采用综合考虑高温事件温度强度、持续时间和发生面积等因子的区域持续性极端高温事件(regional con-tinual high temperature event,RCHTE)判别方法和指标体系,分析中国近50 a RCHTE的时空变化特征。研究表明,中国RCHTE发生强度和频次...采用综合考虑高温事件温度强度、持续时间和发生面积等因子的区域持续性极端高温事件(regional con-tinual high temperature event,RCHTE)判别方法和指标体系,分析中国近50 a RCHTE的时空变化特征。研究表明,中国RCHTE发生强度和频次较多的地区主要位于中国西北(西北西部和内蒙古西部)和东南地区(黄淮南部、江淮、江汉、江南和华南南部等地),而中国东北和西南地区为RCHTE少发区;中国RCHTE发生频次、强度和影响面积在20世纪90年代前略呈减少趋势,90年代后呈现显著增加趋势,各指标在90年代末至21世纪初发生-突变,RCHTE增加趋势更为显著。展开更多
A momentum BP neural network model （MBPNNM） was constructed to retrieve the water depth information for the South Channel of the Yangtze River Estuary using the relationship between the reflectance derived from Land...A momentum BP neural network model （MBPNNM） was constructed to retrieve the water depth information for the South Channel of the Yangtze River Estuary using the relationship between the reflectance derived from Landsat 7 satellite data and the water depth information. Results showed that MBPNNM, which exhibited a strong capability of nonlinear mapping, allowed the water depth information in the study area to be retrieved at a relatively high level of accuracy. Affected by the sediment concentration of water in the estuary, MBPNNM enabled the retrieval of water depth of less than 5 meters accurately. However, the accuracy was not ideal for the water depths of more than 10 meters.展开更多
文摘采用综合考虑高温事件温度强度、持续时间和发生面积等因子的区域持续性极端高温事件(regional con-tinual high temperature event,RCHTE)判别方法和指标体系,分析中国近50 a RCHTE的时空变化特征。研究表明,中国RCHTE发生强度和频次较多的地区主要位于中国西北(西北西部和内蒙古西部)和东南地区(黄淮南部、江淮、江汉、江南和华南南部等地),而中国东北和西南地区为RCHTE少发区;中国RCHTE发生频次、强度和影响面积在20世纪90年代前略呈减少趋势,90年代后呈现显著增加趋势,各指标在90年代末至21世纪初发生-突变,RCHTE增加趋势更为显著。
基金the Key Program of National Natural Science Foundation （Project No.50339010） the Huaihe Valley 0pen Fund Project （No.Hx2007）.
文摘A momentum BP neural network model （MBPNNM） was constructed to retrieve the water depth information for the South Channel of the Yangtze River Estuary using the relationship between the reflectance derived from Landsat 7 satellite data and the water depth information. Results showed that MBPNNM, which exhibited a strong capability of nonlinear mapping, allowed the water depth information in the study area to be retrieved at a relatively high level of accuracy. Affected by the sediment concentration of water in the estuary, MBPNNM enabled the retrieval of water depth of less than 5 meters accurately. However, the accuracy was not ideal for the water depths of more than 10 meters.
