Related Experiment Video
Updated: Oct 31, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
Modelling groundwater quality of the Athabasca River Basin in the subarctic region using a modified SWAT model
Tesfa Worku Meshesha1, Junye Wang2, Nigus Demelash Melaku1
1Athabasca River Basin Research Institute (ARBRI), Athabasca University, 1 University Drive, Athabasca, AB, T9S 3A3, Canada.
Abstract:
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 (R2), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS) values in the satisfied ranges. While the values of R2, 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 R2, 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.
Related Concept Videos
Typical Model Studies
Quality of Water
Testing Water Quality
Modeling and Similitude
Design Example: Creating a Hydraulic Model of a Dam Spillway
Design Example: Analyzing Capacity Contours for Flood Risk Assessment

