Propagating reliable estimates of hydrological forecast uncertainty to many lead times

Bennett, James; Wang, QJ; Li, Ming; Perraud, Jean-Michel

doi:10.1016/j.jhydrol.2021.126798

Cited by 13 publications

(8 citation statements)

References 40 publications

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“…Data-driven approaches can also be applied directly to post-process the hydrological forecasts. Bennett et al (2021a) deployed an ERRIS (error reduction and representation in stages) model to directly correct errors in streamflow prediction up to 168 h ahead (i.e. maximum lead time of 7 d).…”

Section: Serial Pre-and Post-processing Of Hydroclimate Predictions U...mentioning

confidence: 99%

Hybrid forecasting: blending climate predictions with AI models

Slater,

Arnal,

Boucher

et al. 2023

Hydrol. Earth Syst. Sci.

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Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

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Section: Serial Pre-and Post-processing Of Hydroclimate Predictions U...mentioning

confidence: 99%

Hybrid forecasting: blending climate predictions with AI models

Slater,

Arnal,

Boucher

et al. 2023

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…We used streamflow and rainfall (2014-16) as historical sources of "truth" to verify the model before operational release [8]. While there has been studies that presented different verification and performance evaluation metrics and diagnostic plots [37,38], we used some widely used ones for the evaluation of forecasts in ensemble and deterministic forms.…”

Section: Performance Evaluation Methodologymentioning

confidence: 99%

“…Diagnostic plots allow visualisation of verification metrics, and they additionally provide empirical understandings of ensemble hydroclimatic forecasts [37,44]. There are generally six types of popular diagnostic plots [38,45].…”

Section: Diagnostic Plotsmentioning

confidence: 99%

Performance Evaluation of a National 7-Day Ensemble Streamflow Forecast Service for Australia

Bari,

Hasan,

Amirthanathan

et al. 2024

Preprint

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The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau's Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF) and Poor Man's Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pre-processing using the Catchment Hydrologic Pre-Processer (CHyPP) before being fed into the GR4H rainfall-runoff model, which is embedded in the Short-term Water Information Forecasting Tools (SWIFT) hydrological modelling package. The simulated streamflow is then post-processed using the Error Representation and Reduction In Stages (ERRIS). We evaluated the performance of the operational rainfall and streamflow forecasts for 96 catchments using four years of operational data between January 2020 and December 2023. Performance evaluation metrics included CRPS, relative CRPS, CRPSS, and PIT-Alpha for ensemble forecasts and NSE, PCC, MAE, KGE, PBias and RMSE and three categorical metrics CSI, FAR and POD for deterministic forecasts. The skill scores CRPS, relative CRPS, CRPSS and PIT-Alpha, gradually decreased for both rainfall and streamflow as the forecast horizon increased from Day 1 to Day 7. A similar pattern emerged for NSE, KGE, PCC, MAE and RMSE as well as the categorical metrics. Forecast performance also progressively decreased with the higher streamflow regime. Most catchments showed positive performance skills, meaning the ensemble forecast outperformed climatology. Both streamflow and rainfall forecast skills varied spatially across the country – and were generally better in the high runoff generating catchments, and poorer in the drier catchments situated in the western part of the Great Dividing Range, South Australia and mid-west of Western Australia. We did not find any association between model forecast skill and the catchment area. Our findings demonstrate that the 7-day ensemble streamflow forecasting service is robust and draws great confidence from agencies that use these forecasts to support decisions around water resources management.

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“…The use of ML models for post-processing can also be applied directly to the hydrological forecasts. Bennett et al (2021a) deployed an ERRIS (error reduction and representation in stages) error model to directly correct errors in streamflow prediction up to 168 hours ahead (i.e., maximum lead time of 7 days). This approach can be especially beneficial for longer forecast horizons.…”

Section: Parallelmentioning

confidence: 99%

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

Slater

Arnal²,

Boucher³

et al. 2022

Preprint

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Abstract. Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology and Earth system models – into a final prediction product. They are recognised as a promising way of enhancing prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at sub-seasonal to decadal scales, a better appreciation of the strengths of machine learning, plus expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally-expensive offline land model, can minimize the effect of biases that exist within dynamical outputs without explicit bias correction and downscaling, benefit from the strengths of machine learning models, and can learn from large datasets, while combining different sources of predictability with varying time-horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities. These include obtaining physically-explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating modelled initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

show abstract

Propagating reliable estimates of hydrological forecast uncertainty to many lead times

Cited by 13 publications

References 40 publications

Hybrid forecasting: blending climate predictions with AI models

Hybrid forecasting: blending climate predictions with AI models

Performance Evaluation of a National 7-Day Ensemble Streamflow Forecast Service for Australia

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

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