Boreal soils in permafrost regions contain vast quantities of frozen organic material that is released to terrestrial and aquatic environments via subsurface flow paths as permafrost thaws. Longer flow paths may allow chemical reduction of solutes, nutrients, and contaminants, with implications for greenhouse gas emissions and aqueous export. Predicting boreal catchment runoff is complicated by soil heterogeneities related to variability in active layer thickness, soil type, fire history, and preferential flow potential. By coupling measurements of permeability, infiltration potential, and water chemistry with a stream chemistry end-member mixing model, we tested the hypothesis that organic soils and burned slopes are the primary sources of runoff, and that runoff from burned soils is greater due to increased hydraulic connectivity. Organic soils were more permeable than mineral soils, and 25% of infiltration moved laterally upon reaching the organic-mineral soil boundary on unburned hillslopes. A large portion of the remaining water infiltrated into deeper, less permeable soils. In contrast, burned hillslopes displayed poorly defined soil horizons, allowing rapid, mineral-rich runoff through preferential pathways at various depths. On the catchment scale, mineral/organic runoff ratios averaged 1.6 and were as high as 5.2 for an individual storm. Our results suggest that burned soils are the dominant source of water and solutes reaching the stream in summer, whereas unburned soils may provide longer term storage and residence times necessary for production of anaerobic compounds. These results are relevant to predicting how boreal catchment drainage networks and stream export will evolve given continued warming and altered fire regimes.

Experimental design and data collection constitute two main steps of the iterative research cycle (aka the scientific method). To help evaluate competing hypotheses, it is critical to ensure that the experimental design is appropriate and maximizes information retrieval from the system of interest. Scientific hypothesis testing is implemented by comparing plausible model structures (conceptual discrimination) and sets of predictions (predictive discrimination). This research presents a new Discrimination-Inference (DI) methodology to identify prospective data sets highly suitable for either conceptual or predictive discrimination. The DI methodology uses preposterior estimation techniques to evaluate the expected change in the conceptual or predictive probabilities, as measured by the Kullback-Leibler divergence. We present two case studies with increasing complexity to illustrate implementation of the DI for maximizing information withdrawal from a system of interest. The case studies show that highly informative data sets for conceptual discrimination are in general those for which between-model (conceptual) uncertainty is large relative to the within-model (parameter) uncertainty, and the redundancy between individual measurements in the set is minimized. The optimal data set differs if predictive, rather than conceptual, discrimination is the experimental design objective. Our results show that DI analyses highlight measurements that can be used to address critical uncertainties related to the prediction of interest. Finally, we find that the optimal data set for predictive discrimination is sensitive to the predictive grouping definition in ways that are not immediately apparent from inspection of the model structure and parameter values.

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