干旱区生态环境知识资源中心(Arid): Controls of groundwater-dependent vegetation coverage in the yellow river basin, china: Insights from interpretable machine learning

Arid

DOI	10.1016/j.jhydrol.2024.130747
	Controls of groundwater-dependent vegetation coverage in the yellow river basin, china: Insights from interpretable machine learning
	Bai, Taiya; Wang, Xu-Sheng; Han, Peng-Fei
通讯作者	Wang, XS
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2024
卷号	631
英文摘要	Groundwater plays an important role in maintaining vegetation structure and ecological functions in arid regions. However, the relationship between groundwater depth (GD) and fractional vegetation cover (FVC) in a large region with various climate conditions remains poorly understood, primarily due to the complex influence of multiple environmental factors. Machine learning method, a powerful tool for identifying complex and nonlinear processes, has been used to rank explanatory variables on the regional-scale vegetation distribution, while missing the vegetation-groundwater relationship. We developed machine learning models via the extreme gradient boosting (XGBoost) to identify key controls of groundwater-dependent vegetation cover in the Yellow River Basin, China. Two XGBoost models, A and B, were constructed for shallow (GD <= 24.5 m) and deep (GD > 24.5 m) groundwater pixels, respectively, with the same sample number on the resolution of 1 km. Shapely additive explanations (SHAP) method is employed to assess the contributions of climatic, topographic, and edaphic features on FVC. Through an enhanced feature selection method using multicollinearity analysis and the Boruta algorithm, we found that the top four important features in both models were precipitation (P), saturated water content (SWC), air temperature (TA), and potential evapotranspiration (PET) during the growing season. The difference between models A and B indicates the influence of groundwater on vegetation. Shallower groundwater leads to smaller sensitivity of FVC to the precipitation when the monthly P in the growing season is smaller than 68.0 mm, and can also reduce the sensitivity of FVC to the air temperature when the average TA is lower than 13.5degree celsius in the growing season. This study suggests an effective method for recognizing the groundwater effect on the vegetation cover from mixed influences of environmental controls.
英文关键词	Fractional vegetation cover Groundwater depth Interpretable machine learning XGBoost Yellow River Basin
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001182685400001
WOS关键词	CLIMATE FACTORS ; HYDROLOGICAL PROCESSES ; NORMALIZED DIFFERENCE ; BIOMASS ESTIMATION ; INNER-MONGOLIA ; FOREST ; DEPTH ; PLANT ; WATER ; NDVI
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404554
推荐引用方式 GB/T 7714	Bai, Taiya,Wang, Xu-Sheng,Han, Peng-Fei. Controls of groundwater-dependent vegetation coverage in the yellow river basin, china: Insights from interpretable machine learning[J],2024,631.
APA	Bai, Taiya,Wang, Xu-Sheng,&Han, Peng-Fei.(2024).Controls of groundwater-dependent vegetation coverage in the yellow river basin, china: Insights from interpretable machine learning.JOURNAL OF HYDROLOGY,631.
MLA	Bai, Taiya,et al."Controls of groundwater-dependent vegetation coverage in the yellow river basin, china: Insights from interpretable machine learning".JOURNAL OF HYDROLOGY 631(2024).