Storm event analysis at varying watershed scales: Turkey Creek, Santee Experimental Forest, South Carolina
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Morrison, Austin
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Abstract
Coastal areas are expected to see the greatest impact on water resources due to population increase and land development which can affect the regional water budget by reducing evapotranspiration, groundwater recharge/discharge, and increase runoff. This project inspected forested watersheds in coastal South Carolina to understand their stream response to storm events. The objectives of this study were to (1) characterize the watershed conditions based on their land use/land cover, soil drainage class, and topography, (2) compare streamflow patterns using seasonal event hydrographs, and (3) compare results of analytical method of storm event hydrograph separation with that of the chemical method using stable water isotopes. Turkey Creek, a third-order watershed (5,240 ha), includes two first-order sub-watersheds. Physical and chemical hydrograph separation techniques and statistical methods were used for storm event analysis. Average annual rainfall for the study period was 1449 mm. The largest mean ROC, DROC, direct runoff to streamflow ratio, and peak flow rate were observed for the smallest sub-watershed (Conifer) and the lowest for the largest watershed (WS78). The largest baseflow to streamflow ratio was observed in WS78. Conifer sub-watershed experienced the shortest time to peak. Stable water isotope results show surface water samples somewhat isotopically distinct compared to groundwater and rainfall samples. There was a statistical difference in the isotopic signature in WS78 samples, perhaps due to the role of baseflow as groundwater discharge to the stream. Isotope results indicated baseflow contribution was 58-65% of streamflow in contrast to 35-41% as estimated from the hydrograph separation method. Interpretations of the results suggest that storm response was dependent on the antecedent condition and soil type in the watershed. More permeable soils allowed larger baseflow and less permeable soils produced more runoff. Scientists and land managers can use this data to predict runoff changes in areas affected by land development.