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Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model
Berezowski, T.; Nossent, J.; Chormanski, J.; Batelaan, O. (2015). Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model. Hydrol. Earth Syst. Sci. 19(4): 1887-1904. https://hdl.handle.net/10.5194/hess-19-1887-2015
In: Hydrology and Earth System Sciences. European Geosciences Union: Göttingen. ISSN 1027-5606; e-ISSN 1607-7938, meer
Peer reviewed article  

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  • Berezowski, T.
  • Nossent, J.
  • Chormanski, J.
  • Batelaan, O.

Abstract
    As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, 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 hydrological 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 analysis 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 distributed 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 representing a 4_4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by cooccurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis 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.

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