Estimation of future precipitation change in the Yangtze River basin by using statistical downscaling method

Abstract

In this study, the applicability of the statistical downscaling model (SDSM) in downscaling precipitation in the Yangtze River basin, China was investigated. The investigation includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP/NCAR reanalysis data, the validation of the model using independent period of the NCEP/NCAR reanalysis data and the general circulation model (GCM) outputs of scenarios A2 and B2 of the HadCM3 model, and the prediction of the future regional precipitation scenarios. Selected as climate variables for downscaling were measured daily precipitation data from 136 weather stations in the Yangtze River basin. The results showed that: (1) there existed good relationship between the observed and simulated precipitation during the calibration period of 1961-1990 as well as the validation period of 1991-2000. And the results of simulated monthly and seasonal precipitation were better than that of daily. The average R 2 values between the simulated and observed monthly and seasonal precipitation for the validation period were 0.78 and 0.91 respectively for the whole basin, which showed that the SDSM had a good applicability on simulating precipitation in the Yangtze River basin. (2) Under both scenarios A2 and B2, during the prediction period of 2010-2099, the change of annual mean precipitation in the Yangtze River basin would present a trend of deficit precipitation in 2020s; insignificant changes in the 2050s; and a surplus of precipitation in the 2080s as compared to the mean values of the base period. The annual mean precipitation would increase by about 15.29% under scenario A2 and increase by about 5.33% under scenario B2 in the 2080s. The winter and autumn might be the more distinct seasons with more predicted changes of precipitation than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean precipitation in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.

The Potential Impacts of Climate Change Factors on Freshwater Eutrophication: Implications for Research and Countermeasures of Water Management in China

“…The spatial patterns of precipitation have change significantly even though the total amount has remained stable. A recent report [8] indicated that the total amount of precipitation in western China had increased by 35%, with precipitation in South China increased by 5% to 10%, and that in certain parts of North and East China precipitation decreased by 10% to 30% over the last 50 years [65][66][67][68][69], showing an overall "southern flood and northern drought" trend. Due to these significant changes in climate conditions, serious environmental issues are emerging in China including reduction of wetland areas, more frequent natural disasters (flood and drought), water and soil loss, desertification and destruction of aquatic and terrestrial ecosystems, etc.…”

“…Due to these significant changes in climate conditions, serious environmental issues are emerging in China including reduction of wetland areas, more frequent natural disasters (flood and drought), water and soil loss, desertification and destruction of aquatic and terrestrial ecosystems, etc. Global climate change has had a significant impact on human survival and development, especially in China, where water resources are distributed unevenly both temporally and spatially [66,67]. The predicted increase in the frequency and intensity of extreme weather events due to climate change will likely exacerbate this trend.…”

“…As indicated in Table 1 [ [66][67][68][69][70][71][72][73], temperature and precipitation are the most common climate change factors applied in the investigated water eutrophication case studies in China. Wind speed, global radiation, and evaporation have also been considered in certain case studies.…”

“…A warm winter before the algal blooms and an early spring with very high temperatures could explain why the 2007 algae bloom in Taihu Lake occurred two months earlier than usual and was characterized by blue algae growth, which is favored under such extremely warm conditions. Qin et al (2012) analyzed the relationship between the climatic variables and bloom events by utilizing linear correlation analysis and multiple regressions with climate changes factors (i.e., temperature, precipitation, wind speed, global radiation, and sunshine hours) [73], collected from local weather stations from 1987 to 2009, as shown in Figure 9 [67]. According to the results obtained by applying the linear regressions method, bloom events were found to be strongly related to the maximum temperature (Tmax), mean temperature (Tmean), minimum temperature (Tmin), wind speed, and global radiation over the past 23 years at Taihu Lake.…”

“…Sustainability 2016, 8, 229 11 of 17 Figure 9. Correlation between climate change indicators and algal blooms in Taihu Lake, China [67].…”