scholarly journals FLOOD RISK ASSESSMENT UNDER HISTORICAL AND PREDICTED LAND USE CHANGE USING CONTINUOUS HYDROLOGIC MODELING

2015 ◽  
Author(s):  
Jonathan T. Nelson
Land ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 878
Author(s):  
Zhihui Li ◽  
Keyu Song ◽  
Lu Peng

Frequently occurring flood disasters caused by extreme climate and urbanization processes have become the most common natural hazard and pose a great threat to human society. Therefore, urban flood risk assessment is of great significance for disaster mitigation and prevention. In this paper, the analytic hierarchy process (AHP) was applied to quantify the spatiotemporal variations in flood risk in Wuhan during 2000–2018. A comprehensive flood risk assessment index system was constructed from the hazard, sensitivity, and vulnerability components with seven indices. The results showed that the central urban area, especially the area in the west bank of the Yangtze river, had high risk due to its high flood sensitivity that was determined by land use type and high vulnerability with dense population and per unit GDP. Specifically, the Jianghan, Qiaokou, Jiangan, and Wuchang districts had the highest flood risk, more than 60% of whose area was in medium or above-medium risk regions. During 2000–2018, the flood risk overall showed an increasing trend, with Hongshan district increasing the most, and the year of 2010 was identified as a turning point for rapid risk increase. In addition, the comparison between the risk maps and actual historical inundation point records showed good agreement, indicating that the assessment framework and method proposed in this study can be useful to assist flood mitigation and management, and relevant policy recommendations were proposed based on the assessment results.


2010 ◽  
Vol 66 (2) ◽  
pp. 145-156
Author(s):  
Yutaka ICHIKAWA ◽  
Masako TERAMOTO ◽  
Yuusuke NUMA ◽  
Ryosuke NISHIZAWA ◽  
Yasuto TACHIKAWA ◽  
...  

Author(s):  
Jiaheng Zhao ◽  
Huili Chen ◽  
Qiuhua Liang ◽  
Xilin Xia ◽  
Jiren Xu ◽  
...  

AbstractIncreasing resilience to natural hazards and climate change is critical for achieving many Sustainable Development Goals (SDGs). In recent decades, China has experienced rapid economic development and became the second-largest economy in the world. This rapid economic expansion has led to large-scale changes in terrestrial (e.g., land use and land cover changes), aquatic (e.g., construction of reservoirs and artificial wetlands) and marine (e.g., land reclamation) environments across the country. Together with climate change, these changes may significantly influence flood risk and, in turn, compromise SDG achievements. The Luanhe River Basin (LRB) is one of the most afforested basins in North China and has undergone significant urbanisation and land use change since the 1950s. However, basin-wide flood risk assessment under different development scenarios has not been considered, although this is critically important to inform policy-making to manage the synergies and trade-offs between the SDGs and support long-term sustainable development. Using mainly open data, this paper introduces a new framework for systematically assessing flood risk under different social and economic development scenarios. A series of model simulations are performed to investigate the flood risk under different land use change scenarios projected to 2030 to reflect different development strategies. The results are systematically analysed and compared with the baseline simulation based on the current land use and climate conditions. Further investigations are also provided to consider the impact of climate change and the construction of dams and reservoirs. The results potentially provide important guidance to inform future development strategies to maximise the synergies and minimise the trade-offs between various SDGs in LRB.


2014 ◽  
Vol 101 ◽  
pp. 102-113 ◽  
Author(s):  
Frank Canters ◽  
Sven Vanderhaegen ◽  
Ahmed Z. Khan ◽  
Guy Engelen ◽  
Inge Uljee

10.1596/28574 ◽  
2017 ◽  
Author(s):  
Satya Priya ◽  
William Young ◽  
Thomas Hopson ◽  
Ankit Avasthi

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