10 −2 to 4.5 m 3 s −1 .Correspondence to: C. C. Brauer (claudia.brauer@wur.nl)The catchment response can be divided into four phases: (1) soil moisture reservoir filling, (2) groundwater response, (3) surface depression filling and surface runoff and (4) backwater feedback. The first 35 mm of rainfall were stored in the soil without a significant increase in discharge. Relatively dry initial conditions (in comparison to those for past discharge extremes) prevented an even faster and more extreme hydrological response.

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[1] The relation between storage and discharge is an essential characteristic of many rainfall-runoff models. The simple dynamical systems approach, in which a rainfall-runoff model is constructed from a single storage-discharge relation, has been successfully applied to humid catchments. Here we investigate (1) if and when the less humid lowland Hupsel Brook catchment also behaves like a simple dynamical system by hydrograph fitting, and (2) if system parameters can be inferred from streamflow recession rates or more directly from soil moisture storage observations. Only 39% of the fitted monthly hydrographs yielded Nash-Sutcliffe efficiencies above 0.5, from which we can conclude that the Hupsel Brook catchment does not always behave like a simple dynamical system. Model results were especially poor in summer, when evapotranspiration is high and the thick unsaturated zone attenuates the rainfall input. Using soil moisture data to obtain system parameters is not trivial, mainly because there is a discrepancy between local and catchment storage. Parameters obtained with direct storage-discharge fitting led to a strong underestimation of the response of runoff to rainfall, while recession analysis leads to an overestimation.Citation: Brauer, C. C., A. J. Teuling, P. J. J. F. Torfs, and R. Uijlenhoet (2013), Investigating storage-discharge relations in a lowland catchment using hydrograph fitting, recession analysis, and soil moisture data, Water Resour. Res., 49,[4257][4258][4259][4260][4261][4262][4263][4264]