Worth of hydraulic and water chemistry observation data in terms of the reliability of surface water-groundwater exchange flux predictions under varied flow conditions

This study assesses the worth of routinely collected hydraulic data (groundwater head, stream stage and streamflow) and lesser collected water chemistry data (Radon-222, Carbon-14, electrical conductivity (EC)) in the context of making regional-scale surface water-groundwater (SW-GW) exchange flux predictions. Using integrated SW-GW flow and transport numerical models, first-order, second-moment (FOSM) analyses were employed to assess the extent of the uncertainty reduction or lack thereof in SW-GW exchange flux predictions following acquisition of hydraulic and water chemistry observation data. With a case study of the Campaspe River in the Murray-Darling Basin (Australia), we explored the apparent information content of these data during low, regular and high streamflow conditions. Also, a range of spatial and temporal prediction scales were considered: catchment-wide and reach-based spatial scales and annual and monthly temporal scales. Generally, the data worth evaluations showed significant variability across predictions that were dependent on the spatiotemporal scale of the SW-GW exchange, the magnitude and direction of the SW-GW exchange flux and the prevailing streamflow conditions. These dependencies serve to emphasise the importance of prediction specificity with respect to SW-GW exchange. Among existing data, the most worth was found in Radon-222, groundwater hydraulic head, EC, and streamflow data showing average reductions in uncertainty of 41%, 38%, 32%, and 23% respectively. Assessment of type and spatiotemporal locations of potential data showed Radon-222 to be the next most important observation type across many predictions in locations with data paucity of all data types. Hydraulic observation data types were found to inform SW-GW exchange flux best under high- and regular- streamflow conditions when the magnitude of exchange fluxes were largest, whereas the water chemistry data was of highest value for low- and regular-streamflow conditions where groundwater is discharging to the stream.