A new framework for the assessment of potential future disasters caused by typhoons using multi-model ensemble experiments

In this study, a storm surge model of the Salish Sea was developed to simulate storm surge in Puget Sound using the unstructured-grid, finite-volume community ocean model (FVCOM). The model was based on Pacific Northwest National Laboratory’s existing Puget Sound hydrodynamic model that has been validated extensively in previous studies. To identify representative historical storm events, a systematic approach was developed based on the non-tidal residual (NTR) method. A total of 34 storm surge events between 1980 and 2016 were identified and simulated. To accurately simulate storm surge inside Puget Sound, the model was driven by reanalyzed Climate Forecast System Reanalysis meteorological forcing and observed water levels at the entrance of Strait of Juan de Fuca and southern entrance of Johnstone Strait. Model results for total water level, storm surge, and currents were compared with field measurements. A series of error statistics parameters were also calculated to quantify the model’s skills in simulating storm surge in the Salish Sea. Error statistics of model performance metrics demonstrated that the Salish Sea storm surge model was able to simulate storm surge inside Puget Sound with high accuracy. Storm surge propagation into Puget Sound is a nonlinear process that cannot be simply determined by a static approach for the entire domain. The maximum NTR distribution inside Puget Sound suggests that storm surge tends to be amplified in several sub-basins and inlets of the Puget Sound, such as Bellingham Bay, Hood Canal, south Puget Sound, and the multi-inlet basin behind Agate and Rich Passages. Therefore, future study is necessary to further quantify the storm surge level in these high-risk areas through field measurements and refined model simulations.

New York City, New York (NYC), is extremely vulnerable to coastal flooding; thus, verification and improvements in storm surge models are needed in order to protect both life and property. This paper highlights the Stony Brook Storm Surge (SBSS) modeling system. It utilizes surface winds and sea level pressures from the fifth-generation Pennsylvania State University (PSU)-National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) or the Weather Research and Forecasting (WRF) model to drive the Advanced Circulation Model for Coastal Ocean Hydrodynamics (ADCIRC). For this study, the MM5 is utilized at 12-km grid spacing and ADCIRC is run on an unstructured grid down to ~10-m resolution in areas around Long Island and NYC. This paper describes the SBSS and its performance across the NYC region during the 11–12 December 1992 nor'easter and Tropical Storm Floyd on 16–17 September 1999. During the 1992 event, east-northeasterly surface winds of 15–25 m s−1 (30–50 kts) persisted for nearly 24 h, while hurricane-force winds (35–40 m s−1) occurred for a few hours just south of western Long Island. This created a 1.0–1.5-m storm surge around NYC and western Long Island Sound over three tidal cycles. ADCIRC successfully simulated the peak water levels to within ~10%, and it realistically simulated some of the flooding across lower Manhattan. The surface winds for Tropical Storm Floyd were only 5–10 m s−1 weaker than the 1992 event, but no coastal flooding occurred during Floyd, because the storm approached during a low tide. Additional Floyd simulations were completed by shifting the storm's landfall to the spring high tide the previous week, and by doubling the wind speed to mimic a category-1 hurricane. A combination of the spring high tide and a category-1 hurricane scenario during Floyd would have resulted in moderate flooding at several locations around NYC.

The floods associated with the effects of an incoming tropical cyclone have an immense effect in the Philippines, especially with respect to agriculture, industry, livelihood, and public safety. Knowledge of how such storm surge flooding can affect the community is therefore of great importance. In this study, the mechanisms behind Typhoon Haiyan’s anomalous storm surge flooding in 2013, which resulted in more than 6300 casualties and 2.86 billion USD worth of damage in the Philippines, were investigated. The Japan Meteorological Agency (JMA) storm surge model and the FLO-2D flood model were used to simulate Typhoon Haiyan’s storm surge height and the extent of inundation, respectively. The storm surge input data were obtained from JMA typhoon data, and the digital terrain models used were gathered from the airborne interferometric synthetic aperture radar data. The model’s accuracy was also validated using field validation data of the extent of the observed storm surge in affected coastal areas. Topographical analysis of the inundated regions showed the effects of coastal shape, elevation, and position relative to the typhoon’s approach angle on storm surge flow depth and velocity. Storm surge maximum velocity appears to increase as the fluid flows to an increasingly elevated area. Observing fluid velocity in a coastal area with uniform storm surge discharge from all directions also showed that flow velocity tends to increase at the center. Greater flood depths were experienced in areas with lower coastal elevation and not directly located at the coast, compared to higher elevation coastal areas. Greater extents of storm surge flooding are expected in coastal areas that have a concave shape, as fluid is more likely to be dispersed when hitting a convex coast. Extents are likewise observed to be greater in coastal regions that are located perpendicular to the direction of the typhoon. The research also validated the option of using a combination of typhoon and flood models to simulate the inundation flooding caused by extreme weather events.

Development of operational multi-scale storm surge inundated model and application of 2013 typhoon Haiyan

Based on Finite Volume Coastal Ocean Model (FVCOM), this study constructed a numerical model covering the Bohai Sea, Yellow Sea, and East China Sea. National Centers for Environmental Prediction’s Climate Forecast System Reanalysis (NCEP-CFSR) data were used to drive the model to simulate a large storm surge generated by Typhoon Chan-hom. The model was validated by multiple in situ observations of water levels taken at tidal gauge stations. The effects of the nonlinear terms and topography on the modeling of storm surges were then studied. First, the tide–surge interaction during the storm surge process was analyzed. The results show that the tide–surge interaction can suppress the storm surge at the climax of the astronomical tide and benefit the growth of the storm surge at the ebb of the astronomical tide. The tidal constituents M2, S2 and K1 were added to analyze the influences of the amplitudes and periods of tides on the nonlinear reaction. The results indicate that the nonlinear effect will be enhanced by an increased tidal height; additionally, the nonlinear interaction of semidiurnal tides is more significant than that of diurnal tides. The semidiurnal fluctuation that appears near the peak-value time is also attributed to the tide–surge interaction. Moreover, the tide–surge interaction can be influenced by the water depth and position of the area of interest. According to the numerical results, topography has a certain impact on the storm surge: The peak value of the storm surge will decrease with increasing slope. The existence of the Ryukyu Islands reduces the area influenced by the storm surge on the southeast coast of China but expands the high-value storm surge area.

In the storm surge model, the wind drag coefficient Cd is a critical parameter that has a great influence on the forecast of the storm surge level. In the present study, the effect of various wind drag coefficient parameterizations on the storm surge level is investigated in the Bohai Sea, Yellow Sea and East China Sea for Typhoons 7203 and 7303. Firstly, the impacts of initial values of a and b in the linear expression Cd = (a + b × U10) × 10−3 on the pure storm surge model are evaluated based on the data assimilation method. Results indicate that when a and b (i.e., the wind drag coefficients given by Smith, Wu, Geernaert et al. and Mel et al.) are non-zeros, the performance of the model has little difference, and the result from Wu is slightly better. However, they are superior to the performance of the model adopting zero initial values. Then, we discuss the influences of diverse ways of calculating wind drag coefficients, which are inverted by data assimilation method (including both linear and constant Cd) and given in the form of linear formulas, on simulating pure storm surge level. They show that the data assimilation-based coefficients greatly exceed those of the ordinary coefficient formulas. Moreover, the wind drag coefficient in the linear form is a little better than that in constant form when the data assimilation method is used. Finally, the assessment of the impact of astronomical tides on the storm surge level is conducted, and the simulation demonstrates that the storm surge model, which has the combination of four constituents (M2, S2, K1, O1) and wind drag coefficient inverted by the data assimilation method with the linear Cd, exhibits the best performance.

We examine the internal climate variability of a 1000 year long integration of the third version of the Hadley Centre coupled model (HadCM3). The model requires no flux adjustment, needs no spin up procedure prior to coupling and has a stable climate in the global mean. The principal aims are (1) to validate the internal climate variability against observed climate variability, (2) to examine the model for any periodic modes of variability, (3) to use the model estimate of internal climate variability to asses the probability of occurrence of observed trends in climate variables, and (4) to compare HadCM3 with the previous version of the Hadley Centre model, HadCM2. The magnitude and frequency characteristics of the variability of the global mean surface temperature of HadCM3 on annual to decadal time scales is in good agreement with the observations. Observed upward trends in temperature over the last 20 years and longer are inconsistent with the internal variability of the model. The simulated spatial pattern of surface temperature variability is qualitatively similar to that observed, although there is an overestimation of the land temperature variability and regional errors in ocean temperature variability. The model simulates an El Nino Southern Oscillation with an irregular 3–4 year cycle, and with a teleconnection pattern which is much more like the observations than was found in HadCM2. The interdecadal variability of the model ocean in the tropical Pacific, North Pacific and North Atlantic is broadly similar to that in the real world with none of the simulated patterns having any periodic behaviour. HadCM3 simulates an Arctic Oscillation/North Atlantic Oscillation (NAO) in Northern Hemisphere winter which has a spatial pattern consistent with the observations in the Atlantic region, but has too much teleconnection with the North Pacific. The recent observed upward trend in the NAO index is inconsistent with the model internal variability. The variability of the simulated zonal mean atmospheric temperature shows some marked differences to the observed zonal mean temperature variability, although the comparison is confounded by the sparse observational network and its possible contamination by a climate change signal.

Coastal flooding from landfalling tropical cyclones (TCs) is a major hazard with increasing severity in a warming climate and rising seas. It is difficult to predict because of highly complex compound effects of TC induced heavy rainfall, storm surge, and river discharge. This can be further exacerbated by sequential TCs such as Hurricane Irene and Tropical Storm Lee in late August to mid-September 2011, which caused major coastal flooding in the Mid-Atlantic region. This study focuses on better understanding and improving prediction of the compound effects of rain, storm surge, and river discharge using a high-resolution coupled atmosphere-wave-ocean model, namely the Unified Wave INterface–Coupled Model (UWIN-CM) and observations from NDBC buoys, NOAA tide gauges, and USGS estuary sites. UWIN-CM effectively captured the storm track and intensity, surface winds, surface waves, and ocean surface evolution associated with the two storms, compared with the observations. Compound effects of wind, rain, storm surge, and river-stream discharge on coastal flooding are investigated. The storm surge from Hurricane Irene was observed along the coasts Maryland, New Jersey, and New York, Delaware Bay, the lower reaches of the Delaware River, and in lower Chesapeake Bay. Strong onshore wind pushes water upstream, which has the highest compound effects on coastal flooding. Heavy rain and river-stream discharge into the coastal zone contributes mainly to locations upstream away from the open bay water. A new, indirect machine learning method of estimating the spatial extent of coastal flooding using simulated coastal sea surface height is shown.

Estuaries and tidal rivers are highly dynamic transitional zones where marine and riverine processes interact, creating complex hydrodynamic environments. These regions are influenced by natural phenomena such as tidal oscillations, storm surges, and river flow, as well as human activities like water management, hydropower operations, flood protection, and navigation. Effective management of these environments relies on understanding and predicting their hydrodynamic behavior, particularly under extreme conditions such as flooding or abrupt water level changes.This study examines the microtidal Neretva River estuary in Croatia to investigate the interactions between tides, storm surges, and river discharge, and their impacts on water level variability. A modified non-stationary harmonic analysis, based on the NS_Tide model, was developed specifically for microtidal conditions. This model incorporates storm surge and river discharge, improving the predictive accuracy of water levels along the estuary, from tide-dominated downstream sections to discharge-influenced upstream areas. The new version of NS_Tide also allows for a more detailed decomposition of total water levels and tide-surge-river interactions.The results reveal that river discharge is the primary factor influencing water levels at most stations, while the impact of storm surge decreases upstream. Tide-river interactions were observed throughout the study area, whereas tide-surge interactions had minimal effects. The analysis showed that high-frequency discharge fluctuations caused by hydropower operations amplify the S1 tidal constituent in upstream river sections. These fluctuations also modulate the amplitudes of other tidal constituents in estuarine and tidal river regions, highlighting the complex influence of human activities on tidal dynamics.The proposed non-stationary harmonic model proved highly effective for the microtidal Neretva River, capturing the complex interactions between tidal and non-tidal forces under various conditions. Its adaptability to local conditions suggests it could also be applied to mesotidal and macrotidal systems, offering a practical tool for managing estuaries and tidal rivers across diverse environments.

Estimating the geographical flood extent is a key element in impact-based flood forecasting and crucial for countries with long coastlines, and places where storm surges pose a significant risk, such as Denmark. For local flood mitigation measures and climate adaptation strategies, inundation mapping is often performed using physical models. However, in the context of flood forecasting and early warning, these are computationally demanding and therefore may not be able to provide timely forecasts and effective warnings.The Danish Meteorological Institute (DMI) has developed a real-time flood forecasting system for storm surge events in Denmark together with the company SCALGO. This system couples the HBM regional oceanic storm surge forecasting model, developed by DMI, with a rapid inundation mapping, developed by SCALGO, using a 0.4 m resolution Digital Elevation Model (DEM). All inland pixels in the DEM are connected to a coastline pixel through pre-computed hydrological flow paths. The predicted water level from the storm surge model at each coastline pixel is then instantaneously projected inland through the pre-mapped flow paths. This study evaluates the performance of the flood forecasting system on the Oct. 20-21 (2023) storm surge event, with an estimated return period of over 100 years and affecting large parts of southern Denmark (and northern Germany).This flood forecasting system creates a simple inundation mapping based on forecasted sea levels based on a high-resolution DEM modified to account for hydrological flow processes. This real-time flood mapping allows for a visualization of full five-day ocean model forecasts updated continuously at 6h intervals and has been operational for flood warning since October 2022, to supplement DMIs operational ocean forecasting system [1]. The evaluation is performed by comparing the inundation map from the flood forecasting system with media reports, photographs, and other data sources to get an overview of spatial and temporal accuracy and accuracy of the severity of the event. We see a large overlap between areas with forecasted flood risks and actual flooded areas. In some cases, the extent of the flooding differs from the area at risk due to errors in the DEM or local emergency services mitigation strategies.We conclude that the flood forecasting system is useful for identifying coastal areas at risk. While it does not account for detailed physics of flow on land, it is able to reflect the effects of, even very local, geographical variations in sea level that determine the distributions of local-scale flood risk. The current inundation mapping does not currently include the impact of waves, which resulted in larger differences between predictions and actual flooded areas, for easterly-facing locations exposed to large waves. Proposed activities to include the effect of waves will therefore improve the flood forecasting system. [1] Andrée, E., Su, J., Larsen, M. A. D., Madsen, K. S., & Drews, M. (2021). Simulating major storm surge events in a complex coastal region. Ocean Modelling, 162, 101802.

A new marine forecasting system for the New Zealand region has been developed, including global and regional wave models, and regional tide and storm surge models. These form part of an integrated weather-related hazards forecasting capability, which also includes an accurate, data-assimilating high-resolution weather forecasting system and a national-scale flood forecasting system. Predicted variations in sea surface height and depth-averaged current due to tides and storm surge are provided in twice-daily 48-hour regional forecasts. Storm surge is predicted for the New Zealand region using the RiCOM hydrodynamic model on an unstructured grid in depth-averaged mode, using semi-implicit integration in time and a semi-langrangian scheme for advection. The model reproduced the storm surge event resulting from the passage of cyclone “Funa” across New Zealand during January 2008. Root-mean-square (RMS) errors in predicted storm surge height were 4-13 cm. For a second simulated storm, which occurred in Marlborough in late July/early August 2008, the model predicted the storm surge height with RMS errors at Kaikoura and Lyttelton of 7 and 4 cm, respectively. Wave-breaking also contributes to sea levels close to the coast through radiation stresses. The forecast system runs Wavewatch III on nested global and regional structured grids, providing twice-daily 48-hour forecasts. Model developments incorporating wave radiation stresses into the hydrodynamic model forcing are described, and some investigations into the effects of wave forcing on surface setup and coastal inundation are presented. A case study of inundation around the fringes of Hawkes Bay in response to extreme sea levels exacerbated by wave-induced stresses is presented. The case study used the SWAN wave model to provide the near-shore wave characteristics and radiations stresses, and demonstrates the ability of the coupled tide-surge-wave model to simulate and predict coastal flooding on a spatial resolution of a few metres.

Heavy precipitation and storm surges often co-occur and compound together to form sudden and severe flooding events. However, we lack comprehensive observational tools with high temporal and spatial resolution to capture these fast-evolving hazards. Remotely sensed images provide extensive spatial coverage, but they may be limited by adverse weather conditions or platform revisiting schedule. River gauges could provide frequent water height measurement but they are sparsely distributed. Riverine flood and storm surge models, depending on input data quality and calibration process, have various uncertainties. These lead to inevitable temporal and spatial gaps in monitoring inundation dynamics. To fill in the observation gaps, this paper proposes a probabilistic method to estimate daily inundation probability by combining the information from multiple sources, including satellite remote sensing, riverine flood depth, storm surge height, and land cover. Each data source is regarded as a spatial evidence layer, and the weight of evidence is calculated by assessing the association between the evidence presence and inundation occurrence. Within a Bayesian model, the fusion results are daily inundation probability whenever at least one data source is available. The proposed method is applied to estimate daily inundation in Harris, Texas, impacted by Hurricane Harvey. The results agree with the reference water extent, high water mark, and extracted tweet locations. This method could help to further understand flooding as an evolving time-space process and support response and mitigation decisions.

Accurate, real-time forecasting of coastal inundation due to hurricanes and tropical storms is a challenging computational problem requiring high-fidelity forward models of currents and water levels driven by hurricane-force winds. Despite best efforts in computational modeling there will always be uncertainty in storm surge forecasts. In recent years, there has been significant instrumentation located along the coastal United States for the purpose of collecting data—specifically wind, water levels, and wave heights—during these extreme events. This type of data, if available in real time, could be used in a data assimilation framework to improve hurricane storm surge forecasts. In this paper a data assimilation methodology for storm surge forecasting based on the use of ensemble Kalman filters and the advanced circulation (ADCIRC) storm surge model is described. The singular evolutive interpolated Kalman (SEIK) filter has been shown to be effective at producing accurate results for ocean models using small ensemble sizes initialized by an empirical orthogonal function analysis. The SEIK filter is applied to the ADCIRC model to improve storm surge forecasting, particularly in capturing maximum water levels (high water marks) and the timing of the surge. Two test cases of data obtained from hindcast studies of Hurricanes Ike and Katrina are presented. It is shown that a modified SEIK filter with an inflation factor improves the accuracy of coarse-resolution forecasts of storm surge resulting from hurricanes. Furthermore, the SEIK filter requires only modest computational resources to obtain more accurate forecasts of storm surge in a constrained time window where forecasters must interact with emergency responders.

The potential mymargin for improving storm surge simulation is demonstrated by using winds derived from ground-based Global Navigation Satellite System Reflectometry (GNSS-R) that uses BeiDou geostationary Earth orbit (GEO) satellite signals. We reconstruct wind fields by blending GNSS-R coastal winds with the European Center for Median Weather Forecasts (ECMWF) reanalysis product. The reconstructed winds agree well with the weather station data collected at Yangjiang in Guangdong, China. The ECMWF winds and the reconstructed winds are used to force a storm surge model off the Chinese coast during typhoon Utor 2013, respectively. The model storm surges forced by the reconstructed winds agree substantially better with tide-gauge observations than those forced by the ECMWF winds. The average error has been reduced by 30.5% from 24.3 cm with the ECMWF winds to 16.9 cm with the reconstructed winds. This letter suggests that GNSS-R coastal winds can have a positive impact on the accuracy of storm surge hindcasting directly and forecasting indirectly by improving the initial conditions.

Flood protection authorities are not prepared for compound flood risk in estuaries—now and in the face of climate change. Climate projections are rarely downscaled appropriately to assess future changes in storm surge and concurrent river discharge extremes, and their interactions to exacerbate flooding. This is the first time that hourly and fine spatial resolution (7/2.2 km sea level/precipitation), physically consistent, climate projections are used to assess changes in storm surge and river discharge‐driven compound events. The analysis, applied to the Dyfi estuary, western UK, uses 12 downscaled perturbed parameter ensembles for the high‐emissions “RCP8.5” scenario from a global climate model (HadGEM3‐GC3.0). Residual surge and river discharge projections are assessed independently to identify changes in magnitudes and return periods—then combined to identify changing patterns of dependence and timing of compound events. Under RCP8.5 scenario to 2080, river discharge is expected to increase by 28%–29% for 1/20 and 1/50‐year events. Extreme (95th percentile) discharge events are more likely to occur concurrently with extreme surges, and compound events will occur more often, and with a shorter time lag between peak surge and peak discharge—potentially compounding flooding further. The analysis provided forcing conditions representative of future 1 in 20‐year and 1 in 50‐year events used to simulate a potential increased flood footprint in the estuary. The research raises the question of the wider pattern of future compound events throughout the UK, and worldwide, highlighting the critical need for downscaled, coastal and fluvial projections to futureproof flood management strategies.