Traditional land surface models (LSMs) used for numerical weather simulation, climate projection, and as inputs to water management decision support systems, do not treat the LSM lower boundary in a fully process-based fashion. LSMs have evolved from a leaky-bucket approximation to more sophisticated land surface water and energy budget models that typically have a specified bottom layer flux to depict the lowest model layer exchange with deeper aquifers. The LSM lower boundary is often assumed zero flux or the soil moisture content is set to a constant value; an approach that while mass conservative, ignores processes that can alter surface fluxes, runoff, and water quantity and quality. Conversely, groundwater models (GWMs) for saturated and unsaturated water flow, while addressing important features such as subsurface heterogeneity and three-dimensional flow, often have overly simplified upper boundary conditions that ignore soil heating, runoff, snow, and root-zone uptake. In the present study, a state-of-the-art LSM (Common Land Model) and a variably saturated GWM (ParFlow) have been coupled as a single-column model. A set of simulations based on synthet ic data and data from the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS), version 2(d), 18-yr dataset from Valdai, Russia, demonstrate the temporal dynamics of this coupled modeling system. The soil moisture and water table depth simulated by the coupled model agree well with the Valdai observations. Differences in prediction between the coupled and uncoupled models demonstrate the effect of a dynamic water table on simulated watershed flow. Comparison of the coupled model predictions with observations indicates certain cold processes such as frozen soil and freeze/thaw processes have an important impact on predicted water table depth. Comparisons of soil moisture, latent heat, sensible heat, temperature, runoff, and predicted groundwater depth between the uncoupled and coupled models demonstrate the need for improved groundwater representation in land surface schemes.
All Science Journal Classification (ASJC) codes
- Atmospheric Science