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Riverbed temperature profiles are frequently used to estimate vertical river–aquifer exchange fluxes. Often in this approach, strictly vertical flow is assumed. However, riverbeds are heterogeneous structures often characterised by complex flow fields, possibly violating this assumption. We characterise the meter-scale variability of river–aquifer interaction at two sections of the Aa River, Belgium, and compare vertical flux estimates obtained with a 1D analytical solution to the heat transport equation with fluxes simulated with a 3D groundwater model (MODFLOW) using spatially distributed fields of riverbed hydraulic conductivity. Based on 115 point-in-time riverbed temperature profiles, vertical flux estimates that are obtained with the 1D solution are found to be higher near the banks than in the center of the river. The total exchange flux estimated with the 3D groundwater model is around twice as high as the estimate based on the 1D solution, while vertical flux estimates from both methods are within a 10% margin. This is due to an important contribution of non-vertical flows, especially through the riverbanks. Quasi-vertical flow is only found near the center of the river. This quantitative underestimation should be considered when interpreting exchange fluxes based on 1D solutions. More research is necessary to assess conditions for which using a 1D analytical approach is justified to more accurately characterise river–aquifer exchange fluxes.

The objective of this paper is to exploit the potential application of weighted index overlay analysis for assessing groundwater potential mapping at Dhungeta-Ramis sub-basin, Wabi Shebele basin, Ethiopia using remote sensing and geographic information system (GIS) technique. For this purpose, seven groundwater occurrences and movement controlling factors, including, lithology, slope, land use land cover (LULC), rainfall, lineaments, soil, and drainage density were mapped. Then, weight was assigned to thematic maps, and the groundwater prospective of the sub-basin is qualitatively classified into five classes, namely, very good, good, moderate, poor, and very poor which account for 2.22%, 26.93%, 56.74%, 13.84%, and 0.26% landscape, respectively. The cross-validation of the resultant model was carefully carried out using spring, hand-dug, and deep well data. The result reveals that 89% of springs were overlaying good and/or very good groundwater potential zones and 58% of deep well shows the same scenario, whereas 42% of deep well overlays moderate zone. As a result, the map generated using this platform could be used as a preliminary reference in selecting suitable sites for groundwater resource exploitation.

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