A simplified approach to produce probabilistic hydrological model predictions

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Bennett, Bree; Lerat, Julien; Gibbs, Matthew S.; Kuczera, George

doi:10.1016/j.envsoft.2018.07.001

Cited by 30 publications

(28 citation statements)

References 40 publications

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“…The baseline model is given by an AR(1) model, as described in Equations and , and its parameters are estimated by applying the Method of Moments to the “optimal” residuals in Equation (see McInerney et al, 2018). The parameter

{\overset{truê}{ϕ}}_{η}

is computed as the sample lag‐1 autocorrelation coefficient of

\overset{η}{true}

, and σ y is estimated as the sample standard deviation of

\overset{y}{true}

.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…The parameter

{\overset{truê}{μ}}^{*}

is computed as the sample mean of

{\overset{truê}{boldη}}^{*}

, and the parameter

{\overset{truê}{ϕ}}_{η}

is computed as the sample lag‐1 autocorrelation coefficient of

{\overset{truê}{boldη}}^{*} - μ^{*}

(see McInerney et al [2018[ for equations). Note that

{\overset{truê}{μ}}^{*}

will be very close to 0 when

μ_{d}^{(normals)}

is defined by Equation and the dynamic bias window width N b is narrow compared to the length of the calibration period.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…Note that this parameter estimation approach is applied without attempting to estimate parametric uncertainty. When long time series of observed data are used to calibrate parsimonious models, such as GR4J (Perrin et al, 2003) used in this study, the contribution of parametric uncertainty to total uncertainty is generally small (Kavetski, 2018; McInerney et al, 2018; Sun et al, 2017). For this reason, forecasting methods typically neglect parametric uncertainty and focus on forecast rainfall and hydrological uncertainty (e.g., Engeland & Steinsland, 2014; Verkade et al, 2017).…”

Section: Parameter Estimationmentioning

confidence: 99%

“…Whether the treatment of dynamic biases is beneficial in probabilistic subseasonal streamflow forecasting has not been evaluated; Non‐Gaussian innovations (errors) . Innovations, that is, the random error component of AR models, are commonly assumed to follow a Gaussian distribution (Bates & Campbell, 2001; McInerney et al, 2018; Seber & Wild, 1989). A number of studies have found non‐Gaussian assumptions to improve predictive performance.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting

McInerney

Thyer

Kavetski

et al. 2020

Water Resources Research

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Subseasonal streamflow forecasts, with lead times of 1-30 days, provide valuable information for operational water resource management. This paper introduces the multi-temporal hydrological residual error (MuTHRE) model to address the challenge of obtaining "seamless" subseasonal forecaststhat is, daily forecasts with consistent high-quality performance over multiple lead times (1-30 days) and aggregation scales (daily to monthly). The key advance of the MuTHRE model is combining the representation of three temporal characteristics of hydrological residual errors: seasonality, dynamic biases, and non-Gaussian errors. The MuTHRE model is applied in 11 Australian catchments using the hydrological model GR4J and post processed rainfall forecasts from the numerical weather prediction model ACCESS-S, and is evaluated against a baseline model that does not model these error characteristics. The MuTHRE model provides "high" improvements (practically significant in the majority of performance stratifications) in terms of reliability: (i) at short lead times (up to 10 days), due to representing non-Gaussian errors, (ii) stratified by month, due to representing seasonality in hydrological errors, and (iii) in dry years, due to representing dynamic biases in hydrological errors. Forecast performance also improves in terms of sharpness, volumetric bias, and CRPS skill score; these improvements are statistically but not practically significant in the majority of stratifications. Importantly, improvements are consistent across multiple time scales (daily and monthly). This study highlights the benefits of modeling multiple temporal characteristics of hydrological errors and demonstrates the power of the MuTHRE model for producing seamless subseasonal streamflow forecasts that can be utilized for a wide range of applications.

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{\overset{truê}{ϕ}}_{η}

is computed as the sample lag‐1 autocorrelation coefficient of

\overset{η}{true}

, and σ y is estimated as the sample standard deviation of

\overset{y}{true}

.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…The parameter

{\overset{truê}{μ}}^{*}

is computed as the sample mean of

{\overset{truê}{boldη}}^{*}

, and the parameter

{\overset{truê}{ϕ}}_{η}

is computed as the sample lag‐1 autocorrelation coefficient of

{\overset{truê}{boldη}}^{*} - μ^{*}

(see McInerney et al [2018[ for equations). Note that

{\overset{truê}{μ}}^{*}

will be very close to 0 when

μ_{d}^{(normals)}

is defined by Equation and the dynamic bias window width N b is narrow compared to the length of the calibration period.…”

Section: Parameter Estimationmentioning

confidence: 99%

Section: Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting

McInerney

Thyer

Kavetski

et al. 2020

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Pragmatic approaches make use of common sum-of-squared-errors objective functions (with optional response transformations such as logarithmic and Box-Cox). Recent work has highlighted the practical appeal of statistical inference techniques where the likelihood function is closely aligned with common objective functions (e.g., Del Giudice et al, 2018;McInerney et al, 2018). In contrast, the appeal of explicit approaches is their specialized treatment of zero flows in calibration and the consistency of assumptions made in the calibration and prediction stages.…”

Section: Introductionmentioning

confidence: 99%

Benefits of Explicit Treatment of Zero Flows in Probabilistic Hydrological Modeling of Ephemeral Catchments

McInerney

Kavetski

Thyer

et al. 2019

Water Resources Research

Self Cite

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Probabilistic modeling of streamflow in ephemeral catchments, where streamflow is frequently zero or negligible, is a major scientific and operational challenge. This paper evaluates the benefits of an explicit treatment of zero flows in the residual error models used for hydrological model calibration and prediction. In this approach, the lower bound of zero for streamflow is implemented using a censoring approach. The explicit approach is compared to a simpler pragmatic approach, which imposes the zero streamflow bound in prediction but not in calibration. Following a theoretical exposition, empirical comparisons are reported using a daily rainfall‐runoff model (GR4J), four residual error schemes (based on log, log‐sinh, and Box‐Cox [BC] transformations with λ = 0.2 and 0.5), 74 Australian catchments with diverse hydroclimatology, and five performance metrics (reliability, precision, bias, proportion of zero flow days, and Continuous Ranked Probability skill score). The key findings are as follows: (1) in mid‐ephemeral catchments (5–50% zero flows) the explicit approach improves predictive performance, especially reliability, through better characterization of residual errors; (2) BC0.2 and BC0.5 schemes are Pareto optimal in mid‐ephemeral catchments (when the explicit approach is used): BC0.2 achieves better reliability and is recommended for probabilistic prediction, whereas BC0.5 attains lower volumetric bias; (3) in low‐ephemeral catchments (<5% zero flows) the pragmatic approach is sufficient; (4) in high‐ephemeral catchments (>50% zero flows) theoretical limitations result in poor performance of these particular explicit and pragmatic approaches, and further development is needed. The findings provide guidance on improving probabilistic streamflow predictions in ephemeral catchments.

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Using hydraulics to evaluate ecological benefits, risks, and trade‐offs from engineered flooding

Gibbs,

Bice,

Furst

et al. 2023

Hydrological Processes

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Regulation and water extraction have reduced river flow worldwide. In some rivers, this has led to reductions in the frequency and duration of floodplain inundation and, in turn, declining condition of flood‐dependent vegetation. One management response has been the construction and modification of infrastructure, or ‘regulators’, to engineer floodplain inundation at discharges that do not otherwise cause overbank flooding. Such actions have the potential to benefit floodplain vegetation, yet the atypical hydrodynamics of engineered floods may threaten riverine ecological processes and biota and increase the risk of adverse water quality. We investigated the influence of engineered flooding, and associated reductions to in‐channel hydraulics, on key ecological processes. Specifically, the transport and retention of larvae of an iconic fish species (Murray Cod, Maccullochella peelii), and a common microinvertebrate Trichocerca. Analysis of different flow management scenarios for floodplain inundation indicates that maintaining in‐channel velocities greater than 0.2 m s−1 produces higher Trichocerca densities, while velocities greater than 0.3 m s−1 creates suitable habitat for Murray cod larvae, thereby providing targets for avoiding detrimental effects of engineered floods on in‐channel biota. To evaluate responses to regulator operation, velocity was represented by a hydrological model using pre‐computed results from detailed hydrodynamic models. These results were used to relate upstream discharge and downstream water level to the proportion of each reach in the hydrological model meeting each velocity criterion. The model also represents changes in discharge, inundated area and a water quality parameter, dissolved oxygen. Model scenarios for engineered floodplain inundation were used to demonstrate the potential benefits, impacts and trade‐offs between the different metrics identified. The model framework enables a more holistic evaluation of infrastructure operation, extending analysis beyond discharge and inundated area to risks and benefits to key indicators of the ecosystem. This refined integrated approach to the management of regulated river systems may become critical in the future where water resources are projected to further decline under a changing climate.

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A simplified approach to produce probabilistic hydrological model predictions

Cited by 30 publications

References 40 publications

Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting

Multi‐temporal Hydrological Residual Error Modeling for Seamless Subseasonal Streamflow Forecasting

Benefits of Explicit Treatment of Zero Flows in Probabilistic Hydrological Modeling of Ephemeral Catchments

Using hydraulics to evaluate ecological benefits, risks, and trade‐offs from engineered flooding

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