Abstract. Diagnostics of hydrological models are pivotal for a better understanding of catchment functioning, and the analysis of dominating model parameters plays a key role for region-specific calibration or parameter transfer. A major challenge in the analysis of parameter sensitivity is the assessment of both temporal and spatial differences of parameter influences on simulated streamflow response. We present a methodological approach for global sensitivity analysis of hydrological models. The multilevel approach is geared towards complementary forms of streamflow response targets, and combines sensitivity analysis directed to hydrological fingerprints, i.e. temporally independent and temporally aggregated characteristics of streamflow (INDPAS), with the conventional analysis of the temporal dynamics of parameter sensitivity (TEDPAS).The approach was tested in 14 mesoscale headwater catchments of the Ruhr River in western Germany using simulations with the spatially distributed hydrological model mHM. The multilevel analysis with diverse response characteristics allowed us to pinpoint parameter sensitivity patterns much more clearly as compared to using TEDPAS alone. It was not only possible to identify two dominating parameters, for soil moisture dynamics and evapotranspiration, but we could also disentangle the role of these and other parameters with reference to different streamflow characteristics. The combination of TEDPAS and INDPAS further allowed us to detect regional differences in parameter sensitivity and in simulated hydrological functioning, despite the rather small differences in the hydroclimatic and topographic setting of the Ruhr headwaters.

Abstract. Diagnostics of hydrological models is pivotal for a better understanding of catchment functioning. The analysis of dominating parameters for the simulation of streamflow plays a key role for region specific model diagnostics, model calibration or parameter transfer. A major challenge in this analysis of parameter sensitivity is the assessment of both temporal and spatial differences of parameter influences on simulated streamflow response. A methodical approach is presented, wherein a two-tiered global sensitivity analysis on a spatially distributed hydrological model is applied to 14 mesoscale headwater catchments of the river Ruhr in western Germany. The analysis of parameter sensitivity is geared towards two complementary forms of streamflow response targets. The analysis of the temporal dynamics of parameter sensitivity (TEDPAS) is contrasted with sensitivity analysis directed to hydrological fingerprints, i.e. temporally independent and temporally aggregated characteristics of streamflow (INDPAS). The two-tiered approach allows to discern a clarified sensitivity pattern pinpointed to diverse response characteristics, to detect regional differences and to reveal the regional relevance of the response target. Small local differences in the hydroclimatic and topographic setting of the headwaters lead to slight differences in the hydrological functioning, which was revealed by gradual differences in TEDPAS and INDPAS.

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 5 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 10 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.

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