The pathways taken throughout any model-based process are undoubtedly influenced by the modeling team involved and the decision choices they make. For interconnected socioenvironmental systems (SES), such teams are increasingly interdisciplinary to enable a more expansive and holistic treatment that captures the purpose, the relevant disciplines and sectors, and other contextual settings. In practice, such interdisciplinarity increases the scope of what is considered, thereby increasing choices around model complexity and their effects on uncertainty. Nonetheless, the consideration of scale issues is one critical lens through which to view and question decision choices in the modeling cycle. But separation between team members, both geographically and by discipline, can make the scales involved more arduous to conceptualize, discuss, and treat. In this article, the practices, decisions, and workflow that influence the consideration of scale in SESs modeling are explored through reflexive accounts of two case studies. Through this process and an appreciation of past literature, we draw out several lessons under the following themes: (1) the fostering of collaborative learning and reflection, (2) documenting and justifying the rationale for modeling scale choices, some of which can be equally plausible (a perfect model is not possible), (3) acknowledging that causality is defined subjectively, (4) embracing change and reflection throughout the iterative modeling cycle, and (5) regularly testing the model integration to draw out issues that would otherwise be unnoticeable.

It is common in the literature to not consider all sources of uncertainty simultaneously: input, structural, parameter, and observed calibration data uncertainty, particularly in data-sparse environments due to data limitations and the complexities that arise from data limitations when propagating uncertainty downstream in a modelling chain. This paper presents results for the propagation of multiple sources of uncertainty towards the estimation of streamflow uncertainty in a data-sparse environment. Uncertainty sources are separated to ensure low likelihood uncertainty distribution tails are not rejected to examine the interaction of sources of uncertainty. Three daily resolution hydrologic models (HYPE, WATFLOOD, and HEC-HMS), forced with three precipitation ensemble realizations, generated from five gridded climate datasets, for the 1981–2010 period were used to examine the effects of cumulative propagation of uncertainty in the Lower Nelson River Basin as part of the BaySys project. Selected behavioral models produced an average range of Kling-Gupta Efficiency scores of 0.79–0.68. Two alternative methods for behavioral model selection were also considered that ingest streamflow uncertainty. Structural and parameter uncertainty was found to be insufficient, individually, by producing some uncertainty envelopes narrower than observed streamflow uncertainty. Combined structural and parameter uncertainty, propagated to simulated streamflow, often enveloped nearly 100% of observed streamflow values, however, high and low flow years were generally a source for lower reliabilities in simulated results. Including all sources of uncertainty generated simulated uncertainty bounds that enveloped most of the observed flow uncertainty bounds including improvement for high and low flow years across all gauges although the uncertainty bounds generated were of low likelihood. Overall, accounting for each source of uncertainty added value to the simulated uncertainty bounds when compared to hydrometric uncertainty; the inclusion of hydrometric uncertainty was key for identifying the improvements to simulated ensembles.

I reassess a recent analysis of uncertainty in estimates of nitrogen export from stormwater control measures, using structured expert judgment, which concluded that nitrogen export from a watershed in the Piedmont physiographic province of the Chesapeake Bay basin was an order of magnitude greater than from a watershed in the adjacent the Coastal Plain province. Re-analysis of expert responses suggests that hydrographic measurement error is a likely large source of uncertainty in N export from one of the watersheds. Mass-balance estimates of impervious runoff into stormwater drainage systems suggest that nitrogen export from the Coastal Plain watershed is an order of magnitude larger than estimated. This analysis highlights the importance of stormwater drainage infrastructure in driving the hydrology of streams in urban catchments by quarantining impervious runoff from watershed soils.