Forcing Hydrologic Models with GCM Output: Bias Correction vs. the "Delta Change" Method
Document Type
Conference Proceeding
Publication Date
6-2014
Digital Object Identifier (DOI)
https://doi.org/10.1061/9780784413548.214
Abstract
Global climate change is predicted to have impacts on the frequency and severity of flood events. Hydrologic modeling can be used to gain a deeper understanding of the flood risk projected under future climatic conditions. In this study, output from several global circulation models (GCMs) for a range of possible future climate scenarios was used to force a hydrologic model for a case study watershed built using the soil and water assessment tool (SWAT). GCM output was obtained from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset. At the watershed scale, the output was applied with either the "delta change" method or a bias correction. This research compares the differences in SWAT model outputs and associated flood risk projections when using the two different methods of adjusting GCM output. Preliminary results indicate that the delta change method is more useful when simulating extreme events as it better preserves daily climate variability as opposed to using bias-corrected GCM output. The delta change method appears to produce estimates of flood risk that are more realistic than those obtained using GCM output with a bias correction.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
Forcing Hydrologic Models with GCM Output: Bias Correction vs. the "Delta Change" Method, in W. C. Huber (Ed.), World Environmental and Water Resources Congress 2014: Water without Borders, ASCE, p. 2146-2155
Scholar Commons Citation
LaFond, Kaye M.; Griffis, Veronica W.; and Spellman, Patricia, "Forcing Hydrologic Models with GCM Output: Bias Correction vs. the "Delta Change" Method" (2014). School of Geosciences Faculty and Staff Publications. 2172.
https://digitalcommons.usf.edu/geo_facpub/2172
