Global sensitivity analysis (GSA) has long been recognized as an indispensable tool for model analysis. GSA has been extensively used for model simplification, identifiability analysis, and diagnostic tests. Nevertheless, computationally efficient methodologies are needed for GSA, not only to reduce the computational overhead, but also to improve the quality and robustness of the results. This is especially the case for process-based hydrologic models, as their simulation time typically exceeds the computational resources available for a comprehensive GSA. To overcome this computational barrier, we propose a data-driven method called VISCOUS, variance-based sensitivity analysis using copulas. VISCOUS uses Gaussian mixture copulas to approximate the joint probability density function of a given set of input-output pairs for estimating the variance-based sensitivity indices. Our method identifies dominant hydrologic factors by recycling existing input-output data, and thus can deal with arbitrary sample sets drawn from the input-output space. We used two hydrologic models of increasing complexity (HBV and VIC) to assess the performance of VISCOUS. Our results confirm that VISCOUS and the conventional variance-based method can detect similar important and unimportant factors. Furthermore, the VISCOUS method can substantially reduce the computational cost required for sensitivity analysis. Our proposed method is particularly useful for process-based models with many uncertain parameters, large domain size, and high spatial and temporal resolution.

Numerous wetlands in the prairies of Canada provide important ecosystem services, yet are threatened by climate and land-use changes. Understanding the impacts of climate change on prairie wetlands is critical to effective conservation planning. In this study, we construct a wetland model with surface water balance and ecoregions to project future distribution of wetlands. The climatic conditions downscaled from the Weather Research and Forecasting model were used to drive the Noah-MP land surface model to obtain surface water balance. The climate change perturbation is derived from an ensemble of general circulation models using the pseudo global warming method, under the RCP8.5 emission scenario by the end of 21st century. The results show that climate change impacts on wetland extent are spatiotemporally heterogenous. Future wetter climate in the western Prairies will favor increased wetland abundance in both spring and summer. In the eastern Prairies, particularly in the mixed grassland and mid-boreal upland, wetland areas will increase in spring but experience enhanced declines in summer due to strong evapotranspiration. When these effects of climate change are considered in light of historical drainage, they suggest a need for diverse conservation and restoration strategies. For the mixed grassland in the western Canadian Prairies, wetland restoration will be favorable, while the highly drained eastern Prairies will be challenged by the intensified hydrological cycle. The outcomes of this study will be useful to conservation agencies to ensure that current investments will continue to provide good conservation returns in the future.