Reference number : GC-2017-Q1-01557, amendment number : , agreement type : G, recipient legal name : Illman, Walter (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Groundwater models have become indispensable in water supply assessment and contaminant transport studies. The availability of various models, their improved capabilities, and ever-increasing computational resources have advanced models closer to reality. They are becoming increasingly relied upon to make important decisions that have business, policy and societal consequences. One key ingredient to building more reliable models is employing more accurate flow parameters such as hydraulic conductivity ( K ) and specific storage ( S s ). However, the subsurface is heterogeneous at multiple scales and this significantly complicates the characterization of K and S s in unconsolidated and fractured rock formations. 
 
Hydraulic tomography (HT) which relies on the inverse modeling of multiple pumping tests, has emerged as a new method to image the subsurface heterogeneity of K and S s . While HT has been shown to be a robust method for imaging subsurface heterogeneity, there is a critical need to examine whether: 1) other site data such as geological, geophysical, temperature and chemical data can augment pumping test data and improve HT results; 2) HT can effectively map heterogeneities in fractured rock terrains where a large contrast in hydraulic properties between fractures and rock matrix exists; and 3) HT that utilizes river flood pulses as perturbations in groundwater levels can be used to characterize parameters that govern surface-water/groundwater exchange at the basin scale. 
 
Through this research program, we will first examine the effectiveness of integrating other site data with pumping test data on HT results at a well-characterized field site on the University of Waterloo campus. Next, we will also examine whether HT can be improved through data integration at a well-established fractured rock site in Japan where our research group has access to different, yet complementary data. Finally, a computational study will be conducted to investigate the utility of river flood pulses as signals detected in monitoring wells to map the heterogeneity in hydraulic parameters that control surface-water/groundwater exchange. 
 
Collectively, our research program will help determine whether HT can significantly improve subsurface characterization efforts relevant at the resolution and scale necessary for various industries (water, remediation, energy, waste disposal, and mining). The proposed research focuses on conducting complementary computational and field investigations that should significantly advance our capabilities to map subsurface heterogeneity in both porous and fractured geologic media. Because subsurface heterogeneities will be mapped more accurately through HT, development of such capabilities will also allow us to significantly improve the accuracy of groundwater models used to make important business, policy, and societal decisions., program purpose french : Groundwater models have become indispensable in water supply assessment and contaminant transport studies. The availability of various models, their improved capabilities, and ever-increasing computational resources have advanced models closer to reality. They are becoming increasingly relied upon to make important decisions that have business, policy and societal consequences. One key ingredient to building more reliable models is employing more accurate flow parameters such as hydraulic conductivity ( K ) and specific storage ( S s ). However, the subsurface is heterogeneous at multiple scales and this significantly complicates the characterization of K and S s in unconsolidated and fractured rock formations. 
 
Hydraulic tomography (HT) which relies on the inverse modeling of multiple pumping tests, has emerged as a new method to image the subsurface heterogeneity of K and S s . While HT has been shown to be a robust method for imaging subsurface heterogeneity, there is a critical need to examine whether: 1) other site data such as geological, geophysical, temperature and chemical data can augment pumping test data and improve HT results; 2) HT can effectively map heterogeneities in fractured rock terrains where a large contrast in hydraulic properties between fractures and rock matrix exists; and 3) HT that utilizes river flood pulses as perturbations in groundwater levels can be used to characterize parameters that govern surface-water/groundwater exchange at the basin scale. 
 
Through this research program, we will first examine the effectiveness of integrating other site data with pumping test data on HT results at a well-characterized field site on the University of Waterloo campus. Next, we will also examine whether HT can be improved through data integration at a well-established fractured rock site in Japan where our research group has access to different, yet complementary data. Finally, a computational study will be conducted to investigate the utility of river flood pulses as signals detected in monitoring wells to map the heterogeneity in hydraulic parameters that control surface-water/groundwater exchange. 
 
Collectively, our research program will help determine whether HT can significantly improve subsurface characterization efforts relevant at the resolution and scale necessary for various industries (water, remediation, energy, waste disposal, and mining). The proposed research focuses on conducting complementary computational and field investigations that should significantly advance our capabilities to map subsurface heterogeneity in both porous and fractured geologic media. Because subsurface heterogeneities will be mapped more accurately through HT, development of such capabilities will also allow us to significantly improve the accuracy of groundwater models used to make important business, policy, and societal decisions., agreement title english : Data Integration to Significantly Improve Hydraulic Tomography and Groundwater Modelling in Complex Geologic Media, agreement title french : Data Integration to Significantly Improve Hydraulic Tomography and Groundwater Modelling in Complex Geologic Media, agreement number : RGPIN, agreement value : 135000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada