Shallow groundwater dynamics across complex terrain: Influences of landscape position and climate

Friday, 19 December 2014: 11:05 AM
Kelsey G Jencso, University of Montana, Missoula, MT, United States and Brian L McGlynn, Duke University, Nicholas School of the Environment, Durham, NC, United States
Prediction of hillslope and riparian water table dynamics across headwater catchments remains challenging. We suggest that this is partially due to the lack of spatially distributed, high frequency, long duration observations of shallow subsurface flows that connect hillsopes to riparian and stream landscape positions. Further, these observations are rarely considered in the context of both local and watershed scale structure and organization. Here, we provide a synthesis of shallow subsurface hydrologic connectivity duration and magnitude across 9 years of hourly stage recordings for 30 transects of shallow groundwater wells (hillslope size from 0.07 to 4.6 ha) in the Tenderfoot Creek Experimental Forest, MT. Key results include: 1) the annual average water table depth across all hillsope wells was exponentially related (r2 = 0.94) to upslope accumulated area (UAA). 2) K means analysis of the annual hilllsope water table stage duration curves indicated a transition across the continuum of wells from short duration hydrologic connectivity and low stage (UAA = 0.07 – 1ha, n= 90), sustained snowmelt hydrologic connectivity and moderate stage (UAA = 1.2 – 3ha, n = 20), and continuous hydrologic connectivity and highest stage (UAA = 3.1 – 4.6ha, n=11). 3) hillslopes with the largest UAA (n= 11; continuous connectivity and sustained groundwater stage) exhibited groundwater dynamics similar to those in riparian wells. The synthesis of the well water stage responses across space and time suggests strong emergent behavior according to landscape position and topography. These preliminary analyses provide insight into watershed behavior that is requisite for understanding where streamflow originates in the landscape, appropriate model structures for predicting streamflow, predicting landscape position sensitivity to climate variability, and informing land management decisions.