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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #320513
Title: Climate model biases and statistical downscaling for application in hydrologic model

Author
item GAUTAM, SAGAR - University Of Missouri
item COSTELLO, CHRISINE - University Of Missouri
item Baffaut, Claire
item PHUNG, QUANG - University Of Missouri System
item SVOMA, BOHUMIL - University Of Missouri

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2015
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Climate change impact studies use global climate model (GCM) simulations to define future temperature and precipitation. The best available bias-corrected GCM output was obtained from Coupled Model Intercomparison Project phase 5 (CMIP5). CMIP5 data (temperature and precipitation) are available in daily downscaled datasets using bias-correction and constructed analogs at a spatial resolution of (~12 km by 12 km). Downscaling techniques are used to address the scale mismatch between CMIP5 output and finer scale details required for hydrologic modeling. The method used to correct the bias in CMIP5 data compared to observed datasets can be a source of uncertainty in impact assessment studies. The objective of this study was to evaluate two statistical downscaling methods to minimize uncertainty when modeling future climate scenarios in hydrological models. The downscaling methods used include delta and quantile mapping using observed weather datasets from the Goodwater Creek Experimental Watershed (73 km2), located in Audrain and northeastern Boone counties, Missouri. Results indicate that there was little bias between data over the observed record and the CMIP5 data in average annual precipitation using delta method. However, extremes were under-represented for almost all time steps (daily max, monthly max, yearly max, and yearly min). Quantile mapping was able to reproduce the extremes and correct the bias in most cases for the precipitation. Work on downscaling the temperature using quantile mapping is in progress and results will be included in the presentation. These downscaled data will be used to drive SWAT simulations for the study watershed to evaluate the potential for changes in hydrology given future climate scenarios.