Quantifying fine scale discharge to surface water using the amplitude shift method with distributed temperature measurements
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Diurnal temperature fluctuations, as result of solar heating, function as a tracer that continuously exchanges energy between surface water, streambed sediments, and discharging groundwater. Analytical solutions exist that can estimate discharge by extracting the amplitude ratio or diurnal phase shift between pairs of subsurface temperature time series measurements. The research presented here adds to the expanding body of heat tracing literature by applying the amplitude ratio time series discharge estimation to pairs of distributed temperature sensor (DTS) measurements. This research utilized time series data from synthetic data sets, modeled numerically using COMSOL Multiphysics, and physical data sets, modeled in a 10 m long physical sandbox model. Discharge was estimated from spatially averaged temperature data accurately estimates discharge where flow is uniform, because the temperature signal is uniform along a horizontal plane. Error is introduced when spatial variability of diffuse flow exists, resulting in temperature profiles that vary laterally. Spatial averaging inherent to DTS data results in dampening of discrete discharge measurements, because temperature is averaged over sections where discharge is not uniform. This leads to underestimating peak discharge by 14% at localized discharge zones and overestimating discharge as measurements move away from these locations. Uncertainty associated with the resolutions of the DTS sampling interval leads to error in calculated groundwater fluxes. Because the DTS unit measures temperature every meter it is possible for measurements to be offset by as much as half a meter. Non-ideal positioning of the measurement pairs lead to underestimation of localized flux rates by as much as 27%.