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dc.contributor.supervisor Stadnyk, Tricia A. (Civil Engineering) en_US
dc.contributor.author Muhammad, Ameer
dc.date.accessioned 2019-03-07T17:21:41Z
dc.date.available 2019-03-07T17:21:41Z
dc.date.issued 2019 en_US
dc.date.submitted 2019-03-07T17:13:16Z en
dc.identifier.citation Muhammad, A., Evenson, G.R., Stadnyk, T.A., Boluwade, A., Jha, S.K., Coulibaly, P., 2019. Impact of model structure on the accuracy of hydrological modeling of a Canadian Prairie watershed. J. Hydrol. Reg. Stud. 21, 40–56. doi:10.1016/J.EJRH.2018.11.005 en_US
dc.identifier.citation Muhammad, A., Evenson, G.R., Stadnyk, T.A., Boluwade, A., Jha, S.K., Coulibaly, P., 2018. Assessing the importance of potholes in the Canadian Prairie Region under future climate change scenarios. Water (Switzerland) 10. doi:10.3390/w10111657 en_US
dc.identifier.citation Muhammad, A., Stadnyk, T.A., Unduche, F., Coulibaly, P., 2018. Multi-Model Approaches for Improving Seasonal Ensemble Streamflow Prediction Scheme with Various Statistical Post-Processing Techniques in the Canadian Prairie Region. Water 10, 1604. doi:10.3390/w10111604 en_US
dc.identifier.uri http://hdl.handle.net/1993/33783
dc.description.abstract There is an increasing interest in assessing uncertainty and quantifying its impact on hydrological modeling. Four major sources of uncertainty are recognized in hydrologic modeling: (1) input uncertainty mainly due to error in model forcing data, such as error in precipitation measurement; (2) model structure uncertainty arising because models are only an approximation of reality; (3) parameter uncertainty originating because not all the parameters can be measured; and (4) output uncertainty given measurement error in streamflow data against which hydrologic models are calibrated. Despite significant development in computational science, the issue of reducing uncertainty remains a challenge especially its consideration in operational use is often ignored. This research lies in the context of the Natural Sciences and Engineering Research Council (NSERC) funded FloodNET project that aims at developing advanced knowledge, tools, and technologies that will allow Canada to better face the reality of floods. In close collaboration with the Hydrologic Forecasting Centre (HFC) of Manitoba, a modified form of Soil Water Assessment Tool (SWAT) hydrologic model was constructed to better suit Prairie landscape characteristics to minimize and address uncertainty arising from the dynamics of contributing and non-contributing area. The modified model, together with its standard version, was applied to the Upper Assiniboine River Basin (UARB) at Kamsack. Significant improvement was observed in the case of the modified model as compared to the standard version of SWAT model. The modified model was then utilized to assess long-term uncertainty due to coupled impacts of climate and land use change (Prairie pothole removal) on downstream hydrology. Land use change scenarios were combined with future climate change scenarios to assess the importance of pothole wetlands in flood proofing and future water availability. Results suggest that while pothole wetlands are important, climate is the main driver in the future hydrologic regime of the study watershed. Lastly, this study evaluated the performance of the modified model together with a Manitoba HFC operational model to help quantify uncertainty in streamflow forecasting within the UARB. Ensemble Streamflow Prediction (ESP) was investigated using a multi-model approach and post-processing tools developed to improve ensemble decision-making capacity for the HFC. en_US
dc.subject SWAT, Wetlands, Uncertainty, Climate change, Post-processing, Ensemble streamflow prediction en_US
dc.title Uncertainty in streamflow simulation of the Upper Assiniboine River basin en_US
dc.degree.discipline Civil Engineering en_US
dc.contributor.examiningcommittee Asadzadeh, Masoud (Civil Engineering) Coulibaly, Paulin (Civil Engineering) Ali, Genevieve (Geological Sciences) Singh, Vijay (Biological and Agricultural Engineering, Texas A&M University) en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US
dc.description.note May 2019 en_US


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