Scientists analyze record storm surges to help predict future flooding
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WAVES BATTER CHESIL BEACH, UK IN FEBRUARY 2015.
view moreCREDIT: TIM POATE/GERD MASSELINK
Researchers at the University of Southampton have conducted the most detailed spatial analysis to date of storm surges along the coast of the UK and Ireland.
The oceanographers found coastlines in the north of the Irish Sea experience the longest and largest surges, while those occurring around the southwest coast of England have the smallest geographical footprint and last the shortest amount of time.
Across all coastlines investigated, and over a period stretching four decades, the winter seasons of 1989/90 and 2013/14 stood out as having the highest number and most severe storm surge events.
The study findings are published in the June edition of the journal Weather and Climate Extremes and online.
A storm surge is an abnormal rise in seawater level during a storm, measured as above that of the normal tide height for the area. The surge is caused by wind pushing water onshore and is influenced by a storm’s size, speed and where it tracks in relation to the coast. The storm surge footprint is the extent of simultaneous flooding along a stretch of coastline, and influences the damage associated with coastal flooding.
“Storm surges are the most important driver of flooding in many coastal areas,” explains Ivan Haigh a Professor at the University of Southampton and co-author of the study. “If we can understand how the differing characteristics of storms affect surges in many different coastal locations, we can more accurately predict the impact they will have on those localities, how best to counter the effects and how they may increase with climate change. Our research will help improve the accuracy of statistical models used to make these predictions.”
The research, which also involved scientists in Spain, the USA and The Netherlands, examines data on storm surges recorded between 1980 and 2017.
The team identified 270 extreme storm surge events over the study period, based on their duration, footprint size, severity and how frequently a similar event may reoccur. From this they classified eight distinct surge footprint types and linked them to the characteristics of the storms which caused them.
In the course of their research, they found the most extreme surge event was in the winter of 1989/90 – caused by a storm on 26 February 1990 which affected sea levels along the north, east and west coasts. Remembered for extreme flooding in the towns of Towyn and Clwyd in Wales, the event forced five thousand people to be evacuated from their homes and businesses.
The stormiest season in the study period was the winter of 2013/14, which contained the most frequent severe flooding events. In total there were 13 flooding episodes, compared with five in 1989/90. This includes the storm surge of 5 December 2013, which saw some 36 flood warnings in East Anglia and resulted in the loss of properties along the coasts of Norfolk, Suffolk and Essex.
Lead author of the study, Dr Paula Camus of the University of Southampton and Universidad de Cantabria (Spain), comments: “It is crucial we learn lessons from past storm surges in order to help inform our response to future coastal flood risk. Changes to our climate will likely mean more frequent and extreme events, so having accurate data on which to base decisions about infrastructure and emergency response is crucial. We hope our study can better inform the assessment of risk and impacts.”
The researchers acknowledge that their study doesn’t take into account astronomical influence on the height of tides, but say this can be incorporated in the future. They also say their method could be adapted and applied to any coastal region globally.
Ends
Waves topping over sea wall in Blackpool, UK
CREDIT
Sally Brown
The Thames Barrier that protects London from flooding.
CREDIT
John Curtain
Notes to Editors
- The paper ‘Tracking the spatial footprints of extreme storm surges around the coastline of the UK and Ireland’ is published in the June print edition of the journal Weather and Climate Extremes and available to view online at: https://doi.org/10.1016/j.wace.2024.100662.
- Related images with credit lines can be downloaded here: https://safesend.soton.ac.uk/pickup?claimID=QPvVbB9yqKutRMcX&claimPasscode=m6yvkHNJ4ke6m7zS&emailAddr=179280
- For interviews, please contact Peter Franklin, Media Relations, University of Southampton. press@soton.ac.uk 07748 321087
- A timeline of historic coastal flooding events in the UK and other data can be found at: https://www.surgewatch.org/
- More about the School of Ocean and Earth Science at the University of Southampton can be found here: https://www.southampton.ac.uk/about/faculties-schools-departments/school-of-ocean-and-earth-science
- The University of Southampton drives original thinking, turns knowledge into action and impact, and creates solutions to the world’s challenges. We are among the top 100 institutions globally (QS World University Rankings 2024). Our academics are leaders in their fields, forging links with high-profile international businesses and organisations, and inspiring a 22,000-strong community of exceptional students, from over 135 countries worldwide. Through our high-quality education, the University helps students on a journey of discovery to realise their potential and join our global network of over 200,000 alumni. www.southampton.ac.uk
JOURNAL
Weather and Climate Extremes
METHOD OF RESEARCH
Data/statistical analysis
SUBJECT OF RESEARCH
People
ARTICLE TITLE
Tracking the spatial footprints of extreme storm surges around the coastline of the UK and Ireland
Model combines physical parameters and machine learning to predict storm tides
Developed by researchers at the University of São Paulo in Brazil, the system used the port city of Santos as a sample space, and could enhance the efficiency of civil defense activities in the context of extreme weather events.
FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO
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THE STUDY COMBINED PHYSICAL AND NUMERICAL MODELS, WORKING WITH DATA IN DIFFERENT FORMATS VIA A MULTIMODAL ARCHITECTURE
view moreCREDIT: TÂNIA REGO/AGÊNCIA BRASIL
Predicting extreme events is essential to the preparation and protection of vulnerable regions, especially at a time of climate change. The city of Santos on the coast of São Paulo state (Brazil) is Latin America’s largest port and has been the focus for significant case studies, not least because of the storm surges that threaten its infrastructure and the local ecosystems.
An article reporting the results of a study that focused on a critical part of Santos and used advanced machine learning tools to optimize existing extreme event prediction systems has recently been published in Proceedings of the AAAI Conference on Artificial Intelligence. It mobilized a large number of researchers and was coordinated by Anna Helena Reali Costa, full professor at the University of São Paulo’s Engineering School (POLI-USP). The first author is Marcel Barros, a researcher in POLI-USP’s Department of Computer Engineering and Digital Systems.
The models used to predict sea surface heights, high tides, wave heights and so on are based on differential equations comprising temporal and spatial information such as astronomic tide (determined by the relative positions of the Sun, Moon and Earth), wind regime, current velocity and salinity, among many others.
These models are successful in several areas but they are complex and depend on a number of simplifications and hypotheses. Moreover, new measurements and other data sources cannot always be integrated into them to make forecasts more reliable.
Although modelers are increasingly using machine learning methods capable of identifying patterns in data and extrapolating to new situations, a great many examples are required to train the algorithms that perform complex tasks such as those involved in weather forecasting and storm tide prediction.
“Our study combined the two worlds to develop a model based on machine learning that uses physical models as a starting point but refines them by adding measured data. This research field is known as physics-informed machine learning, or PIML,” Barros explained.
Harmonization of these two sources of information is fundamental to develop more precise and accurate forecasts. However, the use of sensor data faces significant technical challenges, owing especially to its irregular nature and problems such as missing data, temporal displacements, and variations in sampling frequencies. Sensors that fail can take days to be brought back online, but the mechanisms for predicting storm tides must be capable of operating continuously without the missing data.
“To address situations with highly irregular data, we developed an innovative technique to represent the passing of time in neural networks. This representation lets the model be told the position and size of the missing data windows, so that it considers them in its predictions of tide and wave heights,” Barros said.
The innovation permits better modeling of complex natural phenomena and can also be used to model other phenomena that involve irregular time series, such as health data, sensor networks in manufacturing, or financial indicators.
“Furthermore, our model combines different kinds of neural networks so as to integrate multimodal data, such as satellite images, tables and forecasts from numerical models, with possible future integration of other types of data, such as text and audio. This approach is an important step toward more robust and adaptable forecasting systems that can handle the complexity and variability of the data associated with extreme weather events,” Reali Costa said.
The model has three key virtues, she added: it combines physical and numerical models; it represents time in neural networks in a new way; and it works with data in different formats by means of multimodal architecture. “The study offers a methodology that can improve the accuracy of predictions of extreme events, such as storm tides in Santos. At the same time, it highlights the challenges and potential solutions for the integration of physical models and sensor data in complex contexts,” she said.
The study was supported by FAPESP via the Center for Artificial Intelligence (C4AI), an Engineering Research Center (ERC) set up by FAPESP and IBM Brazil, and hosted by POLI-USP.
About São Paulo Research Foundation (FAPESP)
The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by awarding scholarships, fellowships and grants to investigators linked with higher education and research institutions in the State of São Paulo, Brazil. FAPESP is aware that the very best research can only be done by working with the best researchers internationally. Therefore, it has established partnerships with funding agencies, higher education, private companies, and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration. You can learn more about FAPESP at www.fapesp.br/en and visit FAPESP news agency at www.agencia.fapesp.br/en to keep updated with the latest scientific breakthroughs FAPESP helps achieve through its many programs, awards and research centers. You may also subscribe to FAPESP news agency at http://agencia.fapesp.br/subscribe.
JOURNAL
Proceedings of the AAAI Conference on Artificial Intelligence
ARTICLE TITLE
Early Detection of Extreme Storm Tide Events Using Multimodal Data Processing
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