DOI: 10.5194/hess-19-2547-2015
Scopus记录号: 2-s2.0-84930672839
论文题名: Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China
作者: Fang G ; H ; , Yang J ; , Chen Y ; N ; , Zammit C
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期: 6 起始页码: 2547
结束页码: 2559
语种: 英语
Scopus关键词: Arid regions
; Atmospheric movements
; Atmospheric temperature
; Climate change
; Climate models
; Linear transformations
; Mapping
; Mathematical transformations
; Meteorology
; Rivers
; Sensitivity analysis
; Stream flow
; Time series
; Watersheds
; Wind
; Bias-correction methods
; Corrected precipitation
; Distributed hydrologic model
; General circulation model
; Meteorological variables
; Nash-Sutcliffe coefficient
; Precipitation correction
; Regional climate models
; Water resources
; arid region
; climate change
; correction
; downscaling
; environmental factor
; general circulation model
; hydrological modeling
; meteorology
; performance assessment
; precipitation (climatology)
; relative humidity
; river basin
; socioeconomic status
; solar radiation
; streamflow
; water resource
; wind velocity
; China
; Kaidu Basin
; Tarim River
; Xinjiang Uygur
英文摘要: Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River basin, northwestern China, and expected to be vulnerable to climate change. It has been demonstrated that regional climate models (RCMs) provide more reliable results for a regional impact study of climate change (e.g., on water resources) than general circulation models (GCMs). However, due to their considerable bias it is still necessary to apply bias correction before they are used for water resources research. In this paper, after a sensitivity analysis on input meteorological variables based on the Sobol' method, we compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations applied over the Kaidu River basin, one of the headwaters of the Tarim River basin. Precipitation correction methods applied include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and quantile mapping (QM), while temperature correction methods are LS, variance scaling (VARI) and DM. The corrected precipitation and temperature were compared to the observed meteorological data, prior to being used as meteorological inputs of a distributed hydrologic model to study their impacts on streamflow. The results show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCM simulations are heavily biased from observed meteorological data, and its use for streamflow simulations results in large biases from observed streamflow, and all bias correction methods effectively improved these simulations; (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g., standard deviation, percentile values) while the LOCI method performed best in terms of the time-series-based indices (e.g., Nash-Sutcliffe coefficient, R 2); (4) for temperature, all correction methods performed equally well in correcting raw temperature; and (5) for simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with those of corrected precipitation; i.e., the PT and QM methods performed equally best in correcting flow duration curve and peak flow while the LOCI method performed best in terms of the time-series-based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other areas and models. © Author(s) 2015. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78508
Appears in Collections: 气候变化事实与影响
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作者单位: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang, China; University of Chinese Academy of Sciences, Beijing, China; Department of Geography, Ghent University, Ghent, Belgium; National Institute of Water and Atmospheric Research, Christchurch, New Zealand
Recommended Citation:
Fang G,H,, Yang J,et al. Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China[J]. Hydrology and Earth System Sciences,2015-01-01,19(6)