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 (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.

Predicting nitrous oxide emissions after the application of solid manure to grassland in the United Kingdom.

Nitrous oxide (N2 O) emission from agricultural soils represents a significant source of greenhouse gas to the atmosphere. We evaluated the suitability of a modified Soil and Water Assessment Tool (SWAT) model to estimate the N2 O flux from the application of solid manure at two grassland sites (North Wyke [NW] and Pwllpeiran [PW]) in the United Kingdom. The simulated N2 O emissions were validated against field observations measured in 2011 and 2012 for model calibration and validation, respectively. The SWAT model predicts water-filled pore space (WFPS) very well with Nash-Sutcliffe efficiency (NSE), R2 , RMSE, and percentage bias (PBIAS) values of 0.67, .72, 0.06, and 3.64, respectively, during the calibration period for NW site, whereas it gives 0.68, .69, 0.07, and 3.04, respectively during the validation period. At PW, the model predicted the NSE, R2 , RMSE, and PBIAS of 0.55, .69, 0.04, and -4.5, respectively, during calibration and 0.63, .71, 0.05, and -2.6, respectively, during the validation period. Compared with WFPS, the model resulted in a slightly lower fit for N2 O emissions for NW (NSE = 0.47, R2  = .63 during calibration, and NSE = 0.55, R2  = .58 during validation) and for PW (NSE = 0.54, R2  = .71 for calibration, and NSE = 0.47, R2  = .69 for validation). Results revealed that the SWAT model performed reasonably well in representing the dynamics of N2 O emissions after solid manure application to grassland.