2020年长江上游和中下游先后发生特大洪水，其中干流编号洪水全部发生在上游，构成了长江流域洪水的主要部分。首先回顾2020年洪水及洪灾情况，然后根据历史上几次特大洪水过程和历年实测资料，分析长江上游洪水特征、洪灾类型及特点，最后提出新时代长江流域洪水整体防御战略及山洪灾害防治战术。研究表明：金沙江洪水是长江上游洪水基础部分，岷江、嘉陵江和干流区间是洪峰的主要来源，三者洪水遭遇是产生上游特大洪水的主因，上游洪水又是全流域特大洪水的基础和重要组成部分。目前造成洪灾死亡人数最多的是山洪以及山洪引起的地质灾害，财产损失最大的是中下游及湖泊地区。未来堤防仍然是防洪的基础，提高沿江城市防洪标准主要手段是控制性水库的联合优化调度，而减少洪涝灾害损失最有效的途径是给洪水以空间的自然解决方案等非工程措施。

In 2020， the upstream and mid-downstream of the Yangtze River experienced massive floods， with the major mainstream floods occurring in the upper Yangtze River. In this study， we first reviewed the floods and their related losses， and then analyzed the characteristics of flooding disasters in the upper Yangtze River based on several catastrophic floods in history. Finally， we proposed the integrated strategies for flood defense and the control tactics for flash floods in the Yangtze River Basin in the new era. Our results show that the floods in the Jinsha River underlay the floods in the upper Yangtze River， whereas the flood peaks were primarily attributed to the inflow from the Minjiang River， the Jialing River and the mainstream interval. The co-occurrence of floods in the aforementioned three tributaries led to the mega-floods in the upper Yangtze River， which formed the foundation and an important component of the basin-wide floods. At present， flash floods and accompanied geological disasters caused the largest number of deaths， and the biggest property losses occurred in the middle-lower reaches and the lake areas. In the future， the embankments will remain the basis of flood control. The main means to improve flood control standards of urbans long the river will rely on the joint optimal operation of reservoirs. In addition， the most effective way to reduce flood losses will be non-engineering measures such as natural solutions that give space to floods.

The regional multi-hazards risk assessment poses difficulties due to data access challenges, and the potential interactions between multi-hazards and social vulnerability. For better natural hazards risk perception and preparedness, it is important to study the nature-hazards risk distribution in different areas, specifically a major priority in the areas of high hazards level and social vulnerability. We propose a multi-hazards risk assessment method which considers social vulnerability into the analyzing and utilize machine learning-enabled models to solve this issue. The proposed methodology integrates three aspects as follows: (1) characterization and mapping of multi-hazards (Flooding, Wildfires, and Seismic) using five machine learning methods including Naïve Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), and K-Means (KM); (2) evaluation of social vulnerability with a composite index tailored for the case-study area and using machine learning models for classification; (3) risk-based quantification of spatial interaction mechanisms between multi-hazards and social vulnerability. The results indicate that RF model performs best in both hazard-related and social vulnerability datasets. The most cities at multi-hazards risk account for 34.12% of total studied cities (covering 20.80% land). Additionally, high multi-hazards level and socially vulnerable cities account for 15.88% (covering 4.92% land). This study generates a multi-hazards risk map which show a wide variety of spatial patterns and a corresponding understanding of where regional high hazards potential and vulnerable areas are. It emphasizes an urgent need to implement information-based prioritization when natural hazards coming, and effective policy measures for reducing natural-hazards risks in future.© 2023. Springer Nature Limited.

This study looks at the nexus between urban growth, climate change, and flood risk in Doha, Qatar, a hot-spot, climate change region that has experienced unprecedented urban growth during the last four decades. To this end, this study overviews the main stages of Doha's urban growth and influencing climatic factors during this period. A physically-based hydrological model was then built to simulate surface runoff and quantify flood risk. Finally, the Pearson correlation was used to verify the potential nexus between flood risk, climate change, and urban growth. Surveying showed that, between 1984 and 2020, urban areas grew by 777%, and bare lands decreased by 54.7%. In addition, Doha witnessed various climatic changes with a notable increase in air temperature (+ 8.7%), a decrease in surface wind speed (- 19.5%), and a decrease in potential evapotranspiration losses (- 33.5%). Growth in urban areas and the perturbation of climatic parameters caused runoff to increase by 422%, suggesting that urban growth contributed more than climatic parameters. Pearson correlation coefficient between flood risk and urban growth was strong (0.83) and significant at p < 0.05. Flood risk has a strong significant positive (negative) correlation with air temperature (wind speed) and a moderate positive (negative) correlation with precipitation (potential evapotranspiration). These results pave the way to integrate flood risk reduction measures in local urban development and climate change adaptation plans.© 2022. The Author(s).

孟印缅三国地处亚热带与热带季风气候区,因自然条件制约,洪涝灾害频繁发生,对“孟中印缅经济走廊”建设将会带来重大影响。开展孟印缅地区的洪水风险评估可为“孟中印缅经济走廊”的建设安全提供必要的信息和科技支撑。利用1980—2016年的降水数据,结合河网、数字高程和土地利用等数据,选取雨季降雨量、暴雨天数、高程、坡度、河网密度、植被覆盖度、土壤可蚀性、人口密度、地均GDP和土地利用10个指标,采用层次分析法和AHP_熵权法对孟印缅地区的洪水灾害风险分布进行了比较研究。研究表明：孟印缅地区高风险区和较高风险区分别占总面积的1.05%和28.76%,高风险区主要分布在印度北部的恒河平原、印度东北部的阿萨姆邦、孟加拉国大部分地区和缅甸南部。受自然、人口和经济条件的制约,孟加拉国是孟印缅三国中洪水风险最高的国家,高风险区和较高风险区分别占总面积的10.61%和65.87%。层次分析法和AHP_熵权法结果间的比较表明,后者比前者识别出更大范围的洪水高风险区。本研究为中国开展周边国家自然灾害的风险评估提供了有效的方法,有助于推进国家孟中印缅经济走廊的建设。

洪水灾害具有发生频率高、破坏性强的特点,风险评估可揭示高风险热点区及其驱动因素,有助于科学、高效构建防洪减灾体系。以珠江流域浔江防洪保护区为研究对象,构建HEC-RAS水动力模型,提取最大流速、水深评估洪水危险性;利用层次分析法和熵权法,考虑人口密度、国内生产总值(GDP)和土地利用等要素,评估承灾体的暴露度和脆弱性;最后计算洪灾风险,分析洪灾风险时空动态变化。结果表明:水动力模型的平均命中率达0.80以上,误报率低于0.28;在浔江防洪保护区内,中高风险区占5.20%以上;1997—2017年间,不同等级的洪水风险均呈加剧趋势,值得警惕的是中高风险区近46.69%区域风险上升趋势显著。研究成果可为科学高效的洪灾风险管理提供决策支持。