| |
|
| Submitter's Name |
Syewoon Hwang |
| Session Name |
Posters - Water and Climate |
| Category |
Climate and Water |
| Poster Number |
69 |
| |
| Author(s) |
Syewoon Hwang, University of Florida (Presenting Author) |
| |
Wendy Graham,
Water institute, University of Florida |
| |
Jeffrey Geurink, Tampa Bay Water |
| |
Alison Adams, Tampa Bay Water |
| |
|
| |
Hydrologic Importance of Spatial Variability in Statistically Downscaled Precipitation Predictions from Global Circulation Models for West-Central Florida |
| |
|
| |
There are a number of statistical techniques that downscale coarse climate information from global circulation models (GCM). However, many of them pay little attention to the small-scale spatial variability of precipitation exhibited by the observed meteorological data which can be an important factor for predicting hydrologic response to climatic forcing. In this study a stochastic downscaling technique was developed to produce bias-corrected daily GCM precipitation fields that honor the spatial autocorrelation structure of observed daily precipitation sequences. This approach is designed to produce bias-corrected daily GCM results which reproduce observed spatial and temporal variability as well as mean climatology.
We used the proposed method to downscale 4 GCM precipitation predictions from 1961 to 2000 over west-central Florida and compared the skill of the method to results obtained using the commonly used bias-correction spatial disaggregation (BCSD) approach. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation index, and variograms for wet (June through September) and dry (October through May) season were calculated for each method. Preliminary results showed that the new stochastic technique reproduced observed temporal and spatial variability and features very well for both wet and dry seasons while the interpolation based BCSD approach significantly underestimated spatial variability (i.e., overestimated spatial correlation).
The two sets of downscaled precipitation scenarios were used with an integrated surface-subsurface hydrologic model to examine hydrologic responses of streamflow and groundwater levels for each climate input scenario for an application in west-central Florida. The results support the hypothesis that accurately representing the spatial variability of precipitation in downscaled GCM predictions is important to reproduce observed hydrologic behavior.
|
| |