LINKING EPISODIC RECHARGE ASSESSMENT WITH STORM CLASSIFICATION

Previous field and modeling studies have illustrated that temporal patterns of moisture drainage below the root zone are often irregularly distributed both intra-annually and inter-annually. Particularly in dry regions, deep drainage can be highly episodic, whereby large percentages of cumulative drainage rates are attributable to a relatively small number of wetting events. This is a salient concept for predictions of climate change impacts on groundwater recharge, for example, because effects may more closely reflect changes to rainfall extremes than to central tendencies in many settings.

This study explores the use of meteorological storm classification methods as a means to improve the description of episodic recharge generation patterns. Individual storm events are classified on a stratiform/convective scale using a fast Fourier transform applied to the reflectivity field of radar data collected by the National Weather Service weather surveillance Doppler radar network (WSR-88D). A calibrated unsaturated zone flow model is then used to assign a drainage generation factor to each corresponding event. In an example application, episodic drainage patterns over a 12 year period are evaluated for a dryland agricultural site in eastern Nebraska’s glacial till region.

Results suggest strong seasonality of deep drainage fluxes developing from annual patterns in precipitation and potential evapotranspiration, with peak drainage rates in late spring to early summer when the area receives frequent convective thunderstorms. Essentially zero drainage generation occurred from July through January, and approximately 75% of storm events produced negligible drainage. Storm classification showed a bimodal distribution between stratiform and convective. Although the strongest drainage generation events were affected by highly convective storm events, stratiform rainfall affected deep drainage to a significant degree, particularly in late winter. The proposed approach holds promise for exploring the linkages between weather events and groundwater.