Abstract

Abstract. Environmental modeling studies aim to infer the impacts on environmental variables that are caused by natural and human-induced changes in environmental systems. Changes in environmental systems are typically implemented as discrete scenarios in environmental models to simulate environmental variables under changing conditions. The scenario development of a model input usually involves several data sources and perhaps other models, which are potential sources of uncertainty. The setup and the parametrization of the implemented environmental model are additional sources of uncertainty for the simulation of environmental variables. Yet to draw well-informed conclusions from the model simulations it is essential to identify the dominant sources of uncertainty. In impact studies in two Austrian catchments the eco-hydrological model Soil and Water Assessment Tool (SWAT) was applied to simulate discharge and nitrate-nitrogen (NO3--N) loads under future changing conditions. For both catchments the SWAT model was set up with different spatial aggregations. Non-unique model parameter sets were identified that adequately reproduced observations of discharge and NO3--N loads. We developed scenarios of future changes for land use, point source emissions, and climate and implemented the scenario realizations in the different SWAT model setups with different model parametrizations, which resulted in 7000 combinations of scenarios and model setups for both catchments. With all model combinations we simulated daily discharge and NO3--N loads at the catchment outlets. The analysis of the 7000 generated model combinations of both case studies had two main goals: (i) to identify the dominant controls on the simulation of discharge and NO3--N loads in the two case studies and (ii) to assess how the considered inputs control the simulation of discharge and NO3--N loads. To assess the impact of the input scenarios, the model setup, and the parametrization on the simulation of discharge and NO3--N loads, we employed methods of global sensitivity analysis (GSA). The uncertainties in the simulation of discharge and NO3--N loads that resulted from the 7000 SWAT model combinations were evaluated visually. We present approaches for the visualization of the simulation uncertainties that support the diagnosis of how the analyzed inputs affected the simulation of discharge and NO3--N loads. Based on the GSA we identified climate change and the model parametrization as being the most influential model inputs for the simulation of discharge and NO3--N loads in both case studies. In contrast, the impact of the model setup on the simulation of discharge and NO3--N loads was low, and the changes in land use and point source emissions were found to have the lowest impact on the simulated discharge and NO3--N loads. The visual analysis of the uncertainty bands illustrated that the deviations in precipitation of the different climate scenarios to historic records dominated the changes in simulation outputs, while the differences in air temperature showed no considerable impact.

Highlights

  • In this paper we demonstrate the utility of global sensitivity analysis (GSA) and uncertainty analysis in a comprehensive setting of an environmental model impact study and address the following points:

  • In general the bootstrapping resulted in narrow confidence intervals for all analyzed model inputs and all signature measures, providing high confidence in the resulting sensitivities

  • The analyses allow for drawing conclusions that are beneficial to consecutive steps of an impact study, for instance in refining the impact study setup and focusing on the most influential components and reducing the uncertainties in the modeling simulation chain

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Summary

Introduction

Predicting the development of natural resources in a changing system involves large uncertainties (Milly et al, 2008). In concurrence with other dynamic processes such as population growth, land use change, or economic development, poses challenges to the management of water supply and water quality (Duran-Encalada et al, 2017; Yates et al, 2015). Human disturbances can exacerbate the impacts of climate and amplify consequences to water quality (Jiménez et al, 2014) on one hand. Stakeholders in environmental systems have to respond to future changes, for instance by adapting farm management practices due to changes in temperatures and precipitation patterns (Schönhart et al, 2018). An impact assessment considers all future changes that can affect the development of the environment of interest as well as those future changes that can introduce uncertainties in the simulation of the environmental variables of interest

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