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

Abstract

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.

Changing freshwater contributions to the Arctic

“…Even among the known unknowns (i.e., input, parameter, and model structural uncertainty), there is the issue of uncertainty propagation through the modeling chain to arrive at a cumulative estimate of downstream (outlet) uncertainty for projected discharge (Pokorny et al, 2021). This issue presents a specific challenge for highlatitude basins, such as the pan-Arctic domain, where data are sparse and the region is highly diverse and complex hydrologically, requiring a relaxation of normally more stringent model calibration guidelines.…”

Evaluation and comparison of CMIP6 and CMIP5 models performance in simulating the runoff

“…Total uncertainty was assessed using reliability, sharpness metrics and Continuous Rank Probability Score (CRSP) (Pokorny et al, 2021;Zhou et al, 2016). Reliability was defined as the percentage of overlap of the LORA reference data set (annual) and the multi-model simulated ensemble bounds (annual)…”

A framework to estimate continuous streamflow at the ungauged site with limited catchment’s hydrological data: Case study Isahaya Catchment

“…The selection of parameters for calibration or optimization needs to be addressed as well as the incorporation of associated uncertainties (Abbaspour et al, 2017, Bock et al, 2018, Pokorny et al, 2021. Due to proximity and similarity within the river basins, DG yielded a fair trend of flow rate estimates at the ungauged site but was inconsistent in determining the actual flow rate with changes in weather.…”