Cumulative Effects of Uncertainty on Simulated Streamflow in a Hydrologic Modeling Environment

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.