Li Zixuan, Zhao Hui, Zou Haitian, Li Yishan, Liu Yuxin, Li Ao. Comparison of soil erosion estimation methods at county scale based on CSLE Model and sampling unit[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(14): 141-148. DOI: 10.11975/j.issn.1002-6819.2019.14.018
Citation: Li Zixuan, Zhao Hui, Zou Haitian, Li Yishan, Liu Yuxin, Li Ao. Comparison of soil erosion estimation methods at county scale based on CSLE Model and sampling unit[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(14): 141-148. DOI: 10.11975/j.issn.1002-6819.2019.14.018

Comparison of soil erosion estimation methods at county scale based on CSLE Model and sampling unit

  • In recent years, it is a significant basic work in soil and water conservation to implement dynamic quantitative monitoring of soil and water loss in countries and key areas nationwide. Soil erosion calculation based on CSLE model can realize quantitative monitoring of soil and water loss. From the perspective of the spatial scope, it is divided into two types as global coverage calculation and sampling unit estimation. It is of great significance to analyze and evaluate the difference and applicability of the two methods for dynamic monitoring of soil and water loss. In this study, Huailai county of Hebei province was taken as an example to evaluate the soil erosion by global coverage calculation and 4% sampling unit estimation. The differences of the two methods were analyzed by data base, factor calculation, soil erosion modulus calculation, soil erosion assessment results, etc., leading to the discussion of the advantages and disadvantages of two methods in the process of the quantitative estimation of soil erosion at the county scale. Our results showed that the area of soil and water loss calculated by global coverage calculation was 59.0 km2 larger than the result based on 4% sampling unit estimation, and the relative difference between the two methods was 12.94 %. By global coverage calculation, the soil erosion area of farm land, garden land, forest land, grass land and others accounted for 30.3%, 9.1%, 24.2%, 56.5% and 4.7% of the land use area, respectively, while by 4% sampling unit estimation, they accounted for 22.8%, 16.3%, 19.8%, 48.1% and 3.0%, respectively. In addition, by global coverage calculation, , the area of farm land, garden land, forest land, grass land and others accounted for 16.0 %, 21.8 %, 31.1 %, 18.4 %, and 12.7 % of the county's land use, respectively, while they accounted for 14.7 %, 10.4 %, 45.3 %, 15.8 %, and 13.8 %, respectively, by 4% sampling unit estimation. Our results also showed that the difference in terrain data and engineering measured data had minor effect on the calculation results of the two methods, while the difference in land use and vegetation cover data affected more. In the global coverage calculation method, it was easily to increase chances of misjudging shrubbery as grassland, and failure to calculate the coverage of undergrowth vegetation under the garden land and forest land, which caused the garden B factor to be underestimated and the woodland B factor to be overestimated, and the proportion of soil erosion from garden land was low, while the proportion of soil erosion from forest land was high. Quantitative estimation of soil erosion at county scale could be achieved by global coverage calculation and sampling unit estimation, but the former was 12.94% larger than the latter, which was related to the accuracy of interpretation of land use and vegetation coverage, the accuracy of factor calculation, and the determination and localization of model parameters. The global coverage calculation method could reflect the spatial distribution characteristics of soil erosion more accurately, which was suitable for the quantitative estimation of soil erosion at medium and small scales, demanding a higher accuracy and comprehensive data source assurance. The sampling unit estimation was applicable to the estimation of large-scale soil erosion in river basins, regions, etc., but the results were greatly influenced by factors such as sampling methods, sampling density, and extrapolation or interpolation methods. The parameter localization should be implemented gradually in the follow-up study, focusing on the impact of the CSLE model estimation results by factor accuracy and remote sensing interpretation accuracy, improvement of factor parameter database, and factor value rating.
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