Optimizing Land Use and Land Cover Allocation for Flood Mitigation Using Land Use Change and Hydrological Models with Goal Programming, Chaiyaphum, Thailand (original) (raw)
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International Journal of Multidisciplinary Research and Growth Evaluation
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Flood risk modeling for optimal rice planning for delta region of Mahanadi river basin in India
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Flood risk management serves to reduce the negative consequences of flood disaster to a certain extent. In agriculture-dominated countries, the extent of damage incurred in crop land by heavy and frequent floods is quite high. The present study aims to develop an optimal rice planning procedure considering the flood risk through hydrodynamic floodplain modeling in flood-prone delta region of Mahanadi river basin in India. As high-resolution topographic data and surveyed river cross sections are unavailable for the study area, MIKE FLOOD model setup is prepared using river cross sections and floodplain elevation model derived from freely available SRTM DEM. In this study, MIKE FLOOD setup is prepared and flood inundation simulation is carried out. Flood inundation extent obtained is compared with RADARSAT-1 image-based inundation extent. Subsequently, flood risk is evaluated for cropping pattern in floodplains using functional relationships between flood characteristics and the expected damage of different rice varieties. Based on the flood risk, an optimal rice planning model is developed for maximizing the net benefits in the floodplain. The average annual expected net benefit of optimal rice allocation model for the study area is to the tune of INR 601 million compared to INR 432 million for normal rice variety cultivation throughout the study area.
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Modeling Earth Systems and Environment, 2016
The Markov Chain and Cellular Automata (Markov-CA) approach have been applied to create the dynamics of land use/land cover (LULC) change modeling in the Tondano watershed, North Sulawesi, Indonesia. The multi temporal of remotely sensed data, Landsat 5 TM in 1997, Landsat 7 TM in 2002 and Landsat 8 LDCM in 2015 were used to produce the LULC maps. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Map (GDEM) data were used as input for the flood modeling created by the Monte Carlo algorithm. The LULC maps in 1997 and 2002 were used to create predictions and modeling LULC map with the Markov-CA approach in the next few years (for the year 2015, 2025, 2035 and 2050). Meanwhile, the LULC map in 2015 with an accuracy of 80.11 % based on the calculation of the Kappa index has been used as a reference map to determine the accuracy of the Markov-CA approach to produce a model of the LULC map in 2015. The result of the accuracy by using cross-correlation matrix between the LULC model in 2015 with the LULC reference in 2015 is 75.88 %. The dynamics of LULC changes showed that area-class forest, dry land, paddy fields and shrubbery would be expected to experience an area decreases in the extent from the year 2015 to 2050, with the rate of change in average: 10.52, 13.22, 14.49 and 1.15 ha/year, respectively. Meanwhile, the area-class bare soil, plantation, settlement and water body would be expected to experience an area increases, with the rate of change in average: 6.79, 11.14, 11.49 and 9.7 ha/year, respectively. Furthermore, flood damage assessment can be calculated by estimating LULC area affected by the flood, which is determined based on the overlay between LULC maps from the result of Markov-CA with flood maps from the result of Monte Carlo algorithm. Under current conditions, estimated flood damage exposure to extreme flood events with return periods of 100 years for the water level scenario Hc = 3 m and Hc = 5 m is more than €520 and €958 million, respectively.
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