Streamflow recession analysis characterizes the storage-outflow relationship in catchments. This relationship, which typically follows a power law, summarizes all catchment-scale subsurface hydrological processes and has long been known to be a key descriptor of the hydrologic response. In this paper, we tested a range of common recession analysis methods (RAMs) andpropose the use of an analytic streamflow distribution model as an alternative method for recession parameter estimation and to objectively compare different RAMs. The used analytical model assumes power law recessions, in combination with a stochastic process for streamflow triggering rainfall events. This streamflow distribution model is used in the present framework to establish reference values for the recession parameters via maximum likelihood estimation.The model-based method has two main advantages: (a) joint estimation of both power law recession parameters (coefficient and exponent), which are known to be strongly correlated, and (b) parameter estimation based on all available streamflow data (no recession selection).The approach is applied to five rainfall-dominated catchments in Switzerland with 40 years of continuous streamflow observations. The results show that the estimated recession parameters are highly dependent on methodological choices and that some RAMs lead to biased estimates.The recession selection method is shown to be of prime importance for a reliable description of catchment-scale recession behaviour, in particular in presence of short streamflow records. The newly proposed model-based RAM yields robust results, which supports the further development of this method for comparative hydrology and opens new perspectives for understanding the recession behaviour of catchments.
Abstract. This paper proposes a systematic assessment of the performance of an analytical modeling framework for streamflow probability distributions for a set of 25 Swiss catchments. These catchments show a wide range of hydroclimatic regimes, including namely snow-influenced streamflows. The model parameters are calculated from a spatially averaged gridded daily precipitation data set and from observed daily discharge time series, both in a forward estimation mode (direct parameter calculation from observed data) and in an inverse estimation mode (maximum likelihood estimation). The performance of the linear and the nonlinear model versions is assessed in terms of reproducing observed flow duration curves and their natural variability. Overall, the nonlinear model version outperforms the linear model for all regimes, but the linear model shows a notable performance increase with catchment elevation. More importantly, the obtained results demonstrate that the analytical model performs well for summer discharge for all analyzed streamflow regimes, ranging from rainfall-driven regimes with summer low flow to snow and glacier regimes with summer high flow. These results suggest that the model's encoding of discharge-generating events based on stochastic soil moisture dynamics is more flexible than previously thought. As shown in this paper, the presence of snowmelt or ice melt is accommodated by a relative increase in the discharge-generating frequency, a key parameter of the model. Explicit quantification of this frequency increase as a function of mean catchment meteorological conditions is left for future research.
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