Search

This paper discusses projections of heavy rainfall events in China during the 21st century based on daily precipitation data from the Fourth Assessment Report's (AR4) Coupled General Circulation Models (CGCM). Results show that all three experimental scenarios (scenarios A2, A1B, and B1) project consistent changes in frequency and intensity of heavy rainfall at the end of 21st century. In the regions of Northeast China and North China, there are no significant changes in frequency but there are remarkable increases in intensity of heavy rainfall, indicating that enhanced intensity is the main contributor to increased ratios of heavy rainfall to total annual precipitation in these regions. In regions of the lower reaches of Yangtze River and South China, increases in the amount of heavy rainfall are closely associated with increased frequency and increased intensity. Projected frequencies of heavy rainfall at the end of 21st century increase by 30.9 ∼ 56.6% in the Yangtze River and 35.9 ∼ 50.2% in South China compared to the period of 1980-1999, and projected intensities increase by 1.0 ∼ 5.7% and 2.8 ∼ 6.3%, respectively. Additionally, the ratios of heavy rainfall to total annual precipitation increase by 2.3 ∼ 5.4% in the Yangtze River and 1.8 ∼ 3.8% in South China. The significant increases of heavy rainfall ratios indicate that as the climate warms, heavy rainfall events are expected to increase at rates that are much faster than increases in total precipitation amounts, indicating that China will experience increased amounts of flooding. These results are substantially consistent among the three IPCC (Intergovernmental Panel on Climate Change) scenarios. The increased probability of heavy rainfall events in China is closely connected with increased transportation of water vapour from the Arabian Sea and the South China Sea. Additionally, atmosphere stratification has become increasingly unstable, which has provided a favorable background for the initiation of heavy rainfall at the end of the 21st century. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Solar radiation (Rs), a critical variable in agricultural and eco-environmental processes, is not measured at many meteorological stations. Prediction of Rs has drawn increasing attention in the recent years. However, due to the dynamic nature of atmosphere, accurate estimation of Rs from routinely measured meteorological variables is a challenging task. Studies have demonstrated that machine-learning approaches outperformed traditional statistical methods. This paper presents an application of Support Vector Machines (SVMs) for monthly mean daily Rs estimates using measured maximum, minimum, and mean air temperatures (Tmax, Tmin, and Tmean, respectively). Twenty-four stations covering different climatic regions-cold (C), severe cold (SC), mild (M), hot summer and cold winter within the Yangtze River Plain (HSCW-A), and hot summer and cold winter within the Sichuan Basin (HSCW-B)-across China were gathered and analysed. Five SVM models with different input attributes were created. These models were also compared with two empirical temperature-based methods. Root mean squared error (RMSE), Nash-Sutcliffe coefficient (NSE), and coefficient of residual mass (CRM) were employed to compare the performances of different methods. The newly developed model, SVM using Tmax-Tmin, and Tmean, outperformed other models with an averaged RMSE of 1.637 MJ m−2 and NSE of 0.813. On a regional scale, when Rs was estimated using the parameters developed at other sites, estimation of Rs within HSCW-B and SC regions were more reliable than in other zones. Especially in HSCW-B region, estimates of Rs using the parameters developed at Zunyi gave better performance (RMSE = 1.117 MJ m−2, NSE = 0.922) than that using the parameters obtained from their own data (RMSE = 1.309 MJ m−2, NSE = 0.894). The results showed that the SVM methodology may be a promising alternative to the traditional approach for predicting solar radiation at any locations where the records of air temperatures are available. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]