Assessing Long-Term Hydrological Impact of Climate Change Using an Ensemble Approach and Comparison with Global Gridded Model-A Case Study on Goodwater Creek Experimental Watershed
by
Sagar Gautam 1,*, Christine Costello 2
, Claire Baffaut 3, Allen Thompson 1, Bohumil M. Svoma 4, Quang A. Phung 1 and Edward J. Sadler 3
Sagar Gautam 1,*, Christine Costello 2, Claire Baffaut 3, Allen Thompson 1, Bohumil M. Svoma 4, Quang A. Phung 1 and Edward J. Sadler 3
1
Department of Bioengineering, University of Missouri, Columbia, MO 65201, USA
2
Industrial & Manufacturing Systems Engineering, University of Missouri, Columbia, MO 65201, USA
3
USDA–ARS, Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USA
4
Salt River Project, Surface Water Resources, Tempe, AZ 85072, USA
*
Author to whom correspondence should be addressed.
Water 2018, 10(5), 564; https://doi.org/10.3390/w10050564
Received: 19 March 2018 / Revised: 19 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
(This article belongs to the Special Issue Hydrological Modelling and Remote Sensing: Selected Papers from the 2017 and 2018 SWAT International Conferences)
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.
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Keywords:
climate change; impact; hydrology; SWAT
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MDPI and ACS Style
Gautam, S.; Costello, C.; Baffaut, C.; Thompson, A.; Svoma, B.M.; Phung, Q.A.; Sadler, E.J. Assessing Long-Term Hydrological Impact of Climate Change Using an Ensemble Approach and Comparison with Global Gridded Model-A Case Study on Goodwater Creek Experimental Watershed. Water 2018, 10, 564.
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