Scientific Leader: Bibi Sarwat Naz
In hydropower and geothermal applications, modeling of shallow subsurface flow is of major importance in order to accurately simulate and predict the exchange of groundwater with streams under low-flow conditions, and the transport of energy. The major challenge is the representation of topographically driven groundwater convergence and streamflow generation, and of the geological heterogeneity across a number of space scales ranging from centimeters to thousands of kilometers in case of continental river systems. Constructing hydrological and geothermal models at this resolution over large spatial scales for scientific and operational applications constitutes a game changer, easily reaching up to 1012 degrees of freedom, where simulations must additionally assimilate observations to mitigate uncertainties in model data.
In EoCoE-II, the integration of hyper-resolved simulation of hydrological fluxes, routing along the river network, and management of storage reservoirs will be performed with a modernized version of ParFlow with adaptive mesh refinement (AMR) capability coupled with HYPERstreamHS model. The added values of these simulations will be shown by feeding ParFlow gridded runoff time series into the operational hydropower model embedded into HYPERstreamHS. The coupling will be specifically developed over the Italian Alpine region.
Previous work on geothermal reservoir characterization showed the successful application of optimal experimental design (OED) within the simulation code SHEMAT-Suite in order to identify optimal drilling locations for assessing uncertain reservoir parameters within a numerical reservoir model. However, the high computational cost has to date limited this approach to a numerical model with significantly reduced number of unknowns. Collaboration with experts in the EoCoE-II consortium will enable us to create a realistic geothermal reservoir model with vastly improved spatial resolution. Combining optimal experimental design for positioning boreholes with state-of-the art HPC techniques will improve the exploration and exploitation of geothermal reservoir systems, as it enables a sophisticated quantification of uncertainties in the subsurfac