干旱区生态环境知识资源中心(Arid): Evaluate River Water Salinity in a Semi-Arid Agricultural Watershed by Coupling Ensemble Machine Learning Technique with SWAT Model

Arid

DOI	10.1111/1752-1688.12958
	Evaluate River Water Salinity in a Semi-Arid Agricultural Watershed by Coupling Ensemble Machine Learning Technique with SWAT Model
	Jung, Chunggil; Ahn, Sora; Sheng, Zhuping; Ayana, Essayas K.; Srinivasan, Raghavan; Yeganantham, Dhanesh
通讯作者	Ahn, S (corresponding author), Texas A&M AgriLife Res, El Paso, TX 79927 USA.
来源期刊	JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
ISSN	1093-474X
EISSN	1752-1688
出版年	2021-10
英文摘要	This study is to establish a new approach to estimate river salinity of semi-arid agricultural watershed and identify drivers by using hydrologic modeling and machine learning. We augmented the limitations of the Soil and Water Assessment Tool (SWAT) to model salinity by coupling with eXtreme Gradient Boosting (XGBoost), a decision-tree-based ensemble machine learning algorithm. Streamflow, precipitation, elevation, main reach length, and dominant soil texture of the top two layers were used along with NO3, NO2, and total phosphorus (TP) output from a calibrated SWAT model are used as predictors to Total Dissolved Solids (TDS) in the XGBoost algorithm. Then, the SWAT model simulations of streamflow, NO3+NO2, and TP from 2000 to 2015 are used as inputs of the XGBoost model to predict monthly water TDS distribution along the river. The predicted river water TDS showed a higher concentration as going downstream from El Paso (inlet) through the Hudspeth canal to Fort Quitman (outlet). Finally, this study carried out cause analysis focusing on soil physical characteristics. The soil salinity level is directly affected by the soil permeability and irrigation water. As a result, the highest TDS is shown in sites with silt loam, whereas the lowest TDS was shown in sites with very cobbly soil. Silt soils can hold more water and are slower to drain than soils of a sand type. These analyses can be used to better understand the mitigation of water salinity.
英文关键词	watershed management machine learning SWAT water salinity soil texture irrigation watershed surface water groundwater interactions
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000712955200001
WOS关键词	CLIMATE-CHANGE ; SOIL-SALINITY ; SALINIZATION ; MANAGEMENT ; REGRESSION ; AQUIFER ; REGION
WOS类目	Engineering, Environmental ; 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/2XILL650/367583
作者单位	[Jung, Chunggil; Ahn, Sora; Sheng, Zhuping] Texas A&M AgriLife Res, El Paso, TX 79927 USA; [Ayana, Essayas K.; Srinivasan, Raghavan; Yeganantham, Dhanesh] Texas A&M Univ, Dept Ecosyst Sci & Management, College Stn, TX USA
推荐引用方式 GB/T 7714	Jung, Chunggil,Ahn, Sora,Sheng, Zhuping,et al. Evaluate River Water Salinity in a Semi-Arid Agricultural Watershed by Coupling Ensemble Machine Learning Technique with SWAT Model[J],2021.
APA	Jung, Chunggil,Ahn, Sora,Sheng, Zhuping,Ayana, Essayas K.,Srinivasan, Raghavan,&Yeganantham, Dhanesh.(2021).Evaluate River Water Salinity in a Semi-Arid Agricultural Watershed by Coupling Ensemble Machine Learning Technique with SWAT Model.JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION.
MLA	Jung, Chunggil,et al."Evaluate River Water Salinity in a Semi-Arid Agricultural Watershed by Coupling Ensemble Machine Learning Technique with SWAT Model".JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION (2021).