基于AFI指数的汉江上游流域洪涝突变辨识

洪涝灾害往往容易在短期内突然发生，从而造成巨大的人员伤亡和财产损失，但目前有关突发性洪涝现象的甄别与分析并没有达成共识。在利用标准化前期降水指数SAPI(Standardized Antecedent Precipitation Index)评估出逐日洪涝状态的基础上，提出突变性洪涝指数AFI(Abrupt Flood Index)以综合反映水量由前期（当天）到后期（后10天）突变及后期洪涝程度，同时定义并计算AFI阈值AFI_t，认为AFI超过AFI_t的日期为临界状态，后期将发生突变性洪涝事件。以汉江上游流域为例计算出该流域1972~2017年逐日AFI指数，并利用AFI_t判别出了处于临界状态的日期。进一步分析表明，AFI指数能够较好地反映突变性洪涝现象，利用AFI指数甄别出的洪涝临界状态有利于识别流域突变性洪涝事件并有助于流域水资源系统应急管理。

Susceptibility Assessment of Flood Disaster in Mountain Cities Based on GIS and Logistic Regression Analysis: A Case Study of Ji'an City,Jiangxi Province

In recent years, the frequent outbreaks of mountain floods have seriously threatened peoples' lives and property. Risk analysis such as flooding susceptibility assessment is one of the critical approaches to prevent and mitigate flooding disaster. However, the inadequate field survey and lack of data might become the significant challenges for the mapping of flood susceptibility. In the era of big data, user-generated data provides new opportunities for flood risk management. This paper takes Ji’an City as the focus area, using the flooding disaster data generated by users on the Internet. 70% flood events were randomly selected as training sample and eight flood-conditioning factors including elevation, slope, aspect, curvature, rainfall, river distance, land use and normalized vegetation index were chosen to evaluate the flooding disaster by logistic regression model. The confusion matrix and ROC curve were used to verify the evaluation results. The results show that: (1) The area with low terrain, close to water system, large rainfall, and construction land have a higher probability of flood occurrence. (2) According to the confusion matrix, the overall accuracy rate of classification is 80.6%.Verified by ROC curves, the AUC value of the training sample and the validation sample is 0.888 and 0.980 respectively. The AUC values are both greater than 0.8, indicating that the evaluation accuracy of the model is relatively high. (3) The proportion of high-risk and extremely high-risk areas is 28.71%, including 80.99% of the flood events in the study area, which shows these areas are densely distributed and highly susceptible. The evaluation outcomes were consistent with the actual situation based on the verification of the flood events from June 1 to June 8, 2020. It can be concluded from the results above that it is feasible to use the data generated by users on the Internet in mountainous areas where the data is not easy to obtain, and the evaluation results can be used to land use planning and flood risk management in Ji'an city.