Technical Note: The impact of spatial scale in bias correction of climate model output for hydrologic impact studies

E P Maurer,W Wang,D L Ficklin

doi:10.5194/hess-20-685-2016

Abstract

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.

Highlights

Climate modeling is an imperfect science, with uncertainties in simulated land-surface climate that vary in space and with the forecast time horizon (Hawkins and Sutton, 2009, 2011)
One way in which applications of quantile mapping vary is in the spatial scale at which it is applied, which can range from the large scale of climate model output down to the finest resolution of observed data
This experiment investigated the effect of the spatial scale at which precipitation is bias corrected on the streamflow produced by a hydrologic model

Summary

Introduction

Climate modeling is an imperfect science, with uncertainties in simulated land-surface climate that vary in space and with the forecast time horizon (Hawkins and Sutton, 2009, 2011). This presents a challenge when projecting climate change impacts at a local and regional scale. The most recent coordinated global climate model (GCM) experiments conducted as part of the fifth Coupled Model Intercomparison Project (CMIP5; Taylor et al, 2012) have been used to simulate historic and future climate These CMIP5 runs have demonstrated improvements over earlier generations of models, both in the representation of physical processes and the simulated fields (Flato et al, 2013; Watterson et al, 2014). Quantile mapping is effective at removing some climate model biases, is relatively simple to apply, and has been incorporated into many statistical downscaling schemes used for local and regional impacts analysis

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