Storm surge frequency reduction in Venice under climate change

Increased tidal levels and storm surges related to climate change are projected to result in extremely adverse effects on coastal regions. Predictions of such extreme and small-scale events, however, are exceedingly challenging, even for relatively short time horizons. Here we use data from observations, ERA-40 reanalysis, climate scenario simulations, and a simple feature model to find that the frequency of extreme storm surge events affecting Venice is projected to decrease by about 30% by the end of the twenty-first century. In addition, through a trend assessment based on tidal observations we found a reduction in extreme tidal levels. Extrapolating the current +17 cm/century sea level trend, our results suggest that the frequency of extreme tides in Venice might largely remain unaltered under the projected twenty-first century climate simulations.

Future storm surge impacts on insurable losses for the North Sea region

The influence of climate change on storm surges including increased mean sea level change and the associated insurable losses are assessed for the North Sea basin. In doing so, the newly developed approach couples a dynamical storm surge model with a loss model. The key element of the approach is the generation of a probabilistic storm surge event set. Together with parametrizations of the inland propagation and the coastal protection failure probability this enables the estimation of annual expected losses. The sensitivity to the parametrizations is rather weak except when the assumption of high level of increased mean sea level change is made. Applying this approach to future scenarios shows a substantial increase of insurable losses with respect to the present day. Superimposing different mean sea level changes shows a nonlinear behavior at the country level, as the future storm surge changes are higher for Germany and Denmark. Thus, the study exhibits the necessity to assess the socio-economic impacts of coastal floods by combining the expected sea level rise with storm surge projections.

Reanalysis of the Hamburg Storm Surge of 1962

In February 1962, Hamburg experienced its most catastrophic storm surge event of the 20th century. This paper analyses the event using the Twentieth Century Reanalysis (20CR) dataset. Responsible for the major flood was a strong low pressure system centred over Scandinavia that was associated with strong north-westerly winds towards the German North Sea coast – the ideal storm surge situation for the Elbe estuary. A comparison of the 20CR dataset with observational data proves the applicability of the reanalysis data for this extreme event.

The Storm Surge Event of the Netherlands in 1953

A disastrous storm surge hit the coast of the Netherlands on 31 January and 1 February 1953. We examine the meteorological situation during this event using the Twentieth Century Reanalysis (20CR) data set. We find a strong pressure gradient between Ireland and northern Germany accompanied by strong north-westerly winds over the North Sea. Storm driven sea level rise combined with spring tide contributed to this extreme event. The state of the atmosphere in 20CR during this extreme event is in good agreement with historical observational data

The 1872 Baltic Sea storm surge

On 13 November 1872, the Baltic Sea coast from Denmark to Pomerania was devastated by an extreme storm surge caused by high winds. This is still the strongest surge on record, and understanding its development can contribute to improved risk assessment and protection. In this paper we trace this event in sea-level pressure and wind data from the “Twentieth Century Reanalysis” (20CR) and compare the results with other observation-based data sources. The analysis shows that, in the ensemble mean of 20CR, the general development is qualitatively well depicted, but with much reduced strength compared to other data sets. The same is true when selecting the ensemble member with maximum wind speeds.

Probability inundation map for a scenario combining storm surge and sea-level rise

Coastal managers require reliable spatial data on the extent and timing of potential coastal inundation, particularly in a changing climate. Most sea level rise (SLR) vulnerability assessments are undertaken using the easily implemented bathtub approach, where areas adjacent to the sea and below a given elevation are mapped using a deterministic line dividing potentially inundated from dry areas. This method only requires elevation data usually in the form of a digital elevation model (DEM). However, inherent errors in the DEM and spatial analysis of the bathtub model propagate into the inundation mapping. The aim of this study was to assess the impacts of spatially variable and spatially correlated elevation errors in high-spatial resolution DEMs for mapping coastal inundation. Elevation errors were best modelled using regression-kriging. This geostatistical model takes the spatial correlation in elevation errors into account, which has a significant impact on analyses that include spatial interactions, such as inundation modelling. The spatial variability of elevation errors was partially explained by land cover and terrain variables. Elevation errors were simulated using sequential Gaussian simulation, a Monte Carlo probabilistic approach. 1,000 error simulations were added to the original DEM and reclassified using a hydrologically correct bathtub method. The probability of inundation to a scenario combining a 1 in 100 year storm event over a 1 m SLR was calculated by counting the proportion of times from the 1,000 simulations that a location was inundated. This probabilistic approach can be used in a risk-aversive decision making process by planning for scenarios with different probabilities of occurrence. For example, results showed that when considering a 1% probability exceedance, the inundated area was approximately 11% larger than mapped using the deterministic bathtub approach. The probabilistic approach provides visually intuitive maps that convey uncertainties inherent to spatial data and analysis.

Evaluation of numerical storm surge models.

"December 1980."

Hurricane Gilbert storm surge data /

At head of title: Coastal Field Data Collection Program.

Storm surge simulation in transformed coordinates /

"November 1976."

Storm surge on the open coast : fundamentals and simplified prediction /

"May 1971."

Verification study of a bathystrophic storm surge model /

"May 1975."

Tree Mortality following Prescribed Fire and a Storm Surge Event in Slash Pine (Pinus elliottii var. densa) Forests in the Florida Keys, USA

In fire-dependent forests, managers are interested in predicting the consequences of prescribed burning on postfire tree mortality. We examined the effects of prescribed fire on tree mortality in Florida Keys pine forests, using a factorial design with understory type, season, and year of burn as factors. We also used logistic regression to model the effects of burn season, fire severity, and tree dimensions on individual tree mortality. Despite limited statistical power due to problems in carrying out the full suite of planned experimental burns, associations with tree and fire variables were observed. Post-fire pine tree mortality was negatively correlated with tree size and positively correlated with char height and percent crown scorch. Unlike post-fire mortality, tree mortality associated with storm surge from Hurricane Wilma was greater in the large size classes. Due to their influence on population structure and fuel dynamics, the size-selective mortality patterns following fire and storm surge have practical importance for using fire as a management tool in Florida Keys pinelands in the future, particularly when the threats to their continued existence from tropical storms and sea level rise are expected to increase.

Surface wave and storm surge prediction during Hurricane Sandy

Hurricane Sandy was the largest storm on historical record in the Atlantic Ocean basin with extensive coastal damage caused by large waves and high storm surge. The primary objectives of this thesis are to compare and evaluate three different spatially-varying surface wind fields of Hurricane Sandy to investigate the impact of the differences between the complex wind fields on predictions of the sea surface evolution, and to evaluate the impact of the storm on the hydrodynamics in Great South Bay (GSB) and the discharge of ocean water into the back-barrier bay from overwash over Fire Island. Three different spatially-varying surface wind fields were evaluated and compared to wind observations, including the parametric Holland (1980) model (H80), the parametric Generalized Asymmetric Holland Model (GAHM), and results from the WeatherFlow Regional Atmospheric Modelling System (WRAMS). The winds were used to drive the coupled Delft3D-SWAN hydrodynamic and ocean wave models on a regional grid. The results indicate that the WRAMS wind field produces wave model predictions in the best agreement with significant wave height observations, followed by the GAHM and H80 wind fields and that a regional atmospheric wind model is best for hindcasting hurricane waves and water levels when detailed observations are available, while a parametric vortex model is best for forecasting hurricane sea surface conditions. Using a series of four connected Delft3D-SWAN grids to achieve finer resolution over Fire Island and GSB, a higher resolution WRAMS was used to predict waves and storm surge. The results indicate that strong local winds have the largest influence on water level fluctuations in GSB. Three numerical solutions were conducted with varying extents of barrier island overwash. The simulations allowing for minor and major overwash indicated good agreement with observations in the east end of GSB and suggest that island overwash provided a significant contribution of ocean water to GSB during the storm. Limiting the overwash in the numerical model directly impacts the total discharge into GSB from the ocean through existing inlets. The results of this study indicate that barrier island overwash had a significant impact on the water levels in eastern GSB.