Application of SWAT model to quantify blue and green water resources in Alberta
Application of SWAT model to quantify blue and green water resources in Alberta
Previously Published Material: This is a three years research project, funded by Alberta Innovates Energy and Environment Solutions on 2014. It is started since March 2014 and the results have been recently submitted for publication in an ISI Journal. The status of the manuscript is "under review". General objectives of the project have been reported in local and regional media. We have also discussed our results with the scientists and local experts trough personal meetings.
Abstract ID#: 33112
English Abstract:
Dynamic nature of water resources, both spatially and temporally, present a significant challenge to sustaining economic developments, ensuring food security, and implementing other development plans in Alberta and elsewhere in the world. A sound knowledge of internal renewable water resources availability and reliability is needed to lay a strong basis for a long-term planning and management. In this study we used the program Soil and Water Assessment Tool (SWAT), in combination with the Sequential Uncertainty Fitting program (SUFI-2), to calibrate and validate a hydrologic model of Alberta. Monthly data from 135 hydrometric stations and wheat yield was used to calibrate the model. This dual-objective calibration increased reliability in the prediction of soil water balance components. The study period was 1993-2007 for calibration and 1983-1993 for validation. The heterogeneous hydro-climatic conditions and the diverse management practices in combination with the scarcity of data in the remote areas and mountainous regions made hydrological modeling challenging. A data discrimination procedure was developed to represent most of the natural and anthropogenic processes in the watersheds. The calibrated model was further used to quantify water resources including blue water flow (river discharge plus deep aquifer recharge), green water flow (evapotranspiration), green water storage (soil moisture), and aquifer recharge at the subbasin spatial and monthly temporal scale. Uncertainty analyses were also performed to predict the errors related to the input data, model structure, and management measures. The results of this study will be integrated with other dynamic predictive models (e.g., climate change, water demand, economic evaluation) to enable analysis of alternative management options in the future.