Calibration of snow parameters in SWAT: comparison of three approaches in the Upper Adige River basin (Italy)     

Ye Tuo  Giorgia Marcolini  Markus Disse  Gabriele Chiogna 《水文科学杂志》2018,63(4):657-678

The Soil and Water Assessment Tool (SWAT) model is generally applied in alpine catchments using a unique set of snow parameters for the entire basin, and calibration is based on discharge records only. This technical note presents three calibration procedures for snow parameters of SWAT considering snow water equivalent (SWE) values computed using a dense network of snow depth measurement stations available in the Upper Adige River basin, Italy. The first two procedures calibrate snow parameters according to the average sub-basin SWE: the first one defines a unique set of parameters for the entire basin, while the second allows for sub-basin variability. The last approach includes the elevation band SWE output in the calibration for each sub-basin and qualitatively compares it to the SWE computed from the available snow depth monitoring stations. This last method provides the best agreement between SWAT model results and SWE data.  相似文献   

Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system     

Bittner  Daniel  Richieri  Beatrice  Chiogna  Gabriele 《Hydrogeology Journal》2021,29(7):2363-2379

Uncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

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