An innovative method for determining hydrological calibration parameters for the WRF-Hydro model in arid regions. (May 2017)

• Record Type: Journal Article
• Authors: Silver, M.; Karnieli, A.; Ginat, H.; Meiri, E.; Fredj, E.
• Is Part Of: Environmental modelling & software. Volume 91(2017)
• Page Start: 47
• Page End: 69
• Publication Date: 2017-05
• Subjects: Hydrology -- Flood forecasting -- Calibration -- Landsat classification -- GIS; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.01.010
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The techniques presented herein allow to directly determine certain crucial calibration parameters for the WRF-Hydro flood forecasting model. Typically, calibrations are chosen by an iterative, empirical, trial and error procedure. We suggest a more systematic methodology to arrive at a usable calibration. Our method is based on physical soil properties and does not depend on observed runoff from certain basins during specific storm events. Three specific calibration variables that most strongly affect the runoff predictions are addressed: topographic slope, saturated hydraulic conductivity, and infiltration. We outline a procedure for creating spatially distributed values for each of the three variables. Simulation runs are performed covering several storm events with calculated calibrations, with default values, and with an expert calibration. We show that our calibration, derived solely from soil physical properties, achieves forecast skill better than the default calibration and at least as good as an expert based calibration. Highlights: This work presents a method for calibrating the WRF-Hydro flood forecasting model. The technique relies only on physical soil characteristics. By determining soil characteristics from remote sensing, the method avoids iterative reruns of the model to validate the calibration. We point out that the methods are applicable in any arid region. The WRF-Hydro results achieved with the calculated calibrations are in line with results from an expert calibration.