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Title: Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments
Abstract. Assessing impacts of climate change on hydrologic systemsis critical for developing adaptation and mitigation strategies for waterresource management, risk control, and ecosystem conservation practices. Suchassessments are commonly accomplished using outputs from a hydrologic modelforced with future precipitation and temperature projections. The algorithmsused for the hydrologic model components (e.g., runoff generation) canintroduce significant uncertainties into the simulated hydrologic variables.Here, a modeling framework was developed that integrates multiple runoffgeneration algorithms with a routing model and associated parameteroptimizations. This framework is able to identify uncertainties from bothhydrologic model components and climate forcings as well as associatedparameterization. Three fundamentally different runoff generationapproaches, runoff coefficient method (RCM, conceptual), variableinfiltration capacity (VIC, physically based, infiltration excess), andsimple-TOPMODEL (STP, physically based, saturation excess), were coupledwith the Hillslope River Routing model to simulate surface/subsurface runoffand streamflow. A case study conducted in Santa Barbara County, California,reveals increased surface runoff in February and March but decreasedrunoff in other months, a delayed (3 d, median) and shortened (6 d,median) wet season, and increased daily discharge especially for theextremes (e.g., 100-year flood discharge, Q100). The Bayesian modelaveraging analysis indicates that the probability of such an increase can be up to85 %. For projected changes in runoff and discharge, general circulationmodels (GCMs) and emission scenarios are two major uncertainty sources,accounting for about half of the total uncertainty. For the changes inseasonality, GCMs and hydrologic models are two major uncertaintycontributors (∼35 %). In contrast, the contribution ofhydrologic model parameters to the total uncertainty of changes in thesehydrologic variables is relatively small (<6 %), limiting theimpacts of hydrologic model parameter equifinality in climate change impactanalysis. This study provides useful information for practices associatedwith water resources, risk control, and ecosystem conservation and forstudies related to hydrologic model evaluation and climate change impactanalysis for the study region as well as other Mediterranean regions.  more » « less
Award ID(s):
1831937
NSF-PAR ID:
10198185
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Hydrology and Earth System Sciences
Volume:
24
Issue:
5
ISSN:
1607-7938
Page Range / eLocation ID:
2253 to 2267
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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