Improving catchment discharge predictions by inferring flow route contributions from a nested-scale monitoring and model setup

Abstract

Abstract. Identifying effective measures to reduce nutrient loads of headwaters in lowland catchments requires a thorough understanding of flow routes of water and nutrients. In this paper we assess the value of nested-scale discharge and groundwater level measurements for the estimation of flow route volumes and for predictions of catchment discharge. In order to relate field-site measurements to the catchment-scale an upscaling approach is introduced that assumes that scale differences in flow route fluxes originate from differences in the relationship between groundwater storage and the spatial structure of the groundwater table. This relationship is characterized by the Groundwater Depth Distribution (GDD) curve that relates spatial variation in groundwater depths to the average groundwater depth. The GDD-curve was measured for a single field site (0.009 km2) and simple process descriptions were applied to relate groundwater levels to flow route discharges. This parsimonious model could accurately describe observed storage, tube drain discharge, overland flow and groundwater flow simultaneously with Nash-Sutcliff coefficients exceeding 0.8. A probabilistic Monte Carlo approach was applied to upscale field-site measurements to catchment scales by inferring scale-specific GDD-curves from the hydrographs of two nested catchments (0.4 and 6.5 km2). The estimated contribution of tube drain effluent (a dominant source for nitrates) decreased with increasing scale from 76–79% at the field-site to 34–61% and 25–50% for both catchment scales. These results were validated by demonstrating that a model conditioned on nested-scale measurements improves simulations of nitrate loads and predictions of extreme discharges during validation periods compared to a model that was conditioned on catchment discharge only.