Karim C. Abbaspour

TL;DR: The SWAT-CUP tool as discussed by the authors is a semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration, and is used to provide statistics for goodness-of-fit.

TL;DR: In this paper, the authors used the SWAT (Soil and Water Assessment Tool) to simulate all related processes affecting water quantity, sediment, and nutrient loads in the Thur River basin, which is a direct tributary to the Rhine.

TL;DR: In this article, the authors build and calibrate an integrated hydrological model of Europe using the Soil and Water Assessment Tool (SWAT) program, and discuss issues with data availability, calibration of large-scale distributed models, and outline procedures for model calibration and uncertainty analysis.

TL;DR: In this paper, the authors describe parameter uncertainties using uniform distributions and fit these distributions iteratively within larger absolute intervals such that two criteria are met: (i) bracketing most of the measured data (>90%) within the 95% prediction uncertainty (95PPU) and (ii) obtaining a small ratio (<1) of the average difference between the upper and lower 95PPU and the standard deviation of measured data.

TL;DR: Five uncertainty analysis procedures for watershed models are compared and if computationally feasible, Bayesian-based approaches are most recommendable because of their solid conceptual basis, but construction and test of the likelihood function requires critical attention.