Abstract. This paper investigates the patterns and controls of aquifer–river exchange in a fast-flowing lowland river by the conjunctive use of streambed temperature anomalies identified with Fibre-optic Distributed Temperature Sensing (FO-DTS) and observations of vertical hydraulic gradients (VHG). FO-DTS temperature traces along this lowland river reach reveal discrete patterns with "cold spots" indicating groundwater up-welling. In contrast to previous studies using FO-DTS for investigation of groundwater–surface water exchange, the fibre-optic cable in this study was buried in the streambed sediments, ensuring clear signals despite fast flow and high discharges. During the observed summer baseflow period, streambed temperatures in groundwater up-welling locations were found to be up to 1.5 °C lower than ambient streambed temperatures. Due to the high river flows, the cold spots were sharp and distinctly localized without measurable impact on down-stream surface water temperature. VHG patterns along the stream reach were highly variable in space, revealing strong differences even at small scales. VHG patterns alone are indicators of both, structural heterogeneity of the stream bed as well as of the spatial heterogeneity of the groundwater–surface water exchange fluxes and are thus not conclusive in their interpretation. However, in combination with the high spatial resolution FO-DTS data we were able to separate these two influences and clearly identify locations of enhanced exchange, while also obtaining information on the complex small-scale streambed transmissivity patterns responsible for the very discrete exchange patterns. The validation of the combined VHG and FO-DTS approach provides an effective strategy for analysing drivers and controls of groundwater–surface water exchange, with implications for the quantification of biogeochemical cycling and contaminant transport at aquifer–river interfaces.
The effect of streambed heterogeneity on groundwater-surface water exchange fluxes inferred from temperature time series
