Investigation of Z-R relationships during tropical storm in GIS using implemented mosaicking algorithms of radar rainfall estimates from ground-based weather radar in the Yom River basin, Thailand

Mahavik, Nattapon; Tantanee, Sarintip; Masthawee, Fatah

doi:10.1007/s12518-021-00383-2

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…Similarly, in the consideration of flooding, rainfall is also recognized as the most influential hazard factor [28,36]. It is expected to achieve more accurate flooding results when using radar estimated rainfall [37]. However, there is unavailable radar estimated data during the study period.…”

Section: Results and Discussion 1) Prioritization Of Flood Hazard Fac...mentioning

confidence: 99%

A Fuzzy AHP Approach to Assess Flood Hazard for Area of Bang Rakam Model 60 Project in Yom River Basin, Northern Thailand

Yodying

Mahavik²,

Tantanee

et al. 2022

App. Envi. Res.

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The Thai government developed the “Bang Rakam Model 60” to solve flood issues in low-lying areas (Phitsanulok and Sukhothai Provinces). In the project, farmers will have to start planting in early April and harvest in July. This research proposes a methodology for assessing flood hazard using a fuzzy analytic hierarchy process (fuzzy AHP) relied on Chang’s extent analysis. It was employed to derive the weight for factor ranking and create a flood hazard map. Eight hazard factors are considered in the methodology: average annual rainfall, drainage density, distance from drainage network, soil water infiltration, land use, elevation, slope, and flow accumulation. The generated flood hazard maps were validated using the repeated flood area from Geo-Informatics and Space Technology Development Agency (GISTDA). Due to the difference of rated opinion on the drainage density factor, the eight experts were divided into two groups of four each. The results of both expert groups indicated that the most pivotal influencing factor to flood hazard is the average annual rainfall. From the first group, it is stated that the highest flood hazard areas are in Phrom Phiram, Mueang Phitsanulok, and Bang Rakam Districts. Whereas, the second group stated that very high flood hazards level occurring mostly in Phrom Phiram District. The flood hazard area was divided into five levels of very low, low, moderate, high, and very high which the first group found that they covered 75.59 km2, 184.44 km2, 211.94 km2, 165.78 km2, and 57.81 km2, respectively, while the second group found that they covered 38.93 km2, 100.22 km2, 175.58 km2, 218.90 km2, and 161.91 km2, respectively. The obtained flood hazard assessment provides crucial information for future flood preparation, response, prevention, mitigation, and recovery initiatives. Moreover, it will guide the government agencies in supplying water and save the compensation budget to victims’ flood-affected farms.

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Section: Results and Discussion 1) Prioritization Of Flood Hazard Fac...mentioning

confidence: 99%

A Fuzzy AHP Approach to Assess Flood Hazard for Area of Bang Rakam Model 60 Project in Yom River Basin, Northern Thailand

Yodying

Mahavik²,

Tantanee

et al. 2022

App. Envi. Res.

Self Cite

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“…However, the rainfall value in the northeastern region gradually decreased to the west side of the area [72] through the Central part and the central part of the northern region because the land is a long way from the nearest sea and there is a friction force which can obstruct tropical storms [18,21]. The upper area of the northern region had higher rainfall in the center of the region due to the high mountain range and abundant forests which can bring orographic rain [73]. Moreover, movement of the Inter-Tropical Convergence Zone (ITCZ) also results in rainfall through the area which the ITCZ passes around [74].…”

Section: Precipitation During 1987 To 2021mentioning

confidence: 99%

Investigation of Temperature, Precipitation, Evapotranspiration, and New Thornthwaite Climate Classification in Thailand

Phumkokrux,

Trivej

2024

Atmosphere

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This study aims (1) to study the trend and characteristics of average annual air temperature (Tann), annual precipitation (Prann), and annual evapotranspiration (PETann) in Thailand over the present period (1987–2021) and (2) to extract the climate pattern in form of a map using the New Thornthwaite Climate Classification method in Thailand considering the present period. The data were prepared by the Thai Meteorological Department. Data variability, the mean of the data calculation in time series, the homogeneity test of data, and abrupt changes were examined. The trends of each variable were calculated using the Mann–Kendal and Sen’s slope test. The results indicated that the high Tann found in Bangkok gradually decreased in the next area. Tann data were heterogeneous with the abrupt change period, and increasing trends were found. Prann values were high in the west side of the southern area and the bottom area of the eastern area; in addition, low rainfall was found in the inner area of the land. Prann data were homogenous with no abrupt change period and slight changes in trends. PETann and %CV spatial distribution were determined for the same pattern of Tann. PETann data were heterogeneous with abrupt change periods and rising trends. The torrid thermal index determined based on the New Thornthwaite Climate Classification results indicated an overall torrid-type climate. A semi-arid climate pattern was found in the small area of the middle of Thailand, and then it shifted toward a moist-type pattern in the next area with an in precipitation. The most climate variability was found to be extreme with the power of temperature changes.

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Investigate ensemble machine learning models to reduce the daily Mean field bias of radar rainfall estimates derived from ZR relationships in the sub-river basins in the middle of Thailand

Mahavik,

Kangerd,

Masthawee

et al. 2024

Environ Dev Sustain

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Investigation of Z-R relationships during tropical storm in GIS using implemented mosaicking algorithms of radar rainfall estimates from ground-based weather radar in the Yom River basin, Thailand

Cited by 5 publications

References 36 publications

A Fuzzy AHP Approach to Assess Flood Hazard for Area of Bang Rakam Model 60 Project in Yom River Basin, Northern Thailand

A Fuzzy AHP Approach to Assess Flood Hazard for Area of Bang Rakam Model 60 Project in Yom River Basin, Northern Thailand

Investigation of Temperature, Precipitation, Evapotranspiration, and New Thornthwaite Climate Classification in Thailand

Investigate ensemble machine learning models to reduce the daily Mean field bias of radar rainfall estimates derived from ZR relationships in the sub-river basins in the middle of Thailand

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