Developing a consistent process-based conceptualization of catchment functioning using measurements of internal state variables

We use isotope data in addition to discharge and groundwater level data to conceptualize the internal processes of runoff generation and tracer transport in a low parameter coupled flow-tracer model that could predict the runoff response and isotopic composition of an upland stream. We used sensitivity analysis to assess the effect of these data on model calibration in terms of parameter identifiability and the model's ability to predict the stream's runoff response, isotopic composition and water age. The results showed that the incorporation of tracer data in particular, clearly increased parameter identifiability and improved the predictive power of models for simulating both streamflow and isotopes. This also resulted in a more consistent process-based conceptualization of catchment functioning. We could also show that using models as learning tools can guide sampling campaigns toward measurements with increased information content for further modeling. We conclude that this is a promising approach for assessing dominant processes in coupled flow-tracer models. This is of value when such models are being used to test hypotheses about the hydrological functioning of catchments, particularly in relation to pollutant transfers.