Initial catchment state, such as soil moisture, strongly controls rainfall-runoff transformation processes. However, due to the high spatial and temporal variability of soil moisture, point measurements may not always be suitable to represent the actual system state of a whole catchment as required in distributed catchment modelling. In this study a fuzzy rule-based system (FRBS) using the TakagiSugeno-Kang approach has been developed using soil moisture and rainfall as input variables to predict the actual discharge at the catchment outlet. Four soil moisture probes from the hydrological test site Dürreych (Black Forest, southwest Germany) were selected, each of them representing a particular runoff generation process (saturation excess flow, infiltration excess flow, slow and fast interflow, return flow). After manual calibration, the simulated peak discharges were very similar to the measured values. Furthermore, the pattern of rule activation in the FRBS reflected the complex, highly nonlinear behaviour of the catchment. Thus, in the FRBS framework, the measurements of soil moisture at representative locations could be used as representation for the actual system state, allowing for an entirely data-driven prediction of the runoff response using rainfall. . Après calage manuel, les pics de débit simulés sont très proches des valeurs mesurées. De plus, le schéma d'activation de la règle au sein du SBRF témoigne du comportement complexe, hautement non-linéaire, du bassin versant. Dans le cadre du SBRF, les mesures d'humidité du sol en des lieux représentatifs peuvent donc être utilisées comme une représentation de l'état actuel du système, ce qui permet une prévision de la transformation de la pluie en débit entièrement basée sur des données.Mots clefs logique floue; modélisation pluie-débit; humidité du sol; réflectométrie dans le domaine temporel (TDR)
Abstract. To precisely map the changes in hydrologic response of catchments (e.g. water balance, reactivity or extremes), we need sensitive and interpretable indicators. In this study we defined nine hydrologically meaningful signature indices: five indices were sampled on the flow duration curve, four indices were closely linked to the distribution of event runoff coefficients. We applied these signature indices to the output from a hydrologic catchment model for three different catchments located in the Nahe basin (Western Germany) to detect differences in runoff behavior resulting from different meteorological input data. The models were driven by measured and simulated (COSMO-CLM) meteorological data. It could be shown that the application of signature indices is a very sensitive tool to assess differences in simulated runoff behavior resulting from climatic data sets of different sources. Specifically, the selected signature indices allow assessing changes in water balance, vertical water distribution, reactivity, seasonality and runoff generation. These indices showed that the hydrological model is very sensitive to biases in mean and spatio-temporal distribution of precipitation and temperature because it acts as a filter for the meteorological input. Besides model calibration and model structural deficits, we found that bias correction of temperature fields and further adjustment of bias correction of precipitation fields is absolutely essential. We conclude that signature indices can act as indirect "efficiency measures" or "similarity measures" for output from regional or local climate models.
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