technical note: the impact of spatial scale in bias correction of climate model output for hydrologic impact studies
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2016
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Abstract
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.
| Reference Key |
maurer2016hydrologytechnical
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|---|---|
| Authors | ;E. P. Maurer;D. L. Ficklin;W. Wang |
| Journal | materials research bulletin |
| Year | 2016 |
| DOI |
10.5194/hess-20-685-2016
|
| URL | |
| Keywords |
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