Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand
Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2007-06-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/11/1373/2007/hess-11-1373-2007.pdf |
| _version_ | 1819141791320899584 |
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| author | D. Sharma A. Das Gupta M. S. Babel |
| author_facet | D. Sharma A. Das Gupta M. S. Babel |
| author_sort | D. Sharma |
| collection | DOAJ |
| description | Global Climate Models (GCMs) precipitation scenarios are often characterized
by biases and coarse resolution that limit their direct application for
basin level hydrological modeling. Bias-correction and spatial
disaggregation methods are employed to improve the quality of ECHAM4/OPYC
SRES A2 and B2 precipitation for the Ping River Basin in Thailand.
Bias-correction method, based on gamma-gamma transformation, is applied to
improve the frequency and amount of raw GCM precipitation at the grid nodes.
Spatial disaggregation model parameters (β,σ<sup>2</sup>),
based on multiplicative random cascade theory, are estimated using
Mandelbrot-Kahane-Peyriere (MKP) function at <i>q</i>=1 for each month.
Bias-correction method exhibits ability of reducing biases from the
frequency and amount when compared with the computed frequency and amount at
grid nodes based on spatially interpolated observed rainfall data. Spatial
disaggregation model satisfactorily reproduces the observed trend and
variation of average rainfall amount except during heavy rainfall events
with certain degree of spatial and temporal variations. Finally, the
hydrologic model, HEC-HMS, is applied to simulate the observed runoff for
upper Ping River Basin based on the modified GCM precipitation scenarios and
the raw GCM precipitation. Precipitation scenario developed with
bias-correction and disaggregation provides an improved reproduction of
basin level runoff observations. |
| first_indexed | 2024-12-22T12:00:04Z |
| format | Article |
| id | doaj.art-5cd4d52ec4174cebacb6efe583eea1a7 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-22T12:00:04Z |
| publishDate | 2007-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-5cd4d52ec4174cebacb6efe583eea1a72022-12-21T18:26:39ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382007-06-011141373139010.5194/hess-11-1373-2007Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, ThailandD. Sharma0A. Das Gupta1M. S. Babel2Water Engineering and Management, Asian Institute of Technology, ThailandWater Engineering and Management, Asian Institute of Technology, ThailandWater Engineering and Management, Asian Institute of Technology, ThailandGlobal Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ<sup>2</sup>), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at <i>q</i>=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.http://www.hydrol-earth-syst-sci.net/11/1373/2007/hess-11-1373-2007.pdf |
| spellingShingle | D. Sharma A. Das Gupta M. S. Babel Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand Hydrology and Earth System Sciences |
| title | Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand |
| title_full | Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand |
| title_fullStr | Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand |
| title_full_unstemmed | Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand |
| title_short | Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand |
| title_sort | spatial disaggregation of bias corrected gcm precipitation for improved hydrologic simulation ping river basin thailand |
| url | http://www.hydrol-earth-syst-sci.net/11/1373/2007/hess-11-1373-2007.pdf |
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