*4.4. Limitations*

Soil erosion estimation is a key to the understanding and management of the ecological environment, particularly in ecologically vulnerable regions [76]. Although it is considered as a widely used approach to soil erosion estimation [74,77], the application of the RUSLE model might be region-specific due to the complexity of the ecological environment [23]. In the case study of the CLB, the gravel content parameter was used to modify the algorithm of soil erodibility factor in the RUSLE model. Such revision has proved to improve soil erosion estimation for the CLB. Despite the improvement, there are some issues that should be addressed in further research:


#### **5. Conclusions**

We estimated and compared the soil erosion of the Chaohu Lake Basin (CLB) in 2017 using the original RUSLE model and the RUSLE model with modified soil erodibility. The average annual soil erosion estimated with the *Kr* algorithm was 0.14 Mg·ha–1·year–1 lower than the estimation result with the original *K* algorithm in the CLB. In other words, taking gravel content into account helps to improve the calculation of soil erodibility and soil erosion estimation. The overall soil erosion in the CLB was low with a majority of slight erosion (accounting for 85.6%), and the mountainous and hilly areas are more prone to soil erosion. The superposition of inappropriate land use and natural factors (including climate, soil properties, and topography) is the main reason for soil erosion of the CLB and should be optimized for soil erosion prevention in the CLB.

Quantitative analysis of soil erosion is highly beneficial in natural resource management and policy-making to relieve the pressure of soil erosion and land degradation. The findings of this study provide useful insights into the spatial distribution of soil erosion and the driving mechanism in this ecologically important region.

**Author Contributions:** Conceptualization, S.H. and L.L.; methodology, S.H.; software, L.C. (Liang Cheng) and L.Y.; validation, S.H. and X.H.; formal analysis, S.H.; data curation, X.H. and T.Z.; writing—original draft preparation, S.H.; writing—review and editing, L.L. and L.C. (Longqian Chen); visualization, S.H.; supervision, project administration, and funding acquisition, L.C. (Longqian Chen). All the authors reviewed and approved the final manuscript version.

**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities under grant number 2018ZDPY07.

**Acknowledgments:** The authors thank the Geospatial Data Cloud for freely providing the Landsat data, the Cold and Arid Regions Sciences Data Center for the soil data, and the National Meteorological Information Center for the rainfall data. Furthermore, we appreciate the editors and reviewers for their constructive comments and suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**


**Table A1.** Measured soil erosion dataset.

#### **References**


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