Modelling groundwater quality of the Athabasca River Basin in the subarctic region using a modified SWAT model

Groundwater is a vital resource for human welfare. However, due to various factors, groundwater pollution is one of the main environmental concerns. Yet, it is challenging to simulate groundwater quality dynamics due to the insufficient representation of nutrient percolation processes in the soil and Water Assessment Tool model. The objectives of this study were extending the SWAT module to predict groundwater quality. The results proved a linear relationship between observed and calculated groundwater quality with coefficient of determination (R-2), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS) values in the satisfied ranges. While the values of R-2, NSE and PBIAS were 0.69, 0.65, and 2.68 during nitrate calibration, they were 0.85, 0.85 and 5.44, respectively during nitrate validation. Whereas the values of R-2, NSE and PBIAS were 0.59, 0.37, and -2.21 during total dissolved solid (TDS) calibration and they were 0.81, 0.80, 7.5 during the validation. The results showed that the nitrate and TDS concentrations in groundwater might change with varying surface water quality. This indicated the requirement for designing adaptive management scenarios. Hence, the extended SWAT model could be a powerful tool for future regional to global scale modelling of nutrient loads and effective surface and groundwater management.

Automated summary

By extending the SWAT module to predict groundwater quality, the study found a linear relationship between observed and calculated values, with satisfactory coefficient of determination (R-2), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS) values. The results demonstrated that nitrate and total dissolved solid (TDS) concentrations in groundwater may vary with changes in surface water quality, indicating the need for adaptive management scenarios to be designed. The extended SWAT model could serve as a powerful tool for future regional to global scale modeling of nutrient loads and effective surface and groundwater management.