Identification of hotspots of flood risk in High Mountain Asia region based on geomorphology and climate data

Author(s):  
Mariam Khanam ◽  
Giulia Sofia ◽  
Efthymios I. Nikolopoulos ◽  
Emmanouil N. Anagnostou

<p>High Mountain Asia (HMA) has the most complex terrain with active hydrologic and geomorphologic processes. Climate change has expedited glacial melt and altered monsoon rain intensity. This has increased flood vulnerability across the region. There have been a few initiatives to measure the vulnerability locally. However, to identify hotspots of flood risk across the region, investigation of the entire HMA region is necessary. Unfortunately, in ungauged basins, the use of traditional floodplain mapping techniques is prevented by the lack of the extensive data required. The present work aims to provide a remote sensing-based flood-risk assessment model that maps and quantifies susceptibility in flood-prone areas. We developed a procedure for floodplain delineation based on high-resolution terrain data and a geomorphic classifier, coupled with satellite-derived extreme rainfall quantiles, and records of past flood events. For this work, we used the unique 8-meter Digital Elevation Models (DEMs) for HMA that are available at the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC). The geomorphic classifier is based on the Hydraulic Scaling Function automatically derived from the DEM, which is used to normalize topography according to the ratio between the local elevations along the drainage network and the riverbanks.  We assess the flood risk hot spots for a specific year based on the spatial distribution of flood losses, drainage density, flood-prone areas, and rainfall. This local flood-risk assessment framework, gradually applied across the entire HMA domain, will increase the awareness of flood risk, towards improved measures for flood risk reduction.</p>

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 104 ◽  
Author(s):  
Qiang Liu ◽  
Hongmao Yang ◽  
Min Liu ◽  
Rui Sun ◽  
Junhai Zhang

Cities located in the transitional zone between Taihang Mountains and North China plain run high flood risk in recent years, especially urban waterlogging risk. In this paper, we take Shijiazhuang, which is located in this transitional zone, as the study area and proposed a new flood risk assessment model for this specific geographical environment. Flood risk assessment indicator factors are established by using the digital elevation model (DEM), along with land cover, economic, population, and precipitation data. A min-max normalization method is used to normalize the indices. An analytic hierarchy process (AHP) method is used to determine the weight of each normalized index and the geographic information system (GIS) spatial analysis tool is adopted for calculating the risk map of flood disaster in Shijiazhuang. This risk map is consistent with the reports released by Hebei Provincial Water Conservancy Bureau and can provide reference for flood risk management.


Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 420
Author(s):  
Zening Wu ◽  
Yuhai Cui ◽  
Yuan Guo

With the progression of climate change, the intensity and frequency of extreme rainfall have increased in many parts of the world, while the continuous acceleration of urbanization has made cities more vulnerable to floods. In order to effectively estimate and assess the risks brought by flood disasters, this paper proposes a regional flood disaster risk assessment model combining emergy theory and the cloud model. The emergy theory can measure many kinds of hazardous factor and convert them into unified solar emergy (sej) for quantification. The cloud model can transform the uncertainty in flood risk assessment into certainty in an appropriate way, making the urban flood risk assessment more accurate and effective. In this study, the flood risk assessment model combines the advantages of the two research methods to establish a natural and social dual flood risk assessment system. Based on this, the risk assessment system of the flood hazard cloud model is established. This model was used in a flood disaster risk assessment, and the risk level was divided into five levels: very low risk, low risk, medium risk, high risk, and very high risk. Flood hazard risk results were obtained by using the entropy weight method and fuzzy transformation method. As an example for the application of this model, this paper focuses on the Anyang region which has a typical continental monsoon climate. The results show that the Anyang region has a serious flood disaster threat. Within this region, Linzhou County and Anyang County have very high levels of risk for flood disaster, while Hua County, Neihuang County, Wenfeng District and Beiguan District have high levels of risk for flood disaster. These areas are the core urban areas and the economic center of local administrative regions, with 70% of the industrial clusters being situated in these regions. Only with the coordinated development of regional flood control planning, economy, and population, and reductions in the uncertainty of existing flood control and drainage facilities can the sustainable, healthy and stable development of the region be maintained.


2011 ◽  
Vol 58 (3) ◽  
pp. 1295-1309 ◽  
Author(s):  
Wen-Ko Hsu ◽  
Pei-Chiung Huang ◽  
Ching-Cheng Chang ◽  
Cheng-Wu Chen ◽  
Dung-Moung Hung ◽  
...  

2012 ◽  
Vol 12 (5) ◽  
pp. 75-81 ◽  
Author(s):  
Jin Gul Joo ◽  
Jung Ho Lee ◽  
Moo Jong Park

2017 ◽  
Vol 9 (11) ◽  
pp. 2005 ◽  
Author(s):  
Jieun Ryu ◽  
Eun Joo Yoon ◽  
Chan Park ◽  
Dong Kun Lee ◽  
Seong Woo Jeon

Climate ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 116
Author(s):  
Saman Armal ◽  
Jeremy R. Porter ◽  
Brett Lingle ◽  
Ziyan Chu ◽  
Michael L. Marston ◽  
...  

Hurricanes and flood-related events cause more direct economic damage than any other type of natural disaster. In the United States, that damage totals more than USD 1 trillion in damages since 1980. On average, direct flood losses have risen from USD 4 billion annually in the 1980s to roughly USD 17 billion annually from 2010 to 2018. Despite flooding’s tremendous economic impact on US properties and communities, current estimates of expected damages are lacking due to the fact that flood risk in many parts of the US is unidentified, underestimated, or available models associated with high quality assessment tools are proprietary. This study introduces an economic-focused Environmental Impact Assessment (EIA) approach that builds upon an our existing understanding of prior assessment methods by taking advantage of a newly available, climate adjusted, parcel-level flood risk assessment model (First Street Foundation, 2020a and 2020b) in order to quantify property level economic impacts today, and into the climate adjusted future, using the Intergovernmental Panel on Climate Change’s (IPCC) Representative Concentration Pathways (RCPs) and NASA’s Global Climate Model ensemble (CMIP5). This approach represents a first of its kind—a publicly available high precision flood risk assessment tool at the property level developed completely with open data sources and open methods. The economic impact assessment presented here has been carried out using residential buildings in New Jersey as a testbed; however, the environmental assessment tool on which it is based is a national scale property level flood assessment model at a 3 m resolution. As evidence of the reliability of the EIA tool, the 2020 estimated economic impact (USD 5481 annual expectation) was compared to actual average per claim-year NFIP payouts from flooding and found an average of USD 5540 over the life of the program (difference of less than USD 100). Additionally, the tool finds a 41.4% increase in average economic flood damage through the year 2050 when environmental change is included in the model.


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