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| Land use change predictions based on the CLUE-S model and total phosphorus load analysis |
| Received:October 12, 2017 |
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| KeyWord:Markov chain;genetic algorithm;CLUE-S model;non-point source pollution;export coefficient method |
| Author Name | Affiliation | E-mail | | WANG Qing-rui | School of Environment, Beijing Normal University, Beijing 100875, China | | | LIU Rui-min | School of Environment, Beijing Normal University, Beijing 100875, China | liurm@bnu.edu.cn | | MEN Cong | School of Environment, Beijing Normal University, Beijing 100875, China | | | GUO Li-jia | School of Environment, Beijing Normal University, Beijing 100875, China | |
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| Abstract: |
| In this study, we analyzed the feasibility of three quantitative land use prediction methods. These three models were the linear extrapolation method, the Markov chain model, and the genetic algorithm. They were combined with the CLUE-S model to predict land use changes in 2020 in the Xiangxi Watershed. Our research provided supporting information for land use plans in the study area and gave indications to reduce the discharge of non-point source pollution. When using either the linear extrapolation method or the Markov chain model, the forest area decreased by more than 1% from 2010 to 2020. This area mainly changed to paddy fields and dry land, which was located in the central area of the watershed on a relatively gentle slope. However, when using the genetic algorithm and appropriate environmental, social, and economic constraints, the areas of paddy field and dryland decreased by 1060 hm2 and 3370 hm2, respectively. This area was mainly converted to forest located in the north of the watershed at high altitude on steep slopes. Based on the export coefficient method, the total phosphorus loads from the whole watershed were calculated to be 11 000 kg and 8000 kg higher in 2020 than in 2010, using the linear extrapolation method and the Markov chain method, respectively. The phosphorus load in 2020 predicted by the genetic algorithm was 24 000 kg lower than in 2010. Spatially, the increased phosphorus load and decreased phosphorus load mainly occurred in the central area and the northern area of the watershed, respectively. This research thus compared three land use area prediction methods and identified the best one. The land use structure in 2020 was simulated and the total phosphorus load and distribution were predicted by integrating the quantity prediction method and the spatial land use distribution model. The result would provide a good reference for future land use planning in the study area. |
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