A simplified approach to produce probabilistic hydrological model predictions

Probabilistic predictions from hydrological models, including rainfall-runoff models, provide valuable information for water and environmental resource risk management. However, traditional “deterministic” usage of rainfall-runoff models remains prevalent in practical applications, in many cases bec...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2018-11, Vol.109, p.306-314
Main Authors: McInerney, David, Thyer, Mark, Kavetski, Dmitri, Bennett, Bree, Lerat, Julien, Gibbs, Matthew, Kuczera, George
Format: Article
Language:English
Subjects:
Bayesian analysis
Case studies
Computer applications
Data processing
Environmental management
Hydrologic analysis
Hydrologic modeling
Hydrologic models
Hydrology
Mathematical models
Maximum likelihood
Maximum likelihood method
Method of moments
Parameter estimation
Performance prediction
Predictions
Probabilistic inference
Probabilistic prediction
Probability
Rainfall-runoff modelling
Rainfall-runoff relationships
Resource management
Risk management
Runoff
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Summary:Probabilistic predictions from hydrological models, including rainfall-runoff models, provide valuable information for water and environmental resource risk management. However, traditional “deterministic” usage of rainfall-runoff models remains prevalent in practical applications, in many cases because probabilistic predictions are perceived to be difficult to generate. This paper introduces a simplified approach for hydrological model inference and prediction that bridges the practical gap between “deterministic” and “probabilistic” techniques. This approach combines the Least Squares (LS) technique for calibrating hydrological model parameters with a simple method-of-moments (MoM) estimator of error model parameters (here, the variance and lag-1 autocorrelation of residual errors). A case study using two conceptual hydrological models shows that the LS-MoM approach achieves probabilistic predictions with similar predictive performance to classical maximum-likelihood and Bayesian approaches—but is simpler to implement using common hydrological software and has a lower computational cost. A public web-app to help users implement the simplified approach is available. •New simplified approach for producing probabilistic hydrological predictions.•Similar performance to maximum-likelihood approach, at lower computational cost.•Web-app available to facilitate uptake of probabilistic predictions.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2018.07.001