Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs

Ehlers, L.B.; Wani, Omar; Koch, Julian; Sonnenborg, Torben O.; Refsgaard, Jens Christian

doi:10.1016/j.advwatres.2019.05.003

Cited by 14 publications

(9 citation statements)

References 56 publications

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“…Bluecat indeed shares some similarities with the nearest neighboring method by Sikorska et al (2015), which may be also used to correct the D-model bias (see, for instance, Ehlers et al, 2019). However, we note that Bluecat infers the conditional probability distribution of the true data, while Sikorska et al (2015) estimate the conditional probability distribution of the simulation error.…”

Section: Discussionmentioning

confidence: 74%

Bluecat: A Local Uncertainty Estimator for Deterministic Simulations and Predictions

Koutsoyiannis

Montanari²

2022

Preprint

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<p>We present a new method for simulating and predicting hydrologic variables with uncertainty assessment and provide example applications to river flows. The method is identified with the acronym ``Bluecat'' and is based on the use of a deterministic model which is subsequently converted to a stochastic formulation. The latter provides an adjustment on statistical basis of the deterministic prediction along with its confidence limits. The distinguishing features of the proposed approach are the ability to infer the probability distribution of the prediction without requiring strong hypotheses on the statistical characterization of the prediction error (e.g. normality, homoscedasticity) and its transparent and intuitive use of the observations. Bluecat makes use of a rigorous theory to estimate the probability distribution of the predictand conditioned by the deterministic model output, by inferring the conditional statistics of observations. Therefore Bluecat bridges the gaps between deterministic (possibly physically-based, or deep learning-based) and stochastic models as well as between rigorous theory and transparent use of data with an innovative and user oriented approach. We present two examples of application to the case studies of the Arno river at Subbiano and Sieve river at Fornacina. The results confirm the distinguishing features of the method along with its technical soundness. We provide an open software working in the R environment, along with help facilities and detailed instructions to reproduce the case studies presented here.</p>

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Section: Discussionmentioning

confidence: 74%

Bluecat: A Local Uncertainty Estimator for Deterministic Simulations and Predictions

Koutsoyiannis

Montanari²

2022

Preprint

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“…Bluecat indeed shares some similarities with the nearest neighboring method by Sikorska et al. (2015), which may be also used to correct the D‐model bias (see, for instance, Ehlers et al., 2019). However, we note that Bluecat infers the conditional probability distribution of the true data, while Sikorska et al.…”

Section: Discussionmentioning

confidence: 76%

Bluecat: A Local Uncertainty Estimator for Deterministic Simulations and Predictions

Koutsoyiannis

Montanari

2022

Water Resources Research

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We present a new method for simulating and predicting hydrologic variables with uncertainty assessment and provide example applications to river flows. The method is identified with the acronym “Bluecat” and is based on the use of a deterministic model which is subsequently converted to a stochastic formulation. The latter provides an adjustment on statistical basis of the deterministic prediction along with its confidence limits. The distinguishing features of the proposed approach are the ability to infer the probability distribution of the prediction without requiring strong hypotheses on the statistical characterization of the prediction error (e.g., normality, homoscedasticity), and its transparent and intuitive use of the observations. Bluecat makes use of a rigorous theory to estimate the probability distribution of the predictand conditioned by the deterministic model output, by inferring the conditional statistics of observations. Therefore Bluecat bridges the gaps between deterministic (possibly physically based, or deep learning‐based) and stochastic models, as well as between rigorous theory and transparent use of data with an innovative and user oriented approach. We present two examples of application to the case studies of the Arno river at Subbiano and Sieve river at Fornacina. The results confirm the distinguishing features of the method along with its technical soundness. We provide an open software working in the R environment, along with help facilities and detailed instructions to reproduce the case studies presented here.

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“…For example, Wang et al [25] presented the Bayesian Joint Probability (BJP) and Zhao et al [26] introduced the General Linear Model Post-processor (GLMPP). There are also hydrological post-processors with different error models that had been evaluated under different climate conditions [40,41], such as post-processors that employ non-parametric methods [42], post-processors based on machine-learning principles [43][44][45][46][47][48], and post-processors based on the copula concept to establish the relation of the dependence among state variables [49][50][51]. This list of hydrological post-processors is not long, and readers can find more details in the work by Li et al [52].…”

Section: Introductionmentioning

confidence: 99%

Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters

Romero-Cuéllar

Gastulo-Tapia

Hernández-López

et al. 2022

Water

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This research develops an extension of the Model Conditional Processor (MCP), which merges clusters with Gaussian mixture models to offer an alternative solution to manage heteroscedastic errors. The new method is called the Gaussian mixture clustering post-processor (GMCP). The results of the proposed post-processor were compared to the traditional MCP and MCP using a truncated Normal distribution (MCPt) by applying multiple deterministic and probabilistic verification indices. This research also assesses the GMCP’s capacity to estimate the predictive uncertainty of the monthly streamflow under different climate conditions in the “Second Workshop on Model Parameter Estimation Experiment” (MOPEX) catchments distributed in the SE part of the USA. The results indicate that all three post-processors showed promising results. However, the GMCP post-processor has shown significant potential in generating more reliable, sharp, and accurate monthly streamflow predictions than the MCP and MCPt methods, especially in dry catchments. Moreover, the MCP and MCPt provided similar performances for monthly streamflow and better performances in wet catchments than in dry catchments. The GMCP constitutes a promising solution to handle heteroscedastic errors in monthly streamflow, therefore moving towards a more realistic monthly hydrological prediction to support effective decision-making in planning and managing water resources.

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Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs

Cited by 14 publications

References 56 publications

Bluecat: A Local Uncertainty Estimator for Deterministic Simulations and Predictions

Bluecat: A Local Uncertainty Estimator for Deterministic Simulations and Predictions

Bluecat: A Local Uncertainty Estimator for Deterministic Simulations and Predictions

Towards an Extension of the Model Conditional Processor: Predictive Uncertainty Quantification of Monthly Streamflow via Gaussian Mixture Models and Clusters

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