Modeling the Impact of Spatiotemporally Variable Vegetation on Groundwater Recharge

Climate and land-use change affect the growth of vegetation and alter plant physiological states such as leaf-area-index (LAI). Because LAI strongly controls canopy interception and transpiration, changes in vegetation growth have the potential to impact the flux of water past the root zone and to the water table in the form of recharge. Presently, vegetation representation is oversimplified in most recharge modeling studies by using fixed parameters such as repeated climatological monthly LAI. This kind of parameterization neglects vegetation responses to dynamic meteorological and land-cover conditions. As a result, the effect of both finer intra-seasonal time-scale and coarser interannual vegetation dynamics on recharge is not considered. We investigate the sensitivity of groundwater recharge in Minnesota (USA) to temporally dynamic vegetation across varying climate and ecoregions. We adopt a stochastic approach using the ensemble Kalman filter (EnKF) with NCAR's Community Land Model (CLMv4.5) to condition uncertain hydrogeologic parameters on a network of water table observations across the state. In our spatial implementation of EnKF, we were able to efficiently leverage the extensive but irregularly distributed groundwater level observations to simultaneously constrain the soil parameters across the state. We present and compare state-wide groundwater recharge estimates from simulations using both transient MODIS vegetation and the typical climatological input. Using the distributed ensemble simulations, we account for spatiotemporally variable ecohydrologic processes and examine the effect of seasonal to interannual variability of vegetation growth on recharge. Our stochastic approach provides more reliable estimates of how climate and land-use change may impact groundwater resources, compared to standard deterministic, fixed-parameter model implementations.