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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]

Understanding the spatiotemporal pattern of precipitation concentration is important for the assessment of flood and drought risk and utilization of water resources. In this study, the daily precipitation concentration index in the Hai River basin in China was calculated based on the Gini coefficient obtained from the observed data of 51 meteorological stations from 1951 to 2018 and spatiotemporal pattern variations were investigated. The trends and abrupt changes of DPCI were tested by the Mann–Kendall and the Pettitt methods, respectively. The relationships among DPCI, percentage of precipitation contributed by the rainiest days, and disaster losses were discussed by the linear correlation analysis. The results showed that the DPCI value ranges between 0.6471 and 0.7938, decreases westward and northward, which is negatively related to latitude and elevation, and is positively related to longitude. Negative trends of the DPCI were found at most stations, and the PCI trends of more than 80% stations were statistically significant. Abrupt changes of the DPCI have a postponement trend from west to east with time. Daily heterogeneity of the rainfall in a year is highly correlated with the heavy rainfall amount of the 25% rainiest days. For a year, higher DPCI coupled with more precipitation is easy to cause a flood disaster; conversely, higher DPCI along with less precipitation is easy to cause a drought disaster. In the future, the risk of flood disasters would be reduced, but the drought disasters would be increased in the Hai River basin. [ABSTRACT FROM AUTHOR]

China's economic development has shifted from a high-speed growth stage to a high-quality development stage, which promotes the sustainable economic development of the Yangtze River (YR) and also improves the response capacity of the YR basin's resilience system to floods. Flood resilience (FR) is not only just the achievement of high-quality economic development (HQED) but also the key to mitigating flood damage (FD). Therefore, based on panel data from 2000 to 2019 from provincial administrative regions (PARs) in the Yangtze River basin (YRb), the entropy weight method and the quantitative regression analysis method were used to empirically investigate the impact of HQED and FR on FD in the YRb. The empirical analysis results show the following. (i) HQED has an inverted U-shaped effect on FD in the YRb, meaning that HQED will reach a certain point and reduce flood losses. (ii) The HQED model, which has been pushed forward, will significantly improve the level of FR. Various mediating effect test methods revealed that FR has a significant negative mediating effect on the process of HQED to mitigate FD. Taken together, this reveals the importance of insisting on implementing an HQED model to mitigate FD in the YRb, gradually deriving an FR that is adapted to floods and has a high self-organizing and self-restoring capacity through high-quality development. [ABSTRACT FROM AUTHOR]

Climate change increases the possibility in varying rainfall and temperature that needs a detail study to estimate flood frequency under changing climate for the Woybo catchment in the Omo River basin of Ethiopia. The impact on flood frequency was evaluated using multiple climate models under RCP4.5 and RCP8.5 emission scenarios for 2020s, 2050s, and 2080s compared to 1976–2005. Hydrologic Engineering Center of Hydrologic Modeling System (HEC-HMS) was used to simulate streamflow after sensitivity analysis, calibration, and validation of the model for the catchment. Flood frequency analysis was initiated after discharge through a longer section followed by frequency analysis by partial duration series approach which provides a better result even though threshold selection is cumbersome. The results from the nonparametric Mann–Kendall test illustrate a slight increase in annual rainfall. The projected flow is expected to increase in autumn, summer, and annually by 8.34, 13.54, and 5.02% in the 2050s and 12.20, 18.06, and 11.87% in 2080s, respectively, under RCP4.5, while it is likely to increase in summer and annually by 15.66 and 5.82% in 2050s and 10.55 and 29.51% in 2080s, respectively, under RCP8.5. Flood frequency was analyzed by using Gumbel's method. The flood magnitude predicts a positive change for 10, 50, 100, 200, 500, and 1000 years recurrent periods under both scenarios. This research paves way to reduce the negative impacts of flooding and plays a vital role in providing estimates of recurrence floods which are used in designing roads, dams, and bridges for sustainable water resources management. [ABSTRACT FROM AUTHOR]