A Retrospective Streamflow Ensemble Forecast for  an Extreme Hydrologic Event: a Case Study of Hurricane Irene and on the Hudson River basin

Saleh, F.; Ramaswamy, V.; Georgas, N.; Blumberg, A.; Pullen, J.

doi:10.5194/hess-2016-104

Cited by 7 publications

(10 citation statements)

References 56 publications

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“…Historically, local ESP systems have utilised a wide variety of physical models and numerical weather prediction (NWP) data sources for simulating rainfall–runoff processes during extreme events such as flash floods or hurricanes (Liechti, Zappa, Fundel, & Germann, ). Overall, previous studies on ensemble flood prediction agree that this approach is a great benefit over the traditional deterministic modelling approach for relatively large river basins (>100 km 2 ) (Amengual, Homar, & Jaume, ; Hally et al, ; Saleh, Ramaswamy, Georgas, Blumberg, & Pullen, ). The effectiveness of the ensemble flood forecasting approach for relatively small urban catchments (<100 km 2 ) remains unclear due to the limited research at this scale.…”

Section: Introductionmentioning

confidence: 59%

“…In August 2011, hurricane Irene caused several deaths and severe property damage to the eastern coast of the United States. Property damage was approximated at about $1.5 billion in New York (http://www.fema.gov/ar/disaster/4020) and $1 billion in New Jersey (Saleh et al, ). During hurricane Irene, a total of between 150 and 250 mm of accumulated precipitation occurred in a period of less than 2 days.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A comparison of SAC‐SMA and Adaptive Neuro‐fuzzy Inference System for real‐time flood forecasting in small urban catchments

Roodsari

Chandler

Kelleher

et al. 2018

J Flood Risk Management

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Growing urbanisation and imperviousness have augmented magnitudes of peak flows, resulting in flooding especially during extreme events. Flood forecast of extreme events can rely on real‐time ensemble flood forecasting systems. Such systems often use predictions from physical models and precipitation ensembles to predict downstream urban flood hydrographs. However, these methods are seldom used in small catchments, where flood predictions may assist emergency management. We explore the relative utility of two models, the Sacramento Model (SAC‐SMA) and an adaptive neuro‐fuzzy inference system (ANFIS) for ensemble flood prediction for nine small urban catchments located near New York City. The models were used to reforecast streamflow for Hurricane Irene (160 mm) and a 35 mm storm across lead times from 3 to 24 hr. Differences in performance between models were small for short (3 hr) lead times, and were similar for the 35 mm storm. Reforecasts of hurricane Irene at 24‐hr lead times show strong performance for SAC‐SMA, but a decline in performance for ANFIS. Model performance did not vary systematically with either catchment size or imperviousness. Our results suggest that model selection is especially important when reforecasting large rain events with longer lead times in small urban catchments.

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Section: Introductionmentioning

confidence: 59%

Section: Methodsmentioning

confidence: 99%

A comparison of SAC‐SMA and Adaptive Neuro‐fuzzy Inference System for real‐time flood forecasting in small urban catchments

Roodsari

Chandler

Kelleher

et al. 2018

J Flood Risk Management

View full text Add to dashboard Cite

show abstract

“…The model is a three‐dimensional hydrodynamic model that covers the Mid‐Atlantic Bight region (Georgas and Blumberg, ; Orton et al, ; Georgas et al, ; ). For this article, storm surge reforecasts produced from the NYHOPS model were created by forcing the NYHOPS model with meteorological inputs provided by 21 ensemble members from the National Oceanic and Atmospheric Administration (NOAA)'s Global Ensemble Forecast System Reforecast (GEFS/R: Hamill et al, ; ; Saleh et al, ). These reforecast data were made to be similar to the operational ensemble system established by the National Weather Service in February 2012.…”

Section: Practical Applications To Storm Surge Predictionmentioning

confidence: 99%

Theory and practice of phase‐aware ensemble forecasting

Schulte

Georgas

2018

Quart J Royal Meteoro Soc

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Skilfully forecasting the intensity of flood events with advanced lead times is necessary for the issuing of flood warnings that subsequently provide adequate time for evacuations and infrastructural preparations. The ensemble mean is often used for deterministic guidance, but it is numerically demonstrated that the presence of large timing differences among ensemble members generates ensemble skewness that renders the ensemble mean an underestimate of the magnitude of an event. We show that one can associate to an ensemble forecast a complementary phase-aware ensemble forecast whose corresponding statistics are unaffected by timing differences among ensemble members. Corresponding to a phase-aware ensemble forecast is a phase-aware mean that remedies the magnitude underestimation problem of the ensemble mean. Uncertainty around the phase-aware mean is captured by phase-aware spread, which quantifies the spread of the magnitude of an event. We show that the uncertainty envelope associated with the phase-aware spread better preserves the structure of the individual ensemble member trajectories. The new methods were applied to storm surge reforecasts for Hurricanes Irene and Sandy at 13 stations located around the New York City metropolitan area. Consistent with theory, the phase-aware mean was found to be a better representation of storm surge magnitude than the ensemble mean. The phase-aware uncertainty envelopes around the phase-aware means were also found to depict the uncertainty of event magnitudes better than traditional uncertainty envelopes. The storm surge applications suggest that such methods should be incorporated into ensemble forecasting frameworks in which timing uncertainty is present, and thus a Matlab toolbox implementing the new methodology has been developed.

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“…The benefit of representing inland precipitation via an atmospheric model accrues to the hydrology model, which then affords the forecasting of river discharge at the coast. Significant buoyancy effects from river discharge are apparent in many coastal regions, and prediction of the local impacts of flood hazards and storm surge are a growing and significant application area of coupled models (Saleh et al 2016).…”

Section: The Sea: the Science Of Ocean Predictionmentioning

confidence: 99%

Coupled ocean-atmosphere forecasting at short and medium time scales

Pullen¹,

Allard²,

Seo³

et al. 2017

j mar res

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Recent technological advances over the past few decades have enabled the development of fully coupled atmosphere-ocean modeling prediction systems that are used today to support short-term (days to weeks) and medium-term (10-21 days) needs for both the operational and research communities. We overview the coupling framework, including model components and grid resolution considerations, as well as the coupling physics by examining heat fluxes between atmosphere and ocean, momentum transfer, and freshwater fluxes. These modeling systems can be run as fully coupled atmosphere-ocean and atmosphere-ocean-wave configurations. Examples of several modeling systems applied to complex coastal regions including Madeira Island, Adriatic Sea, Coastal California, Gulf of Mexico, Brazil, and the Maritime Continent are presented. In many of these studies, a variety of field campaigns have contributed to a better understanding of the underlying physics associated with the atmosphere-ocean feedbacks. Examples of improvements in predictive skill when run in coupled mode versus standalone are shown. Coupled model challenges such as model initialization, data assimilation, and earth system prediction are discussed.

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A Retrospective Streamflow Ensemble Forecast for an Extreme Hydrologic Event: a Case Study of Hurricane Irene and on the Hudson River basin

Cited by 7 publications

References 56 publications

A comparison of SAC‐SMA and Adaptive Neuro‐fuzzy Inference System for real‐time flood forecasting in small urban catchments

A comparison of SAC‐SMA and Adaptive Neuro‐fuzzy Inference System for real‐time flood forecasting in small urban catchments

Theory and practice of phase‐aware ensemble forecasting

Coupled ocean-atmosphere forecasting at short and medium time scales

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