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Travel time is a robust measure of water transport dynamics in catchments. At a given control section along a drainage network, travel time distribution results from an interplay between two main processes: (i) the transport through the hillslopes and (ii) transport through the drainage network. The main scope of this work is to quantify this interplay, specifically we aim to identify the relative impact of hillslopes and channels on the travel time at the catchment scale. A theoretical framework is developed following a bottom-up modelling approach that combines a Boussinesq model for water flow and water travel time in hillslopes, with a geomorphological model for water transport in drainage network. Simple semi-analytical solutions are derived for the first two moments of the travel time distribution within a flow section. We provide some relevant examples based on synthetic rainfall data, exploring the relative impacts of hillslope and channel properties. As expected, the dynamics of the hillslopes control the travel time distribution at the catchment scale. The drainage network typically introduces a lag in the average travel time exiting the hillslopes and reduces the temporal fluctuations of the mean travel time and its variance. Our theoretical model provides meaningful insights on the investigation of the dominant dynamics taking place in catchments: results suggest that hillslopes and their features are the main driver of travel time in catchments. The temporal fluctuations of the travel time moments show a non-linear dependence with the recharge time-series and need to be considered as time-variant. Variability of water ages collected in a single water sample can be very large, the latter feature may have a significant effect on the water quality and on the tracer data analysis, largely governed by the contact times between water and catchment material., QC 20220502

Six conceptually different transport models were applied to the macrodispersion experiment (MADE)-1 field tracer experiment as a first major attempt for model comparison. The objective was to show that complex mass distributions in heterogeneous aquifers can be predicted without calibration of transport parameters, solely making use of structural and flow data. The models differ in their conceptualization of the heterogeneous aquifer structure, computational complexity, and use of conductivity data obtained from various observation methods (direct push injection logging, DPIL, grain size analysis, pumping tests and flowmeter). They share the same underlying physical transport process of advection by the velocity field solely. Predictive capability is assessed by comparing results to observed longitudinal mass distributions of the MADE-1 experiment. The decreasing mass recovery of the observed plume is attributed to sampling and no physical process like mass transfer is invoked by the models. Measures like peak location and strength are used in comparing the modeled and measured plume mass distribution. Comparison of models reveals that the predictions of the solute plume agree reasonably well with observations, if the models are underlain by a few parameters of close values: mean velocity, a parameter reflecting log-conductivity variability, and a horizontal length scale related to conductivity spatial correlation. The robustness of the results implies that conservative transport models with appropriate conductivity upscaling strategies of various observation data provide reasonable predictions of plumes longitudinal mass distribution, as long as key features are taken into account., QC 20210622

Six conceptually different transport models were applied to the macrodispersion experiment (MADE)-1 field tracer experiment as a first major attempt for model comparison. The objective was to show that complex mass distributions in heterogeneous aquifers can be predicted without calibration of transport parameters, solely making use of structural and flow data. The models differ in their conceptualization of the heterogeneous aquifer structure, computational complexity, and use of conductivity data obtained from various observation methods (direct push injection logging, DPIL, grain size analysis, pumping tests and flowmeter). They share the same underlying physical transport process of advection by the velocity field solely. Predictive capability is assessed by comparing results to observed longitudinal mass distributions of the MADE-1 experiment. The decreasing mass recovery of the observed plume is attributed to sampling and no physical process like mass transfer is invoked by the models. Measures like peak location and strength are used in comparing the modeled and measured plume mass distribution. Comparison of models reveals that the predictions of the solute plume agree reasonably well with observations, if the models are underlain by a few parameters of close values: mean velocity, a parameter reflecting log-conductivity variability, and a horizontal length scale related to conductivity spatial correlation. The robustness of the results implies that conservative transport models with appropriate conductivity upscaling strategies of various observation data provide reasonable predictions of plumes longitudinal mass distribution, as long as key features are taken into account.