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It is well known that flooding brings great losses to people's production and life, just because it is unsuspected, is extremely extensive, and has high frequency. More than half of the people in China live in flood-prone areas, and their lives and properties are threatened. Flood risk assessment is one of the measures of flood management, and it is economically and socially important to assess flood risk. The scope of the traditional monitoring and forecasting early warning system is mainly limited to the area affected by flash floods, while the objective reality of real-time monitoring, forecasting, and early warning of flood disasters in the area affected by river floods and by the scheduling of riverine terrace power stations is not possible. The multiphysics remote sensing big data fusion analysis can divide the data into grids according to the grid method in the sliding time window, filter the normal data by information entropy in each grid, judge the remaining data that may be abnormal by using local abnormality factor, and eliminate the abnormal data according to the judgment result. This paper introduces the application of remote sensing in flood control field, proposes the framework of basin flood prediction based on multiphysics field remote sensing big data fusion analysis, and designs each functional module of the system according to the object-oriented idea to realize the functions of data management, image processing, spatial analysis, and simulation output. The method can change the problems of cumbersome data processing and basic parameter rate determination in traditional hydrological methods and can find certain regularity through the connection between all related factors. Meanwhile, the use of artificial intelligence and other technical means makes the calculation speed faster and the obtained results are closer to the actual measured values, which is beneficial to guide the practical work. [ABSTRACT FROM AUTHOR]

Precipitation data from 30 stations in the Huaihe River basin (HRB), a climatic transitional zone in east China, were used to investigate the spatiotemporal variability and trends of extreme precipitation on multitimescales for the period 1961–2010. Results indicated that (1) the spatial pattern of the annual precipitation, rainy days, extreme precipitation, and maximum daily precipitations shows a clear transitional change from the south (high) to the north (low) in the HR; it confirmed the conclusion that the HRB is located in the transitional zone of the 800 mm precipitation contour in China, where the 800 mm precipitation contour is considered as the geographical boundary of the south and the north. (2) Higher value of the extreme precipitation intensity mainly occurs in the middle of the east and the central part of the basin; it reveals a relatively distinct west-east spatial disparity, and this is not in line with the spatial pattern of the extreme precipitation total, the sum of the precipitation in 95th precipitation days. (3) Annual precipitation of 22 stations exhibits increasing trend, and these 22 stations are located from the central to the northern part. There is no significant trend detected for the seasonal precipitation. The summer precipitation exhibits a larger change range; this might cause the variation of the flood and drought in the HBR. However, the increasing trend in winter precipitation may be beneficial to the relief of winter agricultural drought. Rainy days in 12 stations, mostly located in and around the central northeastern part, experienced significant decreasing trend. Extreme precipitation days and precipitation intensity have increasing trends, but no station with significant change trend is detected for the maximum precipitation of the basin. (4) The spatiotemporal variability in the HRB is mainly caused by the geographic differences and is largely influenced by the interdecadal variations of East Asian Summer Monsoon in eastern China. The output of this paper could provide references for the practical decision making and integrated basin management of Huaihe River basin. [ABSTRACT FROM AUTHOR]