A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China

Siqi Zhang, Xuefeng Sang (), Pan Liu, Ziheng Li, Sheng He and Jiaxuan Chang
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Siqi Zhang: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Xuefeng Sang: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Pan Liu: Tianjin University, Tianjin 300072, China
Ziheng Li: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Sheng He: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Jiaxuan Chang: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China

Sustainability, 2024, vol. 16, issue 2, 1-22

Abstract: Monitoring and understanding the development of agricultural management requires fine information on multiple agricultural land use classes. According to the main data bulletin of China’s third national land survey released in 2021, the proportion of arable land in China accounts for 16% of the total land area. After excluding areas that are not suitable for development (slope greater than 25 degrees), the remaining area represents arable land affected by human activities, constituting 96.69% of the total arable land area in the country. Artificially irrigated areas are products of the intertwined interactions between the natural environment and human society. However, the original remote sensing image (China’s land use/cover datasets, CLUDs), which is designed to depict the land use and cover patterns in mainland China, categorizes arable land into two main types: paddy fields and dryland. It lacks a subdivision of artificially irrigated areas. We also found a data discrepancy of more than 10% between the statistical data of rice and the paddy field data in CLUDs. To refine land use data, and then improve the simulation accuracy of the hydrological model, this article proposes a dual-source datasets fusion algorithm, learning based on big data, namely the LUCC statistical data fusion (LUSF) algorithm for integrating the remote sensing-based cropland area dataset and statistics dataset. The runoff simulation results show that, in the Yangtze River Basin, using LUSF datasets, the mean absolute percentage error value of monthly simulated runoff decreased by 0.74%, and the root mean square error value decreased by 0.22 million m 3 . At the basin scale, the absolute error of the simulated runoff is reduced by an average of 433 million m 3 per year, and 36 million m 3 every month. The LUSF datasets corrected the canopy interception coefficient effectively; the runoff simulation error was reduced by 2.96 billion m 3 /a. In the past 40 years, the runoff variation in Dongting Lake and Hanjiang River has been most strongly impacted by changes in the underlying surface. These results reveal that the new data fusion method has some significant improvement over the original method, applicable to the runoff simulation disturbed by strong human activities.

Keywords: land use; remote sensing; statistics; data fusion; Yangtze River Basin; runoff simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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