Parameter calibration and uncertainty estimation of a simple rainfall-runoff model in two case studies

Abstract

The simple rainfall-runoff conceptual KIDS (Kielstau Discharge Simulation) model using PCRaster is applied to simulate continuously daily discharge of the Kielstau and XitaoXi basins. This work focuses on parameter calibration procedure and, in particular, assessment of model prediction uncertainty. We employ a simplistic analysis routine SUFI-2, coupled with the implementation of a Monte Carlo based sampling strategy for the joint investigation of parameter calibration and uncertainty estimation. The scatter plots of model performance and parameter exhibit high equifinality of parameter sets in fitting observations, while their histogram distribution patterns imply that most parameters can be well defined. This study investigates parameter sensitivities and finds interesting local results: soil and groundwater parameters are more sensitive in Kielstau models than in XitaoXi models, and only the soil parameters ‘Sfk’ and ‘Kc’ are found strongly correlated. Finally, the uncertainty bounds are always thin and the global shape of the hydrograph is well approximated for both basins. As the validated uncertainty bounds also represent the desired coverage (P factor >50%) of the observations, and the calculated R factor values are in the targeted range (R factor < 1), it demonstrates the efficiency and suitability of this revised SUFI method for the two case studies.