Future Runoff Variation and Flood Disaster Prediction of the Yellow River Basin Based on CA-Markov and SWAT

Guangxing Ji, Zhizhu Lai, Haibin Xia, Hao Liu and Zheng Wang
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Guangxing Ji: College of Resources and Environmental Sciences, Henan Agricultural University, Zhengzhou 450002, China
Zhizhu Lai: Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
Haibin Xia: Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
Hao Liu: Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China
Zheng Wang: Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China

Land, 2021, vol. 10, issue 4, 1-19

Abstract: The purpose of this paper is to simulate the future runoff change of the Yellow River Basin under the combined effect of land use and climate change based on Cellular automata (CA)-Markov and Soil & Water Assessment Tool (SWAT). The changes in the average runoff, high extreme runoff and intra-annual runoff distribution in the middle of the 21st century are analyzed. The following conclusions are obtained: (1) Compared with the base period (1970–1990), the average runoff of Tangnaihai, Toudaoguai, Sanmenxia and Lijin hydrological stations in the future period (2040–2060) all shows an increasing trend, and the probability of flood disaster also tends to increase; (2) Land use/cover change (LUCC) under the status quo continuation scenario will increase the possibility of future flood disasters; (3) The spring runoff proportion of the four hydrological stations in the future period shows a decreasing trend, which increases the risk of drought in spring. The winter runoff proportion tends to increase; (4) The monthly runoff proportion of the four hydrological stations in the future period tends to decrease in April, May, June, July and October. The monthly runoff proportion tends to increase in January, February, August, September and December.

Keywords: climate change; LUCC; average runoff; high extreme runoff; intra-annual runoff distribution (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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