The widely used, partly-deterministic Soil and Water Assessment Tool (SWAT) requires a large amount of spatial input data, such as a digital elevation model (DEM), land use, and soil maps. Modelers make an effort to apply the most specific data possible for the study area to reflect the heterogeneous characteristics of landscapes. Regional data, especially with fine resolution, is often preferred. However, such data is not always available and can be computationally demanding. Despite being coarser, global data are usually free and available to the public. Previous studies revealed the importance for single investigations of different input maps. However, it remains unknown whether higher-resolution data can lead to reliable results. This study investigates how global and regional input datasets affect parameter uncertainty when estimating river discharges. We analyze eight different setups for the SWAT model for a catchment in Luxembourg, combining different land-use, elevation, and soil input data. The Metropolis–Hasting Markov Chain Monte Carlo (MCMC) algorithm is used to infer posterior model parameter uncertainty. We conclude that our higher resolved DEM improves the general model performance in reproducing low flows by 10%. The less detailed soil-map improved the fit of low flows by 25%. In addition, more detailed land-use maps reduce the bias of the model discharge simulations by 50%. Also, despite presenting similar parameter uncertainty (P-factor ranging from 0.34 to 0.41 and R-factor from 0.41 to 0.45) for all setups, the results show a disparate parameter posterior distribution. This indicates that no assessment of all sources of uncertainty simultaneously is compensated by the fitted parameter values. We conclude that our result can give some guidance for future SWAT applications in the selection of the degree of detail for input data.

Land surface and ecosystem processes affect climate and weather in a range of time scales, from seconds to thousands of years. Land surface/ecosystem models (LSEMs) calculate the fluxes between the biosphere and the atmosphere and other ecosystem dynamics processes. In this study, we develop a calibration procedure, based on the theory of ecosystems hierarchy, to optimize all processes simulated by an LSEM. The procedure was implemented on Optis, a software for hierarchical multi-objective calibration of LSEMs. Optis is based on the multi-objective genetic algorithm, Non-dominated Sorted Genetic Algorithm-II (NSGA-II). The calibration was hierarchically performed from the fastest process (radiative fluxes) to the slowest process (carbon allocation), optimizing nine model outputs. The procedure demonstrated to be efficient, with the nine-objective model optimization reaching about 80% of the performance achieved by the mono-objective optimization. This calibration methodology allows a better global performance of the model, as all simulated variables are optimized.