Optimization and Variants of Quantile-Based Methods for Bias Corrections of Statistically Downscaled Precipitation Data
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 7
Abstract
New optimization and variants of quantile-based methods are developed for bias corrections of monthly and daily general circulation model (GCM)-based statistically downscaled precipitation data. These methods use optimization formulations involving several linear and nonlinear corrections with single and multiple objectives and integrate artificial neural networks (ANNs) with quantile matching (QM) methods. The proposed methods were evaluated at 18 rain gauge sites in Florida using several error and performance measures. Downscaled monthly precipitation data are derived from two statistical downscaling models, including a support vector machine (SVM)-based method developed in this study. Downscaled daily precipitation data from two different climatic zones are also used for the evaluation of bias-correction methods. The methods are assessed based on several performance and error measures, along with their ability to replicate all the moments of the distribution. The selection of the best method among several others for a specific site was found to be dependent on specific performance and error measures adopted for evaluation. The proposed methods not only replicated the observed precipitation data distributions but also minimized the quantitative errors between observed and downscaled precipitation data sets, which could not be accomplished using existing methods. ANN-based methods performed better than QM-based ones in replicating extreme precipitation indices at a daily temporal scale. The multiobjective optimization methods require careful selection of objectives and assignment of weights, with the latter heavily influencing the performance of methods. Variation in performances of methods is observed when methods are calibrated with varying baseline periods with a constant length of test data.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request:
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Monthly precipitation data at several rain gauges in the study region is obtained from United States Historical Climatology Network (USHCN) data set available from Menne et al. (2016)
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The coupled global climate model (CGCM3) is obtained from the Canadian Center for Climate Modeling and Analysis (CCCMA 2013)
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Downscaled monthly precipitation data of CGCM3 model and A1B scenario for the period 1950–2000 using bias correction and spatial disaggregation (BCSD) procedure is obtained from Reclamation (2013)
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Downscaled daily precipitation data from the Canadian Centre for Climate Modeling and Analysis model CanESM2 is obtained from World Climate Research Program’s (WCRP) Coupled Model Intercomparison Project phase 5 data set (WCRP 2011)
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Published In
Journal of Hydrologic Engineering
Volume 25 • Issue 7 • July 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Aug 4, 2019
Accepted: Dec 30, 2019
Published online: Apr 30, 2020
Published in print: Jul 1, 2020
Discussion open until: Sep 30, 2020
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