Quantifying Uncertainty in Streamflow Records

Hamilton, A. S.; Moore, R. D.

doi:10.4296/cwrj3701865

Cited by 95 publications

(82 citation statements)

References 5 publications

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“…For signatures calculated over a long time period, it may be appropriate to incorporate nonstationary error characteristics, such as rating-curve shifts or the example explored by Hamilton and Moore (2012) where the best-practice method for infilling discharge values under ice changed over time. The time period used is important if signatures are used for catchment classification: an unusual event such as a large flood may shift the signature values (Casper et al, 2012).…”

Section: Methods Limitations and Future Developmentsmentioning

confidence: 99%

Uncertainty in hydrological signatures

Westerberg

McMillan

2015

Hydrol. Earth Syst. Sci.

153

141

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Abstract. Information about rainfall-runoff processes is essential for hydrological analyses, modelling and watermanagement applications. A hydrological, or diagnostic, signature quantifies such information from observed data as an index value. Signatures are widely used, e.g. for catchment classification, model calibration and change detection. Uncertainties in the observed data -including measurement inaccuracy and representativeness as well as errors relating to data management -propagate to the signature values and reduce their information content. Subjective choices in the calculation method are a further source of uncertainty.We review the uncertainties relevant to different signatures based on rainfall and flow data. We propose a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrate it in two catchments for common signatures including rainfall-runoff thresholds, recession analysis and basic descriptive signatures of flow distribution and dynamics. Our intention is to contribute to awareness and knowledge of signature uncertainty, including typical sources, magnitude and methods for its assessment.We found that the uncertainties were often large (i.e. typical intervals of ± 10-40 % relative uncertainty) and highly variable between signatures. There was greater uncertainty in signatures that use high-frequency responses, small data subsets, or subsets prone to measurement errors. There was lower uncertainty in signatures that use spatial or temporal averages. Some signatures were sensitive to particular uncertainty types such as rating-curve form. We found that signatures can be designed to be robust to some uncertainty sources. Signature uncertainties of the magnitudes we found have the potential to change the conclusions of hydrological and ecohydrological analyses, such as cross-catchment comparisons or inferences about dominant processes.

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Section: Methods Limitations and Future Developmentsmentioning

confidence: 99%

Uncertainty in hydrological signatures

Westerberg

McMillan

2015

Hydrol. Earth Syst. Sci.

153

141

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“…Sensors in harsh environments, such as those that experience freezing, are particularly prone to malfunction. Research on the quantification of uncertainty in streamflow measurements (e.g., Hamilton and Moore 2012;Jalbert et al 2011;Le Coz 2012;McMillan et al 2012) has rarely been integrated into real-time forecasting applications. A similar situation exists with respect to rainfall uncertainty, which represents another major source of errors in hydrological predictions (e.g., Rossa et al 2011;Renard et al 2011;Zappa et al 2011;Liechti et al 2013).…”

Section: Fig 2 éLectricité De France Operational River Forecaster Amentioning

confidence: 99%

Challenges of Operational River Forecasting

Pagano

Wood

Ramos³

et al. 2014

Journal of Hydrometeorology

155

116

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Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human-natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using humangenerated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.

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“…Typically, analysts assume that the in situ measured data from river flow rate measurements (such as that provided by Environment Canada) are within ±5% of the true value at the 95% confidence interval (Hamilton and Moore, 2012;Papa et al, 2012). Others consider that this random uncertainty associated with the measurement of the flow rate to be negligible (Baldassarre and Montanari, 2009) or as low as 1% of the true value at the 95% confidence interval (Shrestha and Simonovic, 2010).…”

Section: Significance Of the Error Value Ementioning

confidence: 99%

“…In one studies this uncertainty is listed as high as 100% for low flows, 10% for medium flows, and 20% for high flows (Krueger et al, 2010;McMillan et al, 2012). Daily discharge uncertainty is listed with a range of ± 100 -200% for low flows and ±100% for high flows by Harmel and Smith (2007) and up to 50% by Hamilton and Moore (2012) for all magnitudes. Pappenberger et al (2006) reported uncertainty with peak flow rates to range between 8 and 25%, Baldassare and Montanari (2009) reported a range from 6.2% to 42.8% at the 95% confidence interval, and Westerberg et al (2011) give a range between -43 to 73%.…”

Section: Significance Of the Error Value Ementioning

confidence: 99%

Short-Term Peak Flow Rate Prediction and Flood Risk Assessment Using Fuzzy Linear Regression

Khan¹,

Valeo²

2016

J ENVIRON INFORM

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ABSTRACT.A fuzzy linear regression (FLR) method is proposed that uses real-time data to accurately predict daily peak flow rate for the Bow and Elbow Rivers in southern Alberta. FLR model performance was compared to a non-fuzzy, error-in-variables model (EIV). Mean daily flow rate, with a delay of one, two, three or seven days was used as the independent variable. In implementing the FLR, a unique hybrid modelling approach was devised that treated peak flow rate as probabilistic and mean daily flow rate as possibilistic. Three gauge errors, 5%, 10% and 20%, were tested and compared to quantify uncertainty in observed flow rate. The research proposed a new method of computing the exceedance probability of peak flow rate using fuzzy numbers. NSE, PBIAS and RSR and a proposed rating system were used to evaluate and compare the methods. Two different calibration schemes were used, including a quasi-real time system. The tests demonstrated that FLR with a one day lag was a very good predictor of peak flow rate and outperformed EIV for two stations on the Bow River. A test dataset from the floods of June 2013 in Calgary was used for risk assessment. The FLR results demonstrated higher flexibility and sensitivity to the flood as it approached Calgary. The fuzzy method was able to capture the peak flow rate for the majority of the high flow rate days, while the EIV model was unable to predict this data within the 95% confidence interval.

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Quantifying Uncertainty in Streamflow Records

Cited by 95 publications

References 5 publications

Uncertainty in hydrological signatures

Uncertainty in hydrological signatures

Challenges of Operational River Forecasting

Short-Term Peak Flow Rate Prediction and Flood Risk Assessment Using Fuzzy Linear Regression

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