Stable isotope analysis is widely used in environmental tracer studies, e.g. for groundwater flow and discharge quantification. In this context, this study presents an inexpensive approach for the combined use of deuterium (2H) and oxygen-18 (18O) as active semiartificial groundwater tracers by a direct injection of snowmelt into aquifers. This dual isotope approach takes advantage of isotope signature differences between typical groundwater and precipitation water. Aim of this study is the experimental demonstration on laboratory- and field-scale. For this, two column flow experiments were performed using δ2H and δ18O values of snowmelt for breakthrough detection. The differences of the isotope signature between the snowmelt and groundwater were ∆(δ2H) ≈ 61.0 ‰ and ∆(δ18O) ≈ 8.2 ‰. Breakthrough was observed to be almost congruent to a sodium chloride tracer, indicating conservative transport. The low electrical conductivity (EC) of snowmelt (45 µS/cm, i.e. ∆EC ≈ 486 µS/cm to groundwater) was used as an additional easy-to-measure breakthrough indicator. However, the snowmelt EC breakthrough suffered from a slight retardation due to ion exchange. Based on these results, a push-drift-pull tracer test with snowmelt, additionally labeled with uranine, was realized at the field site Pirna, Germany. In the pull phase, a significant isotopic depletion was observed with peak differences of ∆Peak(δ2H) ≈ 24.2 ‰ and ∆Peak(δ18O) ≈ 3.2 ‰, which equals approx. 40 % of the initial difference. The isotope breakthrough was observed to be almost the same as the breakthrough of uranine indicating conservative behavior, while EC breakthrough was affected by ion exchange again.

This data collection includes the Python script used for model data preparation, processing and assessment as described in the article "Evaluation of Decentralized, Closely-Spaced Precipitation Water and Treated Wastewater Infiltration" by Händel et al. submitted to Journal "Water" in 2018. Python script input data as generated from Hydrus 2D/3D models as well as resulting plots as used in the previously mentioned article are included.

This data collection includes the Python script used for image processing and analysis as described in the article "Quantification of uncertainties from image processing and analysis in laboratory-scale DNAPL migration experiments evaluated by reflective optical imaging" by Engelmann et al. submitted to Journal "Water" in 2019. Exemplary raw images generated from laboratory-scale tank experiments for DNAPL migration are included as well.