|
|||||
|
|
Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China |
|
| Authors: | J T Chu J Xia C-Y Xu V P Singh |
| Institution: | 1. Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China 2. Graduate University of the Chinese Academy of Sciences, Beijing, 100039, China 3. Department of Geosciences, University of Oslo, P. O. Box 1047 Blindern, NO-0316, Oslo, Norway 4. Department of Biological and Agricultural Engineering, Texas A & M University, Scoates Hall, 2117 TAMU, College Station, TX, USA |
| Abstract: | A statistical downscaling method (SDSM) was evaluated by simultaneously downscaling air temperature, evaporation, and precipitation in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for downscaling were measured daily mean air temperature, pan evaporation, and precipitation data (1961–2000) from 11 weather stations in the Haihe River basin. The results obtained from SDSM showed that: (1) the pattern of change in and numerical values of the climate variables can be reasonably simulated, with the coefficients of determination between observed and downscaled mean temperature, pan evaporation, and precipitation being 99%, 93%, and 73%, respectively; (2) systematic errors existed in simulating extreme events, but the results were acceptable for practical applications; and (3) the mean air temperature would increase by about 0.7°C during 2011~2040; the total annual precipitation would decrease by about 7% in A2 scenario but increase by about 4% in B2 scenario; and there were no apparent changes in pan evaporation. It was concluded that in the next 30 years, climate would be warmer and drier, extreme events could be more intense, and autumn might be the most distinct season among all the changes. |
| Keywords: | |
| 本文献已被 SpringerLink 等数据库收录！ | |