Watershed Prioritization of Kaeng Lawa Sub-Watershed, Khon Kaen Province Using the Morphometric and Land-Use Analysis: A Case Study of Heavy Flooding Caused by Tropical Storm Podul

Waiyasusri, Katawut; Chotpantarat, Srilert

doi:10.3390/w12061570

Cited by 32 publications

(23 citation statements)

References 43 publications

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“…Finally, flood prioritization was determined based on compound values. While the low values represented high flood priority, the high compound values show low flood priority ranks (Waiyasusri & Chotpantarat 2020). The obtained compound values were between 6.93 -28.57.…”

Section: Flood Priority Rank Based On Morphometric Resultsmentioning

confidence: 91%

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Flood Prioritization Watersheds of the Aras River, Based on Geomorphometric Properties: Case Study Iğdır Province

Utlu

Ghasemlounia

2021

Jeomorfolojik Araştırmalar Dergisi

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Drainage watershed morphometry plays a major role in terms of understanding flood dynamics. Flood potentials are explained considering the linear, areal, and relief morphometry features of the watersheds. In general, there are number of geomorphometric indices in the description of these features. The formal geometries, geomorphology, geology, and general climate characteristics, etc., which play a decisive role in the flood potential in the watershed, can be determined relatively with morphometric indices. In this study, 35 different river watersheds draining their waters to the Aras, located in the center of Iğdır Province, were examined. Geographic information systems (GIS) and statistical software were chosen to analyze and calculate indices for this research. Flood events occur in these 35 different river watersheds and this study evaluates river watersheds in terms of their flood potential using 14 different indices. In the evaluation of the results obtained, the values that have a high impact on the floods are ranked according to their priorities. These results were evaluated in terms of flood priorities using morphometric analysis and principal component analysis methods. Flood priorities of watersheds, obtained through two different methods, are classified as high, medium, or low priorities. The number of common watersheds, determined based on two different methods, is 8 in river watersheds with "high" priority, 11 in river watersheds with "medium" priority, and 7 in river watersheds with "low" priority. According to these results, it is seen that river watersheds with high priority flood potential correspond to the areas where flood events occurred.

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Section: Flood Priority Rank Based On Morphometric Resultsmentioning

confidence: 91%

“…The first of these is the morphometric priority and the other is obtained through the PCA method. (Malik et al, 2019;Martins and Nunes, 2020;Siddiqui et al, 2020;Waiyasusri and Chotpantarat, 2020).…”

Section: Potential Flood Prioritization Of Watershedsmentioning

confidence: 99%

Flood Prioritization Watersheds of the Aras River, Based on Geomorphometric Properties: Case Study Iğdır Province

Utlu

Ghasemlounia

2021

Jeomorfolojik Araştırmalar Dergisi

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“…Over the past 20 years, Krabi's tourist numbers have increased by 543% and Krabi's gross provincial product has grown 431% from the expansion of tourist's population and GPP during 1999-2020. For this reason, Remote sensing technology is necessary in the KEW to survey the area by using satellite images with a multispectral band for land cover surveying [16][17].…”

Section: Applied Environmental Researchmentioning

confidence: 99%

Monitoring the Land Cover Changes in Mangrove Areas and Urbanization using Normalized Difference Vegetation Index and Normalized Difference Built-up Index in Krabi Estuary Wetland, Krabi Province, Thailand

Waiyasusri

2021

App. Envi. Res.

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Krabi Estuary Wetland (KEW) is an outstanding wetland with an estuary environment. At present, the tourism industry has rapidly grown, resulting in the impact of land cover changes. This research aims to assess the changes that have occurred in the KEW from 1999 to 2020 using NDVI and NDBI for monitoring changes in mangrove areas and urbanization in Krabi Province, Thailand. Landsat satellite images in years 1999, 2009 and 2020 were classified by using a band ratio to create land cover maps. The results show that NDVI between 0.41–1.00 clearly shows the mangrove forest area, while NDBI between 0.01–0.40 shows urban and built-up land, and 0.41–1.00 appears as bare land. The NDVI overall accuracy assessment is 82.88%, 97.46% and 88.25% with Kappa values of 0.64, 0.92, and 0.85 for year 1999, 2009 and 2020, respectively. The NDBI overall accuracy assessment is 92.81%, 77.11% and 64% with Kappa values of 0.93, 0.77, and 0.63 for year 1999, 2009 and 2020, respectively. In addition, areas that are sensitive to land-cover change appear around the Chi rat River, Pak Nam Krabi River, and Yuan River, which are tourist areas close to the Krabi and Ao Nang communities. Therefore, it is necessary to speed up the problem solving and find measures to prevent mangrove forest degradation in these 3 mangrove forest areas so that the mangrove forest areas will not decrease rapidly in the future. This research can be valuable for land-cover management in the KEW by policy and decision makers.

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“…Besides the physical factors, socio-economic factors are also contributing factors that accelerate more frequent flooding (Geist & Lambin 2002;Luo et al, 2010;Lambin & Meyfroidt 2010). These factors inevitably cause changes in land use patterns, which is why it is important to study the context of such factors through flood prone risk area analysis (Waiyasusri & Chotpantarat, 2020).…”

Section: Introductionmentioning

confidence: 99%

Flood Prone Risk Area Analysis During 2005 – 2019 in Lam Se Bok Watershed, Ubon Ratchathani Province, Thailand

Waiyasusri¹,

Kulpanich²,

Worachairungreung³

et al. 2021

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This research investigates the application of logistic regression analysis for flood prone risk mapping in the Lam Se Bok watershed area. The study found that floods have occurred as many as 15 times since 2005. In 2019, flooding covered 200.01 km 2 of the watershed (5.51% of the total watershed). Among the areas that flood every year, 15 floods occurred in the lower part of the LSBW basin in Na Udom village, Khok Sawang and Fa Huan village, Rai Khi sub-district, which are in the south of Lue Amnat District, Amnat Charoen Province, as well as in parts of Dum Yai sub-district, Muang Sam Sip district, Ubon Ratchathani. Logistic regression analysis was used to determine the influence of certain variables on this flooding. The variables showing positive β values were mean annual precipitation and distance to a road. The variables showing negative β values included elevation, terrain, slope, soil drainage, distance to stream, land-use, and distance to village, respectively. All of these variables can be analyzed for their Flood Prone Risk area in GIS. The study found that floodprone areas at the very high-level flood prone risk areas, with a total area of 638.59 km 2 (17.59%), high level flood prone risk areas cover an area of 1,848.10 km2 (50.92%). Medium flood prone risk areas cover 794.95 km2 (21.90%). Low flood prone risk areas cover 310.86 km2 (8.56%), the least vulnerable to flooding encompassed 46.35 km2 (1.27%)., and occurred in areas with low elevation and areas with high annual average rainfall when the variable was located in the middle and downstream parts of the LSBW river basin.

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Watershed Prioritization of Kaeng Lawa Sub-Watershed, Khon Kaen Province Using the Morphometric and Land-Use Analysis: A Case Study of Heavy Flooding Caused by Tropical Storm Podul

Cited by 32 publications

References 43 publications

Flood Prioritization Watersheds of the Aras River, Based on Geomorphometric Properties: Case Study Iğdır Province

Flood Prioritization Watersheds of the Aras River, Based on Geomorphometric Properties: Case Study Iğdır Province

Monitoring the Land Cover Changes in Mangrove Areas and Urbanization using Normalized Difference Vegetation Index and Normalized Difference Built-up Index in Krabi Estuary Wetland, Krabi Province, Thailand

Flood Prone Risk Area Analysis During 2005 – 2019 in Lam Se Bok Watershed, Ubon Ratchathani Province, Thailand

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