Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies
Statistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generall...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-02-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/20/685/2016/hess-20-685-2016.pdf |
| _version_ | 1818140645401821184 |
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| author | E. P. Maurer D. L. Ficklin W. Wang |
| author_facet | E. P. Maurer D. L. Ficklin W. Wang |
| author_sort | E. P. Maurer |
| collection | DOAJ |
| description | Statistical downscaling is a commonly used technique for translating
large-scale climate model output to a scale appropriate for assessing
impacts. To ensure downscaled meteorology can be used in climate impact
studies, downscaling must correct biases in the large-scale signal. A simple
and generally effective method for accommodating systematic biases in
large-scale model output is quantile mapping, which has been applied to many
variables and shown to reduce biases on average, even in the presence of
non-stationarity. Quantile-mapping bias correction has been applied at
spatial scales ranging from hundreds of kilometers to individual
points, such as weather station locations. Since water resources and other
models used to simulate climate impacts are sensitive to biases in input
meteorology, there is a motivation to apply bias correction at a scale fine
enough that the downscaled data closely resemble historically observed
data, though past work has identified undesirable consequences to applying
quantile mapping at too fine a scale. This study explores the role of the
spatial scale at which the quantile-mapping bias correction is applied, in
the context of estimating high and low daily streamflows across the western
United States. We vary the spatial scale at which quantile-mapping bias
correction is performed from 2° ( ∼ 200 km) to
1∕8° ( ∼ 12 km) within a statistical downscaling
procedure, and use the downscaled daily precipitation and temperature to
drive a hydrology model. We find that little additional benefit is obtained,
and some skill is degraded, when using quantile mapping at scales finer than
approximately 0.5° ( ∼ 50 km). This can provide
guidance to those applying the quantile-mapping bias correction method for
hydrologic impacts analysis. |
| first_indexed | 2024-12-11T10:47:17Z |
| format | Article |
| id | doaj.art-7e91d985032049df9e150be91080ddbf |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-11T10:47:17Z |
| publishDate | 2016-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-7e91d985032049df9e150be91080ddbf2022-12-22T01:10:26ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382016-02-0120268569610.5194/hess-20-685-2016Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studiesE. P. Maurer0D. L. Ficklin1W. Wang2Santa Clara University, Civil Engineering Department, Santa Clara, CA 95053-0563, USAIndiana University, Department of Geography, Bloomington, IN 47405, USACalifornia State University at Monterey Bay, Department of Science and Environmental Policy and NASA Ames Research Center, Moffett Field, CA 94035, USAStatistical downscaling is a commonly used technique for translating large-scale climate model output to a scale appropriate for assessing impacts. To ensure downscaled meteorology can be used in climate impact studies, downscaling must correct biases in the large-scale signal. A simple and generally effective method for accommodating systematic biases in large-scale model output is quantile mapping, which has been applied to many variables and shown to reduce biases on average, even in the presence of non-stationarity. Quantile-mapping bias correction has been applied at spatial scales ranging from hundreds of kilometers to individual points, such as weather station locations. Since water resources and other models used to simulate climate impacts are sensitive to biases in input meteorology, there is a motivation to apply bias correction at a scale fine enough that the downscaled data closely resemble historically observed data, though past work has identified undesirable consequences to applying quantile mapping at too fine a scale. This study explores the role of the spatial scale at which the quantile-mapping bias correction is applied, in the context of estimating high and low daily streamflows across the western United States. We vary the spatial scale at which quantile-mapping bias correction is performed from 2° ( ∼ 200 km) to 1∕8° ( ∼ 12 km) within a statistical downscaling procedure, and use the downscaled daily precipitation and temperature to drive a hydrology model. We find that little additional benefit is obtained, and some skill is degraded, when using quantile mapping at scales finer than approximately 0.5° ( ∼ 50 km). This can provide guidance to those applying the quantile-mapping bias correction method for hydrologic impacts analysis.http://www.hydrol-earth-syst-sci.net/20/685/2016/hess-20-685-2016.pdf |
| spellingShingle | E. P. Maurer D. L. Ficklin W. Wang Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies Hydrology and Earth System Sciences |
| title | Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies |
| title_full | Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies |
| title_fullStr | Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies |
| title_full_unstemmed | Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies |
| title_short | Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies |
| title_sort | technical note the impact of spatial scale in bias correction of climate model output for hydrologic impact studies |
| url | http://www.hydrol-earth-syst-sci.net/20/685/2016/hess-20-685-2016.pdf |
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