Integrated modelling to assess climate change impacts on groundwater and surface water in the Great Lakes Basin using diverse climate forcing
Climate change in the Great Lakes Basin of North America is expected to have a profound impact on hydrological processes. A greater understanding of these impacts must be developed to inform future adaptation strategies. This study aims to assess potential watershed response to changing climate conditions in southwestern, Ontario, Canada using the fully integrated surface-subsurface model HydroGeoSphere. To account for the uncertainty associated with climate projections and capture a range of possible conditions, a diverse set of meteorological forcings are used, including projections derived from Regional Climate Models (RCMs), a synthetic scenario based on IPCC fifth assessment report predictions, and temporal analogues. Simulation outputs have been compared to assess the potential influence of changing climate on groundwater hydraulic head, surface discharge, and net fluid exchange between surface and subsurface domains. The modelled projections reveal variability in both direction and magnitude of predicted hydrologic change suggesting that simulation outcomes should be interpreted probabilistically, and climate projection uncertainty needs to be characterized. Nonetheless, model outcomes reveal a greater likelihood for a significant reduction in mid-century discharge relative to any significant change in groundwater head or net exchange flux. This study also examines the influence of meteorological input with different temporal resolutions and the importance of RCM lake model coupling for capturing the regional climate influence of the Great Lakes. The latter of these considerations was found to influence spring and summer hydrologic phenomena while the former showed that both daily and monthly normal forcing yield comparable results, if monthly aggregated model outputs are considered. These findings are important for informing the design of future hydrologic investigations especially when working with computationally intensive, large-scale integrated models.
JOURNAL OF HYDROLOGY 卷: 584 文献号: 124682 出版年: 2020