• hydroecology;
  • ecohydrology;
  • meadow

[1] Stream incision is altering the hydroecology of riparian areas worldwide. In the Last Chance watershed in the northern Sierra Nevada, California, logging, overgrazing, and road/railroad construction have caused stream incision, which resulted in drainage of riparian meadow sediments and a succession from native wet meadow vegetation to sagebrush and dryland grasses. Restoration efforts have been initiated to reestablish the ecosystem function of these systems. Original field data including stream stage records, water table hydrographs, sediment hydraulic properties, topographic transects, and aerial imagery of vegetation patterning were used to develop a model of an archetype meadow. Hydrologic behavior was simulated with a finite element model of variably saturated groundwater flow. This model was coupled to an empirical, time-dependent, vegetation threshold relationship between vegetation type and depth to the water table. This was a two-way coupling requiring an iterative approach because water table depth is a determinant of vegetation type, yet the vegetation regime influences water table depth through evapotranspiration. The hydrology and vegetation patterns were analyzed under pristine, degraded (incised), and restored conditions. For the case of deep streambed incision, our hydroecological model predicts the observed shift from mesic (wetter) to xeric (drier) vegetation communities and reproduces their imaged longitudinal zonation. This patterning is explained as a response to groundwater drainage to the stream, which creates dry zones with xeric vegetation adjacent to the stream, while preserving sufficient moisture at the margins of the meadow to support holdout populations of mesic vegetation. The model further predicts the reestablishment of meadow vegetation when the incised channel is filled and a new shallow channel is restored. The coupling of a near-surface hydrologic model to a vegetation response model may be used to design stream restoration projects by predicting vegetation patterning.