Abstract
Potential impacts of climate change on the hydrological components of the Goodwater Creek Experimental Watershed were assessed using climate datasets from the Coupled Model Intercomparison Project Phase 5 and Soil and Water Assessment Tool (SWAT). Historical and future ensembles of downscaled precipitation and temperature, and modeled water yield, surface runoff, and evapotranspiration, were compared. Ensemble SWAT results indicate increased springtime precipitation, water yield, surface runoff and a shift in evapotranspiration peak one month earlier in the future. To evaluate the performance of model spatial resolution, gridded surface runoff estimated by Lund–Potsdam–Jena managed Land (LPJmL) and Jena Diversity-Dynamic Global Vegetation model (JeDi-DGVM) were compared to SWAT. Long-term comparison shows a 6–8% higher average annual runoff prediction for LPJmL, and a 5–30% lower prediction for JeDi-DGVM, compared to SWAT. Although annual runoff showed little change for LPJmL, monthly runoff projection under-predicted peak runoff and over-predicted low runoff for LPJmL compared to SWAT. The reasons for these differences include differences in spatial resolution of model inputs and mathematical representation of the physical processes. Results indicate benefits of impact assessments at local scales with heterogeneous sets of parameters to adequately represent extreme conditions that are muted in global gridded model studies by spatial averaging over large study domains.
Highlights
Climatic shift due to anthropogenic activities has been linked to water supply shortages [1,2], declining biodiversity [3,4,5], ecosystem damage [6], and economic impact [7]
Soil and Water Assessment Tool (SWAT) simulated dry (2007) and wet (2008) years accurately, indicating that the model was suitable for evaluating climate change impacts
A calibrated and validated SWAT model for the Goodwater Creek Experimental Watershed (GCEW) was used to simulate the effect of climate change using downscaled T and P data from 12 Global Circulation Models (GCMs) under four emission scenarios
Summary
Climatic shift due to anthropogenic activities has been linked to water supply shortages [1,2], declining biodiversity [3,4,5], ecosystem damage [6], and economic impact [7]. Increased greenhouse gas (GHG) concentrations alter the radiative balance of the Earth’s atmosphere, causing an increase in average temperature (T) and changes in precipitation (P) patterns [8,9]. Panel on Climate Change (IPCC) reports increases in mean surface temperature of between 0.3 and. 4.8 ◦ C by 2081–2100 relative to 1986–2005, and variable changes in precipitation across the globe for the same period of comparison [10], dependent upon GHG concentrations in the atmosphere. The projected changes in meteorological variables have been reported to impact hydrological components, and agroecosystem functioning [11,12,13]. It is important to develop approaches to increase the ability to predict how future climate changes could impact hydrological components, and correspondingly, agricultural productivity, through the representation of localized systems
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