Intermittent rivers and ephemeral streams(IRES),also known as non-perennial river segments(NPRs),have garnered attention due to their significant roles in watershed hydrology and ecosystem services,especially in the c...Intermittent rivers and ephemeral streams(IRES),also known as non-perennial river segments(NPRs),have garnered attention due to their significant roles in watershed hydrology and ecosystem services,especially in the context of climate change and escalating human activities.Recent advances in machine learning(ML)techniques have significantly improved the analysis of dynamic changes in IRES.Various ML models,including random forest(RF),long short-term memory(LSTM),and U-Net,demonstrate clear advantages in processing complex hydrological data,enhancing the efficiency and accuracy of IRES extraction from remote sensing data.Furthermore,hybrid ML approaches enhance predictive performance in complex hydrological scenarios by integrating multiple algorithms.However,ML methods still face challenges,including high data dependence,computational complexity,and scalability issues with models.This review proposes an IRES monitoring framework that combines satellite data with ML algorithms,integrating remote sensing technologies such as optical imaging and synthetic aperture radar,and evaluates the advantages and limitations of different ML methods.It further highlights the potential of integrating multiple ML techniques and high-resolution remote sensing data to monitor IRES dynamics,conduct ecological assessments,and support sustainable water management,offering a scientific foundation for addressing environmental and anthropogenic pressures.展开更多
Based on the monthly precipitation data for the period 1960-2008 from 616 rainfall stations and the phenology data of main grain crops, the spatial characteristics of drought hazard in China were investigated at a 10 ...Based on the monthly precipitation data for the period 1960-2008 from 616 rainfall stations and the phenology data of main grain crops, the spatial characteristics of drought hazard in China were investigated at a 10 km×10 km grid-cell scale using a GIS-based drought hazard assessment model, which was constructed by using 3-month Standard Precipitation Index （SPI）. Drought-prone areas and heavy drought centers were also identified in this study. The spatial distribution of drought hazard in China shows apparent east-west difference, with the eastern part of China being far more hazardous than the western part. High hazard areas are common in the eastern and central parts of Inner Mongolian Plateau, the central part of Northeast China Plain, the northern part of Heilongjiang, the southeastern part of Qinghai-Tibet Plateau, the central and southern parts of Loess Plateau, the southern part of North China Plain, the northern and southern parts of Yangtze River Plain, and Yun- nan-Guizhou Plateau. Furthermore, obvious differences in drought hazard were found both within and between different agricultural zonings.展开更多
基金National Natural Science Foundation of China,No.41671026。
文摘Intermittent rivers and ephemeral streams(IRES),also known as non-perennial river segments(NPRs),have garnered attention due to their significant roles in watershed hydrology and ecosystem services,especially in the context of climate change and escalating human activities.Recent advances in machine learning(ML)techniques have significantly improved the analysis of dynamic changes in IRES.Various ML models,including random forest(RF),long short-term memory(LSTM),and U-Net,demonstrate clear advantages in processing complex hydrological data,enhancing the efficiency and accuracy of IRES extraction from remote sensing data.Furthermore,hybrid ML approaches enhance predictive performance in complex hydrological scenarios by integrating multiple algorithms.However,ML methods still face challenges,including high data dependence,computational complexity,and scalability issues with models.This review proposes an IRES monitoring framework that combines satellite data with ML algorithms,integrating remote sensing technologies such as optical imaging and synthetic aperture radar,and evaluates the advantages and limitations of different ML methods.It further highlights the potential of integrating multiple ML techniques and high-resolution remote sensing data to monitor IRES dynamics,conduct ecological assessments,and support sustainable water management,offering a scientific foundation for addressing environmental and anthropogenic pressures.
基金National Natural Science Foundation of China, No.40601091 No.40801216+1 种基金 National Key Technology R＆D Program of China, No.2006BAD20B02 No.2006BAC 18B06
文摘Based on the monthly precipitation data for the period 1960-2008 from 616 rainfall stations and the phenology data of main grain crops, the spatial characteristics of drought hazard in China were investigated at a 10 km×10 km grid-cell scale using a GIS-based drought hazard assessment model, which was constructed by using 3-month Standard Precipitation Index （SPI）. Drought-prone areas and heavy drought centers were also identified in this study. The spatial distribution of drought hazard in China shows apparent east-west difference, with the eastern part of China being far more hazardous than the western part. High hazard areas are common in the eastern and central parts of Inner Mongolian Plateau, the central part of Northeast China Plain, the northern part of Heilongjiang, the southeastern part of Qinghai-Tibet Plateau, the central and southern parts of Loess Plateau, the southern part of North China Plain, the northern and southern parts of Yangtze River Plain, and Yun- nan-Guizhou Plateau. Furthermore, obvious differences in drought hazard were found both within and between different agricultural zonings.
