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Title: Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

Abstract

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at ½-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from themore » retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
15010453
Report Number(s):
PNNL-SA-37377
KP1201020
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Climatic Change, 62(1-3):189-216
Additional Journal Information:
Journal Name: Climatic Change, 62(1-3):189-216
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Wood, Andrew W, Leung, Lai R, Sridhar, V, and Lettenmaier, D P. Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs. United States: N. p., 2004. Web. doi:10.1023/B:CLIM.0000013685.99609.9e.
Wood, Andrew W, Leung, Lai R, Sridhar, V, & Lettenmaier, D P. Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs. United States. https://doi.org/10.1023/B:CLIM.0000013685.99609.9e
Wood, Andrew W, Leung, Lai R, Sridhar, V, and Lettenmaier, D P. 2004. "Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs". United States. https://doi.org/10.1023/B:CLIM.0000013685.99609.9e.
@article{osti_15010453,
title = {Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs},
author = {Wood, Andrew W and Leung, Lai R and Sridhar, V and Lettenmaier, D P},
abstractNote = {Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at ½-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.},
doi = {10.1023/B:CLIM.0000013685.99609.9e},
url = {https://www.osti.gov/biblio/15010453}, journal = {Climatic Change, 62(1-3):189-216},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2004},
month = {Thu Jan 01 00:00:00 EST 2004}
}