Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine

Verkade, Jan; Brown, James Dean; Davids, Femke; Reggiani, P.; Weerts, Albrecht

doi:10.1016/j.jhydrol.2017.10.024

Cited by 29 publications

(25 citation statements)

References 49 publications

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“…See Boucher et al (2018) and Hemri (2019) for overviews of QPP, and Demargne et al (2014) and Demeritt et al (2013) for applications in national hydrological forecasting services. QPP has been shown to be effective for correcting biases and underdispersion of probabilistic streamflow forecasts (e.g., Hemri et al, 2015;Verkade et al, 2017;Woldemeskel et al, 2018;Zhao et al, 2011). QPP models can be classified into two broad categories:…”

Section: Introductionmentioning

confidence: 99%

“…For example, the U.S. National Weather Service (NWS) Hydrologic Ensemble Forecasting System (HEFS) uses the ensemble postprocessor (EnsPost) model (Seo et al, 2006), which has been used for forecasts up to 14 days (Brown et al, 2014; Demargne et al, 2014). More generally, ensemble dressing has been used in forecasting with lead times from weekly up to 1 year (e.g., Bennett et al, 2016; Pagano et al, 2013; Verkade et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

McInerney

Thyer

Kavetski

et al. 2020

Water Resources Research

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“…Some related methods are the non-parametric approach of Van Steenbergen et al (2012), the empirical hydrological uncertainty processor of Bourgin et al (2014) or the k-nearest neighbours method of Wani et al (2017). The Quantile Regression (QR) framework (Weerts et al, 2011;Dogulu et al, 2015;Verkade et al, 2017) lies in between in that it 5 introduces an assumption of a linear relationship between the forecasted discharge and the quantiles of interest.…”

Section: Post-processing Approaches 25mentioning

confidence: 99%

A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context

Berthet¹,

Bourgin²,

Perrin³

et al. 2019

Preprint

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An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. While obtaining reliable forecasts is a key issue, it is a challenging task, especially when forecasting high flows in an extrapolation context, i.e. when the event magnitude is larger than what was observed before. In this study, we present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. The experiment setup is based on i) a large set of catchments in France, ii) the GRP rainfall-runoff model designed for flood forecasting and used by the 5 French operational services and iii) an empirical hydrologic uncertainty processor designed to estimate conditional predictive uncertainty from the hydrological model residuals. The variants of the uncertainty processor used in this study differ in the data transformation they used (log, Box-Cox and log-sinh) to account for heteroscedasticity. Different data subsets were selected based on a preliminary event selection. Various aspects of the probabilistic performance of the variants of the hydrologic uncertainty processor, reliability, sharpness and overall quality, were evaluated. Overall, the results highlight the challenge of 10 uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows in an extrapolation context and considerable variability among catchments and across lead times. The increase in statistical treatment complexity did not result in significant improvement, which suggests that a parsimonious and easily understandable data transformation such as the log transformation or the Box-Cox transformation with a parameter between 0.1 and 0.3 can be a reasonable choice for flood forecasting. uncertainty into its two main components is now common: the input uncertainty (mainly the meteorological forecast uncertainty) and the modelling uncertainty, as proposed by Krzysztofowicz (1999). More generally, the predictive uncertainty due to various sources may be explicitly modelled and propagated through the modelling chain, while the "remaining" uncertainty (from the other sources) may then be assessed by statistical post-processing. Modelling each source of uncertainty 5A first approach intends to model each source of uncertainty separately and to propagate these uncertainties through the modelling chain . The heteroscedasticity of the predictive uncertainty distribution results from the separate modelling of each source of uncertainty and from the statistical model specification. While this approach is promising, operational application can be hindered by the challenge of making the hydrological modelling uncertainty explicit, as pointed out by Salamon and Feyen (2009).

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“…To manufacture automotive water tank with excellent comprehensive performance, many scholars at home and abroad have studied the relevant characteristics of the water tank and the water tank bracket. For example, Scholar Asfandiyar et al [1] polycrystalline SnSe-Sn1-vS solid solutions in vacancy engineering and nanostructuring leading to high thermoelectric performance; O'Connor et al [2] surgical treatment of tethered cord syndrome in adults for a systematic review and meta-analysis; Cao et al [3] analysis of differential gene expression in response to anisotropic stretch using a systems model of cardiac myocyte mechanotransduction; Li et al [4] numerical simulation and application of noise for high-power wind turbines with double blades based on large eddy simulation model; Rochling Automotive SE & Co. KG [5] multi-part injection-molded multi-chamber plastic tank having an oblique joining surface in patent application approval process; Iiyama et al [6] a pointestimate based method for soil amplification estimation using high resolution model under uncertainty of stratum boundary geometry; Chandramouli et al [7] coarse large-eddy simulations in a transitional wake flow with flow models under location uncertainty; Winiarski et al [8] performance during competition and competition outcome in relation to testosterone and cortisol among women; Fan et al [9] a review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence; Córcoles-Tendero et al [10] numerical simulation of the heat transfer process in a corrugated tube; Verkade et al [11] estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine; Kumaran et al [12] prediction of surface roughness in abrasive water jet machining of CFRP composites using regression analysis; Harless et al [13] heat transfer and friction characteristics of fully developed gas flow in cross-corrugated tubes; Sydeman et al [14] best practices for assessing forage fish fisheries-seabird resource competition; Henry et al [15] performance during competition and competition outcome in relation to testosterone and cortisol among women; Xu et al [16] study of surface roughness in wire electrochemical micro machining. The above-mentioned scholars have conducted in-depth studies on the various characteristics of the turbines.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Fluid Field in Automotive Water Tank Based on Uncertainty Model

Song¹,

Li²

2020

IJHT

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To solve problems in the formation mechanism of water flow in automotive water tank and its complex working conditions, this paper constructed a theoretical model for the uncertainty of water jet in automotive water tank. Then, based on the secondary development characteristics of FLUENT software, the uncertainty model, and the fluid field simulation module, this paper studied the wall effect of the fluid field of water jet in automotive water tank, and obtained fluid fields of single-nozzle and double-nozzle water jet and the corresponding distribution laws of the wall effect. The research results showed that the wall effect had a great influence on the direction of the water jet and the pressure on the wall surface of the water tank, while its influence on the velocity of the water jet was relatively small; the two streams of water jet converged, which was basically consistent with the laws of the wall effect in the water tank; the smaller the distance between the two water jet streams, the more likely the two streams would converge. The research in this paper provided theoretical basis and reliable technical support for the analysis of the formation mechanism of the water flow in automotive water tank, the optimization of the water tank structure and the application of water tank in various industries.

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Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine

Cited by 29 publications

References 49 publications

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

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

A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context

Numerical Simulation of Fluid Field in Automotive Water Tank Based on Uncertainty Model

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