Abstract
Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land–atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds spatial features found in the spatial pattern of remote-sensing-based ET.
Highlights
The application of spatially distributed hydrological models has become common practice for a wide range of water resources assessments
The empirical orthogonal function (EOF) analysis applied in this study evaluated the differences in spatial patterns between the DK-model outputs in the original configuration and a modified version where three inputs (RD, leaf area index (LAI) and Kc) of the model were replaced by those derived from remote sensing data
The results and discussion are presented in two sections: the first focuses on the sensitivity analysis and parameter optimization of the two-source energy balance model (TSEB) model, and the second features the spatial pattern evaluation of the DK-model using the maps obtained from the TSEB model
Summary
The application of spatially distributed hydrological models has become common practice for a wide range of water resources assessments. The neglect of a specific focus on spatial patterns in model evaluation is a paradox in light of an increasing acknowledgement of the role of patterns in the functioning of hydrological systems (Vereecken et al, 2016). It is against the rationale behind developing and applying distributed models (Freeze and Harlan, 1969; Refsgaard, 1997). If the spatial variability of a hydrological system is not of importance to the modeler, it seems not worth the effort to apply a distributed model, since numerous studies indicate that equal model fidelity can be achieved with a lumped approach when evaluated solely at the catchment outlet (Stisen et al, 2011; Vansteenkiste et al, 2014)
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