Prediction of hydrological responses to land use change.

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ID: 73984
2019
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Abstract
The change of land use and land coverage (LULC) has a direct impact on the underlying surface of the drainage basin, hence alters the rainfall-runoff processes. This study described the procedure to use the CA-Markov model to predict the future distributed land use in the Qinhuai River basin in 2028 based on the historical evolution. The hydrological model HEC-HMS was then utilized to examine the impact of land use change on the hydrological responses of the drainage basin. The major findings were as follows: (1) from 2001 to 2010, major changes detected in land use were substantial conversion of paddy fields, forest land and dryland into urban land (increased by 56.81%), following the trend recognized by CA-Markov model, urban land and dry land were predicted to increase by 17.47% and 14.05% by 2028. (2) the projected land use in 2028 resulted in various degrees of increase in flood peak and volume according to the severity of the floods characterized by flood volumes, the smaller floods were predicted to result in more significant increase with 3.5% increase in flood peak and 2.9% increase in flood volume while these values decreased to 0.4% and 1.1% for a big scale flood; (3) greater increase in urbanization leads to greater change of the flood peak and volume change. For small scale floods, when the proportion of urban land use increases by 30% to 60% hypothetically, the relative increment in flood peaks increase from 4.7% to 8.1% with 4% to 6.6% increase in the flood volume; (4) on the sub-basin scale, the trend of change in urban land use and the flood peak and volume were consistent. The methods and conclusions may shed light on urban land development and management and design of flood control measures in a large river basin.
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gao2019predictionthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gao, Yuqin;Chen, Jiahui;Luo, Hao;Wang, Huaizhi;
Journal The Science of the total environment
Year 2019
DOI S0048-9697(19)34990-3
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