Fuzzy logic-based rainfall—runoff modelling using soil moisture measurements to represent system state

Abstract

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)

Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture

“…Mamdani rules can be intuitively produced or they can also be constructed from available data and expert view (Sen 2004;Casper et al 2007;Tayfur 2012). For example; according to Fig.…”

“…Specific monitoring programs (Merz and Bardossy 1998;Aubert et al 2003;Morbidelli et al 2012) and modelling studies (Gautam et al 2000;Anctil et al 2004;Casper et al 2007;Komma et al 2008;Berthet et al 2009;Brocca et al 2012b;van Steenbergen and Willems 2013) were carried out to investigate the influence of soil moisture on runoff hydrographs.…”

“…The major disadvantage of ANN is that it is a black box model which does not reveal any insight in understanding the underlying physical process. Casper et al (2007) were the first that developed a fuzzy logic based rainfall-runoff model using soil moisture measurements to represent system state. They employed Takagi-SugenoKang (T-S-K) approach where the antecedent part of the fuzzy IF-THEN rules are expressed verbally while the consequent part consists of mathematical expression (Takagi and Sugeno 1985).…”

“…They employed Takagi-SugenoKang (T-S-K) approach where the antecedent part of the fuzzy IF-THEN rules are expressed verbally while the consequent part consists of mathematical expression (Takagi and Sugeno 1985). The antecedent part of the constructed 36 rules involved only soil moisture information while the consequent part mathematically computed discharge (Casper et al 2007). Since such T-S-K fuzzy model involves mathematical expressions, it requires an extra work of the estimation of the model parameters, which is often a difficult task.…”

Hydroinformatics: computational intelligence and technological developments in water science applications—Editorial

“…Fuzzy logic modelling is the approach reported in Casper et al (2007), who developed a rainfall-runoff model based on soil moisture. Four soil moisture probes from a site in the Black Forest in Germany were selected, each of them representing a different runoff generation process (i.e.…”