2021
DOI: 10.1525/elementa.431
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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. Uncertain… Show more

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Cited by 11 publications
(8 citation statements)
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“…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.…”
Section: Influence Of River Regulationmentioning
confidence: 99%
“…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.…”
Section: Influence Of River Regulationmentioning
confidence: 99%
“…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)…”
Section: Multi-model Ensemble Evaluationmentioning
confidence: 99%
“…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.…”
Section: Calibration and Validation Of The Gauged R6-g River Basinmentioning
confidence: 99%