Assimilation of MODIS snow cover area data in a distributed hydrological model

Abstract

As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increas-ing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hy-drological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction – SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The anal-ysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a dis-tributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter rep-resenting a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as geo-morphology, soil texture, land use, precipitation and temper-ature. Moreover, the spatial pattern of sensitivity under dif-ferent response functions is related to different spatial param-eters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity anal-ysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.