The integrated modeling team will address connections between surface and subsurface hydrology by developing new high-performance computing (HPC) models that link these two (normally separate) domains. Integrated process simulation tools will improve predictive understanding of hydrological processes and identify emergent research focus areas. We will build coupled surface/subsurface simulation tools that operate on temporal domains ranging from minutes to years and spatial domains ranging from point (infiltration) to basin (river discharge; groundwater recharge) using parallel computing resources. Initial modeling efforts will be on the adaptation of existing coupled models, which will inform our field instrument deployment.
Central to our goals is the notion of “optimal complexity”– that is, how complex must a model be (or, equivalently, how simple can it be) to predict the flow characteristics of a watershed for a given purpose (Figure). Identifying the appropriate level of complexity is a challenge that can be addressed with high-performance computing, geophysical data, and field measurements. Hydrologic models (of both surface and subsurface) must balance simplicity with complexity in their conceptual and computational frameworks. We will determine appropriate levels of model complexity by incorporating high-density subsurface data from near-surface geophysics into novel HPC techniques and validating the results against geochemical and hydrological field data.
Figure. Optimum complexity in hydrological monitoring. Most hydrological models (left) involve oversimplified subsurface models based on sparse information. Additional data (center) allow increased detail. Comprehensive downhole, geophysical, surface hydrological and tracer data (right) enable much more realistic hydrological models. For a given problem, any one of the levels of complexity shown here might be sufficient; research must determine which level is appropriate for different objectives.