An integrated multi-fingerprint sensitivity-nested approach for regional model parameter estimation and catchment similarity assessment

Abstract. The present study provides a novel approach to the challenge of identifying behavioural parameters in the context of parameter sensitivity and related hydrologic similarity classification. A methodical framework is presented wherein global sensitivity analysis of a spatially distributed conceptual hydrologic model within 14 different mesoscale headwater catchments is combined with a parameter estimation scheme based upon both classification by (1) physiographic and climate and (2) related dynamic response characteristics represented by hydrologic signatures (fingerprints) creating an interface between hydrologic variables of observed and simulated origin. Changing ranks in (3) partial parameter sensitivities within the catchments indicate that hydrologic dynamics might be governed by different hydrologic processes. Model simulated and the respective observed response fingerprints are found to cluster within typical sample regions. These findings show a general model adequacy to represent mesoscale streamflow response processes that relate temporally dominant parameters and allow a reasonable constraint on the parameter space. The senstivity-nested approach may be useful to calibrate hydrologic models sequentially on streamflow sections as well as on constraining (observable) single or combined hydrologic fingerprints and also to transfer results to similar sites, ungauged or anthropogenically altered.