Evaluating water conservation capacity in the Yellow River water conservation area integrating ecological model and machine learning
文献类型：期刊论文
| 作者 | Zhang, Jianglei1,2; Chen, Shaohui2 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 663页码:134202 |
| 关键词 | Water conservation capacity Water conservation quantity the Yellow River Basin Ecological model Variability attribution |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2025.134202 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Accurately assessing the Water Conservation Capacity (WCC) of the Water Conservation Area (WCA) in the Yellow River Basin (YRB) is imperative for regional ecological security, yet it remains challenging because of the intricate interplay among climatic and anthropogenic drivers. This study proposes a novel integrated framework that couples the process-based InVEST model with a data-driven Random Forest (RF) algorithm to evaluate the spatiotemporal dynamics of WCC from 2000 to 2022 using annual water yield and Water Conservation Quantity (WCQ). Results reveal that annual water yield and WCQ range from 80.17 to 218.68 mm and 3.56 to 10.99 mm, and optimal Grade I WCC is predominantly concentrated in the central-southern Yellow River Source Area (YRSA), the Southern Mountain Tributary Area of the Wei River (SMTAWR), and the western Yiluo River Basin (YLRB). RF-based factor importance analysis indicates that climatic factors (precipitation, potential evapotranspiration) and anthropogenic factors (NDVI, population, GDP, flow velocity coefficient) are the primary drivers of WCC, while natural structural factors (soil depth, slope, saturated hydraulic conductivity, plant available water content) exert relatively minor effects. By quantitatively disentangling the relative contributions of climatic, natural structural, and anthropogenic factors to WCC, the proposed InVEST-RF framework advances watershed WCC assessment. Moreover, it provides a transferable methodological tool for ecohydrological evaluations in global watersheds, particularly under the context of changing climate and evolving land use trajectories. |
| URL标识 | 查看原文 |
| WOS关键词 | ECOSYSTEM SERVICES ; FOREST TRANSITION ; INVEST MODEL ; BASIN ; SOIL ; RESOURCES ; PATTERNS ; REGION |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001584938600001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217491]  |
| 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 | Chen, Shaohui |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhang, Jianglei,Chen, Shaohui. Evaluating water conservation capacity in the Yellow River water conservation area integrating ecological model and machine learning[J]. JOURNAL OF HYDROLOGY,2025,663:134202. |
| APA | Zhang, Jianglei,&Chen, Shaohui.(2025).Evaluating water conservation capacity in the Yellow River water conservation area integrating ecological model and machine learning.JOURNAL OF HYDROLOGY,663,134202. |
| MLA | Zhang, Jianglei,et al."Evaluating water conservation capacity in the Yellow River water conservation area integrating ecological model and machine learning".JOURNAL OF HYDROLOGY 663(2025):134202. |
入库方式： OAI收割
来源：地理科学与资源研究所
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