Spatial and temporal change in the potential evapotranspiration sensitivity to meteorological factors in China (1960?2007)

Potential evapotranspiration (E0), as an estimate of the evaporative demand of the atmosphere, has been widely studied in the fields of irrigation management, crop water demand and predictions in ungauged basins (PUBs). Analysis of the sensitivity of E0 to meteorological factors is a basic research on the impact of climate change on water resources, and also is important to the optimal allocation of agricultural water resources. This paper dealt with sensitivity of E0 over China, which was divided into ten drainage systems, including Songhua River basin, Liaohe River basin, Haihe River basin, Yellow River basin, Yangtze River basin, Pearl River basin, Huaihe River drainage system, Southeast river drainage system, Northwest river drainage system and Southwest river drainage system. In addition, the calculation method of global radiation in Penman-Monteith formula was improved by optimization, and the sensitivities of Penman-Monteith potential evapotranspiration to the daily maximum temperature (STmax), daily minimum temperature (STmin), wind speed (SU2), global radiation (SRs) and vapor pressure (SVP) were calculated and analyzed based on the long-term meteorological data from 653 meteorological stations in China during the period 1960-2007. Results show that: (1) the correlation coefficient between E0 and pan evaporation increased from 0.61 to 0.75. E0 had the decline trends in eight of ten drainage systems in China, which indicates that “pan evaporation paradox” commonly exists in China from 1960 to 2007. (2) Spatially, Tmax was the most sensitive factor in Haihe River basin, Yellow River basin, Huaihe River drainage system, Yangtze River basin, Pearl River basin and Southeast river drainage system, and VP was the most sensitive factor in Songhua River Basin, Liaohe River basin, Northwest river drainage system while Rs was the most sensitive factor in Southwest river drainage system. For the nation-wide average, the most sensitive factor was VP, followed by Tmax, Rs, U2 and Tmin. In addition, the changes in sensitivity coefficients had a certain correlation with elevation. (3) Temporally, the maximum values of STmax and SRs occurred in July, while the maximum values of STmin, SVP and SU2 occurred in January. Moreover, trend analysis indicates that STmax had decline trends, while STmin, SU2, SRs and SVP had increasing