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Unravel compound soil erosion zones in source region of Yellow River combining rule-based and interpretable machine learning

文献类型：期刊论文

作者Zhang, Biao2,8; Fang, Haiyan2,3,7; Wu, Shufang2,8; Feng, Hao2; Zhang, Guotao4; Gao, Xing5; Niu, Baicheng1,6
刊名JOURNAL OF HYDROLOGY
出版日期2026
卷号664页码:134596
关键词Multiple erosive forces Geographical zoning Spatiotemporal variation High-altitude cold and arid regions
ISSN号0022-1694
DOI10.1016/j.jhydrol.2025.134596
产权排序3
文献子类Article
英文摘要The Yellow River source region (SRYR), a climate transition zone highly sensitive to human and environmental changes, faces growing compound soil erosion risks. However, the spatiotemporal distribution of compound soil erosion zones remains unclear. This study integrate machine learning with decision rules to propose a zone framework for compound soil erosion zones, revealing the spatiotemporal variation of the compound erosion zone and synergistic mechanisms. The results showed:(1) The freeze-thaw(FT) erosion was the most widely distributed in the SRYR, water erosion intensity decreased significantly(691.1 t km-2 yr-1) from 1980 to 2020, wind erosion showed decadal fluctuations; (2) The XGBoost-optimized compound soil erosion zone (XGCSEZ) and rule-based compound erosion zone (CSEZ) showed partial consistency (OA = 71.4 %, Kappa = 68.5 %), better identifying in compound soil erosion(i.e.water-wind erosion: 2.0 % increased to 7.3 % in 1980s, water-FT erosion:6.0 % increased to 11.8 % in 2020s);(3) Soil texture plays the most important role (28.1 %) in the compound erosion zone, followed by terrain (27.0 %), climate (25.4 %), landform (15.5 %) and human activities (4.0 %), with clay(SHAP value = 0.54), elevation(0.51), FTCD(0.39), NDVI(0.34), and slope(0.34) as important feature; (4) The XGCSEZ showed vertical distribution of elevation and undergoes significant spatiotemporal changes due to grain for green project(GGP) divided into three stages: binary compound transformation (1980-1995) (I), single-force erosion (water and wind) expansion (1995-2000) (II), and compound soil erosion diversification (2000-2020) (III) due to warm and humid climate and sustainable ecological management. Vegetation is an important stabilizing regulator. This work advances science-based management of compound erosion in high-altitude regions.
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WOS研究方向Engineering ; Geology ; Water Resources
语种英语
WOS记录号WOS:001625418400002
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/219400]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Wu, Shufang
作者单位1.Qinghai Normal Univ, Sch Natl Secur & Emergency Management, Xining 810008, Qinghai, Peoples R China
2.Northwest A&F Univ, Coll Water Resources & Architectural Engn, State Key Lab Soil & Water Conservat & Desertifica, Yangling 712100, Shaanxi, Peoples R China;
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China;
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China;
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;
6.Qinghai Normal Univ, Key Lab Tibetan Plateau Land Surface Proc & Ecol C, Minist Educ, Xining 810008, Peoples R China;
7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China;
8.Northwest A&F Univ, Coll Water Resources & Architectural Engn, Yangling 712100, Shaanxi, Peoples R China;
推荐引用方式
GB/T 7714		 Zhang, Biao,Fang, Haiyan,Wu, Shufang,et al. Unravel compound soil erosion zones in source region of Yellow River combining rule-based and interpretable machine learning[J]. JOURNAL OF HYDROLOGY,2026,664:134596.
APA Zhang, Biao.,Fang, Haiyan.,Wu, Shufang.,Feng, Hao.,Zhang, Guotao.,...&Niu, Baicheng.(2026).Unravel compound soil erosion zones in source region of Yellow River combining rule-based and interpretable machine learning.JOURNAL OF HYDROLOGY,664,134596.
MLA Zhang, Biao,et al."Unravel compound soil erosion zones in source region of Yellow River combining rule-based and interpretable machine learning".JOURNAL OF HYDROLOGY 664(2026):134596.

入库方式： OAI收割

来源：地理科学与资源研究所

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