Article

The Impact of Tidal Phase on Hurricane Sandy's Flooding Around New York City and Long Island Sound

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... On the other hand, at the eastern part of Manhattan (i.e., in the Upper East River and Western Long Island Sound), where the tidal range is greater than 1.5 m, the maximum surge height coincided nearly at the time of the local normal low tide. Georgas et al. (2014) found that the flood level along the Upper East River and Western Long Island Sound could be up to 0.9-1.2 m higher if Sandy happened to have come ashore 7-10 h earlier. ...
... Non-linear tide-surge interactions also contribute to the storm-induced water levels (e.g., Lyddon et al., 2018;Marsooli and Lin, 2018). Georgas et al. (2014) used a "bathtub" technique to predict the effects of tidal phase on the extent and depth of flooding in NYC. In the bathtub technique, the storm tide calculated in the coastal ocean is assumed to horizontally extent over land and inundate areas with an elevation equal to or lower than the calculated storm tide level. ...
... The faster floodwater speed calculated based on the 6 h early experiment can be due to the interactions of storm surge and tides in the surrounding waters of Manhattan. Model results for the 6 h early experiment show that the storm tide in Western Long Island Sound and the East River is much larger than that based on the control run, which is consistent with findings from Georgas et al. (2014). The higher storm tides in Western Long Island Sound propagate into the East River and toward NY/NJ Harbor, in contrast to the control run that the storm tide propagates from NY/NJ Harbor to the East River ( Figure 13A). ...
Article
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This study investigates the effects of tidal phase on coastal flooding in New York City during Hurricane Sandy. A micro-scale hydrodynamic model is developed for Manhattan – the most densely populated borough of New York City – to accurately simulate coastal flooding in built environments. The model accounts for the effects of urban features on coastal flooding by resolving seawalls, buildings, and roads in the computational mesh. Model validation against high-water-mark measurements shows a root-mean-square-error of 15 cm and a bias of 7 cm. A series of numerical experiments are performed to investigate the effects of tide timing on the extent and depth of flooding in Manhattan. Model results show that the peak storm tide at The Battery tide gauge station, which is located immediately off of the southern tip of Manhattan, would have been 8.2% larger than the measured peak storm tide if Hurricane Sandy had arrived 12 h earlier. However, the extent of flooding would have been only 3% larger. If Sandy had arrived 6 h earlier, the peak storm tide would have been 27.2% smaller but the extent of coastal flooding in Manhattan would have been 69% larger. The model results indicate that the peak storm tide alone is not a good indicator for the extent of coastal flooding in urban areas. The floodwater velocity substantially impacts the extent of coastal flooding, suggesting that extra caution should be taken in using flood maps that are generated based on static modeling techniques, i.e., “bathtubbing,” that neglect the principles of fluid dynamics.
... The SIT-NYHOPS under-prediction was at least partially the result of inferior wind forecasts from the operational models [12]. Forbes et al. [13] showed that the NWS Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model [14] could realistically simulate the Sandy water levels using an ensemble of tracks and intensities from the National Hurricane Center. ...
... Forbes et al. [13] showed that the NWS Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model [14] could realistically simulate the Sandy water levels using an ensemble of tracks and intensities from the National Hurricane Center. Both Forbes et al. [13] and Georgas et al. [12] illustrated that Sandy's observed flooding was not a worst-case scenario for this event given the variations in Sandy's track and phase of the tide landfall that might have occurred. ...
... Wind and pressure forcing for SLOSH are geometrically circular, thus unrepresentative of the large asymmetries observed during Sandy. Georgas et al. (2014) [12] illustrated the tidal uncertainty aspect of Sandy's water level prediction, but did not run SIT-NYHOPS days before landfall with an ensemble of forecasts. ...
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This paper describes storm surge simulations made for Sandy (2012) for the Metropolitan New York (NYC) area using the Advanced Circulation (ADCIRC) model forced by the Weather Research and Forecasting (WRF) model. The atmospheric forecast uncertainty was quantified using 11-members from an atmospheric Ensemble Kalman Filter (EnKF) system. A control WRF member re-initialized every 24 h demonstrated the capability of the WRF-ADCIRC models to realistically simulate the 2.83 m surge and 4.40 m storm tide (surge + astronomical tide) above mean lower low water (MLLW) for NYC. Starting about four days before landfall, an ensemble of model runs based on the 11 “best” meteorological predictions illustrate how modest changes in the track (20–100 km) and winds (3–5 m s−1) of Sandy approaching the New Jersey coast and NYC can lead to relatively large (0.50–1.50 m) storm surge variations. The ensemble also illustrates the extreme importance of the timing of landfall relative to local high tide. The observed coastal flooding was not the worst case for this particular event. Had Sandy made landfall at differing times, locations and stages of the tide, peak water levels could have been up to 0.5 m higher than experienced.
... However, the same drawbacks of an excessive amount of data required and complicated calculation process still exist. Georgas et al. [10][11][12][13] designed and operated the Stevens Flood Advisory System (SFAS, http:// steve ns. edu/ SFAS) in 2015. ...
... where A t and B t are represented by Eqs. (12) and (13), respectively [41]: ...
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The northern Gulf of Mexico coast is affected by the North Atlantic hurricane season, which causes storm surge disasters every year and brings serious economic losses to the southern USA; therefore, it is necessary to make an accurate advance prediction of storm surge level. In this paper, a model with simple structure, fast computation speed, and accurate prediction results has been constructed based on nonlinear auto-regressive exogenous (NARX) neural network. Five types of data collected from observation stations are selected as the input factors of the model. To improve the model's computational efficiency, a neuron pruning strategy based on sensitivity analysis is introduced. By analyzing the output weights of the neurons in the hidden layer on the sensitivity of the model prediction output, the model structure can be adjusted accordingly. Moreover, a modular prediction method is introduced based on the tide harmonic analysis data so as to make the model prediction results more accurate. At last, a complete storm surge level prediction model, pruned modular (PM)-NARX, is constructed. In this paper, the model is trained by using historical data and used for storm surge level prediction along the northern Gulf of Mexico coast in 2020. The simulation test results show that the correlation between the predicted data and the observed data is stable above 0.99 at 12 h in advance and the model is able to produce the results within one minute. The prediction speed, accuracy, and stability are higher than those of conventional models. In addition, two sets of follow-up tests show that the prediction accuracy of the model can still maintain a high level. The above can prove that the pruned modular (PM)-NARX model can effectively provide early warning before the storm surge to avoid property damage and human casualties.
... Probabilistic forecasts are particularly useful for events with higher uncertainties, such as Tropical Cyclones (TCs) where a small change in the storm speed or direction can lead to large variations in storm surge over a broad coastal region . This is even more true for areas like the US Northeast where moderate-to-large tide ranges significantly impact total water levels (Georgas et al., 2014;Colle et al., 2015). Uncertainties generated from ensemble forecasts may be used to address risk thresholds based on specific criteria. ...
... an ensemble-based forecasting system developed at the Stevens Institute of Technology. SFAS is the evolution of the publicly-available coastal ocean forecasting system initiated in 2007 with the New York Harbor Observing and Prediction System (Georgas and Blumberg, 2010;Orton et al., 2012;Georgas et al., 2014). The system models rainfall-driven hydrology, tide, and storm surge to predict total water levels Jordi et al., 2019). ...
Article
Ensemble-based probabilistic forecasting of storm surge is increasingly being used to provide metrics for emergency management decisions such as the near-worst case scenario. The Stevens Flood Advisory System is an ensemble prediction system used to forecast total water levels over a broad coastal region and street-scale flood levels for several New York Harbor (NYH) critical infrastructure sites. As a part of our continuous assessment of this system’s performance, we evaluate its prediction of storm tide and resurgence during Tropical Cyclone Isaias (2020), which tracked northward along the Pennsylvania/New Jersey border and caused the largest storm surge in NYH since Hurricane Sandy. Isaias specific track and speed generated an unusual flood event consisting of a storm surge, a blowout, then a significant resurgence that caused minor flooding. The analysis shows that the super-ensemble spread provided a better estimate of uncertainties than sub-ensembles based only on one meteorological forcing system. Because of ensemble averaging, the central forecast under-predicted peak water levels and the resurgence peak though these were predicted by some of the ensemble members. The impacts of errors in forecast storm arrival time and resolution-related biases in coarse global atmospheric models on the predictions are noted. A limited comparison for this single storm with the National Hurricane Center’s forecast show SFAS providing better accuracy and spread. Advantages and challenges of SFAS and other similar mid-latitude flood forecast systems are identified along with recommendations for analysis and improvement.
... The Stevens ECOM (sECOM) three-dimensional hydrodynamic model (Blumberg et al. 1999;Georgas and Blumberg 2009;Georgas et al. 2014;Orton et al. 2012) has been providing highly accurate operational storm surge forecasts on its NYHOPS grid for over a decade (New York Harbor Observing and Prediction System; http://stevens.edu/ NYHOPS), with typical water level RMS errors of * 0.10 m (Georgas and Blumberg 2009), 0.15 m for Tropical Storm Irene (Orton et al. 2012, #5970), and 0.17 m for Hurricane Sandy (Georgas et al. 2014). ...
... The Stevens ECOM (sECOM) three-dimensional hydrodynamic model (Blumberg et al. 1999;Georgas and Blumberg 2009;Georgas et al. 2014;Orton et al. 2012) has been providing highly accurate operational storm surge forecasts on its NYHOPS grid for over a decade (New York Harbor Observing and Prediction System; http://stevens.edu/ NYHOPS), with typical water level RMS errors of * 0.10 m (Georgas and Blumberg 2009), 0.15 m for Tropical Storm Irene (Orton et al. 2012, #5970), and 0.17 m for Hurricane Sandy (Georgas et al. 2014). The NYHOPS grid includes the Mid-Atlantic and Northeastern U.S. coastline from Maryland to Rhode Island and for flood hazard assessment studies is nested inside a NW Atlantic model grid captures the large-scale influence of winds from Nova Scotia to Cape Hatteras and out to * 2000 km distance offshore. ...
Article
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Cities and towns along the tidal Hudson River are highly vulnerable to flooding through the combination of storm tides and high streamflows, compounded by sea level rise. Here a three-dimensional hydrodynamic model, validated by comparing peak water levels for 76 historical storms, is applied in a probabilistic flood hazard assessment. In simulations, the model merges streamflows and storm tides from tropical cyclones (TCs), offshore extratropical cyclones (ETCs) and inland “wet extratropical” cyclones (WETCs). The climatology of possible ETC and WETC storm events is represented by historical events (1931–2013), and simulations include gauged streamflows and inferred ungauged streamflows (based on watershed area) for the Hudson River and its tributaries. The TC climatology is created using a stochastic statistical model to represent a wider range of storms than is contained in the historical record. TC streamflow hydrographs are simulated for tributaries spaced along the Hudson, modeled as a function of TC attributes (storm track, sea surface temperature, maximum wind speed) using a statistical Bayesian approach. Results show WETCs are important to flood risk in the upper tidal river (e.g., Albany, New York), ETCs are important in the estuary (e.g., New York City) and lower tidal river, and TCs are important at all locations due to their potential for both high surge and extreme rainfall. The raising of floods by sea level rise is shown to be reduced by ~ 30–60% at Albany due to the dominance of streamflow for flood risk. This can be explained with simple channel flow dynamics, in which increased depth throughout the river reduces frictional resistance, thereby reducing the water level slope and the upriver water level.
... Their work illustrated that the incorporation of sea level dynamics into river routing enhanced the model performance in reproducing river water levels. One approach to model the coastal estuarine transition zone is to extend 3-dimensional hydrodynamic estuary ocean models further upstream using a finer resolution mesh to represent the coastal areas in more detail compared to open oceans (Georgas et al., 2009;Cho et al., 2012;Orton et al., 2012;Chen and Liu, 2014;Georgas et al., 2014;Blumberg et al., 2015;Wu and Lin, 2015). Other studies adopted integrated modeling approaches using 1-dimensional (1-D) hydraulic models with upstream boundary conditions from hydrologic models and downstream boundary conditions from storm surge models or tidal influences (Mashriqui et al., 2010;Roe et al., 2010;Mashriqui et al., 2014;Zope et al., 2015;Gharbi et al., 2016). ...
... The operational framework links the 2-D modeling software HEC-RAS 5.0 (Brunner, 2016) to an operational hydrologic model of the Hudson River basin ) and a regional scale operational storm surge model for the New York Harbor (Georgas et al., 2009;Georgas et al., 2014). The utility of this methodology was demonstrated by retrospectively forecasting two extreme events, hurricanes Irene and Sandy at the confluence of the Hackensack River, Passaic River and Newark Bay. ...
Article
Estuarine regions can experience compound impacts from coastal storm surge and riverine flooding. The challenges in forecasting flooding in such areas are multi-faceted due to uncertainties associated with meteorological drivers and interactions between hydrological and coastal processes. The objective of this work is to evaluate how uncertainties from meteorological predictions propagate through an ensemble-based flood prediction framework and translate into uncertainties in simulated inundation extents. A multi-scale framework, consisting of hydrologic, coastal and hydrodynamic models, was used to simulate two extreme flood events at the confluence of the Passaic and Hackensack rivers and Newark Bay. The events were Hurricane Irene (2011), a combination of inland flooding and coastal storm surge, and Hurricane Sandy (2012) where coastal storm surge was the dominant component. The hydrodynamic component of the framework was first forced with measured streamflow and ocean water level data to establish baseline inundation extents with the best available forcing data. The coastal and hydrologic models were then forced with meteorological predictions from 21 ensemble members of the Global Ensemble Forecast System (GEFS) to retrospectively represent potential future conditions up to 96 hours prior to the events. Inundation extents produced by the hydrodynamic model, forced with the 95th percentile of the ensemble-based coastal and hydrologic boundary conditions, were in good agreement with baseline conditions for both events. The USGS reanalysis of Hurricane Sandy inundation extents was encapsulated between the 50th and 95th percentile of the forecasted inundation extents, and that of Hurricane Irene was similar but with caveats associated with data availability and reliability. This work highlights the importance of accounting for meteorological uncertainty to represent a range of possible future inundation extents at high resolution (∼m).
... Coastal flooding caused by storms is often associated with storm surge, which is defined as the difference between the observed and predicted tidal water level at the coast. Coastal flooding in the Mid-Atlantic and Northeast US has received extensive attention in the recent literature, because of the extreme nature of Hurricane Sandy (Hall and Sobel 2013, Georgas et al 2014, Lopeman et al 2015, as well as regional surge projections (Lin et al 2012, Little et al 2015, and flooding trends (Talke et al 2014, Reed et al 2015. The atmospheric storm type of focus in these studies was mainly hurricanes, with the exception of Talke et al (2014) who examined the influence of the North Atlantic Oscillation (NAO), which is known to correlate with the distribution of extratropical cyclone (ETC) paths (Serreze et al 1997). ...
... These RLs minimally change if we remove Hurricane Sandy from the analysis. Furthermore, our choice to focus on a relatively short return periods is motivated in part by the existence of a large body of literature regarding the return period of extreme surge such as caused by Hurricane Sandy (Lin et al 2012, Hall and Sobel 2013, Zervas 2013, Lopeman et al 2015, Georgas et al 2014. Notably, the RL estimates for Sandy vary substantially among these studies, most likely because the observational record is not long enough to robustly constrain the most extreme events (see also: Dangendorf et al 2016 for the influence of a single extreme event on longer RLs). ...
Article
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This letter examines the magnitude, spatial footprint, and paths of hurricanes and extratropical cyclones (ETCs) that caused strong surge along the east coast of the US between 1979 and 2013. Lagrangian cyclone track information, for hurricanes and ETCs, is used to associate surge events with individual storms. First, hurricane influence is examined using ranked surged events per site. The fraction of hurricanes among storms associated with surge decreases from 20%–60% for the top 10 events to 10%–30% for the top 50 events, and a clear latitudinal gradient of hurricane influence emerges for larger sets of events. Secondly, surges on larger spatial domains are examined by focusing on storms that cause exceedance of the probabilistic 1-year surge return level at multiple stations. Results show that if the strongest events in terms of surge amplitude and spatial extent are considered, then hurricanes are most likely to create the hazards. However, when slightly less strong events that still impact multiple areas during the storm life cycle are considered, the relative importance of hurricanes shrinks as that of ETCs grows. Furthermore we find distinct paths for ETCs causing multi-site surge at individual segments of the US east coast.
... It is also important to note however that the NARR-forced NYHOPS still under-predicted Hurricane Sandy's peak surge at The Battery by ~2 ft, or ~20% of the observed ~10 ft surge (Figure 4). During the actual event in October 2012 the NYHOPS OFS forecast forced by the deterministic North American Mesoscale (NAM) model at 12 km resolution under-predicted Sandy's surge at The Battery by approximately 3 ft, while it was within 1 ft when, after the fact, the same model was forced with a more accurate forecast [9] or a high-fidelity reanalysis [10]. ...
... It is also important to note however that the NARR-forced NYHOPS still under-predicted Hurricane Sandy's peak surge at The Battery by~2 feet, or~20% of the observed~10 feet surge (Figure 4). During the actual event in October 2012 the NYHOPS OFS forecast forced by the deterministic North American Mesoscale (NAM) model at 12 km resolution under-predicted Sandy's surge at The Battery by approximately 3 feet, while it was within 1 foot when, after the fact, the same model was forced with a more accurate forecast [9] or a high-fidelity reanalysis [10]. ...
Article
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This article presents the results and validation of a comprehensive, multi-decadal, hindcast simulation performed using the New York Harbor Observing and Prediction System´s (NYHOPS) three-dimensional hydrodynamic model. Meteorological forcing was based on three-hourly gridded data from the North American Regional Reanalysis of the US National Centers for Environmental Prediction. Distributed hydrologic forcing was based on daily United States Geologic Survey records. Offshore boundary conditions for NYHOPS at the Mid-Atlantic Bight shelf break included hourly subtidal water levels from a larger-scale model ran for the same period, tides, and temperature and salinity profiles based on the Simple Ocean Data Assimilation datasets. The NYHOPS model's application to hindcast total water level and 3D water temperature and salinity conditions in its region over three decades was validated against observations from multiple agencies. Average indices of agreement were: 0.93 for storm surge (9 cm RMSE, 90% of errors less than 15 cm), 0.99 for water temperature (1.1 • C RMSE, 99% of errors less than 3 • C), and 0.86 for salinity (1.8 psu RMSE, 96% of errors less than 3.5 psu). The model's skill in simulating bottom water temperature, validated against historic data from the Long Island Sound bottom trawl survey, did not drift over the years, a significant and encouraging finding for multi-decadal model applications used to identify climatic trends, such as the warming presented here. However, the validation reveals residual biases in some areas such as small tributaries that receive urban discharges from the NYC drainage network. With regard to the validation of storm surge at coastal stations, both the considerable strengths and remaining limitations of the use of North American Regional Reanalysis (NARR) to force such a model application are discussed.
... Recent studies show the promise of adopting streamflow ensemble forecast techniques due to advantages over deterministic forecasts (Habets et al., 2004;Younis et al., 2008;Boucher et al., 2011;Schellekens et al., 2011;Verkade and Werner, 2011;Alfieri et al., 2013) as well as a way of accounting for uncertainties in hydrological forecasting (Chen and Yu, 2007;Demeritt et al., 2007;Davolio et al., 2008;Pappenberger et al., 2008;Reggiani and Weerts, 2008;Cloke and Pappenberger, 2009;Bao et al., 2011;Bogner and Pappenberger, 2011;Cuo et al., 2011;Schellekens et al., 2011;Alfieri et al., 2012;Amengual et al., 2015). Other advantages include the ability to distinguish between an extreme event forecast that is more or less likely to occur within the model's forecast horizon (Buizza, 2008;Golding, 2009) and better decision making with respect to operational hydrological concerns McCollor and Stull, 2008;Boucher et al., 2012). Furthermore, ensemble-based stream-flow forecasts tend to be more consistent between successive forecasts . ...
... Apart from this work, the framework is currently operational and fully automated on the Pharos (Greek for "lighthouse") Linux supercomputer at Stevens Institute of Technology. It produces four forecast cycles of ensemble river discharge per day, simulated at hourly time steps, which feed into the New York Harbor Observing and Prediction System (NYHOPS) (Bruno et al., 2006;Georgas et al., 2007Georgas et al., , 2014. NYHOPS was developed at Stevens Institute of Technology's Davidson Laboratory to generate forecasts of the Atlantic coast, New York Harbor and Hudson River region through in situ monitoring equipment and hydrodynamic modeling (Blumberg et al., 2015). ...
Article
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This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ∼ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.
... Sandy's landfall produced a catastrophic storm surge stretching from New Jersey to Rhode Island that made it the sixth costliest US tropical cyclone on record since 1900 [6]. In spite of the incredible damage Sandy generated, two recent modeling studies demonstrated that Sandy's storm surge was not the worst-case scenario for the NY/NJ Bight [7,8]. ...
... While dynamical downscaling is one viable method for long-term predictions of storm surge, it is computationally expensive and requires complex methodology to run long integrations utilizing GCM's with varying grid resolutions. The complex bathymetry and coastal geometry of the Mid-Atlantic Bight also hinder attempts to simulate storm surge with dynamical models for long periods of time as well [8]. ...
Article
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This paper reviews the historical and potential future trends of extratropical cyclones (ECs) along the United States (US) East Coast and western Atlantic, as well as potential changes in coastal flooding, heavy precipitation, and damaging winds. Most models project a steady decrease in the number of ECs for the US East Coast and western Atlantic region by the middle to later twenty-first century, while there is an increase in more intense (<980 hPa) cyclones and heavy precipitation; however, there is also been large interdecadal and interannual variability. Potential biases may exist in the models because of difficulty capturing: (a) the Atlantic storm track sensitivity to the Gulf Stream SST gradient, (b) latent heating within these storms, and (c) dynamical interactions at jet level. More work is needed to determine future changes in hybrid storms (e.g., Sandy 2012) and diagnostics to better understand the future cyclone changes in the models.
... Storm surges due to Typhoon Haishen was accurately simulated by coupling the mesoscale meteorological model (WRF) and the storm surge model (GeoClaw) [9]. The surface wind and mean sea level pressure predictions from mesoscale atmospheric models such as the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) Model have been used to drive the storm surge models [10,11,12,13,14,15,16]. Most of these models were run in hindcast mode. ...
... As a result, optical and biogeochemical conditions, including sediment concentrations, primary production, nutrient fluxes, and organic matter quality, are highly variable (Aurin et al., 2010;Byrd et al., 2020;Goebel et al., 2006;Sherman, Tzortziou, Turner, Goes, & Grunert, 2023;Turner et al., 2022;Varekamp et al., 2014). LIS water quality conditions can be further complicated by non-fluvial urban coastal processes, including export of wastewater from point sources, benthic resuspension of sediments and nutrients, wetland tidal exchanges, industrial discharge, and polluted stormwater runoff from urban landscapes (Georgas et al., 2014;Signell et al., 2000). ...
Article
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Dissolved organic matter and its colored component, Colored Dissolved Organic Matter (CDOM), play a major role in global carbon budgets, and their fluxes provide an essential link between terrestrial and aquatic biogeochemical cycles. Satellite observations can uniquely capture the hydro‐biogeochemical connectivity of terrestrial and aquatic landscapes, across scales. Yet, accurate satellite retrievals of CDOM and dissolved organic carbon (DOC) dynamics remain challenging in urbanized estuaries and coasts. Here, we present an advanced unified algorithm for space‐based retrieval of coastal CDOM and DOC dynamics and its application in Long Island Sound—one of the world's most heavily urbanized estuaries that is becoming increasingly vulnerable to climate change stressors. A rich bio‐optical data set, encompassing a wide range of environmental conditions, was integrated into the algorithm training to retrieve DOC concentrations and CDOM spectral shape (i.e., spectral slope S275–295)—a proxy for DOC quality. The new algorithms were applied to full‐resolution satellite imagery from the Sentinel‐3 Ocean and Land Color Instrument (OLCI) after thoroughly evaluating the performance of six ocean color atmospheric correction approaches (ACOLITE, BAC, C2RCC, MUMM, l2gen, and Polymer). Evaluation of the algorithms yielded mean absolute percent differences of 28%, 12%, and 10% for aCDOM(300), S275–295, and DOC, respectively. Application of the algorithms to multi‐year satellite OLCI imagery captured, for the first time, the coupled impact of seasonal transitions, wind regimes, freshwater inputs, anthropogenic disturbances, and hydrological extremes (both intense precipitation and droughts) on DOC fluxes and CDOM quality at the ecosystem scale. Results have important implications for improved predictions of coastal biogeochemical fluxes in complex urban−estuary systems.
... As a result, optical and biogeochemical conditions, including sediment concentrations, primary production, nutrient fluxes, and organic matter quality, are highly variable (Aurin et al., 2010;Byrd et al., 2020;Goebel et al., 2006;Sherman, Tzortziou, Turner, Goes, & Grunert, 2023;Turner et al., 2022;Varekamp et al., 2014). LIS water quality conditions can be further complicated by non-fluvial urban coastal processes, including export of wastewater from point sources, benthic resuspension of sediments and nutrients, wetland tidal exchanges, industrial discharge, and polluted stormwater runoff from urban landscapes (Georgas et al., 2014;Signell et al., 2000). ...
... New York City is located at the western end of the sound. . Long Island Sound has experienced several devastating storms including Hurricane Sandy (Georgas et al., 2014) and Hurricane Carol in 1954(Colle et al., 2008. ...
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Storm-surge models are commonly used to assess the impacts of hurricanes and coastal storms in coastal areas. Including the impact of the projected future sea level rise (SLR) in these models is a necessary step for a realistic flood risk assessment. Commonly, SLR is superimposed linearly on the estimated water elevation. This approach, while efficient, may lead to inaccuracies. Further, developing a new model with updated data that include the impacts of SLR (i.e., nonlinear approach) is time consuming. We compare the linear and nonlinear approaches to include the effect of SLR to predict Maximum Water/Flood Elevations (MWE) as a result of storm surge and SLR. After a simplified theoretical analysis, a number of idealized cases based on the typical coastal bodies of water are modeled to assess the impact of SLR on MWE using the linear superposition and nonlinear approaches. Additionally, two case studies are carried out: Narragansett Bay, RI and Long Island Sound, CT (USA). Results show that for the idealized cases with variable depth, in general, the linear superposition of SLR to MWE is conservative (i.e., predicts a larger flood elevation) relative to the nonlinear approach. However, if a constant depth is considered, results are not consistent (i.e. linear superposition can overestimate or underestimate MWE, and the results depended on the geometry). The simulated MWE from the Narragansett Bay simulation confirms the outcome of idealized cases showing that linear assumption is conservative up to 10\% relative to the nonlinear approach. For this study, Hurricane Sandy and a Synthetic Storm from {the US Army Corps of Engineers} North Atlantic Comprehensive Coastal Study (NACCS) dataset are simulated. Long Island Sound model results are also consistent with the idealized case. In general, based on the results of the idealized and real studies, a discrepancy of up to 10% between the linear and nonlinear approaches is expected in estimation of MWE which can be under- or over-estimation of flood elevation.
... New York City is located at the western end of the sound. Long Island Sound has experienced several devastating storms including Hurricane Sandy (Georgas et al. 2014) and Hurricane Carol in 1954(Colle et al. 2008. ...
Article
Full-text available
Storm-surge models are commonly used to assess the impacts of hurricanes and coastal storms in coastal areas. Including the impact of the projected future sea level rise (SLR) in these models is a necessary step for a realistic flood risk assessment. Commonly, SLR is superimposed linearly on the simulated water elevation. This approach, while efficient, may lead to inaccuracies. Furthermore, developing a new model with updated data (e.g., boundary conditions, bathymetry) that include the effects of SLR (i.e., nonlinear approach) is time consuming. We compare the linear and nonlinear approaches to include the effect of SLR in predicting maximum water/flood elevations (MWE) as a result of storm surge. After a simplified theoretical analysis, a number of idealized cases based on the typical coastal bodies of water are modeled to assess the impact of SLR on MWE using the linear superposition and nonlinear approaches. Additionally, two case studies are carried out: Narragansett Bay, RI, and Long Island Sound, CT (USA). Results show that for the idealized cases with variable depth, in general, the linear superposition of SLR to MWE is conservative (i.e., predicts a larger flood elevation) relative to the nonlinear approach. However, if a constant depth is considered, results are not consistent (i.e., linear superposition can overestimate or underestimate MWE, and the results depended on the geometry). The simulated MWE from the Narragansett Bay simulation confirms the outcome of idealized cases showing that linear assumption is conservative up to 10% relative to the nonlinear approach. For this study, Hurricane Sandy and a synthetic storm from the US Army Corps of Engineers North Atlantic Comprehensive Coastal Study (NACCS) dataset are simulated. Long Island Sound model results are also consistent with the idealized case. In general, based on the results of the idealized and real case studies, a discrepancy of up to 10% between the linear and nonlinear approaches is expected in estimation of MWE which can be under- or over-estimation of flood elevation, depending on the geometry.
... More broadly, a recent study found that future climate change effects on tropical cyclones will have only a small effect on extreme sea levels in New York Bight, relative to the effects of sea-level rise 9,11,20 . Sandy had a worst-case timing with respect to the evening high tide 21 and a near-worst case storm track 22 . Any differences from this scenario in a hypothetical world without climate change would likely have reduced damages. ...
Article
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In 2012, Hurricane Sandy hit the East Coast of the United States, creating widespread coastal flooding and over 60billioninreportedeconomicdamage.Thepotentialinfluenceofclimatechangeonthestormitselfhasbeendebated,butsealevelrisedrivenbyanthropogenicclimatechangemoreclearlycontributedtodamages.Toquantifythiseffect,herewesimulatewaterlevelsanddamagebothastheyoccurredandastheywouldhaveoccurredacrossarangeoflowersealevelscorrespondingtodifferentestimatesofattributablesealevelrise.Wefindthatapproximately60 billion in reported economic damage. The potential influence of climate change on the storm itself has been debated, but sea level rise driven by anthropogenic climate change more clearly contributed to damages. To quantify this effect, here we simulate water levels and damage both as they occurred and as they would have occurred across a range of lower sea levels corresponding to different estimates of attributable sea level rise. We find that approximately 8.1B (4.7B–14.0B, 5th–95th percentiles) of Sandy’s damages are attributable to climate-mediated anthropogenic sea level rise, as is extension of the flood area to affect 71 (40–131) thousand additional people. The same general approach demonstrated here may be applied to impact assessments for other past and future coastal storms. Sea level rise amplifies coastal storm impacts, but the role of anthropogenic climate change is poorly resolved. Here the authors reassess Hurricane Sandy, using a dynamic flood model to show that anthropogenic sea level rise added a central estimate of $8 billion in damages.
... This effect is a typical non-linear surge and tide interaction as suggested by Prandle and Wolf (1978) for the Thames estuary. If the storm surge had peaked 6 h earlier or later (during high tide), the total water level would not necessarily have been as high as a linear superposition of tide and surge heights, as noticed for hurricane Sandy in the New York Harbor region by Georgas et al. (2014). The SSVBES 3D modeling results for sea surface elevations during the storm tide of August 2016 (ST1) are shown in Fig. 13 as well as the observed time series at Capitania, Praticagem, and Palmas. ...
Article
We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.0∘S, 46.3∘W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets.
... The height and timing of high tide relative to the peak storm surge is also an doi: 10.1111/nyas.14011 important factor for New York City coastal flooding (e.g., Colle et al., 2015;Colle et al., 2008;Georgas et al., 2014;Kemp and Horton, 2013). Storm tide can be defined as the combination of tide level and storm surge, measured as a value above a given year's MSL. ...
... The vertical resolution of the NYHOPS grid is 10 sigma (bottom-following) layers at depths shallower than 200 m, providing forecasts at 150,680 points (Georgas et al., 2014). ...
Article
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This study presents a flood prediction framework that uses multi‐scale operational models representing meteorological, coastal, hydrologic and hydraulic stormwater components. A dual‐drainage (street‐sewer) model forms the framework core and receives inputs from coastal and meteorological models. The framework was tested in a flood prone area within the City of Hoboken, New Jersey. Hoboken’s aging combined sewer system is regularly overwhelmed by rainfall, storm surge or their combination resulting in nuisance flooding. The utility of the framework was demonstrated by retrospectively forecasting (72‐hours horizon) two contrasting extreme flood events, a rainfall dominated event (Hurricane Irene) and a surge dominated event (Hurricane Sandy). The simulations showed that overland flow from storm surge and low‐lying topography were major factor in surcharging the sewer system resulting in flooding. This modelling approach captures multi‐scale interactions and demonstrates the importance of holistically representing short‐term stressors in urban‐coastal systems. The framework can be run in ensemble mode to account for uncertainty in atmospheric forcing and aid decision making but this option was not explored in this study. Despite the limitations in expanding the dual‐drainage domain to the entire city of Hoboken, the study offers interesting perspectives on leveraging existing models and predicting system response to different stressors. This article is protected by copyright. All rights reserved.
... The completed multi-decadal high-resolution three-dimensional hindcast for Long Island Sound was based on the New York Harbor Observing and Prediction System (NYHOPS) model (www.stevens.edu/NYHOPS; Georgas et al. 2007;Blumberg and Georgas 2008;Georgas 2010;Georgas and Blumberg 2010;DiLiberto et al. 2011;Orton et al. 2012;Wilkin and Hunter 2013;Georgas et al. 2014). Meteorological forcing to Long Island Sound in NYHOPS was based on 3-hourly gridded data from the North American Regional Reanalysis (NARR, Messinger et al. 2006) of the US National Centers for Environmental Prediction (NCEP). ...
Technical Report
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The project had the following objectives: 1) To address the paucity of physical environmental data during Long Island Sound’s (LIS) observed warming trend and accompanying fisheries shift by running a hindcast of the LIS circulation using the New York Harbor Observing and Prediction System (NYHOPS), an operational, comprehensive, high-resolution, three-dimensional, numerical model. 2) To explore and understand climate-forced links between the physical and ecological environment of the Sound by studying the statistical correlations of historic ecological data (mainly fish trawl survey data) to the physical environmental data from the NYHOPS model with a goal to explain the recent ecological regime changes and, 3) To project the impacts of climate change and variability on the Sound’s ecosystem and its living marine resources, by forcing NYHOPS with Intergovernmental Panel for Climate Change (IPCC)-class global climate models.
... The operational NYHOPS model is now producing 108hr forecasts that are updated every 6 h (Georgas et al., 2016a). The hydrodynamic code is based on sECOM-MPI, a parallel Princeton Ocean Model derivative code similar to the one used in PATH (Blumberg and Georgas, 2008;Georgas, 2010;Bhushan et al., 2010;Georgas et al., 2009a;Georgas et al., 2007;Georgas et al., 2014;Jordi et al., 2017;Marsooli et al., 2017). sECOM was also coupled to a water quality model that operationally predicted Chromophoric Dissolved Organic Matter (CDOM) concentrations based on the RCA (Row Column AESOP, HydroQual, 2009;NYCDEP, 2013) code similar to the one in PATH (Georgas et al., 2009b). ...
Article
A simulation of transport and fate of pathogen indicators for the New York City open waters was completed using a coupled numerical model. Modeled concentrations for Enterococci in receiving waters were extracted from the model and compared with NYC beach observations and NYC Harbor Surveys to validate and bias-correct the result. Results, both before and after bias-correction, were then used to calculate model-based contact recreation advisories and compare to existing advisories: NYC DEP advisory guidance for waterbodies, and NYC DOHMH advisory guidance for NYC beaches. The model was able to simulate the transport and fate of Enterococci. Receiving water Enterococci concentrations grew responsively to rainfall, and decreased notably after rainfall. The model showed the ability to take complexity of natural effects into account, and was conservative in most cases after bias-correction. Based on an exceedance criterion of 110 cfu/100 mL, simulated Enterococci concentrations exceeded that criterion 54% (109%) of the total advisory days that existing NYC DOHMH guidance would suggest throughout all NYC beaches, on average, before (after) bias-correction. The model exceeded that criterion 217% (246%) of the total advisory days that existing NYC DEP guidance would suggest throughout all NYC waterbodies, on average, before (after) bias-correction. Simulation results were provided to NYC DEP and NYC DOHMH for the refinement of existing advisories.
... NOAA's Hurricane Research Division used to generate real-time hurricane winds (H*Wind) from surface wind observations on buoys, automated observation platforms, ships, etc. (Powell et al. 1998), but the H*Wind winds were only available prior to landfall. Consequently, surface wind and air pressure predictions from mesoscale atmospheric models such as the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) Model have been used to drive the storm surge models (Lin et al. 2010;Zhong et al. 2010;Di Liberto et al. 2011;Chen et al. 2013;Georgas et al. 2014;Wang et al. 2014;Zambon et al. 2014). Most of these models were run in hindcast mode. ...
Article
Through a case study of Hurricane Arthur (2014), the Weather Research and Forecasting (WRF) Model and the Finite Volume Coastal Ocean Model (FVCOM) are used to investigate the sensitivity of storm surge forecasts to physics parameterizations and configurations of the initial and boundary conditions in WRF. The turbulence closure scheme in the planetary boundary layer affects the prediction of the storm intensity: the local closure scheme produces lower equivalent potential temperature than the nonlocal closure schemes, leading to significant reductions in the maximum surface wind speed and surge heights. On the other hand, higher-class cloud microphysics schemes overpredict the wind speed, resulting in large overpredictions of storm surge at some coastal locations. Without cumulus parameterization in the outermost domain, both the wind speed and storm surge are grossly underpredicted as a result of large precipitation decreases in the storm center. None of the choices for the WRF physics parameterization schemes significantly affect the prediction of Arthur's track. Sea surface temperature affects the latent heat release from the ocean surface and thus storm intensity and storm surge predictions. The large-scale atmospheric circulation models provide the initial and boundary conditions for WRF, and influence both the track and intensity predictions, thereby changing the spatial distribution of storm surge along the coastline. These sensitivity analyses underline the need to use an ensemble modeling approach to improve the storm surge forecasts.
... This static bottom boundary is appropriate based on the limited impact of air-sea coupling affecting Sandy's wind field [Zambon et al., 2014]. This WRF product is similar to the one used in previous studies [Georgas et al., 2014;Glenn et al., 2016;Seroka, 2016] and covers the entire MAB ( Figure 1). Data were output hourly from a series of six 36 h forecast runs reinitialized at 00:00 GMT daily starting on 25 October. ...
Article
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Hurricane Sandy (2012) was the second costliest tropical cyclone to impact the United States and resulted in numerous lives lost due to its high winds and catastrophic storm surges. Despite its impacts little research has been performed on the circulation on the continental shelf as Sandy made landfall. In this study integrated ocean observing assets and regional ocean modeling were used to investigate the coastal ocean response to Sandy's large wind field. Sandy's unique cross-shelf storm track, large size, and slow speed resulted in along-shelf wind stress over the coastal ocean for nearly 48 hours before the eye made landfall in southern New Jersey. Over the first inertial period (∼18 hours) this along-shelf wind stress drove onshore flow in the surface of the stratified continental shelf and initiated a two-layer downwelling circulation. During the remaining storm forcing period a bottom Ekman layer developed and the bottom Cold Pool was rapidly advected offshore ∼70 kilometers. This offshore advection removed the bottom Cold Pool from the majority of the shallow continental shelf and limited ahead-of-eye-center sea surface temperature (SST) cooling, which has been observed in previous storms on the MAB such as Hurricane Irene (2011). This cross-shelf advective process has not been observed previously on continental shelves during tropical cyclones and highlights the need for combined ocean observing systems and regional modeling in order to further understand the range of coastal ocean responses to tropical cyclones.
... These hydrologic losses are adjusted for inflation but do not include losses from coastal storm tide flooding due to Extratropical and/or Tropical Cyclones (ETCs and TCs, respectively). Storm tide flooding can be defined as coastal flooding caused by a storm's surge pushing ocean waters to rise above local astronomical tide levels, with the phase of the local astronomical tide having important effects on total water levels above ground [2]. ...
... However, at smaller spatial scales (including Long Island Sound), this assumption has not been evaluated. To investigate the potential magnitude of tidal-range change at Pelham Bay, we ran a series of 35-day tidal simulations for Long Island Sound using the Stevens Institute Estuarine and Coastal Ocean model (sECOM) on the New York Harbor Observing and Prediction System (NYHOPS) domain (Georgas and Blumberg, 2009;Georgas et al., 2014;Orton et al., 2012). These simulations included only the astronomical constituents (M2, S2, N2, K2, K1, O1, and Q1) and shallow-water, overtide constituents (M4, M6) that are provided to this model domain as open-boundary conditions at the edge of the continental shelf. ...
Article
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New York City (NYC) is threatened by 21st-century relative sea-level (RSL) rise because it will experience a trend that exceeds the global mean and has high concentrations of low-lying infrastructure and socioeconomic activity. To provide a long-term context for anticipated trends, we reconstructed RSL change during the past ~1500 years using a core of salt-marsh sediment from Pelham Bay in The Bronx. Foraminifera and bulk-sediment δ¹³C values were used as sea-level indicators. The history of sediment accumulation was established by radiocarbon dating and recognition of pollution and land-use trends of known age in down-core elemental, isotopic, and pollen profiles. The reconstruction was generated within a Bayesian hierarchical model to accommodate multiple proxies and to provide a unified statistical framework for quantifying uncertainty. We show that RSL in NYC rose by ~1.70 m since ~575 CE (including ~0.38 m since 1850 CE). The rate of RSL rise increased markedly at 1812–1913 CE from ~1.0 to ~2.5 mm/yr, which coincides with other reconstructions along the US Atlantic coast. We investigated the possible influence of tidal-range change in Long Island Sound on our reconstruction using a regional tidal model, and we demonstrate that this effect was likely small. However, future tidal-range change could exacerbate the impacts of RSL rise in communities bordering Long Island Sound. The current rate of RSL rise is the fastest that NYC has experienced for >1500 years, and its ongoing acceleration suggests that projections of 21st-century local RSL rise will be realized.
... A major uncertainty in storm surge modeling is the sea surface drag coefficient parameterization [Cardone and Cox, 2009;Lin and Chavas, 2012;Resio and Westerink, 2008]. Hindcast studies of two recent TCs with sECOM on the NYHOPS grid have found that replacing the wind drag parameterization used by the operational system [Large and Pond, 1981] with a wave-slope sensitive parameterization [Taylor and Yelland, 2001] gives improved results and RMS errors of 0.15 m for Tropical Storm Irene [Orton et al., 2012], and 0.17 m for Hurricane Sandy [Georgas et al., 2014], and the latter is used here. Recent studies have presented evidence that the drag coefficient hits a ceiling at high wind speeds [e.g., Powell et al., 2003], but this saturation has not been demonstrated for the shallow coastal ocean. ...
Article
Recent studies of flood risk at New York Harbor (NYH) have shown disparate results for the 100-year storm tide, providing an uncertain foundation for the flood mitigation response after Hurricane Sandy. Here, we present a flood hazard assessment that improves confidence in our understanding of the region's present-day potential for flooding, by separately including the contribution of tropical cyclones (TCs) and extratropical cyclones (ETCs), and validating our modeling study at multiple stages against historical observations. The TC assessment is based on a climatology of 606 synthetic storms developed from a statistical-stochastic model of North Atlantic TCs. The ETC assessment is based on simulations of historical storms with many random tide scenarios. Synthetic TC landfall rates and the final TC and ETC flood exceedance curves are all shown to be consistent with curves computed using historical data, within 95% confidence ranges. Combining the ETC and TC results together, the 100-year return period storm tide at NYH is 2.70 m (2.51-2.92 at 95% confidence), and Hurricane Sandy's storm tide of 3.38 m was a 260-year (170-420) storm tide. Deeper analyses of historical flood reports from estimated Category-3 hurricanes in 1788 and 1821 lead to new estimates and reduced uncertainties for their floods, and show that Sandy's storm tide was the largest at NYH back to at least 1700. The flood exceedance curves for ETCs and TCs have sharply different slopes due to their differing meteorology and frequency, warranting separate treatment in hazard assessments. This article is protected by copyright. All rights reserved.
... These hydrologic losses are adjusted for inflation but do not include losses from coastal storm tide flooding due to Extratropical and/or Tropical Cyclones (ETCs and TCs, respectively). Storm tide flooding can be defined as coastal flooding caused by a storm's surge pushing ocean waters to rise above local astronomical tide levels, with the phase of the local astronomical tide having important effects on total water levels above ground [2]. ...
Article
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This paper presents the automation, website interface, and verification of the Stevens Flood Advisory System (SFAS, http://stevens.edu/SFAS). The fully-automated, ensemble-based flood advisory system dynamically integrates real-time observations and river and coastal flood models forced by an ensemble of meteorological models at various scales to produce and serve street scale flood forecasts over urban terrain. SFAS is applied to the Greater NY/NJ Metropolitan region, and is used routinely by multiple forecast offices and departments within the US National Weather Service (NWS), regional and municipal Offices of Emergency Management, as well as the general public. Every six hours, the underlying H³E (Hydrologic-Hydraulic-Hydrodynamic Ensemble) modelling framework, prepares, runs, data-assimilates, and integrates results from 375 dynamic model simulations to produce actionable, probabilistic ensemble forecasts of upland and coastal (storm surge) flooding conditions with an 81-h forecast horizon. Meteorological forcing to the H³E models is provided by 125 weather model ensemble members as well as deterministic weather models from major weather agencies (NCEP, ECMWF, CMC) and academia. The state-of-the-art SFAS, a replacement of the well-known, but deterministic, Storm Surge Warning System (SSWS) that was highlighted during Hurricanes Irene and Sandy and more recently extratropical cyclone Jonas, has been operational since the end of 2015.
... Apart from this work, the framework is currently operational and fully automated on the Pharos (lighthouse) Linux supercomputer at Stevens Institute of Technology, producing 4 forecast cycles of ensemble river discharge per day, simulated 5 at hourly time step, that feed into the New York Harbor Observing and Prediction System (NYHOPS) (Bruno et al., 2006;Georgas et al., 2007;Georgas et al., 2014). NYHOPS was developed at Stevens Institute of Technology's Davidson Laboratory to generate forecasts of the Atlantic Coast, New York Harbor, and Hudson River region through in-situ monitoring equipment and hydrodynamic modeling (Blumberg et al., 2015). ...
Article
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This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River Basin, USA (~ 36,000 km2) using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation datasets reforecast by the 21 ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were used to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 hours pre-event utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modelling framework was implemented on the Hudson River basin, it is flexible and re-locatable in other parts of the world.
... will depend on where people with different vulnerabilities are living in the future. Recent advances in detailed modeling of "street-level" flooding in well-mapped neighborhoods (Blumberg et al., 2015) can contribute to answering such questions as can similar advances in modeling the timing of storm surges in relation to tides (Georgas et al., 2014). Intersecting predictions of inundation with patterns of social vulnerability such as that indexed by the "Social Vulnerability Index" (SoVI; e.g., Guillard-Gonçalves et al., 2014) would represent a valuable contribution to disaster planning. ...
Technical Report
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An interdisciplinary, collaborative program is needed to facilitate predictions of the inter-connected factors that will impact coastal systems and the resilience of coastal communities over the next few decades. Two interdisciplinary workshops were held, in 2014 and 2015, to develop consensus as to the needs and scope that might be included in such a program. This report integrates the outcomes of those workshops with a review of recent literature on the subject. Workshop participants agreed that the program should focus on building innovative enhancements of objective decision-making utilizing model results. It must integrate natural and social sciences and facilitate a cyber-supported network of modelers, scholars and stakeholders from academia, federal agencies, local and state governments, non-governmental organizations and the private sector. Observational data, imagery, and numerical models should support trans-disciplinary research to advance resilience regionally and locally. Improved resilience of low-income communities in flood prone areas should be a priority. The scientific community at large can initiate and evolve a network of interdisciplinary scientists and supporting cyber-infrastructure with emphasis on complex coastal systems. Collaborations must be facilitated with rigorous data and model standards, open source model code, and effective communication with a hierarchy of scientists and operational end users. Model projections are needed to support local government officials in assessing resilience, planning for humanitarian assistance and identifying the most vulnerable communities, environments, and facilities. Integrative methodologies should utilize historical data, probabilistic analyses, physics-based numerical models, socioeconomic models and complex systems models. New cyber networks and workshops can enable scientists and stakeholders with diverse backgrounds to collaborate and share methods, standards and models for solving coastal problems. The most important outcome of this initiative must be: developing viable long-range resilience programs that enable continually evolving adaptive management strategies underpinned by advanced numerical modeling.
... Great progress has been made understanding the wave, current, infiltration, sediment transport, and wind processes that combine to produce overtopping and flooding of beaches and changes to shorelines and coastal communities. Storm impacts depend on the storm timing, duration, magnitude, and location (Georgas et al. 2014). In addition, interactions between tidal currents, wind-driven cur-rents, and wave-driven flows during high water levels may amplify forces on the beach and increase transport of sediment and pollutants (Mulligan et al. 2008). ...
... Beyond a statistical methodology, accounting for SLR and its uncertainty in federal and municipal flood standards also requires institutional changes, which have proven to be an obstacle for effective risk management (e.g., Moser and Ekstrom 2010). Finally, our model is a 'bath tub' model in that it accounts for mean wave height, which is often but not always a good approximation (Lin et al. 2012;Georgas et al. 2014). We have assumed a historic distribution of storms, which imperfectly samples the true probability distribution which may change in a warming climate (Christensen et al. 2013). ...
Article
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Sea-level rise (SLR) causes estimates of flood risk made under the assumption of stationary mean sea level to be biased low. However, adjustments to flood return levels made assuming fixed increases of sea level are also inaccurate when applied to sea level that is rising over time at an uncertain rate. To accommodate both the temporal dynamics of SLR and their uncertainty, we develop an Average Annual Design Life Level (AADLL) metric and associated Design Life SLR (DL-SLR) allowances. The AADLL is the flood level corresponding to a time-integrated annual expected probability of occurrence (AEP) under uncertainty over the design life of an asset; DL-SLR allowances are the adjustment from 2000 levels that maintain current average probability over the design life. Given non-stationary and uncertain sea-level rise, AADLL flood levels and DL-SLR allowances provide estimates of flood protection heights and offsets for different planning horizons and different levels of confidence in SLR projections in coastal areas. Here we employ probabilistic sea-level rise projections to illustrate the calculation of AADLL flood levels and DL-SLR allowances for a set of long-duration tide gauges along U.S. coastlines.
... The bathymetry and topography information in the model is based on the region's best available datasets, compiled by FEMA Region 2 for their recent coastal storm surge study [21]. The grid is doubly-nested, with a large, coarse grid modeling the Northwest Atlantic (the Stevens Northwest Atlantic Predictions grid, SNAP), a finer-resolution regional grid (the NYHOPS grid) that resolves New York Harbor regions at ~100 m [20,22], and a 30 m resolution grid that covers Jamaica Bay. In storm surge modeling studies, a common simplified approach to representing the effects of wetlands is to treat them as enhanced landscape roughness features, through Mannings-n. ...
Article
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Here, we demonstrate that reductions in the depth of inlets or estuary channels can be used to reduce or prevent coastal flooding. A validated hydrodynamic model of Jamaica Bay, New York City (NYC), is used to test nature-based adaptation measures in ameliorating flooding for NYC's two largest historical coastal flood events. In addition to control runs with modern bathymetry, three altered landscape scenarios are tested: (1) increasing the area of wetlands to their 1879 footprint and bathymetry, but leaving deep shipping channels unaltered; (2) shallowing all areas deeper than 2 m in the bay to be 2 m below Mean Low Water; (3) shallowing only the narrowest part of the inlet to the bay. These three scenarios are deliberately extreme and designed to evaluate the leverage each approach exerts on water levels. They result in peak water level reductions of 0.3%, 15%, and 6.8% for Hurricane Sandy, and 2.4%, 46% and 30% for the Category-3 hurricane of 1821, respectively (bay-wide OPEN ACCESS J. Mar. Sci. Eng. 2015, 3 655 averages). These results suggest that shallowing can provide greater flood protection than wetland restoration, and it is particularly effective at reducing " fast-pulse " storm surges that rise and fall quickly over several hours, like that of the 1821 storm. Nonetheless, the goal of flood mitigation must be weighed against economic, navigation, and ecological needs, and practical concerns such as the availability of sediment.
... In addition, FEMA uses dynamic models for its floodmapping studies (e.g., FEMA, 2014a), and the National Oceanic and Atmospheric Administration (NOAA) similarly uses dynamic models for forecasting neighborhood flooding during hurricanes. Further, prior studies of New York Harbor have shown that dynamic models can reproduce past storm-tide events with a typical accuracy of 0.5 ft (e.g., Colle et al., 2008;Orton et al., 2012;Georgas et al., 2014). ...
... The Hurricane Sandy field-verified inundation area ( Fig. 3.1), a surface interpolated using field-verified high-water marks and storm-sensor data from the U.S. Geological Survey, clearly equaled and exceeded the 1983 100-and 500year floodplains, most strikingly along the southern coasts of Brooklyn and Queens and along the eastern and southern shores of Staten Island. Northern Queens and the Bronx experienced less flooding relative to the other boroughs in part because the Long Island Sound was at low tide when Sandy made landfall (Georgas et al., 2014). ...
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Abstract. In recent centuries, human activities have greatly modified the geomorphology of coastal regions. However, studies of historical and possible future changes in coastal flood extremes typically ignore the influence of geomorphic change. Here, we quantify the influence of 20th Century manmade changes to Jamaica Bay, New York City, on present-day storm tides. We develop and validate a hydrodynamic model for the 1870s, based on detailed maps of bathymetry, seabed characteristics, topography, and tide observations, for use alongside a present-day model. Predominantly through dredging, landfill, and inlet stabilization, the average water depth of the bay increased from 1.7 to 4.5 m, tidal surface area decreased from 92 to 72 km2, and the inlet minimum cross-sectional area expanded from 4800 to 8900 m2. Total (freshwater plus salt) marsh habitat area has declined from 61 to 15 km2 and intertidal unvegetated habitat area from 17 to 4.6 km2. A probabilistic flood hazard assessment with simulations of 144 storm events reveals that the landscape changes caused an increase of 0.28 m (12 %) in the 100-year storm tide, even larger than the influence of global sea level rise of about 0.23 m since the 1870s. Specific anthropogenic changes to estuary depth, area and inlet depth and width are shown through targeted modeling and dynamics-based considerations to be the most important drivers of increasing storm tides.
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The objective of this work was to evaluate the benefits of using multi-model meteorological ensembles in representing the uncertainty of hydrologic forecasts. An inter-comparison experiment was performed using meteorological inputs from different models corresponding to Hurricane Irene (2011), over three sub-basins of the Hudson River basin. The ensemble-based precipitation inputs were used as forcing in a hydrological model to retrospectively forecast hourly streamflow, with a 96-hour lead time. The inputs consisted of 73 ensemble members, namely one high-resolution ECMWF deterministic member, 51 ECMWF members and 21 NOAA/ESRL (GEFS Reforecasts v2) members. The precipitation inputs were resampled to a common grid using the bilinear resampling method that was selected upon analysing different resampling methods. The results show the advantages of forcing hydrologic forecasting systems with multi-model ensemble forecasts over using deterministic and single model ensemble forecasts. The work showed that using the median of all 73 ensemble streamflow forecasts relatively improved the Nash–Sutcliffe Efficiency and lowered the biases across the examined sub-basins, compared to using the ensemble median from an individual model. This research contributes to the growing literature that demonstrates the promising capabilities of multi-model systems to better describe the uncertainty in streamflow predictions.
Article
The objective of this work was to evaluate the benefits of using multi-model meteorological ensembles in representing the uncertainty of hydrologic forecasts. An inter-comparison experiment was performed using meteorological inputs from different models corresponding to Hurricane Irene (2011), over three sub-basins of the Hudson River basin. The ensemble-based precipitation inputs were used as forcing in a hydrological model to retrospectively forecast hourly streamflow, with a 96-hour lead time. The inputs consisted of 73 ensemble members, namely one high-resolution ECMWF deterministic member, 51 ECMWF members and 21 NOAA/ESRL (GEFS Reforecasts v2) members. The precipitation inputs were resampled to a common grid using the bilinear resampling method that was selected upon analysing different resampling methods. The results show the advantages of forcing hydrologic forecasting systems with multi-model ensemble forecasts over using deterministic and single model ensemble forecasts. The work showed that using the median of all 73 ensemble streamflow forecasts relatively improved the Nash–Sutcliffe Efficiency and lowered the biases across the examined sub-basins, compared with using the ensemble median from an individual model. This research contributes to the growing literature that demonstrates the promising capabilities of multi-model systems to better describe the uncertainty in streamflow predictions.
Chapter
Seas are rising and seriously impacting coasts and coastal communities globally. Global warming is causing melting of glaciers and steric expansion of water volume. Locally and regionally, other effects including land subsidence and the slowing of ocean currents such as the Gulf Stream are causing additional rises. By midcentury, relative sea levels in cities like New York and Miami may exceed those of the year 2000 by up to 1.2 m (~ 4 ft.).
Chapter
Most people tend to think of coasts as material “things”. What you see when you look at a coast at any instant in time may be a beach composed of sand or a coastal wetland consisting of vegetation, mud and crabs and perhaps some methane or hydrogen sulfide gas. But in previous times it may have been very different and it probably will be different in the future. In reality, coasts are not “things” but processes; they are not static but are constantly becoming something new. This has always been the natural way with coasts. The ever-changing coastal process involves the interplay of solid material (e.g., sand and mud), chemistry (e.g., the pH of the Earth’s oceans), forces, energy fluxes and transfers (e.g., physical, chemical, biological, and solar), biological activity and ecological evolution, and, now, profound human interaction. We may reasonably expect coastal change to be accelerated in response to the climate changes that are now underway in the Anthropocene, a new geologic epoch in which human activities are causing profound and enduring modifications to the earth’s surface.
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Skillfully 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.
Chapter
Computational models are essential tools to support resilience planning for Jamaica Bay, or indeed anywhere (Hawes and Reed, 2006; Pickett et al., 2004; Walker et al., 2002; Gallopin, 2002). Models are simplifications of reality, constructed to highlight the interactions among physical, ecological, and social components of a system. Models connect observations with hypotheses and theories about how physical and social systems work, allowing scientists to articulate and test system understanding against data. Although there are physical and conceptual models, in the early twenty-first century, most models are deployed on computers and are increasingly used in distributed computing environments accessible through the Internet.
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A new, high-resolution, hydrodynamic model that encompasses the urban coastal waters of New Jersey along the Hudson River Waterfront opposite New York City, New York, has been developed and validated for simulating inundation during Hurricane Sandy. A 3.1-m-resolution square model grid combined with a high-resolution lidar elevation dataset permits a street-by-street focus to inundation modeling. The waterfront inundation model is a triple-nested Stevens Institute Estuarine and Coastal Ocean Hydrodynamic Model (sECOM) application; sECOM is a successor model to the Princeton Ocean Model family of models. Robust flooding and drying of land in the model physics provides for the dynamic prediction of flood elevations and velocities across land features during inundation events. The inundation model was forced by water levels from the extensively validated New York Harbor Observing and Prediction System (NYHOPS) hindcast of that hurricane. Validation against 56 watermarks and 16 edgemarks provided via the USGS and through an extensive crowdsourcing effort consisting of photographs, videos, and personal stories shows that the model is capable of computing overland water elevations quite accurately throughout the entire surge event. The correlation coefficient (R2) between the watermark observations and the model results is 0.92. The standard deviation of the residual error is 0.07 m. Comparisons to the 16 flood edgemarks suggest that the model was able to reproduce flood extent to within 20 m. Because the model was able to capture the spatial and temporal variation of water levels in the region observed during Hurricane Sandy, it was used to identify the flood pathways and suggest where flood-preventing interventions could be built.
Article
A multilinear regression (MLR) approach is developed to predict 3-hourly storm surge during the cool-season months (1 October-31 March 31) between 1979 and 2012 using two different atmospheric reanalysis datasets and water-level observations at three stations along the New York-New Jersey coast (Atlantic City, New Jersey; the Battery in New York City; and Montauk Point, New York). The predictors of the MLR are specified to represent prolonged surface wind stress and a surface sea level pressure minimum for a boxed region near each station. The regression underpredicts relatively large (≥95th percentile) storm maximum surge heights by 6.0%-38.0%. A bias-correction technique reduces the average mean absolute error by 10%-15% at the various stations for storm maximum surge predictions. Using the same forecast surface winds and pressures from the North American Mesoscale (NAM) model between October and March 2010-14, raw and bias-corrected surge predictions at the Battery are compared with raw output from a numerical hydrodynamic model's [the Stevens Institute of Technology New York Harbor Observing and Prediction System (SIT-NYHOPS)] predictions. The accuracy of surge predictions between the SIT-NYHOPS output and bias-corrected MLR model at the Battery are similar for predictions that meet or exceed the 95th percentile of storm maximum surge heights.
Conference Paper
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The coastal northeast United States was heavily impacted by hurricanes Irene and Sandy. Track forecasts for both hurricanes were quite accurate days in advance. Intensity forecasts, however, were less accurate, with the intensity of Irene significantly over-predicted, and the rapid acceleration and intensification of Sandy just before landfall under-predicted. By operating a regional component of the Integrated Ocean Observing System (IOOS), we observed each hurricane's impact on the ocean in real-time, and we studied the impacted ocean's influence on each hurricane's intensity. Summertime conditions on the wide Mid-Atlantic continental shelf consist of a stratified water column with a thin (10m-20m) warm surface layer (24-26C) covering bottom Cold Pool water (8-10C). As the leading edge of Irene tracked along the coast, real-time temperature profiles from an underwater glider documented the mixing and broadening of the thermocline that rapidly cooled the surface by up to 8C, well before the eye passed over. Atmospheric forecast sensitivity studies indicate that the over prediction of intensity in Irene could be eliminated using the observed colder surface waters. In contrast, Hurricane Sandy arrived in the late Fall of 2012 after seasonal cooling had already deepened and decreased surface layer ocean temperatures by 8C. The thinner layer of cold bottom water still remaining before Sandy was forced offshore by downwelling favorable winds, resulting in little change in ocean surface temperature as Sandy crossed and mixed the shelf waters. Atmospheric sensitivity studies indicate that because there was little ocean cooling, there was little reduction in hurricane intensity as Sandy came ashore. Results from Irene and Sandy illustrate the important role of the U.S. IOOS in providing the best estimate of the rapidly evolving ocean conditions to atmospheric modelers forecasting the intensity of hurricanes. Data from IOOS may enable improved hurricane forecasting in the future.
Article
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Numerical simulations of the storm tide that flooded the US Atlantic coastline during Hurricane Sandy (2012) are carried out using the National Weather Service (NWS) Sea Lakes and Overland Surges from Hurricanes (SLOSH) storm surge prediction model to quantify its ability to replicate the height, timing, evolution and extent of the water that was driven ashore by this large, destructive storm. Recent upgrades to the numerical model, including the incorporation of astronomical tides, are described and simulations with and without these upgrades are contrasted to assess their contributions to the increase in forecast accuracy. It is shown, through comprehensive verifications of SLOSH simulation results against peak water surface elevations measured at the National Oceanic and Atmospheric Administration (NOAA) tide gauge stations, by storm surge sensors deployed and hundreds of high water marks collected by the U.S. Geological Survey (USGS), that the SLOSH-simulated water levels at 71% (89%) of the data measurement locations have less than 20% (30%) relative error. The RMS error between observed and modeled peak water levels is 0.47 m. In addition, the model’s extreme computational efficiency enables it to run large, automated ensembles of predictions in real-time to account for the high variability that can occur in tropical cyclone forecasts, thus furnishing a range of values for the predicted storm surge and inundation threat.
Conference Paper
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The New York Harbor Observation and Prediction System, now in its 3 rd generation (NYHOPS v3), combines a network of real time sensors and a hydrodynamic forecasting computer model to assess prevailing ocean, environmental and meteorological conditions and to provide long and short term forecasts of the mentioned conditions. The older NYHOPS v2 model used spatially uniform surface heat flux forcing. Barometric pressure gradient forcing has also been neglected. The scope of this work was to assess sensitivity of the NYHOPS Sea Surface Temperature (SST) predictions to the spatial variability of the surface boundary condition. We compared two runs using different meteorological forcing: 1) Spatially varying wind stress and air pressure forcing, but spatially uniform heat flux forcing for the entire NYHOPS region (NYHOPS v2 surface boundary conditions with air pressure). 2) Spatially varying wind stress, air pressure, and heat flux forcing (NYHOPS v3 surface boundary conditions with air pressure). The SST modeled with NYHOPS was then compared against the validated GOES 12 satellite SST mapped on the NYHOPS grid nodes. A remarkable improvement (error reduction to the extent of 60%) in the prediction of SST by NYHOPS v3 was observed compared to NYHOPS v2. Similar error ranges were observed on comparison of NYHOPS v3 modeled SST and the satellite SST against in-situ observations, indicating that NYHOPS provides an effective SST prediction tool.
Article
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We briefly describe the new NYHOPS v3 OFS (Operational hydrodynamic Forecast System) and quantify its performance against National Ocean Service (NOS) standard OFS evaluation metrics. Given the relatively large area of the NYHOPS v3 OFS (including the NY/NJ Harbor estuary, Long Island Sound, and their coastal ocean), and the proliferation of sensor networks, the presented skill assessment is one of the most extensive performed to date: model results are compared to in situ observations of water level, currents, temperature, salinity, and waves from over 100 locations, collected in a 2 year period. The model's ability to describe the hydrodynamic conditions in the extensive area it is employed is remarkable. The average index of agreement for water level is 0.98, for currents is 0.87, for water temperature is 0.98, for salinity is 0.77, and for significant wave heights is 0.88. Respective, average root-mean-square errors are: 10cm for water level, 13cm/s and 9° for currents, 1.4°C for water temperatures, 2.8psu for salinities, and 32cm for significant wave heights.
Article
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Three of the nine highest recorded water levels in the New York Harbor (NYH) region have occurred since 2010 (Mar. 2010, Aug. 2011, and Oct. 2012), and eight of the largest twenty have occurred since 1990. To investigate whether this cluster of high waters is a random occurrence or indicative of intensified storm tides, we recover archival tide gauge data back to 1844 and evaluate the trajectory of the annual maximum storm tide (AMST). Approximately half of long-term variance is anti-correlated with decadal-scale variations in the North Atlantic Oscillation (NAO), while long-term trends explain the remainder. The 10-year storm-tide has increased by 0.28 m. Combined with a 0.44 m increase in local sea-level since 1856, the 10-year flood-level has increased by approximately 0.72 ± 0.25 m, and magnified the annual probability of overtopping the typical Manhattan seawall from less than 1% to about 20-25%.
Article
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Three real-time storm surge forecasting systems [the eight-member Stony Brook ensemble (SBSS), the Stevens Institute of Technology's New YorkHarbor Observing and Prediction System (SIT-NYHOPS), and the NOAA Extratropical Storm Surge (NOAA-ET) model] are verified for 74 available days during the 2007-08 and 2008-09 cool seasons for five stations around the New York City-Long Island region. For the raw storm surge forecasts, the SIT-NYHOPS model has the lowest root-mean-square errors (RMSEs) on average, while the NOAA-ET has the largest RMSEs after hour 24 as a result of a relatively large negative surge bias. The SIT-NYHOPS and SBSS also have a slight negative surge bias after hour 24. Many of the underpredicted surges in the SBSS ensemble are associated with large waves at an offshore buoy, thus illustrating the potential importance of nearshore wave breaking (radiation stresses) on the surge predictions. A bias correction using the last 5 days of predictions (BC) removes most of the surge bias in the NOAA-ET model, with the NOAA-ET-BC having a similar level of accuracy as the SIT-NYHOPS-BC for positive surges. A multimodel surge ensemble (ENS-3) comprising the SBSS control member, SITNYHOPS, and NOAA-ET models has a better degree of deterministic accuracy than any individual member. Probabilistically, the ALL ensemble (eight SBSS members, SIT-NYHOPS, and NOAA-ET) is underdispersed and does not improve after applying a bias correction. The ENS-3 improves the Brier skill score (BSS) relative to the best deterministic member (SIT-NYHOPS), and the ENS-3 has a larger BSS and better reliability than the SBSS and ALL ensembles, thus illustrating the benefits of a multimodel storm surge ensemble.
Article
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Detailed simulations, comparisons with observations, and model sensitivity experiments are presented for the August 2011 tropical cyclone Irene and a March 2010 nor'easter that affected the New York City (NYC) metropolitan area. These storms brought strong winds, heavy rainfall, and the fourth and seventh highest gauged storm tides (total water level), respectively, at the Battery, NYC. To dissect the storm tides and examine the role of various physical processes in controlling total water level, a series of model experiments was performed where one process was omitted for each experiment, and results were studied for eight different tide stations. Neglecting remote meteorological forcing (beyond ∼250 km) led to typical reductions of 7–17% in peak storm tide, neglecting water density variations led to typical reductions of 1–13%, neglecting a parameterization that accounts for enhanced wind drag due to wave steepness led to typical reductions of 3–12%, and neglecting atmospheric pressure gradient forcing led to typical reductions of 3–11%. Neglecting freshwater inputs to the model domain led to reductions of 2% at the Battery and 9% at Piermont, 14 km up the Hudson River from NYC. Few storm surge modeling studies or operational forecasting systems incorporate the “estuary effects” of freshwater flows and water density variations, yet joint omission of these processes for Irene leads to a low-bias in storm tide for NYC sites like La Guardia and Newark Airports (9%) and the Battery (7%), as well as nearby vulnerable sites like the Indian Point nuclear plant (23%).
Article
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The importance of sea-level rise in shaping coastal landscapes is well recognized within the earth science community, but as with many natural hazards, communicating the risks associated with sea-level rise remains a challenge. Topography is a key parameter that influences many of the processes involved in coastal change, and thus, up-to-date, high-resolution, high-accuracy elevation data are required to model the coastal environment. Maps of areas Subject to potential inundation have great utility to planners and managers concerned with the effects of sea-level rise. However, most of the maps produced to date are simplistic representations derived from older, coarse elevation data. In the last several years, vast amounts of high quality elevation data derived from lidar have become available. Because of their high vertical accuracy and spatial resolution, these lidar data are an excellent source of up-to-date information from which to improve identification and delineation of vulnerable lands. Four elevation datasets of varying resolution and accuracy were processed to demonstrate that the improved quality of lidar data leads to more precise delineation of coastal lands Vulnerable to inundation. A key component of the comparison was to calculate and account for the vertical uncertainty of the elevation datasets. This comparison shows that lidar allows for a Much more detailed delineation of the potential inundation zone when compared to other types of elevation models. It also shows how the certainty of the delineation of lands vulnerable to a given sea-level rise scenario is much improved when derived from higher resolution lidar data.
Article
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Hurricanes are one of the world's most destructive natural forces. Storm surge, the water pushed onto shore by the winds swirling around the hurricane, can wipe out entire communities in a matter of hours. Prior to the advent of numerical modeling of storm surge in the late 1960's, this was the largest cause of loss of life from a hurricane. Even today, as hurricane Katrina drastically demonstrated, we're still susceptible to storm surge, and it is critical that the National Weather Service (NWS) provide guidance on storm surge to emergency managers and the public so they can be prepared and react responsibly. The National Hurricane Center (NHC) forecasters produce forecasts of storm surge as part of their advi- sory package. They base their storm surge forecasts on the results of the Sea Lake and Overland Surges from Hurricanes (SLOSH) model (Jelesnianski et al. 1992). The SLOSH model was designed to be used operation- ally, so the various inputs had to be available, assumed or parameterized. The key inputs which can't be pre- pared before the storm threatens are the wind and pres- sure fields used to describe the hurricane over time. To allow forecasters to provide those fields and run the model, SLOSH was designed with a simplified, paramet- ric wind and pressure model which requires only readily available information. Specifically, SLOSH requires the track of the storm, the radius of maximum winds (Rmax) over time, and the pressure difference between the cen- ter of the storm and the ambient (or peripheral) pressure (DelP) over time. Since these parameters are either in the NHC's official advisory or can be computed from it, NHC forecasters can produce a storm surge forecast based on the current NHC hurricane forecast. Because emergency managers and evacuation planners require guidance on potential storm surge flooding well in advance of a hurricane, NHC also pro- vides composites of hypothetical storms grouped by Saffir-Simpson category, forward speed and direction. Each composite is generated by joining the results of running several dozen hypothetical storms through the SLOSH model. The hypothetical storms used in one composite are identical except that their positions have been shifted by a uniform amount. The composite is formed by determining the maximum value a given area attains at any time during any of the runs. While these composites are invaluable tools for evacuation planning, they are not directly associated with the current hurri-
Article
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Understanding the broad-scale ramifications of accelerated sea level rise requires maps of the land that could be inundated or eroded. Producing such maps requires a combination of elevation information and models of shoreline erosion, wetland accretion, and other coastal processes. Assessments of coastal areas in the United States that combine all of these factors have focused on relatively small areas, usually 25 to 30 kilometers wide. In many cases, the results are as sensitive to uncertainty regarding geological processes as to the rate of sea level rise. This paper presents maps illustrating the elevations of lands close to sea level. Although elevation contours do not necessarily coincide with future shorelines, the former is more transparent and less dependent on subjective modeling. Several methods are available for inferring elevations given limited data. This paper uses the USGS 1-degree digital elevation series and NOAA shoreline data to illustrate the land below the 1.5- and 3.5-meter contours for areas the size of entire U.S. states or larger. The maps imply that approximately 58,000 square kilometers of land along the Atlantic and Gulf coasts lie below the 1.5-meter contour. Louisiana, Florida, Texas, and North Carolina account for more than 80 percent of the low land. Outside of those four states, the largest vulnerable populated region is the land along the Eastern Shore of Chesapeake Bay stretching from Dorchester County, Maryland to Accomac County, Virginia.
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Our long-term goal is to interprete chromophoric dissolved organic matter (CDOM) sources, distributions, and dynamics in and around the NY/NJ Harbor Estuary, with a focus on significant freshwater events, through the creation of a robust, deterministic, high-resolution, four-dimensional, predictive model of CDOM fate and transport, validated against in-situ and remote sensing observations. An existing four-dimensional hydrodynamic and CDOM source tracking model was significantly updated and compared against a) concurrent datasets of in situ (EcoShuttle) CDOM observations available for the New York/New Jersey Harbor and, b) satellite-derived (SeaWiFS) surface CDOM distributions for the Harbor and its New York Bight Approaches (Bight Apex). The New York Harbor Observation and Prediction System (NYHOPS), a hydrodynamic/CDOM forecasting model incorporating CDOM fluorescence source strengths and first-order decay through photodegradation, was updated to a new high-resolution version shown to better capture the relevant hydrodynamic scales and associated CDOM sources and transport. The NYHOPS CDOM fate module was singificantly changed to include a more robust and deterministic bio-kinetic formulation of CDOM absorption loss due to photobleaching. Existing high-resolution local observations of CDOM fluorescence, absorption and other related variables, were used to locally calibrate the NYHOPS bio-kinetic module. CDOM distributions based on NYHOPS were compared to both the in-situ observations and SeaWiFS-derived spatial distributions, to continue interpretation of the CDOM data and facilitate an understanding of the processes that control CDOM distributions in estuarine and coastal waters.
Conference Paper
The New York Harbor Observing and Prediction System (NYHOPS) has been quasioperational since late 2003. The system provides hindcasts/nowcasts/ forecasts of water properties in an urban ocean environment. The environment includes the waters of the New York /New Jersey Harbor Estuary, from the coast eastward to the continental shelf break of the New York Bight and all of Long Island Sound in one contiguous fashion. Each day NYHOPS produces a 24-hour hindcast/nowcast and a 48-hour forecast of the physical state of water level, currents, temperature, and salinity. The model hindcast/nowcast/forecast skill assessment has been quantified for the entire year 2004 in terms of mean error (ME), standard deviation of the error (SD), RMS error and correlation coefficient. For the hindcast/nowcast, at most of the locations, errors in computed water levels are less than 8.5% of the local tidal range and correlations between the data and model exceed 0.94. The mean RMS errors and correlations degrade to 10 % and 0.91 for the 1st day forecast and to 14 % and 0.83 for the 2nd day forecast. The performance of the model for salinity and temperature are very encouraging. The vertical salinity stratification and its temporal variations in the different regions in the model domain are accurately reproduced in the model. For the hindcast/nowcast, the correlation coefficients range at various stations from 0.64 to 0.79 for salinity, and are typically greater everywhere than 0.96 for temperature. The 1st day and 2nd day forecasts of salinity and temperature have almost the same skill as the hindcast. The experience gained over more than one year of quasi-operational status has led to a NYHOPS system that is capable of describing the entire spectrum of time scales of the computed quantities. NYHOPS data and model products are all available in real-time at www.stevens.edu/maritimeforecast). Future extensions of NYHOPS include the assimilation of temperature, salinity and CODAR derived surface current measurements.
Article
Three-dimensional simulations of estuarine circulation in the New York Harbor complex, Long Island Sound, and the New York Bight have been conducted using the Estuarine, Coastal and Ocean Model (ECOM) within the framework of a single grid system. The model grid is curvilinear and orthogonal, with resolution from 100 m in rivers to about 50 km in the bight. The model forcing functions consist of (1) meteorological data; (2) water level elevation and temperature and salinity fields along the open boundary; and (3) freshwater inflows from 30 rivers, 110 wastewater treatment plants, and 268 point sources from combined sewer overflows and surface runoffs. Because the goal of this study is to maximize, to the extent possible, the predictive skill of the modeling system, the motivation for and a detailed description of the construction of these boundary forcing functions are presented. Two 12-month periods are considered: (1) October 1988 to September 1989 for model calibration; and (2) October 1994 to September 1995 for model validation. For model calibration, the results are compared with water levels at 14 stations, currents at six stations, and temperature and salinity at 35 stations. Model validation is accomplished using data from an extensive hydrodynamic monitoring program. Mean errors in predicted elevations and currents are less than 10% and 15%, respectively. Correlation coefficients for salinity and temperature are as high as 0.86 and 1.0, respectively. The level of skill shown by these statistical measures suggests that the model is capable of describing the entire spectrum of time scales for the computed quantities, from the semidiurnal to the annual scales.
Article
In response to renewed studies of potential hurricane barriers across Lake Pontchartrain, the U.S. Army Engineer Research and Development Center conducted a survey of the New England hurricane barriers. This survey revealed a number of common factors pertaining to the projects. First, most of the projects have not been tested with storm water elevations near their design elevation. An exception is the Charles River dam, which helped prevent flooding in Boston during the Blizzard of 1978. For the lower levels experienced, all projects performed as designed. Second, there is little information in the literature regarding flushing, sedimentation, or other environmental effects of the New England barriers. All except Charles River were constructed in an era when environmental studies were minimal compared to today. Third, long-term maintenance requirements were underestimated for the projects with mechanical components. In particular, the 1960s electromechanical controls at Providence and New Bedford need upgrading. Fourth, many people are unaware that the Corps of Engineers has built and efficiently operated hurricane barriers for more than 40 years. A public education campaign would be beneficial to the USACE. The New England and New Jersey barriers are excellent examples of cooperation and operational coordination between the USACE and municipal agencies. At least six major challenges will confront designers of Gulf Coast hurricane barriers compared to the earlier projects.
Article
Measurements of the momentum flux were made by the Reynolds flux and dissipation methods on a deep water stable tower operated by the Bedford Institute of Oceanography . A modified Gill propeller-vane anemometer was used to measure the velocity. Drag coefficients from 196 Reynolds flux measurements agree well with those previously reported. Based on 192 runs, a comparison of the dissipation and Reynolds flux results shows excellent agreement on average, for wind speeds from 4 to 20 m sSUB-SUB1. The much more extensive dissipation data set (1086 h from the tower and 505 h from the weathership PAPA, CCGS Quadra) was used to investigate the dependence of the drag coefficient on wind speed, fetch and stability. Some time series of the momentum flux and drag coefficient are shown to demonstrate additional sources of variation in the drag coefficient. (from authors' abstract)
Article
It is proposed that the sea surface roughness zo can be predicted from the height and steepness of the waves, zo/Hs = A(Hs/Lp)B, where Hs and Lp are the significant wave height and peak wavelength for the combined sea and swell spectrum; best estimates for the coefficients are A = 1200, B = 4.5. The proposed formula is shown to predict well the magnitude and behavior of the drag coefficient as observed in wave tanks, lakes, and the open ocean, thus reconciling observations that previously had appeared disparate. Indeed, the formula suggests that changes in roughness due to limited duration or fetch are of order 10% or less. Thus all deep water, pure windseas, regardless of fetch or duration, extract momentum from the air at a rate similar to that predicted for a fully developed sea. This is confirmed using published field data for a wide range of conditions over lakes and coastal seas. Only for field data corresponding to extremely young waves (U10/cp > 3) were there appreciable differences between the predicted and observed roughness values, the latter being larger on average. Significant changes in roughness may be caused by shoaling or by swell. A large increase in roughness is predicted for shoaling waves if the depth is less than about 0.2Lp. The presence of swell in the open ocean acts, on average, to significantly decrease the effective wave steepness and hence the mean roughness compared to that for a pure windsea. Thus the predicted open ocean roughness is, at most wind speeds, significantly less than is observed for pure wind waves on lakes. Only at high wind speeds, such that the windsea dominates the swell, do the mean open ocean values reach those for a fully developed sea.
Even. Downloaded from www.worldscientific.com by Dr
  • J Extr
J. of Extr. Even. Downloaded from www.worldscientific.com by Dr. Nickitas Georgas on 07/31/14. For personal use only.
Surge-tide interaction in the southern North Sea. Ninth Liege Colloquium. Hydrodynamics of Estuaries and Fjords
  • D Prandle
  • J Wolf
Prandle, D and Wolf J (1978). Surge-tide interaction in the southern North Sea. Ninth Liege Colloquium. Hydrodynamics of Estuaries and Fjords. University of Liege, May 2-6, 1977.
Hurricane Sandy Event Recap Report: Impact Forecasting. http:// thoughtleadership.aonbenfield.com/Documents/20130514 if hurricane sandy event recap
  • Aon Benfield
Aon Benfield (2013). Hurricane Sandy Event Recap Report: Impact Forecasting. http:// thoughtleadership.aonbenfield.com/Documents/20130514 if hurricane sandy event recap.pdf [March 15, 2014].
Forecasting and Dissecting Hurricane Sandy's Storm Tide for Geophysical Research Letters
  • P M Orton
  • N Georgas
  • A Blumberg
  • S Vinogradov
Orton, PM, Georgas, N., Blumberg, A. and Vinogradov, S. (in preparation). Forecasting and Dissecting Hurricane Sandy's Storm Tide for Geophysical Research Letters.
Weighing Sea Barriers as Protection for New Yorkafter-hurri- cane-sandy-debating-costly-sea-barriers-in-new-york-area.html?pagewanted¼ all& r¼0
  • M Navarro
  • Ny
Navarro, M [NY TIMES] (2012). Weighing Sea Barriers as Protection for New York. New York Times, November 7, 2012. http://www.nytimes.com/2012/11/08/nyregion/after-hurri- cane-sandy-debating-costly-sea-barriers-in-new-york-area.html?pagewanted¼ all& r¼0 [March 15, 2014].
The New York Bight -Shelf Harbor Dynamic Study: Ocean Forecast Sensitivity to Forecasts of Atmospheric Forcing
  • N Georgas
  • A F Blumberg
Georgas, N and Blumberg AF (2008). The New York Bight -Shelf Harbor Dynamic Study: Ocean Forecast Sensitivity to Forecasts of Atmospheric Forcing. Office of Naval Research, ONR Grant N00014-06-1-1027. 50pp.
Chapter 3: Sea Level Rise
  • R Horton
  • C Little
  • D Bader
  • C Rosenzweig
Horton, R, Little C, Bader D and Rosenzweig C (submitted). Chapter 3: Sea Level Rise. In: Rosenzweig C and Solecki W (eds.) New York City Panel on Climate Change 2014 Report: Sixth Assessment Report. New York, NY, USA.
Surge " wasn't even close " to breaching hurricane barrier in Stamfordsurge-wasnt-even- close-to-breaching-hurricane-barrier-in-stamford
  • Ct News
CT News (2012). Surge " wasn't even close " to breaching hurricane barrier in Stamford. October 30, 2012. http://blog.ctnews.com/sandy/2012/10/30/surge-wasnt-even- close-to-breaching-hurricane-barrier-in-stamford/ [March 15, 201].
Probabilistic guidance for hurricane storm surge. 19th Conference on Probability and Statistics in the Atmospheric Sciences
  • A A Taylor
  • B Glahn
Taylor, AA and Glahn B (2008). Probabilistic guidance for hurricane storm surge. 19th Conference on Probability and Statistics in the Atmospheric Sciences. January 21-24, 2008, New Orleans, Louisiana.
Downloaded from www.worldscientific.com by Dr
  • J Of Extr
  • Even
J. of Extr. Even. Downloaded from www.worldscientific.com by Dr. Nickitas Georgas on 07/31/14. For personal use only.
Surge "wasn't even close" to breaching hurricane barrier in Stamford
  • Ct News
CT News (2012). Surge "wasn't even close" to breaching hurricane barrier in Stamford. October 30, 2012. http://blog.ctnews.com/sandy/2012/10/30/surge-wasnt-evenclose-to-breaching-hurricane-barrier-in-stamford/ [March 15, 201].
SLOSH: Sea, Lake, and Overland Surges from Hurricanes. NOAA Technical Report NWS 48. US Dept. of Commerce, National Oceanic and Atmospheric Administration
  • C Jelesnianski
  • Chen J Shaffer
Jelesnianski, C, Chen J and Shaffer WA (1992). SLOSH: Sea, Lake, and Overland Surges from Hurricanes. NOAA Technical Report NWS 48. US Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service.
Silver Spring, MD: U.S. Department of Commerce, National Oceanic and Atmospheric Administration
National Oceanic Atmospheric Administration [NOAA] (2000). Tidal Datums and Their Applications. Silver Spring, MD: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Special Publication NOS CO-OPS 1. http://tidesandcurrents.noaa.gov/publications/tidal datums and their applications.pdf [March 15, 2014].
  • P Orton
  • S Vinogradov
  • N Georgas
  • A Blumberg
Orton, P, Vinogradov S, Georgas N and Blumberg A (submitted). Chapter 5: Hydrodynamic Mapping of Future Coastal Flood Hazards for New York City. In: Rosenzweig C and Solecki W (eds.) New York City Panel on Climate Change 2014 Report: Sixth Assessment Report. New York, NY, USA.
Ch. 2: Our Changing Climate
  • J Walsh
  • D Wuebbles
  • K Hayhoe
  • J Kossin
  • K Kunkel
  • G Stephens
  • P Thorne
  • R Vose
  • M Wehner
  • J Willis
  • D Anderson
  • S Doney
  • R Feely
  • P Hennon
  • V Kharin
  • T Knutson
  • F Landerer
  • T Lenton
  • Kennedy J Somerville
Walsh, J, Wuebbles D, Hayhoe K, Kossin J, Kunkel K, Stephens G, Thorne P, Vose R, Wehner M, Willis J, Anderson D, Doney S, Feely R, Hennon P, Kharin V, Knutson T, Landerer F, Lenton T, Kennedy J and Somerville R (2014). Ch. 2: Our Changing Climate. In: Melillo JM, Richmond TC and Yohe GW (eds.) Climate Change Impacts in the United States: The Third National Climate Assessment, U.S. Global Change Research Program, pp. 19-67.