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Date: Mon, 11 Aug 2003 10:01:27 -0700 (PDT)
From: Civil and Environmental Engineering <ceginfo@u.washington.edu>
To: faculty@ce.washington.edu, <cegrads@u.washington.edu>,
   <zabow@u.washington.edu>, <dennis@atmos.washington.edu>,
   <arun.kumar@noaa.gov>
Subject: Final Ph.D. Exam for Andrew Wood
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The final examination for the Ph.D. degree for Aandrew Wood will be held
on Monday, August 18, 2003 in 264 Wilcox at 1:00.
--------------------------------------------------------------------------
> USING CLIMATE MODEL ENSEMBLE FORECASTS FOR SEASONAL HYDROLOGIC PREDICTION
> Andrew W. Wood
>
> [Professor Dennis P. Lettenmaier (Chair)
> Department of Civil and Environmental Engineering]
>
> ABSTRACT
> Seasonal hydrologic forecasting has long played an invaluable role in the
> development and use of water resources.  Despite notable advances in the
> science and practice of climate prediction, current approaches of
> hydrologists and water managers largely fail to incorporate seasonal
> climate forecast information that has become operationally available
> during the last decade.  This study is motivated by the view that a
> combination of hydrologic and climate prediction methods affords a new
> opportunity to improve hydrologic forecast skill.  A relatively direct
> statistical approach for achieving this combination (i.e., downscaling)
> was demonstrated that used ensemble climate model forecasts with a six
> month lead time produced by the NCEP/CPC Global Spectral Model (GSM) as
> input to the macroscale Variable Infiltration Capacity hydrologic model
> to produce ensemble runoff and streamflow forecasts.  The approach
> involved the bias correction of climate model precipitation and
> temperature fields, and spatial and temporal disaggregation from monthly
> climate model scale (about 2 degrees latitude by longitude) fields to
> daily hydrology model scale (1/8 degrees) inputs.  A qualitative
> evaluation of the approach in the eastern U.S. suggested that it was
> successful in translating climate forecast signals to local hydrologic
> variables and streamflow, but that the dominant influence on forecast
> results tended to be persistence in initial hydrologic conditions.  The
> suitability of the statistical downscaling approach for supporting
> hydrologic simulation was then assessed (using a continuous retrospective
> 20-year climate simulation from the DOE Parallel Climate Model) relative
> to dynamical downscaling via a regional, meso-scale climate model.  The
> statistical approach generally outperformed the dynamical approach, in
> that the dynamical approach alone required additional bias-correction to
> reproduce the retrospective hydrology as well as the statistical
> approach.  Finally, using 21 years of retrospective forecasts for the
> western U.S., the skill of the GSM-based hydrologic forecasts was assessed
> relative to NWS Extended Streamflow Prediction (ESP) method forecasts.
> Because of unexceptional GSM climate forecasts, the GSM-based and ESP
> hydrologic forecasts generally showed similar skill.  During strong ENSO
> anomalies, however, GSM-based forecasts yielded higher forecast skill in
> the Sacramento-San Joachin and Columbia River basins, but lower skill in
> the Colorado and upper Rio Grande River basins.
>
> <|--------------------------------------------------------------------|>
>  | Andrew W. Wood                                206-685-1796 (w/lab) |
>  | University of Washington                      206-910-3938  (cell) |
>  | Civil and Env. Engineering                    503-280-8763  (home) |
>  | Box 352700                                    206-685-3836   (fax) |
>  | Seattle, WA 98195-2700          http://www.ce.washington.edu/~aww/ |
> <|--------------------------------------------------------------------|>
>
>
>


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