In this paper, variations of surface water flow and its climatic causes in China are analyzed using hydrological and meteorological observational data, as well as the impact data set （version 2.0） published by the N...In this paper, variations of surface water flow and its climatic causes in China are analyzed using hydrological and meteorological observational data, as well as the impact data set （version 2.0） published by the National Climate Center in November 2009. The results indicate that surface water resources showed an increasing trend in the source region of the Yangtze River over the past 51 years, especially after 2004. The trend was very clearly shown, and there were quasi-periods of 9 years and 22 years, where the Tibetan Plateau heating field enhanced the effect, and the plateau monsoon entered a strong period. Precipitation notably increased, and glacier melt water increased due to climate change, all of which are the main climatic causes for increases in water resources in the source region. Based on global climate model prediction, in the SRESA1B climate change scenarios, water resources are likely to increase in this region for the next 20 years.展开更多
The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in ...The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error （LMMSE） estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers＇ positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound （CRLB）.展开更多
文摘In this paper, variations of surface water flow and its climatic causes in China are analyzed using hydrological and meteorological observational data, as well as the impact data set （version 2.0） published by the National Climate Center in November 2009. The results indicate that surface water resources showed an increasing trend in the source region of the Yangtze River over the past 51 years, especially after 2004. The trend was very clearly shown, and there were quasi-periods of 9 years and 22 years, where the Tibetan Plateau heating field enhanced the effect, and the plateau monsoon entered a strong period. Precipitation notably increased, and glacier melt water increased due to climate change, all of which are the main climatic causes for increases in water resources in the source region. Based on global climate model prediction, in the SRESA1B climate change scenarios, water resources are likely to increase in this region for the next 20 years.
基金supported by the Fundamental Research Funds for the Central Universities(ZYGX2009J016)
文摘The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error （LMMSE） estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers＇ positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound （CRLB）.
