Evaluate River Water Salinity in a Semi-Arid Agricultural Watershed by Coupling Ensemble Machine Learning Technique with SWAT Model

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.

Automated summary

This study established a new approach to estimate river salinity in a semi-arid agricultural watershed using hydrologic modeling and machine learning. By analyzing soil physical characteristics, it was found that soil salinity levels are directly affected by soil permeability and irrigation water, showing higher TDS levels in silt loam sites and lower TDS levels in very cobbly soil sites.