| Title: | Technical note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies |
| Authors: | Maurer, E.P., D.L. Ficklin and W. Wang |
| Year: | 2016 |
| Journal: | Hydrology and Earth System Sciences |
| Volume (Issue): | 20(2) |
| Pages: | 685-696 |
| Article ID: | |
| DOI: | 10.5194/hess-20-685-2016 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | hydrologic only |
| Primary Application Category: | climate change |
| Secondary Application Category: | hydrologic assessment |
| Watershed Description: | Colombia river, Upper Colorado River and Sierra Nevada River, which drains portions of 11 western U.S. states (and the Colombia drains part of western Canada). |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | |
| 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 degree (~200 km) to
1/8 degree (~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 degree
(~50 km). This can provide guidance to those applying the
quantile-mapping bias correction method for hydrologic impacts
analysis. |
| Language: | English |
| Keywords: | |