Multi-geospatial flood hazard modelling for a large and complex river basin with data sparsity: a case study of the Lam River Basin, Vietnam

Dung, Nguyen Ba; Long, Nguyễn Quốc; An, Dang Tran; Minh, Dang Tuyet

doi:10.1007/s41748-021-00215-8

Cited by 13 publications

(6 citation statements)

References 64 publications

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“…Based on the classification of the hazard of flood disaster causing factors, the stability of disaster-pregnant environment and the vulnerability level of the disaster bearing bodies 34 , 35 , the flood risk of the Lijiang River Basin was calculated and the flood risk evaluation map of the Lijiang River Basin was obtained (Fig. 7 ).…”

Section: Results and Analysismentioning

confidence: 99%

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

Tang

Liju

et al. 2023

Sci Rep

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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.

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Section: Results and Analysismentioning

confidence: 99%

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

Tang

Liju

et al. 2023

Sci Rep

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“…The geography is gradually tilted from northern to southern and eastern to western; the altitudes from 120 a.m.s.l to 1200 m a.m.s.l and strongly separated with three typical zones the plains, midlands and highlands (Nguyen et al, 2021;Hung et al, 2017). There areas of forestry, agricultural lands and other types of land are irregularly distributed on the whole study basin (Dinh and Sima, 2022;Dung et al, 2021). The area has a tropical monsoon climate which comprises a dry season and a rainy season all year round, creating the background of a mean annual temperature approximately of 24.5C, rainfall of about 2100 mm and a relative humidity of 85% (Nguyen et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Soil Properties Variation Valued in Relation to Land Use and Management Practices in Vietnam

Dinh,

Shima

2024

IJARe

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Background: Land use and management practices (LUMPs) have been accelerated globally to serve the increasing socio-economic requirements in recent decades. Methods: This study focuses to assess the impacts of the LUMPs on the properties and composition of soil in the Lam River Basin (LRB), Vietnam based on analysis of ninety-six soil samples collected in the soil profiles (0_60 cm). Result: The results indicated that the sand and clay ratios (34.0~35.7% and 16.2~19.9%) and bulk density (BD) (1.07±0.05~1.34 ± 0.06 Mg m-3) in the natural forest lands (NFLs) were lower and higher perturbed than the plantation forest lands (PFLs) (31.1~ 51.5%, 5.7~38.2% and 0.86 ± 0.03~1.12±0.05 Mg m-3) in the 0-20 cm topsoil layer. The soil texture is mainly sandy clay to clay loam and the BD tends slightly increase in the topsoil layer, then decrease in the 20-60 cm subsoil layer. There is no difference in soil pH between collected soil samples (CSSs) and tends to increase with increasing the subsoil layer. The base cations (BCs) (Ca2+, K+, Mg2+: 0.01~0.29, 0.06~0.11 and 0.06~0.07 Cmolc kg-1) and total organic contents (TOCs) (total carbon (TC), total nitrogen (TN), total phosphorus (TP): 24.11; 2.80 and 0.19 g kg-1) of the NFLs are higher than those of the CSSs (TC, TN, TP: 14.37~23.91, 1.62~2.28 and 0.08~0.17 g kg-1) in the topsoil layer while these values were not different in the topsoil layer.

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“…A slope with a low gradient will lead to water accumulation and slow infiltration, which can increase the risk of flooding [78,79] Both runoff flow and vertical percolation are influenced by the slope of the terrain [32]. In the KRB, the slope ranged from 2 to 74.30 (see Figure 3c) and was classified into six levels: nearly flat (0-3), gently sloping (3)(4)(5)(6)(7)(8), sloping (8)(9)(10)(11)(12)(13)(14)(15), moderately steep (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and extremely severe (>30), as proposed by Rahmati et al [32]. The class with the smallest slope range was assigned the highest weight because of the relatively flat terrain, while the class with the largest slope range was given the lowest weight due to its higher runoff potential.…”

Section: Slopementioning

confidence: 99%

“…The detailed information regarding these factors is summarized in Table 5. In order to provide a more descriptive analysis, ratings were assigned to the classes within each thematic layer based on their relative importance for flood hazard, which are very high risk (5), high risk (4), moderate risk (3), low risk (2), and very low risk (1). These ratings were determined by incorporating expert knowledge and conducting a comprehensive literature review to ensure that the reclassification accurately reflected the significance of each factor in contributing to flood hazards.…”

Section: Assigned Weights Of Criterionmentioning

confidence: 99%

“…The identification of flood hazard zones is a crucial element of funding allocations, as highlighted by Bubeck et al [3] and Chapi et al [4]. To effectively control and reduce floods, flood hazard mapping is a fundamental requirement [5]. Numerous techniques have been developed to study floods, such as frequency ratios [6][7][8], logistic regression modeling [9], artificial neural networks [10][11][12], bivariate statistical approaches [13], time series modeling [14][15][16], and machine learning [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process

Syakira,

Tan,

Zulkafli

et al. 2023

Water

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The use of satellite precipitation products can overcome the limitations of rain gauges in flood hazard mapping for mitigation purposes. Hence, this study aims to evaluate the capabilities of three global precipitation measurement (GPM) integrated multisatellite retrievals for GPM (IMERG) products in tropical flood hazard mapping in the Kelantan River Basin (KRB), Malaysia, using the GIS-based analytic hierarchy process (AHP) method. In addition to the precipitation factor, another eleven factors that contribute to flooding in the KRB were included in the AHP method. The findings demonstrated that the spatial pattern and percentage area affected by floods simulated under the IMERG-Early (IMERG-E), IMERG-Late (IMERG-L), and IMERG-Final (IMERG-F) products did not differ significantly. The receiver operating characteristics curve analysis showed that all three IMERG products performed well in generating flood hazard maps, with area under the curve values greater than 0.8. Almost all the recorded historical floods were placed in the moderate-to-very-high flood hazard areas, with only 1–2% found in the low flood hazard areas. The middle and lower parts of the KRB were identified as regions of “very high” and “high” hazard levels that require particular attention from local stakeholders.

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Multi-geospatial flood hazard modelling for a large and complex river basin with data sparsity: a case study of the Lam River Basin, Vietnam

Cited by 13 publications

References 64 publications

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

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

Soil Properties Variation Valued in Relation to Land Use and Management Practices in Vietnam

Assessment of Three GPM IMERG Products for GIS-Based Tropical Flood Hazard Mapping Using Analytical Hierarchy Process

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