Quantitative Attribution Analysis of Driving Factors of Soil Erosion in Xiaolangdi Reservoir Area of the Yellow River

YU Wanyang; ZHAO Longshan; ZHANGJinsong; MENG Ping; BA Yinji; ZHANGJinxin

doi:10.13870/j.cnki.stbcxb.2023.03.021

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Quantitative Attribution Analysis of Driving Factors of Soil Erosion in Xiaolangdi Reservoir Area of the Yellow River

更新时间：2025-03-12

- Quantitative Attribution Analysis of Driving Factors of Soil Erosion in Xiaolangdi Reservoir Area of the Yellow River
- Vol. 37, Issue 3, Pages: 155-163(2023)
- 作者机构：
  
  1. 贵州大学林学院,贵阳,550025
  2. 中国林业科学研究院林业研究所,北京,100091
  3. 中国地质调查局烟台海岸带地质调查中心,山东,烟台,264004
- 作者简介：
- 基金信息：
- DOI：10.13870/j.cnki.stbcxb.2023.03.021
  CLC： S157.1
- Published：2023
- 稿件说明：
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YU Wanyang, ZHAO Longshan, ZHANGJinsong, et al. Quantitative Attribution Analysis of Driving Factors of Soil Erosion in Xiaolangdi Reservoir Area of the Yellow River[J]. 2023, 37(3): 155-163.
DOI：

YU Wanyang, ZHAO Longshan, ZHANGJinsong, et al. Quantitative Attribution Analysis of Driving Factors of Soil Erosion in Xiaolangdi Reservoir Area of the Yellow River[J]. 2023, 37(3): 155-163. DOI： 10.13870/j.cnki.stbcxb.2023.03.021.

摘要

为评价小浪底库区30年来土壤侵蚀特征及其影响因素

基于RUSLE模型

估算小浪底库区1990—2020年土壤侵蚀模数

分析土壤侵蚀时空变化特征

并结合地理探测器定量分析植被覆盖度、土地利用类型、海拔、坡度和降雨量等影响因子对土壤侵蚀格局的影响。结果表明:(1)小浪底库区土壤侵蚀模数从1990年的3 150 t/(km2·a)下降至2020年的1 554 t/(km2·a)

土壤流失总量减少50.00%。高等级土壤侵蚀持续向低等级侵蚀转变

从1990—2020年

剧烈、极强烈、强烈、中度和轻度侵蚀面积分别下降53.92%

64.51%

55.65%

39.68%和3.28%

而微度侵蚀面积则上升41.13%。现阶段土壤侵蚀强度以微度侵蚀为主

其次是轻度侵蚀

两者分别占总侵蚀面积的60.02%和24.08%。(2)小浪底库区严重的土壤侵蚀主要分布在库区西南部(平陆县、陕州区)、东南部(济源市、孟津县)和中部(垣曲县)等人类活动集中的部分地区

但在时空上呈收缩聚集的特征。(3)植被覆盖度与土地利用类型对小浪底库区土壤侵蚀强度的解释力高于其他因子

植被覆盖度最高可解释42.09%的土壤侵蚀

土地利用类型最高可解释28.64%的土壤侵蚀

双因子的协同作用增强对土壤侵蚀的解释力。低海拔(＜718 m)、缓坡(8°~16°)地区由于人类活动可及性高

植被覆盖度较低是土壤侵蚀的高风险区

可加强这些区域的生态恢复及水土保持措施建设。

Abstract

In order to evaluate the characteristics of soil erosion and its driving factors in the Xiaolangdi Reservoir area in the past 30 years

this study estimated the soil erosion modulus in the Xiaolangdi Reservoir area from 1990 to 2020 based on the Revised Universal Soil Loss Equation (RUSLE)

and analyzed the spatial-temporal variation characteristics of soil erosion. Then combined with geographic detector

the effects of vegetation coverage

land use type

altitude

slope and rainfall on soil loss patterns were quantitatively analyzed. Results showed that: (1) The soil erosion modulus in the Xiaolangdi reservoir area decreased from 3 150 t/(km2·a) in 1990 to 1 554 t/(km2·a) in 2020

and the total soil erosion decreased by 50.00%. The intensity of soil erosion also changed from high level to lower level. From 1990 to 2020

the area of extremely severe

very severe

severe

moderate and slight erosion decreased by 53.92%

64.51%

55.65%

39.68% and 3.28%

respectively. While the area of very slight erosion increased by 41.13%. At the present stage

the main erosionintensity was very slight erosion

followed by slight erosion

which accounted for 60.02% and 24.08% of the total erosion area

respectively. (2) Serious soil erosion in the Xiaolangdi Reservoir area was mainly distributed in the southwest (Pinglu County

Shanzhou District)

southeast (Jiyuan City

Mengjin County) and central (Yuanqu County) of the reservoir area where human activities are concentrated

but it tended to shrink and gather in time and space. (3) Vegetation coverage and land use type had higher explanatory power than other factors

vegetation coverage could explain up to 42.09% of soil erosion

while land use type could explain up to 28.64% of soil erosion

and the interaction of different factors enhanced the explanatory power of soil erosion risk. Low altitude (< 718 m) and gentle slope (8°~16°) areas were high risk areas for soil erosion due to the high accessibility of human activities and low vegetation coverage. Ecological restoration and soil and water conservation measures in these areas should be strengthened.

关键词

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references

李占斌,朱冰冰,李鹏.土壤侵蚀与水土保持研究进展[J].土壤学报,2008,45(5):802-809.

Borrelli P, Robinson D A, Panagos P, et al. Land use and climate change impacts on global soil erosion by water (2015-2070)[J].Proceedings of the National Academy of Sciences of the United States of America,2020,117(36):21994-22001.

王猛,王鹤松,姜超,等.基于RUSLE和地理探测器模型的西南地区土壤侵蚀格局及定量归因[J].应用基础与工程科学学报,2021,29(6):1386-1402.

Li P F, Mu X M, Holden J, et al. Comparison of soil erosion models used to study the Chinese Loess Plateau[J].Earth Science Reviews,2017,170:17-30.

陈正发,龚爱民,宁东卫,等.基于RUSLE模型的云南省土壤侵蚀和养分流失特征分析[J].水土保持学报,2021,35(6):7-14.

Sun W Y, Shao Q Q, Liu J Y, et al. Assessing the effects of land use and topography on soil erosion on the Loess Plateau in China[J].Catena,2014,121:151-163.

魏艳红,焦菊英.黄土丘陵沟壑区不同土地利用方式下小流域侵蚀产沙特征[J].水土保持学报,2021,35(3):96-103.

Gao J B, Wang H, Zuo L Y. Spatial gradient and quantitative attribution of karst soil erosion in Southwest China[J].Environmental Monitoring and Assessment,2018,190:e730.

Gao J B, Wang H. Temporal analysis on quantitative attribution of karst soil erosion:A case study of a peak-cluster depression basin in Southwest China[J].Catena,2019,172:369-377.

Liang S Z X, Fang H Y. Quantitative analysis of driving factors in soil erosion using geographic detectors in Qiantang River catchment, Southeast China[J].Journal of Soils and Sediments,2021,21:134-147.

王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.

董亚宁,范博凯,李少鹏,等.生态文明视角下黄河流域生态保护和高质量发展研究[J].生态经济,2022,38(2):217-222.

Zhao Q H, Ding S Y, Ji X Y, et al. Relative contribution of the Xiaolangdi dam to runoff changes in the Lower Yellow River[J].Land,2021,10(5):e521.

周申蓓,黄媛媛,吕玲玲.小浪底工程建设后黄河河岸带生态系统服务价值变化[J].人民黄河,2022,44(9):122-125,154.

章文波,谢云,刘宝元.利用日雨量计算降雨侵蚀力的方法研究[J].地理科学,2002,22(6):705-711.

Sharply A N, Williams J R. EPIC-erosion/productivity impact calculator[M].1.1 Model determination. Technical bulletin 1768. United States Department of Agriculture,1989.

McCool D K, Foster G R, Weesies G A. Slope length and steepness factors (LS). Predicting soil erosion by water:A guide to conservation planning with the revised universal soil loss equation (RUSLE). Agriculture handbook No 703[M].Washington:USDA,1997.

Liu B Y, Nearing M A, Shi P J, et al. Slope length effects on soil loss for steep slopes[J].Soil Science Society of America Journal,2000,64(5):1759-1763.

蔡崇法,丁树文,史志华,等.应用USLE模型与地理信息系统IDRISI预测小流域土壤侵蚀量的研究[J].水土保持学报,2000,14(2):19-24.

Jin F M, Yang W C, Fu J X, et al. Effects of vegetation and climate on the changes of soil erosion in the Loess Plateau of China[J].Science of the Total Environment,2021,773:e145514.

中华人民共和国水利部.土壤侵蚀分类分级标准SL 190-2007[S].北京:中国水利水电出版社,2008.

闫瑞,张晓萍,李够霞,等.基于RUSLE的北洛河上游流域侵蚀产沙模拟研究[J].水土保持学报,2017,31(4):32-37.

Kou P L, Xu Q, Jin Z, et al. Complex anthropogenic interaction on vegetation greening in the Chinese Loess Plateau[J].Science of the Total Environment,2021,778:e146065.

Zhang S H, Fan W W, Li Y Q, et al. The influence of changes in land use and landscape patterns on soil erosion in a watershed[J].Science of the Total Environment,2017,574:34-35.

穆兴民,李朋飞,刘斌涛,等.1901-2016年黄土高原土壤侵蚀格局演变及其驱动机制[J].人民黄河,2022,44(9):36-45.

王封,刘金铜,付同刚,等.基于RUSLE模型的太行山区土壤侵蚀时空分异特征及影响因子研究[J].中国生态农业学报,2022,30(7):1064-1076.

Sun X Y, Jiang Z, Liu F, et al. Monitoring spatio-temporal dynamics of habitat quality in Nansihu Lake basin, eastern China, from 1980 to 2015[J].Ecological Indicators,2019,102:716-723.

Renard K G, Foster G R, Weesies G A, et al. Predicting soil erosion by water:A guide to conservation planning with the revised universal soil loss equation (RUSLE). Agriculture handbook No 703[M].Washington:USDA,1997.

Zhang Z H, Song Y Z, Wu P. Robust geographical detector[J].International Journal of Applied Earth Observation and Geoinformation,2022,109:e102782.

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