Vulnerability Assessment of Soil Erosion in Southwestern Hubei Province Based on RUSLE Model and Geographic Detector

YANG Yanpan; TIAN Pei; SHEN Chenzhu; PING Yaodong; JIA Tinghui; YUE Xue; HUANG Jianwu

doi:10.13870/j.cnki.stbcxb.2024.01.015

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Vulnerability Assessment of Soil Erosion in Southwestern Hubei Province Based on RUSLE Model and Geographic Detector

更新时间：2025-03-12

- Vulnerability Assessment of Soil Erosion in Southwestern Hubei Province Based on RUSLE Model and Geographic Detector
- Vol. 38, Issue 1, Pages: 91-103(2024)
- 作者机构：
  
  1. 华中师范大学地理过程分析与模拟湖北省重点实验室,武汉,430079
  2. 华中师范大学城市与环境科学学院,武汉,430079
- 作者简介：
- 基金信息：
- DOI：10.13870/j.cnki.stbcxb.2024.01.015
  CLC： X825
- Published：2024
- 稿件说明：
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YANG Yanpan, TIAN Pei, SHEN Chenzhu, et al. Vulnerability Assessment of Soil Erosion in Southwestern Hubei Province Based on RUSLE Model and Geographic Detector[J]. 2024, 38(1): 91-103.
DOI：

YANG Yanpan, TIAN Pei, SHEN Chenzhu, et al. Vulnerability Assessment of Soil Erosion in Southwestern Hubei Province Based on RUSLE Model and Geographic Detector[J]. 2024, 38(1): 91-103. DOI： 10.13870/j.cnki.stbcxb.2024.01.015.

摘要

[目的] 水土流失是鄂西南突出的生态问题

合理评估该区域的土壤侵蚀脆弱性

并探究其驱动机制是采取有效水土保持措施的前提。[方法] 从人地耦合系统视角分析自然和社会经济因素对鄂西南土壤侵蚀的影响

从暴露度、敏感性和适应能力3个方面构建了鄂西南土壤侵蚀脆弱性评价指标体系

并采用综合赋权法确定指标权重。[结果] (1)2010—2020年鄂西南土壤侵蚀脆弱性总体上呈现先降低后升高的趋势

强烈及以上强度的土壤侵蚀脆弱性呈现零星的碎片化分布格局

且主要集中在鄂西南的中南部和东部宜昌市辖区的西部地区;(2)研究区敏感性最高的区域集中在海拔800~1 500 m

敏感性在坡度25°~35°最大

并呈现出向两侧递减的趋势

当坡度＞15°时

较高及以上敏感性面积急剧增加;(3)较高及以上土壤侵蚀脆弱性高于土壤侵蚀强度

较低及以下土壤侵蚀脆弱性低于土壤侵蚀强度

土壤侵蚀脆弱性与土壤侵蚀强度存在协同变化趋势;(4)土壤侵蚀脆弱性的分布格局是多因素协同作用造成的

地理探测器的分析表明坡度、植被覆盖度、教育质量和城镇化率对土壤侵蚀脆弱性的解释力较强。[结论] 未来需要高度重视对坡度>15°地区植被覆盖的保护

并通过提高当地的教育质量

加强水土保持的宣传力度

提升当地居民的水土保持意识。

Abstract

[Objective] Soil erosion is a prominent ecological problem in southwestern Hubei Province. A reasonable assessment of the vulnerability of soil erosion in this area and exploration of its driving mechanisms are prerequisites for effective soil and water conservation measures. [Methods] The effects of natural and socio-economic factors on soil erosion in southwest Hubei were analyzed from the perspective of the human-land coupling system. The evaluation index system of soil erosion vulnerability in southwest Hubei was constructed from three aspects: exposure

sensitivity

and adaptability. The comprehensive weighting method was used to determine the index weight. [Results] (1) From 2010 to 2020

the vulnerability of soil erosion in southwest Hubei generally showed a trend of decreasing first and then increasing. The vulnerability of soil erosion with strong and above intensity showed a fragmented distribution pattern

which was mainly concentrated in the central and southern parts of southwest Hubei and the western part of Yichang municipal district in the east. (2) The area with the highest sensitivity in the study area was concentrated at an altitude of 800 ~ 1 500 m

and the sensitivity was the largest at a slope of 25° ~ 35°

and showed a decreasing trend to both sides. When the slope was greater than 15°

the area with higher and higher sensitivity increased sharply; (3) Soil erosion vulnerability was higher than soil erosion intensity at higher and above soil erosion levels

and lower than soil erosion intensity at lower and below soil erosion levels. There was a synergistic trend between soil erosion vulnerability and soil erosion intensity. (4) The distribution pattern of soil erosion vulnerability was caused by a multi-factor synergy. The analysis of the geographical detector showed that slope

vegetation coverage

education quality

and urbanization rate have strong explanatory power for soil erosion vulnerability. [Conclusion] In the future

it is necessary to attach great importance to the protection of vegetation coverage in areas with a slope>15°

and to enhance the awareness of local residents about soil and water conservation by improving the quality of local education and strengthening the publicity of soil and water conservation.

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references

王迪,郝萌萌,陈帅,等.中国滑坡敏感性及危险因素评价[J].自然灾害,2021,108:3045-3059. WANG D, HAO M M, CHEN S, et al. Sensitivity and risk factor evaluation of landslides in China[J].Natural Calamities,2021,108:3045-3059.

FERRER J, GUO Z Z, MEDINA V, et al. A framework to project future rainfall scenarios: An application to shallow landslide-triggering summer rainfall in Wanzhou County China[J].Water,2022,14(6):e873.

REICHSTEIN M, CAMPS-VALLS G, STEVENS B, et al. Deep learning and process understanding for data-driven earth system science[J].Nature,2019,566(7743):195-204.

DOLOJAN N L J, MORIGUCHI S, HASHIMOTO M, et al. Mapping method of rainfall-induced landslide hazards by infiltration and slope stability analysis[J].Landslides,2021,18(6):2039-2057.

SHU H P, HVRLIMANN M, MOLOWNY-HORAS R, et al. Relation between land cover and landslide susceptibility in Val d' Aran, Pyrenees (Spain): Historical aspects, present situation and forward prediction[J].The Science of the Total Environment,2019,693:e133557.

ZHOU X Z,WEN H J,LI Z W,et al.An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost[J].Geocarto International, 2022, 37(26):13419-13450.

CHEN H Y, DENG T T, DU T, et al. An RF and LSSVM-NSGA-II method for the multi-objective optimization of high-performance concrete durability[J].Cement and Concrete Composites,2022,129:e104446.

DENGN D, LI Y X, MA J Q, et al. A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit[J].Frontiers in Environmental Science,2022,10:e1009433.

HAKIM W L, REZAIE F, NUR A S, et al. Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea[J].Journal Environmental Manage ment,2022,305:e114367.

EKMEKCIOLU Ö, KOC K, ÖZGER M, et al. Exploring the additional value of class imbalance distributions on interpretable flash flood susceptibility prediction in the Black Warrior River Basin, Alabama, United States[J].Journal of Hydrology,2022,610:e127877.

JAAFARI A, PANAHI M, MAFI-GHOLAMI D, et al. Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides[J].Applied Soft Computing,2022,116:e108254.

SA T, ÖRNEK H K. Classification rule mining based on Pareto-based Multiobjective Optimization[J].Applied Soft Computing,2022,127:e109321.

CHENG J Y, DAI X A, WANG Z K, et al. Landslide susceptibility assessment model construction using typical machine learning for the Three Gorges Reservoir Area in China[J].Remote Sensing,2022,14(9):e2257.

MERGHADI A, YUNUS A P, DOU J, et al. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance[J].Earth-Science Reviews,2020,207:e103225.

王毅,方志策,牛瑞卿,等.基于深度学习的滑坡灾害易发性分析[J].地球信息科学学报,2021,23(12):2244-2260. WANG Y,FANG Z C, NIU R Q, et al. Landslide susceptibility analysis based on deep learning[J].Journal of Geo-Information Science,2021,23(12):2244-2260.

刘纪平,梁恩婕,徐胜华,等.顾及样本优化选择的多核支持向量机滑坡灾害易发性分析评价[J].测绘学报,2022,51(10):2034-2045. LIU J P, LIANG E J, XU S H, et al. Multi-kernel support vector machine considering sample optimization selection for analysis and evaluation of landslide disaster susceptibility[J].Acta Geodaetica et Cartographica Sinica,2022,51(10):2034-2045.

SAHA A, PAL S C, SANTOSH M, et al. Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: The present and future scenarios[J].Journal of Cleaner Production, 2021,320:e128713.

徐胜华,刘纪平,王想红,等.熵指数融入支持向量机的滑坡灾害易发性评价方法:以陕西省为例[J].武汉大学学报(信息科学版),2020,45(8):1214-1222. XU S H, LIU J P, WANG X H, et al. Landslide susceptibility assessment method incorporating index of entropy based on support vector machine: A case study of Shaanxi Province[J].Geomatics and Information Science of Wuhan University,2020,45(8):1214-1222.

王念秦,郭有金,刘铁铭,等.基于支持向量机模型的滑坡危险性评价[J].科学技术与工程,2019,19(35):70-78. WANG N Q, GUO Y J, LIU T M, et al. Landslide susceptibility assessment based on support vector machine model[J].Science Technology and Engineering,2019,19(35):70-78.

王倩,薛云,张维,等.基于支持向量机的滑坡易发性评价[J].湖南城市学院学报,2021,30(1):22-28. WANG Q, XUE Y, ZHANG W, et al. Landslide susceptibility mapping based on support vector machine models[J].Journal of Hunan City University,2021,30(1):22-28.

TABATABAEI M, SALEHPOUR J A, AHMAD H S. Suspended sediment load prediction using non-dominated sorting genetic algorithm II[J].International Soil and Water Conservation Research,2019,7(2):119-129.

BAI Z G, LIU Q M, LIU Y. Landslide susceptibility mapping using GIS-based machine learning algorithms for the Northeast Chongqing Area, China[J].Arabian Journal of Geosciences,2021,14(24):e2831.

LIU Y M, ZHANG W C, ZHANG Z J, et al. Risk factor detection and landslide susceptibility mapping using geo-detector and random forest models: The 2018 Hokkaido Eastern Iburi Earthquake[J].Remote Sensing, 2021,13(6):e1157.

PHAM B T, VAN D D, ACHARYA T D, et al. Performance assessment of artificial neural network using Chi-square and backward elimination feature selection methods for landslide susceptibility analysis[J].Environmental Earth Sciences,2021,80(20):e686.

SUN D L, GU Q Y, WEN H J, et al. Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization[J].Gondwana Research,2022,123(2023):89-106.

邢昕芙,吴承龙,李金辉,等.采用修正逻辑回归方法对降雨滑坡的敏感性评价[J].自然灾害,2021,106(1):97-117. XING X F, WU C L, LI J H, et al. Susceptibility assessment for rainfall-induced landslides using a revised logistic regression method[J].Natural Hazards,2021,106(1):97-117.

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