GIS-based risk assessment of flood disaster in the Lijiang River Basin

Abstract

This study is designed to provide a scientific reference for the establishment of rainstorm and flood disaster prevention system in Guilin region and improve the risk assessment of rainstorm and flood disasters. To realize the goal, a flood risk evaluation model is established by weight analysis methods including the entropy weight method and the analytic hierarchy process from 3 aspects, i.e., risk of disaster causing factors, sensitivity of disaster-pregnant environment and vulnerability of disaster bearing body. For the model, the daily precipitation 1980–2020 of 6 representative national meteorological stations in the Lijiang River Basin was used as reference data of disaster causing factors; six indicators, i.e., NDVI, river network density, geological hazard, slope, slope aspect and terrain undulation were selected as the sensitivity of disaster-pregnant environment; NPP, potential of farmland production, and population density were taken as the criteria for determining the vulnerability of disaster bearing capacity. Meanwhile, ArcGIS was used for analysis and calculation to complete the risk assessment of flood disaster in Lijiang River Basin, Guangxi. The results indicate that: (1) the hazard level of flood disaster causing factors in Lijiang River Basin shows a decreasing distribution pattern from north to south, and high-risk areas cover 3108.47 km2, accounting for 21.29%; (2) the stability grade of disaster-pregnant environment shows a decreasing trend from the surrounding mountains to the plains, and the low-stability and lower-stability areas are mostly found in the low-lying areas around Lijiang River, with an area of 4218.63 km2, accounting for 28.69%; (3) the vulnerability of the disaster bearing body is generally at a low level, and the areas with high level cover 246.96 km2, accounting for only 1.69%; (4) under the combined effect of the above factors, the northern part of Guilin City in the Lijiang River Basin has a high risk of flood disaster.

GIS-based flood susceptibility mapping using bivariate statistical model in Swat River Basin, Eastern Hindukush region, Pakistan

“…The NDVI is another key flood-causing factor in the way that lower NDVI weights indicate high flood susceptibility while higher NDVI weights represent a lower risk of flooding (Ullah et al, 2019b;Rehman et al, 2022). Alternatively, the lowest NDVI values represent unhealthy vegetation, mostly occupying the elevated snowcapped northern areas (Shrestha et al, 2020;Ziwei et al, 2023). Based on the NDVI measurements, our analysis revealed that all, except one, classes are highly flood susceptible.…”

“…The normal value of the NDVI is Frontiers in Environmental Science frontiersin.org ranging from −1 to +1 (Khosravi et al, 2016b;Riazi et al, 2023). The positive NDVI value is considered active vegetation coverage like dense forest, the value close to zero represents barren areas, while the negative values are referred to the water body (Wang et al, 2020;Ziwei et al, 2023). For the preparation of the NDVI map, satellite data were collected from the Landsat 8 collection 1, of the USGS department, and the value was calculated with the following formula (Eq.…”

“…There is a solid relationship between LULC and flood since land use strongly influences decreasing and increasing surface runoff (Ahmad et al, 2018;Ziwei et al, 2023). A LULC pattern determines the type of land use by individuals and natural processes (Ullah et al, 2019b).…”

Urban growth’s implications on land surface temperature in a medium-sized European city based on LCZ classification

“…Climate change has been shown to elevate urban temperature, intensifying extreme heat events, increasing their frequency, duration and strength, and raising the risk of heat exposure in the cities 1 , 2 negatively influencing ageing societies' health and urban environment 3 – 5 . It is also the main ecological driver of the hydrological balance 6 , 7 . In urban areas, temperatures can reach even a dozen degree Celsius higher than in rural 2 , 8 , 9 .…”

“…It is known that the impervious surfaces increase the risk of the floods in urban areas due to insufficient drainage 7 and that the spatial arrangement and structure of the impervious surfaces (their shape and size) are having several impacts on LST. The higher the spatial density of the impervious areas the higher the increase of LST compared to natural areas 15 – 17 .…”