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 'S fk ' and 'K c ' 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.
Abstract. The KIDS model (Kielstau Discharge Simulation model) is a simple rainfall-runoff model developed originally for the Kielstau catchment. To extend its range of application we applied it to a completely different catchment, the XitaoXi catchment in China. Kielstau is a small (51 km2) lowland basin in Northern Germany, with large proportion of wetland area. And XitaoXi is a mesoscale (2271 km2) mountainous basin in the south of China. Both catchments differ greatly in size, topography, landuse, soil properties, and weather conditions. We compared two catchments in these features and stress on the analysis how the specific catchment characteristics could guide the adaptation of KIDS model and the parameter estimation for streamflow simulation. The Nash and Sutcliffe coefficient was 0.73 for Kielstau and 0.65 for XitaoXi. The results suggest that the application of KIDS model may require adjustments according to the specific physical background of the study basin.
Abstract. This paper investigates the variations of model performance caused by different model structures in both flow processes and model complexity level. Two case studies indicate that model efficiency is strongly dependent on model structure. The resulting substantial variation in both the model efficiency and the hydrographs from different model structures is used to estimate the structural uncertainty. The results help to select the most appropriate model adapted to local situations, which reveal great conformity with the actual hydrological patterns in both study basins.
The impact of different grid resolutions of spatial input data on modelled river runoff are investigated using the simple rainfall-runoff model KIDS (Kielstau Discharge Simulations) in PCRaster modelling language for two watersheds -Kielstau and XitaoXi. In this study, the grid-based spatial data are aggregated to coarser resolutions to support the multi-resolution, multi-calibration and multi-site analysis for grid-scale investigations. Daily streamflow is simulated and model parameters are calibrated at each spatial resolution. The study suggests that re-calibration is critically needed when the grid resolution is changed. Altering grid sizes has an apparent impact on the parameter distribution patterns. Resolution uncertainty bands obtained by the overlapping hydrographs generated with different resolutions of input data are reported with a sufficient coverage of the observations for both basins. The analysis of model efficiency in terms of IC-ratio (a ratio between the input grid area and the catchment area) indicates that coarser resolutions with an IC-ratio of <0.001 may be used as an effective alternative for conducting preliminary analyses in streamflow simulation for the Kielstau basin. The modelling outputs are more sensitive to the spatial distribution of input data at the XitaoXi watershed, showing that accurate input data are required to achieve optimum modelling performance.
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