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Case Study – The 2011 Japan Earthquake

Cambridge iGCSE Geography > The Natural Environment > Earthquakes and Volcanoes > Case Study – The 2011 Japan Earthquake

Background Information

Location : The earthquake struck 250 miles off the northeastern coast of Japan’s Honshu Island at 2:46 pm (local time) on March 11, 2011.

Japan 2011 Earthquake map

Magnitude : It measured 9.1 on the Moment Magnitude scale, making it one of the most powerful earthquakes ever recorded.

Japan is a highly developed country with advanced infrastructure, technology, and a robust economy. The nation has a high GDP, an efficient healthcare system, and extensive education. However, it’s also located in the Pacific Ring of Fire, making it prone to earthquakes.

What caused the 2011 Japan earthquake?

Japan is located on the eastern edge of the Eurasian Plate. The Eurasian plate, which is continental, is subducted by the Pacific Plate, an oceanic plate forming a subduction zone to the east of Japan. This type of plate margin is known as a destructive plate margin . The process of subduction is not smooth. Friction causes the Pacific Plate to stick. Pressure builds and is released as an earthquake.

Friction has built up over time, and when released, this caused a massive ‘megathrust’ earthquake. The enormous tension released as the plates shifted caused the seafloor to uplift, triggering the earthquake and subsequent tsunami .

The amount of energy released in this single earthquake was 600 million times the energy of the Hiroshima nuclear bomb.

Scientists drilled into the subduction zone soon after the earthquake and discovered a thin, slippery clay layer lining the fault. The researchers think this clay layer allowed the two plates to slide an incredible distance, some 164 feet (50 metres), facilitating the enormous earthquake and tsunami.

The earthquake occurred at a relatively shallow depth of 20 miles below the surface of the Pacific Ocean. This, combined with the high magnitude, caused a tsunami (find out more about  how a tsunami is formed  on the BBC website).

What were the primary effects of the 2011 Japan earthquake?

• Ground Shaking : Extensive damage to buildings and infrastructure.
• Landfall: Some coastal areas experienced land subsidence as the earthquake dropped the beachfront in some places by more than 50 cm.

What were the secondary effects of the 2011 Japan earthquake?

• Tsunami : A giant tsunami wave resulted in widespread destruction along the coast.
• Fatalities : Around 16,000 deaths were reported, mainly resulting from the tsunami.
• Injuries : 26,152 were injured, mainly as a result of the tsunami.
• Nuclear Crisis : The Fukushima Daiichi nuclear power plant was damaged, leading to radiation leaks.
• Economic Loss : Estimated at over $235 billion.
• Displacement : Around 340,000 people were displaced from their homes.
• Damage: The tsunami destroyed or damaged 332,395 buildings, 2,126 roads, 56 bridges, and 26 railways. Three hundred hospitals were damaged, and 11 were destroyed.
• Environmental Damage : Coastal ecosystems were heavily impacted.
• Blackouts: Over 4.4 million households were left without electricity in North-East Japan.
• Transport: Rural areas remained isolated for a long time because the tsunami destroyed major roads and local trains and buses. Sections of the Tohoku Expressway were damaged. Railway lines were damaged, and some trains were derailed.

What were the immediate responses to the 2011 Japan earthquake?

Tsunami Warnings and Prediction :

• The Japan Meteorological Agency issued tsunami warnings three minutes after the earthquake.
• Scientists predicted where the tsunami would hit using modelling and forecasting technology.

Search and Rescue Operations:

• Rescue workers and 100,000 members of the Japan Self-Defence Force were dispatched within hours.
• Some individuals were rescued from beneath rubble with the aid of sniffer dogs.

Radiation Protection Measures:

• The government declared a 20 km evacuation zone around the Fukushima nuclear power plant.
• Evacuees from the area around the nuclear power plant were given iodine tablets to reduce radiation poisoning risk.

International Assistance:

• Japan received help from the US military.
• Search and rescue teams from New Zealand, India, South Korea, China, and Australia were sent.

Access and Evacuation :

• Access was restricted to affected areas due to debris and mud, complicating immediate support.
• Hundreds of thousands were evacuated to temporary shelters or relocated.

Health Monitoring :

• Those near the Fukushima Daiichi nuclear meltdown had radiation levels checked and their health monitored.
• Measures were taken to ensure individuals did not receive dangerous exposure to radiation.

What were the long-term responses to the 2011 Japan earthquake?

Reconstruction Policy and Budget:

• Establishment of the Reconstruction Policy Council in April 2011.
• Approval of a budget of 23 trillion yen (£190 billion) for recovery over ten years.
• Creation of ‘Special Zones for Reconstruction’ to attract investment in the Tohoku region.

Coastal Protection Measures:

• Implementing coastal protection policies like seawalls and breakwaters designed for a 150-year recurrence interval of tsunamis.

Legislation for Tsunami-Resilient Communities:

• Enactment of the ‘Act on the Development of Tsunami-resilient Communities’ in December 2011.
• Emphasis on human life, combining infrastructure development with measures for the largest class tsunami.

Economic Challenges and Recovery:

• Japan’s economy wiped 5–10% off the value of stock markets post-earthquake.
• Long-term response priority: rebuild infrastructure, restore and improve the economy’s health.

Transportation and Infrastructure Repair:

• Repair and reopening of 375 km of the Tohoku Expressway by the 24th of March 2011.
• Restoration of the runway at Sendai Airport by the 29th of March, a joint effort by the Japanese Defence Force and the US Army.

Utility Reconstruction:

• Energy, water supply, and telecommunications infrastructure reconstruction.
• As of November 2011: 96% of electricity, 98% of water, and 99% of the landline network had been restored.

How does Japan prepare for earthquakes, and what was its impact?

Japan has a comprehensive earthquake preparedness program, including:

• Strict Building Codes : Buildings are constructed to withstand seismic activity.
• Early Warning Systems : Advanced technology provides early warnings to citizens.
• Education and Drills : Regular earthquake drills in schools, offices, and public places.

Impact of the 2011 Earthquake

The extensive preparation in Japan likely saved lives and reduced damage during the 2011 earthquake. However, the unprecedented magnitude of the event still led to significant destruction, particularly with the tsunami and nuclear crisis.

The 2011 Japan earthquake illustrates the complexity of managing natural disasters in even the most developed and prepared nations. The event prompted further refinements in disaster preparedness and response in Japan and globally, highlighting the need for continuous assessment and adaptation to seismic risks.

The 2011 earthquake occurred off Japan’s Honshu Island, measuring 9.1 on the Moment Magnitude scale, one of the strongest ever recorded.

Triggered by a ‘megathrust’ in a destructive plate margin, the Pacific Plate subducted the Eurasian Plate, releasing energy equivalent to 600 million Hiroshima bombs.

Primary effects included extensive ground shaking and significant land subsidence in coastal areas.

Secondary effects included a massive tsunami, around 16,000 deaths, 26,152 injuries, a nuclear crisis at Fukushima, over $235 billion in economic loss, displacement of 340,000 people, and widespread damage to infrastructure and the environment.

Immediate responses included rapid tsunami warnings, extensive search and rescue operations, radiation protection measures, international assistance, and evacuation strategies.

Long-term responses focused on reconstruction policies, coastal protection, tsunami-resilient community development, economic recovery, and transportation and utility restoration.

Japan’s extensive earthquake preparedness, including strict building codes and early warning systems, likely reduced damage, but the magnitude still caused significant destruction.

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Learning from Megadisasters: A Decade of Lessons from the Great East Japan Earthquake

March 11, 2021 Tokyo, Japan

Authors: Shoko Takemoto,  Naho Shibuya, and Keiko Sakoda

Today marks the ten-year anniversary of the Great East Japan Earthquake (GEJE), a mega-disaster that marked Japan and the world with its unprecedented scale of destruction. This feature story commemorates the disaster by reflecting on what it has taught us over the past decade in regards to infrastructure resilience, risk identification, reduction, and preparedness, and disaster risk finance.  Since GEJE, the World Bank in partnership with the Government of Japan, especially through the Japan-World Bank Program on Mainstreaming Disaster Risk Management in Developing Countries has been working with Japanese and global partners to understand impact, response, and recovery from this megadisaster to identify larger lessons for disaster risk management (DRM).

Among the numerous lessons learned over the past decade of GEJE reconstruction and analysis, we highlight three common themes that have emerged repeatedly through the examples of good practices gathered across various sectors.  First is the importance of planning. Even though disasters will always be unexpected, if not unprecedented, planning for disasters has benefits both before and after they occur. Second is that resilience is strengthened when it is shared .  After a decade since GEJE, to strengthen the resilience of infrastructure, preparedness, and finance for the next disaster, throughout Japan national and local governments, infrastructure developers and operators, businesses and industries, communities and households are building back better systems by prearranging mechanisms for risk reduction, response and continuity through collaboration and mutual support.  Third is that resilience is an iterative process .  Many adaptations were made to the policy and regulatory frameworks after the GEJE. Many past disasters show that resilience is an interactive process that needs to be adjusted and sustained over time, especially before a disaster strikes.

As the world is increasingly tested to respond and rebuild from unexpected impacts of extreme weather events and the COVID-19 pandemic, we highlight some of these efforts that may have relevance for countries around the world seeking to improve their preparedness for disaster events.

Introduction: The Triple Disaster, Response and Recovery

On March 11th, 2011 a Magnitude 9.0 earthquake struck off the northeast coast of Japan, near the Tohoku region. The force of the earthquake sent a tsunami rushing towards the Tohoku coastline, a black wall of water which wiped away entire towns and villages. Sea walls were overrun. 20,000 lives were lost. The scale of destruction to housing, infrastructure, industry and agriculture was extreme in Fukushima, Iwate, and Miyagi prefectures. In addition to the hundreds of thousands who lost their homes, the earthquake and tsunami contributed to an accident at the Fukushima Daiichi Nuclear Power Plant, requiring additional mass evacuations. The impacts not only shook Japan’s society and economy as a whole, but also had ripple effects in global supply chains. In the 21st century, a disaster of this scale is a global phenomenon.

The severity and complexity of the cascading disasters was not anticipated. The events during and following the Great East Japan Earthquake (GEJE) showed just how ruinous and complex a low-probability, high-impact disaster can be. However, although the impacts of the triple-disaster were devastating, Japan’s legacy of DRM likely reduced losses. Japan’s structural investments in warning systems and infrastructure were effective in many cases, and preparedness training helped many act and evacuate quickly. The large spatial impact of the disaster, and the region’s largely rural and elderly population, posed additional challenges for response and recovery.

Ten years after the megadisaster, the region is beginning to return to a sense of normalcy, even if many places look quite different. After years in rapidly-implemented temporary prefabricated housing, most people have moved into permanent homes, including 30,000 new units of public housing . Damaged infrastructure has been also restored or is nearing completion in the region, including rail lines, roads, and seawalls.

In 2014, three years after GEJE, The World Bank published Learning from Megadisasters: Lessons from the Great East Japan Earthquake . Edited by Federica Ranghieri and Mikio Ishiwatari , the volume brought together dozens of experts ranging from seismic engineers to urban planners, who analyzed what happened on March 11, 2011 and the following days, months, and years; compiling lessons for other countries in 36 comprehensive Knowledge Notes . This extensive research effort identified a number of key learnings in multiple sectors, and emphasized the importance of both structural and non-structural measures, as well as identifying effective strategies both pre- and post-disaster. The report highlighted four central lessons after this intensive study of the GEJE disaster, response, and initial recovery:

1) A holistic, rather than single-sector approach to DRM improves preparedness for complex disasters; 2) Investing in prevention is important, but is not a substitute for preparedness; 3) Each disaster is an opportunity to learn and adapt; 4) Effective DRM requires bringing together diverse stakeholders, including various levels of government, community and nonprofit actors, and the private sector.

Although these lessons are learned specifically from the GEJE, the report also focuses on learnings with broader applicability.

Over recent years, the Japan-World Bank Program on Mainstreaming DRM in Developing Countries has furthered the work of the Learning from Megadisasters report, continuing to gather, analyze and share the knowledge and lessons learned from GEJE, together with past disaster experiences, to enhance the resilience of next generation development investments around the world. Ten years on from the GEJE, we take a moment to revisit the lessons gathered, and reflect on how they may continue to be relevant in the next decade, in a world faced with both seismic disasters and other emergent hazards such as pandemics and climate change.

Through synthesizing a decade of research on the GEJE and accumulation of the lessons from the past disaster experience, this story highlights three key strategies which recurred across many of the cases we studied. They are:

1) the importance of planning for disasters before they strike, 2) DRM cannot be addressed by either the public or private sector alone but enabled only when it is shared among many stakeholders , 3) institutionalize the culture of continuous enhancement of the resilience .

For example, business continuity plans, or BCPs, can help both public and private organizations minimize damages and disruptions . BCPs are documents prepared in advance which provide guidance on how to respond to a disruption and resume the delivery of products and services. Additionally, the creation of pre-arranged agreements among independent public and/or private organizations can help share essential responsibilities and information both before and after a disaster . This might include agreements with private firms to repair public infrastructures, among private firms to share the costs of mitigation infrastructure, or among municipalities to share rapid response teams and other resources. These three approaches recur throughout the more specific lessons and strategies identified in the following section, which is organized along the three areas of disaster risk management: resilient infrastructure; risk identification, reduction and preparednes s ; and disaster risk finance and insurance.

Lessons from the Megadisaster

Resilient Infrastructure

The GEJE had severe impacts on critical ‘lifelines’—infrastructures and facilities that provide essential services such as transportation, communication, sanitation, education, and medical care. Impacts of megadisasters include not only damages to assets (direct impacts), but also disruptions of key services, and the resulting social and economic effects (indirect impacts). For example, the GEJE caused a water supply disruption for up to 500,000 people in Sendai city, as well as completely submerging the city’s water treatment plant. [i] Lack of access to water and sanitation had a ripple effect on public health and other emergency services, impacting response and recovery. Smart investment in infrastructure resilience can help minimize both direct and indirect impacts, reducing lifeline disruptions. The 2019 report Lifelines: The Resilient Infrastructure Opportunity found through a global study that every dollar invested in the resilience of lifelines had a $4 benefit in the long run.

In the case of water infrastructure , the World Bank report Resilient Water Supply and Sanitation Services: The Case of Japan documents how Sendai City learned from the disaster to improve the resilience of these infrastructures. [ii] Steps included retrofitting existing systems with seismic resilience upgrades, enhancing business continuity planning for sanitation systems, and creating a geographic information system (GIS)-based asset management system that allows for quick identification and repair of damaged pipes and other assets. During the GEJE, damages and disruptions to water delivery services were minimized through existing programs, including mutual aid agreements with other water supply utility operators. Through these agreements, the Sendai City Waterworks Bureau received support from more than 60 water utilities to provide emergency water supplies. Policies which promote structural resilience strategies were also essential to preserving water and sanitation services. After the 1995 Great Hanshin Awaji Earthquake (GHAE), Japanese utilities invested in earthquake resistant piping in water supply and sanitation systems. The commonly used earthquake-resistant ductile iron pipe (ERDIP) has not shown any damage from major earthquakes including the 2011 GEJE and the 2016 Kumamoto earthquake. [iii] Changes were also made to internal policies after the GEJE based on the challenges faced, such as decentralizing emergency decision-making and providing training for local communities to set up emergency water supplies without utility workers with the goal of speeding up recovery efforts. [iv]

Redundancy is another structural strategy that contributed to resilience during and after GEJE. In Sendai City, redundancy and seismic reinforcement in water supply infrastructure allowed the utility to continue to operate pipelines that were not physically damaged in the earthquake. [v] The Lifelines report describes how in the context of telecommunications infrastructure , the redundancy created through a diversity of routes in Japan’s submarine internet cable system  limited disruptions to national connectivity during the megadisaster. [vi] However, the report emphasizes that redundancy must be calibrated to the needs and resources of a particular context. For private firms, redundancy and backups for critical infrastructure can be achieved through collaboration; after the GEJE, firms are increasingly collaborating to defray the costs of these investments. [vii]

The GEJE also illustrated the importance of planning for transportation resilience . A Japan Case Study Report on Road Geohazard Risk Management shows the role that both national policy and public-private agreements can play. In response to the GEJE, Japan’s central disaster legislation, the DCBA (Disaster Countermeasures Basic Act) was amended in 2012, with particular focus on the need to reopen roads for emergency response. Quick road repairs were made possible after the GEJE in part due to the Ministry of Land, Infrastructure, Transport and Tourism (MLIT)’s emergency action plans, the swift action of the rapid response agency Technical Emergency Control Force (TEC-FORCE), and prearranged agreements with private construction companies for emergency recovery work. [viii] During the GEJE, roads were used as evacuation sites and were shown effective in controlling the spread of floods. After the disaster, public-private partnerships (PPPs) were also made to accommodate the use of expressway embankments as tsunami evacuation sites. As research on Resilient Infrastructure PPPs highlights, clear definitions of roles and responsibilities are essential to effective arrangements between the government and private companies. In Japan, lessons from the GEJE and other earthquakes have led to a refinement of disaster definitions, such as numerical standards for triggering force majeure provisions of infrastructure PPP contracts. In Sendai City, clarifying the post-disaster responsibilities of public and private actors across various sectors sped up the response process. [ix] This experience was built upon after the disaster, when Miyagi prefecture conferred operation of the Sendai International Airport   to a private consortium through a concession scheme which included refined force majeure definitions. In the context of a hazard-prone region, the agreement clearly defines disaster-related roles and responsibilities as well as relevant triggering events. [x]

Partnerships for creating backup systems that have value in non-disaster times have also proved effective in the aftermath of the GEJE. As described in Resilient Industries in Japan , Toyota’s automotive plant in Ohira village, Miyagi Prefecture lost power for two weeks following GEJE. To avoid such losses in the future, companies in the industrial park sought to secure energy during power outages and shortages by building the F-Grid, their own mini-grid system with a comprehensive energy management system. The F-Grid project is a collaboration of 10 companies and organizations in the Ohira Industrial Park. As a system used exclusively for backup energy would be costly, the system is also used to improve energy efficiency in the park during normal times. The project was supported by funding from Japan’s “Smart Communities'' program. [xi] In 2016, F-grid achieved a 24 percent increase in energy efficiency and a 31 percent reduction in carbon dioxide emissions compared to similarly sized parks. [xii]

Schools are also critical infrastructures, for their education and community roles, and also because they are commonly used as evacuation centers. Japan has updated seismic resilience standards for schools over time, integrating measures against different risks and vulnerabilities revealed after each disaster, as documented in the report Making Schools Resilient at Scale . After the 2011 GEJE, there was very little earthquake-related damage; rather, most damage was caused by the tsunami. However, in some cases damages to nonstructural elements like suspending ceilings in school gymnasiums limited the possibility of using these spaces after the disaster. After the disaster, a major update was made to the policies on the safety of nonstructural elements in schools, given the need for higher resilience standards for their function as post-disaster evacuation centers [xiii] .

Similarly, for building regulations , standards and professional training modules were updated taking the lessons learned from GEJE. The Converting Disaster Experience into a Safer Built Environment: The Case of Japan report highlights that, legal framework like, The Building Standard Law/Seismic Retrofitting Promotion Law, was amended further enhance the structural resilience of the built environment, including strengthening structural integrity, improving the efficiency of design review process, as well as mandating seismic diagnosis of large public buildings. Since the establishment of the legal and regulatory framework for building safety in early 1900, Japan continued incremental effort to create enabling environment for owners, designers, builders and building officials to make the built environment safer together.

Cultural heritage also plays an important role in creating healthy communities, and the loss or damage of these items can scar the cohesion and identity of a community. The report Resilient Cultural Heritage: Learning from the Japanese Experience shows how the GEJE highlighted the importance of investing in the resilience of cultural properties, such as through restoration budgets and response teams, which enabled the relocation of at-risk items and restoration of properties during and after the GEJE. After the megadisaster, the volunteer organization Shiryō-Net was formed to help rescue and preserve heritage properties, and this network has now spread across Japan. [xiv] Engaging both volunteer and government organizations in heritage preservation can allow for a more wide-ranging response. Cultural properties can play a role in healing communities wrought by disasters: in Ishinomaki City, the restoration of a historic storehouse served as a symbol of reconstruction [xv] , while elsewhere repair of cultural heritage sites and the celebration of cultural festivals served a stimulant for recovery. [xvi] Cultural heritage also played a preventative role during and after the disaster by embedding the experience of prior disasters in the built environment. Stone monuments which marked the extent of historic tsunamis served as guides for some residents, who fled uphill past the stones and escaped the dangerous waters. [xvii] This suggests a potential role for cultural heritage in instructing future generations about historic hazards.

These examples of lessons from the GEJE highlight how investing in resilient infrastructure is essential, but must also be done smartly, with emphasis on planning, design, and maintenance. Focusing on both minimizing disaster impacts and putting processes in place to facilitate speedy infrastructure restoration can reduce both direct and indirect impacts of megadisasters.  Over the decade since GEJE, many examples and experiences on how to better invest in resilient infrastructure, plan for service continuity and quick response, and catalyze strategic partnerships across diverse groups are emerging from Japan.

Risk Identification, Reduction, and Preparedness

Ten years after the GEJE, a number of lessons have emerged as important in identifying, reducing, and preparing for disaster risks. Given the unprecedented nature of the GEJE, it is important to be prepared for both known and uncertain risks. Information and communication technology (ICT) can play a role in improving risk identification and making evidence-based decisions for disaster risk reduction and preparedness. Communicating these risks to communities, in a way people can take appropriate mitigation action, is a key . These processes also need to be inclusive , involving diverse stakeholders--including women, elders , and the private sector--that need to be engaged and empowered to understand, reduce, and prepare for disasters. Finally, resilience is never complete . Rather, as the adaptations made by Japan after the GEJE and many past disasters show, resilience is a continuous process that needs to be adjusted and sustained over time, especially in times before a disaster strikes.

Although DRM is central in Japan, the scale of the 2011 triple disaster dramatically exceeded expectations. After the GEJE, as Chapter 32 of Learning From Megadisasters highlights, the potential of low-probability, high-impact events led Japan to focus on both structural and nonstructural disaster risk management measures. [xviii] Mitigation and preparedness strategies can be designed to be effective for both predicted and uncertain risks. Planning for a multihazard context, rather than only individual hazards, can help countries act quickly even when the unimaginable occurs. Identifying, preparing for, and reducing disaster risks all play a role in this process.

The GEJE highlighted the important role ICT can play in both understanding risk and making evidence-based decisions for risk identification, reduction, and preparedness. As documented in the World Bank report Information and Communication Technology for Disaster Risk Management in Japan , at the time of the GEJE, Japan had implemented various ICT systems for disaster response and recovery, and the disaster tested the effectiveness of these systems. During the GEJE, Japan’s “Earthquake Early Warning System” (EEWS) issued a series of warnings. Through the detection of initial seismic waves, EEWS can provide a warning of a few seconds or minutes, allowing quick action by individuals and organizations. Japan Railways’ “Urgent Earthquake Detection and Alarm System” (UrEDAS) automatically activated emergency brakes of 27 Shinkansen train lines , successfully bringing all trains to a safe stop. After the disaster, Japan expanded emergency alert delivery systems. [xix]

The World Bank’s study on Preparedness Maps shows how seismic preparedness maps are used in Japan to communicate location specific primary and secondary hazards from earthquakes, promoting preparedness at the community and household level. Preparedness maps are regularly updated after disaster events, and since 2011 Japan has promoted risk reduction activities to prepare for the projected maximum likely tsunami [xx] .

Effective engagement of various stakeholders is also important to preparedness mapping and other disaster preparedness activities. This means engaging and empowering diverse groups including women, the elderly, children, and the private sector. Elders are a particularly important demographic in the context of the GEJE, as the report Elders Leading the Way to Resilience illustrates. Tohoku is an aging region, and two-thirds of lives lost from the GEJE were over 60 years old. Research shows that building trust and social ties can reduce disaster impacts- after GEJE, a study found that communities with high social capital lost fewer residents to the tsunami. [xxi] Following the megadisaster, elders in Ofunato formed the Ibasho Cafe, a community space for strengthening social capital among older people. The World Bank has explored the potential of the Ibasho model for other contexts , highlighting how fueling social capital and engaging elders in strengthening their community can have benefits for both normal times and improve resilience when a disaster does strike.

Conducting simulation drills regularly provide another way of engaging stakeholders in preparedness. As described in Learning from Disaster Simulation Drills in Japan , [xxii] after the 1995 GHAE the first Comprehensive Disaster Management Drill Framework was developed as a guide for the execution of a comprehensive system of disaster response drills and establishing links between various disaster management agencies. The Comprehensive Disaster Management Drill Framework is updated annually by the Central Disaster Management Council. The GEJE led to new and improved drill protocols in the impacted region and in Japan as a whole. For example, the 35th Joint Disaster simulation Drill was held in the Tokyo metropolitan region in 2015 to respond to issues identified during the GEJE, such as improving mutual support systems among residents, governments, and organizations; verifying disaster management plans; and improving disaster response capabilities of government agencies. In addition to regularly scheduled disaster simulation drills, GEJE memorial events are held in Japan annually to memorialize victims and keep disaster preparedness in the public consciousness.

Business continuity planning (BCP) is another key strategy that shows how ongoing attention to resilience is also essential for both public and private sector organizations. As Resilient Industries in Japan demonstrates, after the GEJE, BCPs helped firms reduce disaster losses and recover quickly, benefiting employees, supply chains, and the economy at large. BCP is supported by many national policies in Japan, and after the GEJE, firms that had BCPs in place had reduced impacts on their financial soundness compared to firms that did not. [xxiii] The GEJE also led to the update and refinement of BCPs across Japan. Akemi industrial park in Aichi prefecture, began business continuity planning at the scale of the industrial park three years before the GEJE. After the GEJE, the park revised their plan, expanding focus on the safety of workers. National policies in Japan promote the development of BCPs, including the 2013 Basic Act for National Resilience, which was developed after the GEJE and emphasizes resilience as a shared goal across multiple sectors. [xxiv] Japan also supports BCP development for public sector organizations including subnational governments and infrastructure operators. By 2019, all of Japan’s prefectural governments, and nearly 90% of municipal governments had developed BCPs. [xxv] The role of financial institutions in incentivizing BCPs is further addressed in the following section.

The ongoing nature of these preparedness actions highlights that resilience is a continuous process. Risk management strategies must be adapted and sustained over time, especially during times without disasters. This principle is central to Japan’s disaster resilience policies. In late 2011, based on a report documenting the GEJE from the Expert Committee on Earthquake and Tsunami Disaster Management, Japan amended the DCBA (Disaster Countermeasures Basic Act) to enhance its multi-hazard countermeasures, adding a chapter on tsunami countermeasures. [xxvi]

Disaster Risk Finance and Insurance

Disasters can have a large financial impact, not only in the areas where they strike, but also at the large scale of supply chains and national economy. For example, the GEJE led to the shutdown of nuclear power plants across Japan, resulting in a 50% decrease in energy production and causing national supply disruptions. The GEJE has illustrated the importance of disaster risk finance and insurance (DRFI) such as understanding and clarifying contingent liabilities and allocating contingency budgets, putting in place financial protection measures for critical lifeline infrastructure assets and services, and developing mechanisms for vulnerable businesses and households to quickly access financial support. DRFI mechanisms can help people, firms, and critical infrastructure avoid or minimize disruptions, continue operations, and recover quickly after a disaster.

Pre-arranged agreements, including public-private partnerships, are key strategies for the financial protection of critical infrastructure. The report Financial Protection of Critical Infrastructure Services (forthcoming) [xxvii] shows how pre-arranged agreements between the public sector and private sector for post-disaster response can facilitate rapid infrastructure recovery after disasters, reducing the direct and indirect impacts of infrastructure disruptions, including economic impacts. GEJE caused devastating impacts to the transportation network across Japan. Approximately 2,300 km of expressways were closed, representing 65 percent of expressways managed by NEXCO East Japan , resulting in major supply chain disruptions [xxviii] .  However, with the activation of pre-arranged agreements between governments and local construction companies for road clearance and recovery work, allowing damaged major motorways to be repaired within one week of the earthquake. This quick response allowed critical access for other emergency services to further relief and recovery operations.

The GEJE illustrated the importance of clearly defining post-disaster financial roles and responsibilities among public and private actors in order to restore critical infrastructure rapidly . World Bank research on Catastrophe Insurance Programs for Public Assets highlights how the Japan Railway Construction, Transport and Technology Agency  (JRTT) uses insurance to reduce the contingent liabilities of critical infrastructure to ease impacts to government budgets in the event of a megadisaster. Advance agreements between the government, infrastructure owners and operators, and insurance companies clearly outline how financial responsibilities will be shared in the event of a disaster. In the event of a megadisaster like GEJE, the government pays a large share of recovery costs, which enables the Shinkansen bullet train service to be restored more rapidly. [xxix]

The Resilient Industries in Japan   report highlights how diverse and comprehensive disaster risk financing methods are also important to promoting a resilient industry sector . After the GEJE, 90% of bankruptcies linked to the disaster were due to indirect impacts such as supply chain disruptions. This means that industries located elsewhere are also vulnerable: a study found that six years after GEJE, a greater proportion of bankruptcy declarations were located in Tokyo than Tohoku. [xxx] Further, firms without disaster risk financing in place had much higher increases in debt levels than firms with preexisting risk financing mechanisms in place. [xxxi] Disaster risk financing can play a role pre-disaster, through mechanisms such as low-interest loans, guarantees, insurance, or grants which incentivize the creation of BCPs and other mitigation and preparedness measures.  When a disaster strikes, financial mechanisms that support impacted businesses, especially small or medium enterprises and women-owned businesses, can help promote equitable recovery and help businesses survive. For financial institutions, simply keeping banks open after a major disaster can support response and recovery. After the GEJE, the Bank of Japan (BoJ) and local banks leveraged pre-arranged agreements to maintain liquidity, opening the first weekend after the disaster to help minimize economic disruptions. [xxxii] These strategies highlight the important role of finance in considering economic needs before a disaster strikes, and having systems in place to act quickly to limit both economic and infrastructure service impacts of disasters.

Looking to the Future

Ten years after the GEJE, these lessons in the realms of resilient infrastructure, risk identification, reduction and preparedness, and DRFI are significant not only for parts of the world preparing for tsunamis and other seismic hazards, but also for many of the other types of hazards faced around the globe in 2021. In Japan, many of the lessons of the GEJE are being applied to the projected Nankai Trough and Tokyo Inland earthquakes, for example through modelling risks and mapping evacuation routes, implementing scenario planning exercises and evacuation drills , or even prearranging a post-disaster reconstruction vision and plans. These resilience measures are taken not only individually but also through innovative partnerships for collaboration across regions, sectors, and organizations including public-private agreements to share resources and expertise in the event of a major disaster.

The ten-year anniversary of the GEJE finds the world in the midst of the multiple emergencies of the global COVID-19 pandemic, environmental and technological hazards, and climate change. Beyond seismic hazards, the global pandemic has highlighted, for example, the risks of supply chain disruption due to biological emergencies. Climate change is also increasing hazard exposure in Japan and around the globe. Climate change is a growing concern for its potential to contribute to hydrometeorological hazards such as flooding and hurricanes, and for its potential to play a role in secondary or cascading hazards such as fire. In the era of climate change, disasters will increasingly be ‘unprecedented’, and so GEJE offers important lessons on preparing for low-probability high-impact disasters and planning under uncertain conditions in general.

Over the last decade, the World Bank has drawn upon the GEJE megadisaster experience to learn how to better prepare for and recover from low-probability high-impact disasters. While we have identified a number of diverse strategies here, ranging from technological and structural innovations to improving the engagement of diverse stakeholders, three themes recur throughout infrastructure resilience, risk preparedness, and disaster finance. First, planning in advance for how organizations will prepare for, respond to, and recover from disasters is essential, i.e. through the creation of BCPs by both public and private organizations. Second, pre-arranged agreements amongst organizations for sharing resources, knowledge, and financing in order to mitigate, prepare, respond and recover together from disasters and other unforeseen events are highly beneficial. Third, only with continuous reflection, learning and update on what worked and what didn’t work after each disasters can develop the adaptive capacities needed to manage ever increasing and unexpected risks. Preparedness is an incremental and interactive process.

These lessons from the GEJE on the importance of BCPs and pre-arranged agreements both emphasize larger principles that can be brought to bear in the context of emergent climate and public health crises. Both involve planning for the potential of disaster before it strikes. BCPs and pre-arranged agreements are both made under blue-sky conditions, which allow frameworks to be put in place for advanced mitigation and preparedness, and rapid post-disaster response and recovery. While it is impossible to know exactly what future crises a locale will face, these processes often have benefits that make places and organizations better able to act in the face of unlikely or unpredicted events. The lessons above regarding BCPs and pre-arranged agreements also highlight that neither the government nor the private sector alone have all the tools to prepare for and respond to disasters. Rather, the GEJE shows the importance of both public and private organizations adopting BCPs, and the value of creating pre-arranged agreements among and across public and private groups. By making disaster preparedness a key consideration for all organizations, and bringing diverse stakeholders together to make plans for when a crisis strikes, these strengthened networks and planning capacities have the potential to bear benefits not only in an emergency but in the everyday operations of organizations and countries.

Back to Top

Additional Resources

Program Overview

• Japan-World Bank Program on Mainstreaming Disaster Risk Management in Developing Countries

Reports and Case Studies Featuring Lessons from GEJE

• Learning from Megadisasters: Lessons from the Great East Japan Earthquake  (PDF)
• Lifelines: The Resilient Infrastructure Opportunity  (PDF)
• Resilient Water Supply and Sanitation Services: The Case of Japan  (PDF)
• Japan Case Study Report on Road Geohazard Risk Management  (PDF)
• Resilient Infrastructure PPPs  (PDF)
• Making Schools Resilient at Scale  (PDF)
• Converting Disaster Experience into a Safer Built Environment: The Case of Japan  (PDF)
• Resilient Cultural Heritage: Learning from the Japanese Experience  (PDF)
• Information and Communication Technology for Disaster Risk Management in Japan
• Resilient Industries in Japan : Lessons Learned in Japan on Enhancing Competitiveness in the Face of Disasters by Natural Hazards (PDF)
• Preparedness Maps for Community Resilience: Earthquakes. Experience from Japan  (PDF)
• Elders Leading the Way to Resilience  (PDF)
• Ibasho: Strengthening community-driven preparedness and resilience in Philippines and Nepal by leveraging Japanese expertise and experience  (PDF)
• Learning from Disaster Simulation Drills in Japan  (PDF)
• Catastrophe Insurance Programs for Public Assets  (PDF)
• PPP contract clauses unveiled: the World Bank’s 2017 Guidance on PPP Contractual Provisions
• Learning from Japan: PPPs for infrastructure resilience

Audiovisual Resources on GEJE and its Reconstruction Processes in English

• NHK documentary: 3/11-The Tsunami: The First 3 Days
• NHK: 342 Stories of Resilience and Remembrance
• Densho Road 3.11: Journey to Experience the Lessons from the Disaster - Tohoku, Japan
• Sendai City: Disaster-Resilient and Environmentally-Friendly City
• Sendai City: Eastern Coastal Area Today, 2019 Fall

[i]   Resilient Water Supply and Sanitation Services  report, p.63

[ii]   Resilient Water Supply and Sanitation Services  report, p.63

[iii]   Resilient Water Supply and Sanitation Services  report, p.8

[iv]   Resilient Water Supply and Sanitation Services  report, p.71

[v]   Resilient Water Supply and Sanitation Services  report, p.63

[vi]   Lifelines: The Resilient Infrastructure Opportunity  report, p.115

[vii] Lifelines: The Resilient Infrastructure Opportunity  report, p.133

[viii]   Japan Case Study Report on Road Geohazard Risk Management  report, p.30

[ix]   Resilient Infrastructure PPPs  report, p.8-9

[x]   Resilient Infrastructure PPPs  report, p.39-40

[xi]   Resilient Industries in Japan  report, p.153.

[xii]   Lifelines: The Resilient Infrastructure Opportunity  report, p. 132

[xiii]   Making Schools Resilient at Scale  report, p.24

[xiv]   Resilient Cultural Heritage  report, p.62

[xv]   Learning from Megadisasters  report, p.326

[xvi]   Resilient Cultural Heritage  report, p.69

[xvii]   Learning from Megadisasters  report, p.100

[xviii] Learning from Megadisasters  report, p.297.

[xix]  J-ALERT, Japan’s nationwide early warning system, had 46% implementation at GEJE, and in communities where it was implemented earthquake early warnings were successfully received. Following GEJE, GOJ invested heavily in J-ALERT adoption (JPY 14B), bearing 50% of implementation costs. In 2013 GOJ spent JPY 773M to implement J-ALERT in municipalities that could not afford the expense. In 2014 MIC heavily promoted the L-ALERT system (formerly “Public Information Commons”), achieving 100% adoption across municipalities. Since GEJE, Japan has updated the EEWS to include a hybrid method of earthquake prediction, improving the accuracy of predictions and warnings.

[xx]  Related resources: NHK, “#1 TSUNAMI BOSAI: Science that Can Save Your Life”  https://www3.nhk.or.jp/nhkworld/en/ondemand/video/3004665/  ; NHK “BOSAI: Be Prepared - Hazard Maps”  https://www3.nhk.or.jp/nhkworld/en/ondemand/video/2084002/

[xxi]  Aldrich, Daniel P., and Yasuyuki Sawada. "The physical and social determinants of mortality in the 3.11 tsunami." Social Science & Medicine 124 (2015): 66-75.

[xxii]   Learning from Disaster Simulation Drills in Japan  Report, p. 14

[xxiii]  Matsushita and Hideshima. 2014. “Influence over Financial Statement of Listed Manufacturing Companies by the GEJE, the Effect of BCP and Risk Financing.” [In Japanese.] Journal of Japan Society of Civil Engineering 70 (1): 33–43.  https://www.jstage.jst.go.jp/article/jscejsp/70/1/70_33/_pdf/-char/ja .

[xxiv]   Resilient Industries in Japan  report, p. 56

[xxv]  MIC (Ministry of Internal Affairs and Communications). 2019. “Survey Results of Business Continuity Plan Development Status in Local Governments.” [In Japanese.] Press release, MIC, Tokyo.  https://www.fdma.go.jp/pressrelease/houdou/items/011226bcphoudou.pdf .

[xxvi]   Japan Case Study Report on Road Geohazard Risk Management  report, p.17.

[xxvii]  The World Bank. 2021. “Financial Protection of Critical Infrastructure Services.” Technical Report – Contribution to 2020 APEC Finance Ministers Meeting.

[xxviii]   Resilient Industries in Japan  report, p. 119

[xxix]  Tokio Marine Holdings, Inc. 2019. “The Role of Insurance Industry to Strengthen Resilience of Infrastructure—Experience in Japan.” APEC seminar on Disaster Risk Finance.

[xxx]  TDB (Teikoku DataBank). 2018. “Trends in Bankruptcies 6 Years after the Great East Japan Earthquake.” [In Japanese.] TDB, Tokyo.  https://www.tdb.co.jp/report/watching/press/pdf/p170301.pdf .

[xxxi]  Matsushita and Hideshima. 2014. “Influence over Financial Statement of Listed Manufacturing Companies by the GEJE, the Effect of BCP and Risk Financing.” [In Japanese.] Journal of Japan Society of Civil Engineering 70 (1): 33–43.  https://www.jstage.jst.go.jp/article/jscejsp/70/1/70_33/_pdf/-char/ja .

[xxxii]   Resilient Industries in Japan  report, p. 145

Nepal Earthquake Case Studies

About the Project

On April 25, 2015, Nepal and its people experienced a 7.8 magnitude earthquake. On May 12, another major earthquake of 7.2 magnitude hit the country. In practice, his means that millions of Nepalis have lived and died under the weight of falling buildings, landslides, floods, hunger, and homelessness brought about by massive seismic shifts across the Himalayan belt. Most will refer to this as an earthquake, singular. But this is no singular disaster. The country has experienced more than 300 seismic events since April 25, 2015, and nearly 9000 people died as a direct result of the two most major earthquakes.

For most of Nepal’s approximately 30 million people, living uncertainty is old hat. Consider the legacies of civil war (1996-2006) followed by a decade of political instability and current struggles to write a viable constitution. But the spring of 2015 has cracked open new forms of vulnerability for most Nepalis. These quakes have caused enormous destruction to the nation’s rich cultural heritage, in the Kathmandu Valley and beyond. The countryside has experienced vast devastation. More than half a million homes have been destroyed or are precariously habitable. This equates to about 2.5 million internally displaced. More than 3,500 schools have been destroyed and nearly as many health posts. There has been widespread damage to highways and road networks; glacial lakes are in danger of bursting; landslides are a constant threat, and have continued to wipe out settlements; many hydroelectric dams have been damaged; water borne illness and other public health challenges loom as monsoon has arrived. Even so, Nepalis are showing incredible resilience, creativity, and deep commitments to helping each other through this suffering.

This project – in the context of ANTH 55: Anthropology of Global Health – explores the human impacts of these disasters by asking students to engage in collective research and writing of case studies focused on specific areas of inquiry related to the earthquake.

The assumption of this project is not that students will become “experts” either on Nepal or on the health effects of earthquakes, but that they will amass sufficient knowledge about their area of inquiry so that they can contribute to an effort to expand knowledge and understanding of this event to others, and expand in the process their own conceptualization of what “global health” is, where and how it occurs, and how it links to many other aspects of human life.

Earthquake case studies

Earthquake case studies Below are powerpoint presentations discussing the primary and secondary effects and immediate and long-term responses for both the Kobe, Japan and Kashmir, Pakistan earthquakes.

Effects of the Italian earthquake – http://www.bbc.co.uk/learningzone/clips/the-italian-earthquake-the-aftermath/6997.html Responses to Italian earthquake – http://www.bbc.co.uk/learningzone/clips/the-italian-earthquake-the-emergency-response/6998.html The Kobe earthquake – http://www.bbc.co.uk/learningzone/clips/the-kobe-earthquake/3070.html General effects & responses & Kobe (Rich) & Kashmir (Poor)

O Ltb Eartqaukes Cs from donotreply16 Kobe earthquake (Rich country)

Koberevision from cheergalsal Haiti 2010 – Poor country Picture Facts On 12th January, an earthquake measuring 7.0 on the Richter scale struck close to Haiti’s capital Port-au-Prince The earthquake occurred at a destructive plate margin between the Caribbean and North American Plates, along a major fault line. The earthquakes focus was 13km underground, and the epicentre was just 25km from Port-au-Prince Haiti has suffered a large number of serious aftershocks after the main earthquake

Primary effects About 220,000 people were killed and 300,000 injured The main port was badly damaged, along with many roads that were blocked by fallen buildings and smashed vehicles Eight hospitals or health centres in Port-au-Prince collapsed or were badly damaged. Many government buildings were also destroyed About 100,000 houses were destroyed and 200,000 damaged in Port-au-Prince and the surrounding area. Around 1.3 million Haitians were displaced (left homeless)

Secondary effects Over 2 million Habitats were left without food and water. Looting became a serious problem The destruction of many government buildings hindered the government’s efforts to control Haiti, and the police force collapsed The damage to the port and main roads meant that critical aid supplies for immediate help and longer-term reconstruction were prevented from arriving or being distributed effectively Displaced people moved into tents and temporary shelters, and there were concerns about outbreaks of disease. By November 2010, there were outbreaks of Cholera There were frequent power cuts The many dead bodies in the streets, and under the rubble, created a health hazard in the heat. So many had to be buried in mass graves

Short-term responses The main port and roads were badly damaged, crucial aid (such as medical supplies and food) was slow to arrive and be distributed. The airport couldn’t handle the number of planes trying to fly in and unload aid American engineers and diving teams were used to clear the worst debris and get the port working again, so that waiting ships could unload aid The USA sent ships, helicopters, 10,000 troops, search and rescue teams and $100 million in aid The UN sent troops and police and set up a Food Aid Cluster to feed 2 million people Bottled water and water purification tablets were supplied to survivors Field hospitals were set up and helicopters flew wounded people to nearby countries The Haitian government moved 235,000 people from Port-au-Prince to less damaged cities

Long-term responses Haiti is dependent on overseas aid to help it recover New homes would need to be built to a higher standard, costing billions of dollars Large-scale investment would be needed to bring Haiti’s road, electricity, water and telephone systems up to standard, and to rebuild the port Sichuan, China 2008 – Poor country case study Picture On 12th May at 14:28pm, the pressure resulting from the Indian Plate colliding with the Eurasian Plate was released along the Longmenshan fault line that runs beneath. This led to an earthquake measuring 7.9 on the Richter scale with tremors lasting 120 seconds.

Primary effects · 69,000 people were killed · 18,000 missing · 374,000 were injured · between 5 -11 million people were missing · 80% of buildings collapsed in rural areas such as Beichuan county due to poorer building standards · 5 million buildings collapsed

Secondary effects · Communication were brought to a halt – neither land nor mobile phones worked in Wenchuan · Roads were blocked and damaged and some landslides blocked rivers which led to flooding · Fires were caused as gas pipes burst · Freshwater supplies were contaminated by dead bodies

Immediate responses · 20 helicopters were assigned to rescue and relief effects immediately after the disaster · Troops parachuted in or hiked to reach survivors · Rescuing survivors trapped in collapsed buildings was a priority · Survivors needed food, water and tents to shelter people from the spring rains. 3.3 million new tents were ordered.

Long-term responses · Aid donations specifically money – over £100 million were raised by the Red Cross · One million temporary small were built to house the homeless · The Chinese government pledged a $10 million rebuilding funds and banks wrote off debts by survivors who did not have insurance

Case Study: Predicting the Next Big Earthquake

Recent earthquake activity.

USGS Recent Worldwide Earthquake Activity To explore individual earthquakes in more depth, click on the UTC Date-Time field. Show me how Hide Details for accessing USGS Recent Worldwide Earthquake Activity Scroll the list to look over earthquakes that have occurred in the last seven days. To explore individual earthquakes in more depth, follow the COMMENTS links. Scroll to the bottom of the list to view recent Earthquakes plotted on a world map. What is the magnitude of the most recent recorded earthquake? How many earthquakes were recorded for the last seven days? Of those earthquakes, how many were of a magnitude 7.0 or greater? IRIS Seismic Monitor Click on the map to zoom to specific regions. Click on individual earthquakes to see lists of others nearby. Show me how Hide Details for accessing the IRIS Seismic Monitor Click on the map to zoom to specific regions. Click on individual earthquakes to see lists of others nearby. Where are earthquakes concentrated?

Where does Earth Quake?

Predicting the Next Big One!

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3.9: Case Studies

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• Chris Johnson, Matthew D. Affolter, Paul Inkenbrandt, & Cam Mosher
• Salt Lake Community College via OpenGeology

Video explaining the seismic activity and hazards of the Intermountain Seismic Belt and the Wasatch Fault, a large intraplate area of seismic activity.

North American Earthquakes

Basin and Range Earthquakes —Earthquakes in the Basin and Range Province, from the Wasatch Fault (Utah) to the Sierra Nevada (California), occur primarily in normal faults created by tensional forces. The Wasatch Fault, which defines the eastern extent of the Basin and Range province, has been studied as an earthquake hazard for more than 100 years.

New Madrid Earthquakes (1811-1812) —Historical accounts of earthquakes in the New Madrid seismic zone date as far back as 1699 and earthquakes continue to be reported in modern times [ 11 ]. A sequence of large (M w >7) occurred from December 1811 to February 1812 in the New Madrid area of Missouri [ 12 ]. The earthquakes damaged houses in St. Louis, affected the stream course of the Mississippi River, and leveled the town of New Madrid. These earthquakes were the result of intraplate seismic activity [ 9 ].

Charleston (1868) —The 1868 earthquake in Charleston South Carolina was a moment magnitude 7.0, with a Mercalli intensity of X, caused significant ground motion, and killed at least 60 people. This intraplate earthquake was likely associated with ancient faults created during the breakup of Pangea. The earthquake caused significant liquefaction [ 13 ]. Scientists estimate the recurrence of destructive earthquakes in this area with an interval of approximately 1500 to 1800 years.

Great San Francisco Earthquake and Fire (1906) —On April 18, 1906, a large earthquake, with an estimated moment magnitude of 7.8 and MMI of X, occurred along the San Andreas fault near San Francisco California. There were multiple aftershocks followed by devastating fires, resulting in about 80% of the city being destroyed. Geologists G.K. Gilbert and Richard L. Humphrey, working independently, arrived the day following the earthquake and took measurements and photographs [ 14 ].

Alaska (1964) —The 1964 Alaska earthquake, moment magnitude 9.2, was one of the most powerful earthquakes ever recorded. The earthquake originated in a megathrust fault along the Aleutian subduction zone. The earthquake caused large areas of land subsidence and uplift, as well as significant mass wasting.

Video from the USGS about the 1964 Alaska earthquake.

Loma Prieta (1989) —The Loma Prieta, California, earthquake was created by movement along the San Andreas Fault. The moment magnitude 6.9 earthquake was followed by a magnitude of 5.2 aftershock. It caused 63 deaths, buckled portions of the several freeways, and collapsed part of the San Francisco-Oakland Bay Bridge.

This video shows how shaking propagated across the Bay Area during the 1989 Loma Prieta earthquake.

This video shows the destruction caused by the 1989 Loma Prieta earthquake.

Global Earthquakes

Many of history’s largest earthquakes occurred in megathrust zones, such as the Cascadia Subduction Zone (Washington and Oregon coasts) and Mt. Rainier (Washington).

Shaanxi, China (1556) —On January 23, 1556 an earthquake of an approximate moment magnitude 8 hit central China, killing approximately 830,000 people in what is considered the most deadly earthquake in history. The high death toll was attributed to the collapse of cave dwellings ( yaodong ) built in loess deposits, which are large banks of windblown, compacted sediment (see Chapter 5 ). Earthquakes in this are region are believed to have a recurrence interval of 1000 years. [ 15 ].

Lisbon, Portugal (1755) —On November 1, 1755 an earthquake with an estimated moment magnitude range of 8–9 struck Lisbon, Portugal [ 13 ], killing between 10,000 to 17,400 people [ 16 ]. The earthquake was followed by a tsunami.

Valdivia, Chile (1960) —The May 22, 1960 earthquake was the most powerful earthquake ever measured, with a moment magnitude of 9.4–9.6 and lasting an estimated 10 minutes. It triggered tsunamis that destroyed houses across the Pacific Ocean in Japan and Hawaii and caused vents to erupt on the Puyehue-Cordón Caulle (Chile).

Video describing the tsunami produced by the 1960 Chili earthquake.

Tangshan, China (1976) —Just before 4 a.m. (Beijing time) on July 28, 1976 a moment magnitude 7.8 earthquake struck Tangshan (Hebei Province), China, and killed more than 240,000 people. The high death toll is attributed to people still being asleep or at home and most buildings being made of URM.

Sumatra, Indonesia (2004) —On December 26, 2004, slippage of the Sunda megathrust fault generated a moment magnitude 9.0–9.3 earthquake off the coast of Sumatra, Indonesia [ 17 ]. This megathrust fault is created by the Australia plate subducting below the Sunda plate in the Indian Ocean [ 18 ]. The resultant tsunamis created massive waves as tall as 24 m (79 ft) when they reached the shore and killed more than an estimated 200,000 people along the Indian Ocean coastline.

Haiti (2010) —The moment magnitude 7 earthquake that occurred on January 12, 2010, was followed by many aftershocks of magnitude 4.5 or higher. More than 200,000 people are estimated to have died as a result of the earthquake. The widespread infrastructure damage and crowded conditions contributed to a cholera outbreak, which is estimated to have caused thousands more deaths.

Tōhoku, Japan (2011) —Because most Japanese buildings are designed to tolerate earthquakes, the moment magnitude 9.0 earthquake on March 11, 2011, was not as destructive as the tsunami it created. The tsunami caused more than 15,000 deaths and tens of billions of dollars in damage, including the destructive meltdown of the Fukushima nuclear power plant.

9. Hildenbrand TG, Hendricks JD (1995) Geophysical setting of the Reelfoot rift and relations between rift structures and the New Madrid seismic zone. U.S. Geological Survey, Washington; Denver, CO

11. Feldman J (2012) When the Mississippi Ran Backwards: Empire, Intrigue, Murder, and the New Madrid Earthquakes of 1811 and 1812. Free Press

12. Fuller ML (1912) The New Madrid earthquake. Central United States Earthquake Consortium, Washington, D.C.

13. Talwani P, Cox J (1985) Paleoseismic evidence for recurrence of Earthquakes near Charleston, South Carolina. Science 229:379–381

14. Gilbert GK, Holmes JA, Humphrey RL, et al (1907) The San Francisco earthquake and fire of April 18, 1906 and their effects on structures and structural materials. U.S. Geological Survey, Washington, D.C.

15. Boer JZ de, Sanders DT (2007) Earthquakes in human history: The far-reaching effects of seismic disruptions. Princeton University Press, Princeton

16. Aguirre B.E. (2012) Better disaster statistics: The Lisbon earthquake. J Interdiscip Hist 43:27–42

17. Rossetto T, Peiris N, Pomonis A, et al (2007) The Indian Ocean tsunami of December 26, 2004: observations in Sri Lanka and Thailand. Nat Hazards 42:105–124

18. Satake K, Atwater BF (2007) Long-Term Perspectives on Giant Earthquakes and Tsunamis at Subduction Zones. Annual Review of Earth and Planetary Sciences 35:349–374. https://doi.org/10.1146/annurev.earth.35.031306.140302

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• NEWS EXPLAINER
• 12 September 2023
• Clarification 13 September 2023
• Update 14 September 2023

Why was the Morocco earthquake so deadly?

• Michael Marshall 0

Michael Marshall is a science journalist in Devon, UK.

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Collapsing buildings have been responsible for many of the casualties in the Morocco earthquake. Credit: Mohamed Messara/EPA-EFE/Shutterstock

Morocco is dealing with the aftermath of its most devastating earthquake for decades. The tremor, which hit on 8 September in the High Atlas mountain range, around 70 kilometres southwest of Marrakesh, has killed more than 2,800 people, with thousands more injured. The death toll seems likely to rise as rescue and recovery efforts continue.

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doi: https://doi.org/10.1038/d41586-023-02880-3

Updates & Corrections

Clarification 13 September 2023 : This article has been updated to make clear that the 1,739 historical earthquakes did not all happen in Morocco, but their activity was felt there.

Update 14 September 2023 : This article was updated on 14 September with additional information from researchers in Morocco.

Peláez, J. A. et al. Seismol. Res. Lett. 78 , 614–621 (2007).

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Bhuj Earthquake India 2001 – A Complete Study

Bhuj earthquake india.

Gujarat : Disaster on a day of celebration : 51st Republic Day on January 26, 2001

• 7.9 on the Richter scale.
• 8.46 AM January 26th 2001
• 20,800 dead

Basic Facts

• Earthquake: 8:46am on January 26, 2001
• Epicenter: Near Bhuj in Gujarat, India
• Magnitude: 7.9 on the Richter Scale

Geologic Setting

• Indian Plate Sub ducting beneath Eurasian Plate
• Continental Drift
• Convergent Boundary

Specifics of 2001 Quake

Compression Stress between region’s faults

Depth: 16km

Probable Fault: Kachchh Mainland

Fault Type: Reverse Dip-Slip (Thrust Fault)

The earthquake’s epicentre was 20km from Bhuj. A city with a population of 140,000 in 2001. The city is in the region known as the Kutch region. The effects of the earthquake were also felt on the north side of the Pakistan border, in Pakistan 18 people were killed.

Tectonic systems

The earthquake was caused at the convergent plate boundary between the Indian plate and the Eurasian plate boundary. These pushed together and caused the earthquake. However as Bhuj is in an intraplate zone, the earthquake was not expected, this is one of the reasons so many buildings were destroyed – because people did not build to earthquake resistant standards in an area earthquakes were not thought to occur. In addition the Gujarat earthquake is an excellent example of liquefaction, causing buildings to ‘sink’ into the ground which gains a consistency of a liquid due to the frequency of the earthquake.

India : Vulnerability to earthquakes

• 56% of the total area of the Indian Republic is vulnerable to seismic activity .
• 12% of the area comes under Zone V (A&N Islands, Bihar, Gujarat, Himachal Pradesh, J&K, N.E.States, Uttaranchal)
• 18% area in Zone IV (Bihar, Delhi, Gujarat, Haryana, Himachal Pradesh, J&K, Lakshadweep, Maharashtra, Punjab, Sikkim, Uttaranchal, W. Bengal)
• 26% area in Zone III (Andhra Pradesh, Bihar, Goa, Gujarat, Haryana, Kerala, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttaranchal, W. Bengal)
• Gujarat: an advanced state on the west coast of India.
• On 26 January 2001, an earthquake struck the Kutch district of Gujarat at 8.46 am.
• Epicentre 20 km North East of Bhuj, the headquarter of Kutch.
• The Indian Meteorological Department estimated the intensity of the earthquake at 6.9 Richter. According to the US Geological Survey, the intensity of the quake was 7.7 Richter.
• The quake was the worst in India in the last 180 years.

What earthquakes do

• Casualties: loss of life and injury.
• Loss of housing.
• Damage to infrastructure.
• Disruption of transport and communications.
• Breakdown of social order.
• Loss of industrial output.
• Loss of business.
• Disruption of marketing systems.
• The earthquake devastated Kutch. Practically all buildings and structures of Kutch were brought down.
• Ahmedabad, Rajkot, Jamnagar, Surendaranagar and Patan were heavily damaged.
• Nearly 19,000 people died. Kutch alone reported more than 17,000 deaths.
• 1.66 lakh people were injured. Most were handicapped for the rest of their lives.
• The dead included 7,065 children (0-14 years) and 9,110 women.
• There were 348 orphans and 826 widows.

Loss classification

Deaths and injuries: demographics and labour markets

Effects on assets and GDP

Effects on fiscal accounts

Financial markets

Disaster loss

• Initial estimate Rs. 200 billion.
• Came down to Rs. 144 billion.
• No inventory of buildings
• Non-engineered buildings
• Land and buildings
• Stocks and flows
• Reconstruction costs (Rs. 106 billion) and loss estimates (Rs. 99 billion) are different
• Public good considerations

Human Impact: Tertiary effects

• Affected 15.9 million people out of 37.8 in the region (in areas such as Bhuj, Bhachau, Anjar, Ganhidham, Rapar)
• High demand for food, water, and medical care for survivors
• Humanitarian intervention by groups such as Oxfam: focused on Immediate response and then rehabilitation
• Of survivors, many require persistent medical attention
• Region continues to require assistance long after quake has subsided
• International aid vital to recovery

Social Impacts

• 80% of water and food sources were destroyed.
• The obvious social impacts are that around 20,000 people were killed and near 200,000 were injured.
• However at the same time, looting and violence occurred following the quake, and this affected many people too.
• On the other hand, the earthquake resulted in millions of USD in aid, which has since allowed the Bhuj region to rebuild itself and then grow in a way it wouldn’t have done otherwise.
• The final major social effect was that around 400,000 Indian homes were destroyed resulting in around 2 million people being made homeless immediately following the quake.

Social security and insurance

• Ex gratia payment: death relief and monetary benefits to the injured
• Major and minor injuries
•  Cash doles
• Government insurance fund
• Group insurance schemes
• Claim ratio

Demographics and labour market

• Geographic pattern of ground motion, spatial array of population and properties at risk, and their risk vulnerabilities.
• Low population density was a saving grace.
• Extra fatalities among women
• Effect on dependency ratio
• Farming and textiles

Economic  Impacts

• Total damage estimated at around $7 billion. However $18 billion of aid was invested in the Bhuj area.
• Over 15km of tarmac road networks were completely destroyed.
• In the economic capital of the Gujarat region, Ahmedabad, 58 multi storey buildings were destroyed, these buildings contained many of the businesses which were generating the wealth of the region.
• Many schools were destroyed and the literacy rate of the Gujarat region is now the lowest outside southern India.

Impact on GDP

• Applying ICOR
• Rs. 99 billion – deduct a third as loss of current value added.
• Get GDP loss as Rs. 23 billion
• Adjust for heterogeneous capital, excess capacity, loss Rs. 20 billion.
• Reconstruction efforts.
• Likely to have been Rs. 15 billion.

Fiscal accounts

• Differentiate among different taxes: sales tax, stamp duties and registration fees, motor vehicle tax, electricity duty, entertainment tax, profession tax, state excise and other taxes. Shortfall of Rs. 9 billion of which about Rs. 6 billion unconnected with earthquake.
• Earthquake related other flows.
• Expenditure:Rs. 8 billion on relief. Rs. 87 billion on rehabilitation.

Impact on Revenue Continue Reading

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The Parkfield, California, Earthquake Experiment

September 28, 2004— m 6.0 earthquake captured.

The Parkfield Experiment is a comprehensive, long-term earthquake research project on the San Andreas fault. Led by the USGS and the State of California, the experiment's purpose is to better understand the physics of earthquakes - what actually happens on the fault and in the surrounding region before, during and after an earthquake. Ultimately, scientists hope to better understand the earthquake process and, if possible, to provide a scientific basis for earthquake prediction. Since its inception in 1985, the experiment has involved more than 100 researchers at the USGS and collaborating universities and government laboratories. Their coordinated efforts have led to a dense network of instruments poised to "capture" the anticipated earthquake and reveal the earthquake process in unprecedented detail.

Moderate-size earthquakes of about magnitude 6 have occurred on the Parkfield section of the San Andreas fault at fairly regular intervals - in 1857, 1881, 1901, 1922, 1934, and 1966. The first, in 1857, was a foreshock to the great Fort Tejon earthquake which ruptured the fault from Parkfield to the southeast for over 180 miles. Available data suggest that all six moderate-sized Parkfield earthquakes may have been "characteristic" in the sense that they all ruptured the same area on the fault. If such characteristic ruptures occur regularly, then the next quake would have been due before 1993.

These pages describe the scientific background for the experiment, including the tectonic setting at Parkfield, the historical earthquake activity on this section of the San Andreas fault, the monitoring and data collecting activities currently being carried out, and plans for future research. Data are available to view in real-time and download.

Scientific Advances

While the greatest scientific payoff is expected when the earthquake occurs, our understanding of the earthquake process has already been advanced through research results from Parkfield. Some of the highlights are described.

Real-time data from instrumentation networks running at Parkfield are available for viewing and downloading.

Parkfield Earthquake Shake Table Exhibit

The Art-Science of Earthquakes by D.V. Rogers November 23, 2009 ( video )

The exhibit was a geologically interactive, seismic machine earthwork temporarily installed in Parkfield in 2008. Rogers presented the history, conceptual premise, documentation of the work, and also put forward the idea of how early 21st century cultural practice could be used to encourage earthquake awareness and preparedness.

Pictures and interactive, 360-degree panorama .

Lessons From the Best-Recorded Quake in History

USGS Public Lecture by Andy Michael October 26, 2006 ( video )

New data from the 2004 Parkfield earthquake provide important lessons about earthquake processes, prediction, and the hazards assessments that underlie building codes and mitigation policies.

Research Scientist: John Langbein , Earthquake Science Center.

Suggestions or feedback?

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Study: Heavy snowfall and rain may contribute to some earthquakes

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When scientists look for an earthquake’s cause, their search often starts underground. As centuries of seismic studies have made clear, it’s the collision of tectonic plates and the movement of subsurface faults and fissures that primarily trigger a temblor.

But MIT scientists have now found that certain weather events may also play a role in setting off some quakes.

In a study appearing today in Science Advances , the researchers report that episodes of heavy snowfall and rain likely contributed to a swarm of earthquakes over the past several years in northern Japan. The study is the first to show that climate conditions could initiate some quakes.

“We see that snowfall and other environmental loading at the surface impacts the stress state underground, and the timing of intense precipitation events is well-correlated with the start of this earthquake swarm,” says study author William Frank, an assistant professor in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “So, climate obviously has an impact on the response of the solid earth, and part of that response is earthquakes.”

The new study focuses on a series of ongoing earthquakes in Japan’s Noto Peninsula. The team discovered that seismic activity in the region is surprisingly synchronized with certain changes in underground pressure, and that those changes are influenced by seasonal patterns of snowfall and precipitation. The scientists suspect that this new connection between quakes and climate may not be unique to Japan and could play a role in shaking up other parts of the world.

Looking to the future, they predict that the climate’s influence on earthquakes could be more pronounced with global warming.

“If we’re going into a climate that’s changing, with more extreme precipitation events, and we expect a redistribution of water in the atmosphere, oceans, and continents, that will change how the Earth’s crust is loaded,” Frank adds. “That will have an impact for sure, and it’s a link we could further explore.”

The study’s lead author is former MIT research associate Qing-Yu Wang (now at Grenoble Alpes University), and also includes EAPS postdoc Xin Cui, Yang Lu of the University of Vienna, Takashi Hirose of Tohoku University, and Kazushige Obara of the University of Tokyo.

Seismic speed

Since late 2020, hundreds of small earthquakes have shaken up Japan’s Noto Peninsula — a finger of land that curves north from the country’s main island into the Sea of Japan. Unlike a typical earthquake sequence, which begins as a main shock that gives way to a series of aftershocks before dying out, Noto’s seismic activity is an “earthquake swarm” — a pattern of multiple, ongoing quakes with no obvious main shock, or seismic trigger.

The MIT team, along with their colleagues in Japan, aimed to spot any patterns in the swarm that would explain the persistent quakes. They started by looking through the Japanese Meteorological Agency’s catalog of earthquakes that provides data on seismic activity throughout the country over time. They focused on quakes in the Noto Peninsula over the last 11 years, during which the region has experienced episodic earthquake activity, including the most recent swarm.

With seismic data from the catalog, the team counted the number of seismic events that occurred in the region over time, and found that the timing of quakes prior to 2020 appeared sporadic and unrelated, compared to late 2020, when earthquakes grew more intense and clustered in time, signaling the start of the swarm, with quakes that are correlated in some way.

The scientists then looked to a second dataset of seismic measurements taken by monitoring stations over the same 11-year period. Each station continuously records any displacement, or local shaking that occurs. The shaking from one station to another can give scientists an idea of how fast a seismic wave travels between stations. This “seismic velocity” is related to the structure of the Earth through which the seismic wave is traveling. Wang used the station measurements to calculate the seismic velocity between every station in and around Noto over the last 11 years.

The researchers generated an evolving picture of seismic velocity beneath the Noto Peninsula and observed a surprising pattern: In 2020, around when the earthquake swarm is thought to have begun, changes in seismic velocity appeared to be synchronized with the seasons.

“We then had to explain why we were observing this seasonal variation,” Frank says.

Snow pressure

The team wondered whether environmental changes from season to season could influence the underlying structure of the Earth in a way that would set off an earthquake swarm. Specifically, they looked at how seasonal precipitation would affect the underground “pore fluid pressure” — the amount of pressure that fluids in the Earth’s cracks and fissures exert within the bedrock.

“When it rains or snows, that adds weight, which increases pore pressure, which allows seismic waves to travel through slower,” Frank explains. “When all that weight is removed, through evaporation or runoff, all of a sudden, that pore pressure decreases and seismic waves are faster.”

Wang and Cui developed a hydromechanical model of the Noto Peninsula to simulate the underlying pore pressure over the last 11 years in response to seasonal changes in precipitation. They fed into the model meteorological data from this same period, including measurements of daily snow, rainfall, and sea-level changes. From their model, they were able to track changes in excess pore pressure beneath the Noto Peninsula, before and during the earthquake swarm. They then compared this timeline of evolving pore pressure with their evolving picture of seismic velocity.

“We had seismic velocity observations, and we had the model of excess pore pressure, and when we overlapped them, we saw they just fit extremely well,” Frank says.

In particular, they found that when they included snowfall data, and especially, extreme snowfall events, the fit between the model and observations was stronger than if they only considered rainfall and other events. In other words, the ongoing earthquake swarm that Noto residents have been experiencing can be explained in part by seasonal precipitation, and particularly, heavy snowfall events.

“We can see that the timing of these earthquakes lines up extremely well with multiple times where we see intense snowfall,” Frank says. “It’s well-correlated with earthquake activity. And we think there’s a physical link between the two.”

The researchers suspect that heavy snowfall and similar extreme precipitation could play a role in earthquakes elsewhere, though they emphasize that the primary trigger will always originate underground.

“When we first want to understand how earthquakes work, we look to plate tectonics, because that is and will always be the number one reason why an earthquake happens,” Frank says. “But, what are the other things that could affect when and how an earthquake happens? That’s when you start to go to second-order controlling factors, and the climate is obviously one of those.”

This research was supported, in part, by the National Science Foundation.

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Press mentions.

A new study conducted by MIT researchers suggests “heavy snowfall could be a factor in triggering swarms of earthquakes,” reports Evan Bush for NBC News . "Those big snowfall events seem to correlate well with the start of these big earthquake swarms," says Prof. William Frank. "We shouldn’t forget the climate itself can also play a role in changing the stress state at depth where earthquakes are happening." 

BBC Science Focus

BBC Science Focus reporter Alex Hughes spotlights a new study by MIT scientists that suggests more heavy snowfall and rain linked to climate change could increasingly contribute to earthquakes worldwide. “The researchers made these conclusions based on how weather patterns in northern Japan have seemingly contributed to a new 'swarm' of earthquakes,” writes Hughes, “a pattern of multiple, ongoing quakes – that is thought to have begun in 2020.”

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Earthquake shakes U.S. East Coast

An earthquake struck the East Coast of the United States on Friday morning, according to the U.S. Geological Survey, causing buildings to shake and rattling nerves from Maryland to Maine.

The USGS measured the quake as a 4.8 temblor with its epicenter near Lebanon, New Jersey. It struck a little before 10:30 a.m. ET. An aftershock of magnitude-4.0 hit right around 6 p.m. ET.

The morning earthquake was the strongest recorded in the Northeast in more than a decade, according to USGS records .

There were no immediate reports of major destruction or any fatalities. Local and regional officials from cities in the earthquake zone said inspections had been launched to ensure that buildings, bridges and other infrastructure were not damaged.

Follow here for live updates on the earthquake.

James Pittinger, mayor of Lebanon, New Jersey, called the earthquake “the craziest thing I’ve ever experienced.”  In an interview with MSNBC , he said he had not received reports of any significant damage so far, but added that the shaking caused his dog to run for cover and objects to fall off his shelves.

While a 4.8-magnitude temblor is not considered a major earthquake, even minor shaking can cause damage on the East Coast, which does not take similar precautions as other earthquake hot spots around the world.

New York Gov. Kathy Hochul said the quake was felt across the state.

“My team is assessing impacts and any damage that may have occurred, and we will update the public throughout the day,” she wrote on X .

New York City Mayor Eric Adams said in an afternoon news briefing that no major injuries or impacts to infrastructure were reported, and that people in the city should “go about their normal day.”

Ground stops were temporarily issued at Newark Liberty International Airport in New Jersey and John F. Kennedy International Airport in New York City, according to the Federal Aviation Administration's website. Flight disruptions at the Newark airport continued into the afternoon .

The Port Authority Transit Corp., which operates a rapid transit route between Pennsylvania and New Jersey, suspended service in the aftermath of the quake.

“Crews will inspect the integrity of the line out of an abundance of caution,” PATCO said in an update on X . “Once inspection is complete, service will resume. No timeframe. Updates to follow.”

New York’s Metropolitan Transportation Authority said that there had been no impact to its service but that teams will be inspecting train lines. New Jersey Transit alerted riders of 20-minute delays due to bridge inspections following the earthquake.

While earthquakes in the northeast U.S. are rare, Buffalo, New York, was struck by a 3.8-magnitude quake in February 2023 — the strongest recorded in the area in 40 years.

A 4.1-magnitude earthquake struck the tri-state area in 2017, centered near Little Creek, Delaware,  according to the U.S. Geological Survey . And before that, a 5.8-magnitude quake  shook central Virginia in 2011,  and was felt across much of the East Coast, forcing hundreds of thousands people to evacuate buildings in New York, Washington and other cities.

New Jersey Gov. Phil Murphy said in a post on X that the state has activated its emergency operations center and asked the public not to call 911 unless they are experiencing an emergency.

Frederik J. Simons, a professor of geosciences at Princeton University, told NBC News that the earthquake occurred on a shallow fault system in New Jersey and lasted about 35 seconds.

“The shallower or the closer it is, the more we feel it as humans,” he said.

The quake originated at a depth of less than 3 miles,  according to the USGS . 

Earthquakes on the East Coast can be felt at a great distance and can cause more pronounced shaking in comparison to those on the West Coast because rocks in the region are often older, harder and more dense.  

“These are competent rocks that transmit energy well,” Simons said.

The earthquake ruptured within a fault zone known as the Ramapo system, Simons said. It’s a zone in relatively ancient rock that contains old faults and cracks from ancient tectonic processes. These old faults slowly accumulate stress and occasionally something slips, Simons said.

“There are cracks in it and now and then a little motion accumulates, the stress keeps growing, at very slow rates,” he said. “It’s like an old house creaking and groaning.”

Simons said this was one of the largest earthquakes in New Jersey in recent history. The last notable one was a magnitude-3.1 temblor in Freehold Township in September 2020. 

“I’m on campus at Princeton University for the biggest one I’ve felt in a lifetime,” he said. “This shaking was violent, strong and long.”

Some videos captured the moment of the earthquake, including one from a coffee shop in New Jersey.

The East Coast quake struck two days after a powerful 7.4-magnitude temblor shook the island of Taiwan, killing at least 12 people and injuring more than 1,000 others. The two incidents are not thought to be related, said Dara Goldberg, a USGS geophysicist.

“We’re much too far of a distance for the stress on the fault of Taiwan to affect New York,” she said.

Denise Chow is a reporter for NBC News Science focused on general science and climate change.

Evan Bush is a science reporter for NBC News. He can be reached at [email protected].

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Open Access

Peer-reviewed

Research Article

Rapid determination of seismic influence field based on mobile communication big data—A case study of the Luding Ms 6.8 earthquake in Sichuan, China

Roles Formal analysis, Software, Writing – original draft, Writing – review & editing

Affiliation Zhejiang Earthquake Agency, Hangzhou, China

Roles Data curation, Formal analysis, Methodology

* E-mail: [email protected]

Affiliation Beijing Earthquake Agency, Beijing, China

Roles Resources, Software, Supervision

Affiliation Zhejiang Development and Planning Institute, Hangzhou, China

Roles Data curation, Formal analysis

Roles Conceptualization

Roles Data curation

• Dongping Li, 
• Qingquan Tan, 
• Zhiyi Tong, 
• Jingfei Yin, 
• Min Li, 
• Huanyu Li, 
• Haiqing Sun

• Published: May 10, 2024
• https://doi.org/10.1371/journal.pone.0298236
• Peer Review
• Reader Comments

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal–spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution.

Citation: Li D, Tan Q, Tong Z, Yin J, Li M, Li H, et al. (2024) Rapid determination of seismic influence field based on mobile communication big data—A case study of the Luding Ms 6.8 earthquake in Sichuan, China. PLoS ONE 19(5): e0298236. https://doi.org/10.1371/journal.pone.0298236

Editor: Rahul Priyadarshi, Siksha O Anusandhan, INDIA

Received: September 15, 2023; Accepted: January 19, 2024; Published: May 10, 2024

Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This study was supported by the Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (2021D07), Project of Spark Program of Earthquake Sciences, China Earthquake Administration (XH23001B), Zhejiang Provincial Natural Science Foundation of China(LTGG24D040002).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

For a long time, most of the disaster distribution data related to post-earthquake rescue has been obtained by using expensive special equipment. However, due to the uncertainty of when and where an earthquake disaster may occur, the large-scale deployment and maintenance of professional equipment can incur considerable costs. It is also difficult to ensure the connectivity of this equipment and the full coverage of all affected people when an earthquake occurs [ 1 ]. With their ever-increasing popularity, smartphones, as the most widely used electronic devices, have been equipped with computing, communication, storage, and sensing capabilities. Even in disaster scenarios, the probability of people holding smartphones is still very high. Therefore, smartphones are capable of constructing direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate disaster distribution with higher temporal–spatial resolution [ 2 ].

Several earthquakes in recent years have shown that when the intensity of the epicenter reaches VIII, communication base stations will usually be out of service, which directly leads to a precipitous drop in the acquisition of smartphone location data after an earthquake. A large amount of smartphone location data disappears at a large scale after an earthquake, and the closer to the hardest hit area, the more obvious the data drop is. In addition, in the event of a nondestructive earthquake, there will be such phenomena as an increase in communication volume at the epicenter and location changes due to the flow of people avoiding the disaster. The change and distribution of mobile communication big data play an indicative role in estimating the extent of devastation in the first instances [ 3 ]. After the Wenchuan earthquake in 2008, communication facilities in the disaster area were severely damaged, and many mobile base stations stopped service, resulting in communication outages in the areas where these stations are located. We analyzed the affected areas and the extent of devastation by collecting the out-of-service data of mobile base stations and mapped the distribution range of the affected areas, which was highly consistent with the intensity distribution data obtained from the post-earthquake field survey ( Fig 1 ).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0298236.g001

(We have collected information after the Wenchuan Ms 8.0 earthquake through 2 specialized BBS forums: https://www.txrjy.com/ ; https://club.mscbsc.com/ . Although the data we collected are incomplete and limited, we still have relatively and accurately demonstrated the distribution of the seismic influence field and the approximate area of the macroscopic epicenter through spatial interpolation).

With the prevalence of smartphones and the development of mobile Internet services, in combination with the popularization of global positioning technology, the technology of population data estimation based on precise geographic location has become increasingly mature [ 4 ]. When a user enters a certain geographic space, it is possible to obtain and verify the user’s location information, and the current population size of a geographic space can be inferred through a differentiated model. After an earthquake, we can use the changes in smartphone thermal data to infer the extent of damage to mobile communication infrastructure caused by the earthquake [ 5 , 6 ]. The data sources involved in this work are the various smartphone APP vendors. Because the data is location data of point groups reported in a certain area, personal privacy is not involved, and the data size covers billions of terminals.

Since 2012, telecom operators have successively applied location data analysis to mobile networks. Verizon, a U.S. operator, engages in business consulting by collecting information on the apps used and websites visited by its users, as well as their geographic locations [ 7 ]. The highway monitoring project of French telecom operator Orange and the smart footprint project of Spanish telecom company Telefonica are designed to provide location-derived information to users. Represented by the I-LOV project in Germany, many research institutions have participated in the construction of disaster emergency rescue systems based on smartphone signal searches [ 8 ]. At the World Internet Conference in Wuzhen in November 2014, the comprehensive analysis of China Mobile big data demonstrated a dynamic people flow big data analysis platform, and in December of the same year, the mobile location of big data provided decision-making information for the government in the emergency response to the stampede at the Bund in Shanghai [ 9 , 10 ]. "Location big data" is not merely the result of technological transformation in the computer industry. Cross-border thinking and big data thinking are also applicable to various other industries. Similar work has been conducted in the field of natural disaster research, where MyShake has been developed and built as a global smartphone seismic network that can detect and be triggered by P waves. With the constant downloading of MyShake, the scale of the seismic network has been rapidly developed, forming a global seismic network that utilizes personal smartphones to provide acceleration waveforms [ 11 ]. Since 2017, experts have started participating in the research on rapid acquisition of disaster situations and post-earthquake crowd flow analysis based on mobile Internet location data, and some scholars have begun using mobile location information data to study and track the distribution of earthquake-stricken areas and the responses of people after the earthquake [ 12 ]. Researchers have used smartphone data to analyze the crowd dynamics during the 2017 Jiuzhaigou earthquake. By analyzing the call and SMS data, they identified the densely populated areas and the migration trajectories during the evacuation process, which provided support for the development of effective urgent evacuation strategies [ 13 ]. The MIT Media Lab used smartphone data to analyze changes in human mobility and access to critical urban services in the aftermath of the 2015 Nepal earthquake, and the findings underscored the importance of considering post-disaster mobility dynamics in emergency response and recovery planning [ 14 ]. European-Mediterranean Seismological Centre used smartphone data to dynamically assess post-earthquake population displacement. By analyzing detailed call records [ 15 ], EMSC identified changes in human mobility patterns in the affected areas and quantified the displacement level, which provided information for post-disaster recovery and planning work. Taking an earthquake-stricken area as the main research object, Gao Na compared the demographic data obtained by smartphone location and found the role of annual difference data in the field of sudden disaster emergency relief [ 16 ]. The Nie group from the Institute of Geology, China Earthquake Administration used smartphone location data to analyze the indicators related to earthquake disasters and proposed to regard seismic intensity as a sensitive indicator for smartphone location data [ 17 ]. Zhang used the pre-earthquake and post-earthquake Internet smartphone location data in an earthquake area and adopted the standard deviation ellipse model in spatial econometric analysis to analyze the spatial distribution characteristics of the disappeared smartphone location data in the earthquake area and the oriented direction of its discrete point sets, which determined the direction of the seismic influence field, and further provided technical support for post-disaster situation assessment and emergency response services [ 18 ]. In the process of seismic data processing, relevant experts can address earthquake emergency response, rescue guidance, and other studies by integrating and mining a large amount of complex and multisource data [ 19 ]. In conclusion, the application of "mobile location big data" during earthquake emergency responses can improve the scientificity and accuracy of the decision-making processes during an earthquake and enhance the capabilities of earthquake early warnings and emergency responses [ 20 ].

Materials and methods

Principles of smartphone location big data.

The popularity of smartphones and the development of the Internet (according to the report of "Worldwide Quarterly Smartphone Tracker" of the International Data Corporation (IDC), as of the first quarter of 2022, the global smartphone penetration rate is approximately 82%) has ushered in the era of mobile Internet. In addition, with the development and wide use of the global positioning system (GPS), the generation and development of location-based services have become inevitable trends. Location-based services not only offer convenience but also provide new data sources and possibilities for business intelligence analysis, public affairs management, academic research, and other efforts. A large number of users around the world generate numerous information for sharing every day, and the shared information can be accessed via application interfaces [ 21 ]. The geographic data generated through smartphone location sharing services has brought a new revolution to GIS. The most fundamental issue involved in research using the shared data in mobile networks as a data source is the acquisition of data and the supporting platforms for the analysis and computation of acquired data. Population data estimation applies geo-fencing technology to push notifications. When a smartphone enters a geographical spatial scope, the location information of the smartphone can be acquired and logged. Since the size of a population is highly correlated to the number of smartphones, the current population size can be inferred by means of model simulation at different times and in different areas [ 22 ]. The volume of shared information in mobile networks is huge, which poses a great challenge in storing and analyzing this massive amount of data. Therefore, most of the research on mobile terminal location information focus on studying and predicting individual mobility patterns. Such analysis of individual mobility patterns, combined with the information from users’ social network applications, can be suitably applied to public affairs management, such as user profiling, service recommendations, and market predictions [ 23 ]. Communication data providers can also upload smartphone location data to cloud servers for real-time analysis and storage. Cloud servers can process large-scale location data, and the results of these cluster location data analyses can facilitate the development of more targeted emergency strategies in response to natural disasters. With the support of a large amount of data, the study of human activities on a larger scale will be of greater significance for urban and rural planning, population distribution, socio-economic indicators, and other aspects [ 24 , 25 ].

Data preprocessing

A magnitude 6.8 earthquake struck Luding County, Sichuan Province (at 29.59 degrees north latitude and 102.08 degrees east longitude) at 12:52 p.m. Beijing time on September 5, 2022, with a focal depth of 16 kilometers. The earthquake caused heavy casualties, with 46 deaths, and severe damage to water, electricity, transportation, and communication facilities and other infrastructure. The region was highly deformed due to crustal movements, and there have been other violent earthquakes. Since 1900, 21 earthquakes with a magnitude of 6.0 or greater have occurred within 200 km of the epicenter, and a 6.2-magnitude earthquake occurred 27 km from the epicenter in 1975. At the epicenter, we collected a total of 1,806,100 smartphone location records and 4,856 spatial grid locations within a range of 300 km from east to west and 220 km from north to south, and the collection scope covers areas of VI degree and above.

The analysis in this study used GIS data is obtained from the open-source data Open Street Map (OSM), which is available for free and can be downloaded from the portal website of Digital Crete ( https://www.openstreetmap.org/ ) The OSM data contains a series of data layers such as highways, railways, water systems, buildings, transportation facilities, etc. In this study, we only used data from residential points and areas, The DEM data adopts Copernicus DEM, which is a global open-source DEM data released by the European Space Agency(ESA) and can be downloaded from the ESA portal website ( https://panda.copernicus.eu/panda ). The DEM data of ESA has a 10 meter (EA-10) resolution for the European part and a 30 meter resolution for the global range. In this study, we used a 30 meter resolution in a raster format (Tif).The above data does not require authorization. The smartphone location records data obtained was authorized by the telecommunications company to the Zhejiang Earthquake Agency and provided to the author for use. The smartphone location records data used in this article uses Geohash encoding with an accuracy of Geohash7 (approximately 120m * 150m). This data is used to count the number of mobile devices in each Geohash grid within the earthquake zone range per minute.The data is group smartphone location data and does not involve the personal privacy of individual mobile phone users, so there is no concern about personal privacy leakage. All maps in this article are created using ARCGIS 10.6, The coordinates of the map are WGS84,The maps are oriented with North as up, and at this scale all maps in this article have an extent of 300km × 200km.The GIS data is obtained from open-source data websites and has been verified against the place name data of the epicenter area. All data does not involve copyright or legal disputes.

The epicenter of this earthquake was a mountainous area. The population density near the epicenter was not high, with only a few settlements and scenic areas for tourists. After the earthquake, the communication facilities in the disaster area were damaged, which led to a substantial reduction in mobile terminal connections. The disaster avoidance behavior of people and the rapid repair of mobile communication facilities also caused changes in the number of mobile terminals in the disaster area. In addition, there was also a huge quantitative difference between different time periods and areas. Therefore, we extracted the population information in different time periods. Specifically, starting from 10:00 a.m. on the 9th day, the data coverage was extracted every 1 hour, with 24 time periods in total. In this way, the dynamic damage condition of communication facilities in the earthquake area could be reflected in a relatively comprehensive way. In this work, due to the vast data size, accuracy and efficiency contradicted each other. Although the adoption of high precision could contribute to a more detailed representation of population distribution, the problem of high computational complexity would occur. Moreover, the terrain of the disaster area was complicated, and the shadowing effect of the mountains had a certain impact on the accurate positioning of smartphone locations [ 26 ]. Therefore, an excessive pursuit of accuracy would result in a certain amount of repeated calculation points and affect the calculation efficiency. In order to analyze the population in the earthquake area in a faster way, while balancing accuracy and efficiency, we selected a 150 m grid size to analyze terminal location distribution.

Population distribution simulation based on density analysis

Principle of kernel density analysis..

Within the 150 m grid, the population is not perfectly uniformly distributed. Thus, a mathematical approach is needed to simulate population density. We regard the center of the grid as a point. The value of the point is the size of population in the grid, and the distribution of population density is represented by calculating point density [ 27 ]. There are three commonly used methods for calculating point density: the quadrat density method, the kernel density method, and the Voronoi diagram density method. The quadrat density method randomly selects a number of quadrats in the space of the area being simulated and calculates the density of each quadrat by counting the number of individuals in each quadrat, with the average of the density of all quadrats as the density of the large area. However, random sampling is characterized by a certain degree of subjectivity, so the simulation results are relatively larger. This method is applicable to the sampling survey of a static population, but it has a poor effect in simulating a population with strong mobility and high density. The Voronoi diagram density method calculates a distance-based plane partition in geometric space by using data points as generators of a Voronoi diagram. There are n non-coincident seed points in the plane, and the plane is divided into n regions in such a way that the distance from a point in each region to the seed point in the region in which the point is located is closer than the distance from it to any seed point in any other region, and each region is called a Voronoi seed point region. Due to the abrupt density changes at cell junctions and the neglect of continuity in the occurrence of spatial phenomena, Voronoi diagrams also have certain limitations in population distribution estimation [ 28 , 29 ].

However, the above problems can be solved by the kernel density method. The value of kernel density gradually decreases with increasing center radiation distance, with consideration to the distance attenuation effect of the center point on its surrounding locations [ 30 ]. Conceptually, each point is covered with a smooth curved surface, and the surface value is highest at the location of that point. As the distance increases, the surface value decreases until the value turns to zero at a distance equal to the search radius. Each pixel value of the output raster is the sum of all surface values superimposed on the pixel. Thus, the kernel density estimation method can transform a point set into a surface that exhibits continuous density variation. It also is possible to transform a discrete set of points into a smooth density variation diagram, thus demonstrating their spatial distribution pattern. The higher the density value, the greater the aggregation extent of the point is. Kernel density analysis has obvious advantages in the simulation of population distribution, as population distribution is featured with clustering, and the farther away from the center, the less dense the population distribution is [ 31 ].

Spatial calculation method of kernel density.

https://doi.org/10.1371/journal.pone.0298236.g002

Population distribution simulation based on kernel density analysis.

We adopted the kernel density method and used the collected smartphone point data to simulate the population distribution within a range of 300 km from east to west and 220 km from north to south. Figs 3 and 4 show the simulated population distribution of Luding based on smartphone data. As shown in Fig 3 , the area near the epicenter is sparsely populated, with only a certain amount of population distributed in Moxi Town, Detou Town, and Dewei Township. In particular, the population density is very low in the west of the epicenter, with few large settlements located in the area. The population is concentrated in the eastern plain areas, which are far from the epicenter. Among these areas, Luding County is 40 km away from the epicenter, while Shimian County is 47 km away from the epicenter. The rest of the population is sporadically distributed along the bottom of the terrain ditches and the traffic lines. Fig 5 shows a heat map of population near the epicenter at 12:00 p.m. on September 5. It can be seen in Fig 5 that at 12:00 p.m., the area near the epicenter, where the government of Moxi Town is located, was densely populated, with only a few people sporadically distributed in the surrounding area. There were also few people in Detuo Town, and its neighboring township Dewei had a certain amount of population.

https://doi.org/10.1371/journal.pone.0298236.g003

https://doi.org/10.1371/journal.pone.0298236.g004

https://doi.org/10.1371/journal.pone.0298236.g005

To reflect the population change before and after the earthquake, we compared the population density at 13:00 pm after the earthquake on September 5 ( Fig 6 ) with the data at 12:00 pm before the earthquake ( Fig 5 ) and calculated the density difference between the 2 time periods so that the communication outage and the movement of people caused by the earthquake can be reflected more objectively. Figs 5 and 6 show the changes in smartphone density before and after the earthquake in the earthquake area and the epicentral area, respectively. As shown in Fig 6 , after the earthquake, the population density of highly seismic regions near the epicenter in the IX degree zone in the southern part of Luding County, such as Moxi Town, decreased significantly; as shown in Fig 7 , the population density of areas farther away from the epicenter (such as Hanyuan County) did not change much, and the overall density decrease and increase of Xingjing County in the VI degree zone were relatively in balance. Yucheng District in Ya’an City, the most densely populated area, in the V degree zone, experienced an increase in the number of smartphones turned on. In the earthquake area of Luding County, the population density in Moxi Town, Detou Town, Yanzigou Town, Dewei Town and its surrounding area, as well as Wanggangping Township, Caoke Township and its surrounding area in Shimian County dropped sharply, from which it could be inferred that a significant number of out-of-service base stations and power outages occurred in these areas. Along National Highway 318 from Luding County to Tianquan County, there was a certain increase in population density, and the population on the periphery of Luding County also increased to a certain extent, indicating that tourists had already begun to evacuate out of the scenic areas half an hour after the earthquake.

https://doi.org/10.1371/journal.pone.0298236.g006

https://doi.org/10.1371/journal.pone.0298236.g007

Results and discussion

Analysis of population change in the segmented zones of the disaster area.

After the earthquake, the population distribution in the disaster area displayed a dynamic change. Starting from the size of population affected by out-of-service base stations after the earthquake, to the repair of communication and electric lines, and then to the subsequent evacuation to the outside along the traffic lines, the number and location of the population had been changing.

We selected several typical settlements in the Ⅸ and VII degree zones for the time series analysis of communication terminal volume. Moxi Town, Caoke Township, and Detou Town in the Ⅸ degree zone were chosen, of which Moxi Town was closer to the epicenter and was also one of the settlements heavily struck by the earthquake. Fig 8 shows an analysis of the population data by area and time period within 24 hours since 12:00 pm on September 5. At 1:00 pm, communication and power systems were severely damaged after the earthquake, resulting in a sharp drop in communication terminal volume. However, the communication repair was made very quickly. After the emergency communication vehicles entered the earthquake area, communication was partially restored. As of 7:00 pm that night, part of the communication and power systems had been restored. The communication terminal volume began to grow slowly. At 06:00 am on September 6, the population evacuated from the scenic areas and the rescue forces entering the disaster area were superimposed. As shown in Fig 8 , the crowd flow in the three settlements showed a sharp increase; the situation in Caoke Township and Detou Township was basically similar, and the time series curves of the communication terminal volume in the two places were highly consistent.

https://doi.org/10.1371/journal.pone.0298236.g008

We also examined statistics on changes in Shimian County, Luding County, and Yidong Town in the VII degree zone ( Fig 9 ) within 24 hours. Because of the extremely uneven population distribution in this zone, in which Shimian Town and Luding County Town were relatively densely populated and the damage to communication facilities after the earthquake was less serious than that in the Ⅸ degree zone, the decline in communication terminal volume was relatively slower than that in the other zones. At 11:00 pm, the communication terminal volume showed a natural decline due to equipment shutdowns. Similarly, the population change in the three typical settlements in the VII degree zone is highly consistent. What is different from the Ⅸ degree zone is that there was a continuous outflow of people after the partial restoration of communication.

https://doi.org/10.1371/journal.pone.0298236.g009

Simulation of out-of-service base station situations in the disaster area

https://doi.org/10.1371/journal.pone.0298236.g010

https://doi.org/10.1371/journal.pone.0298236.g011

After the intensity map was officially released, we also calculated the changes in simulated population based on smartphones in different intensity zones before and after the earthquake, and in order to exclude the impact of smartphone shutdowns for lunch breaks on the out-of-service situation, we also calculated the changes in the smartphone-based population in the same time period and the same area on September 4. In the calculation of the final result, as the base, the data on September 4 was subtracted so that we could obtain the real out-of-service situation on September 5. Table 1 shows the statistics of the out-of-service rate based on smartphone population data. In Table 2 , it can be seen that the out-of-service rate in the VI degree zone is -1.62%, while the out-of-service rate on September 4 is 1.32%. The out-of-service rate decreased after the earthquake, which means that after the earthquake, some people in this zone were affected and kept learning about the disaster through their mobile terminals. By using the same method and after deduction of the base, we finally calculated that the out-of-service rate in the VII degree zone at 19.42%, which is contrary to the traditional view that service outages at communication base stations will occur only in the VIII degree zone; thus, it is inferred that there exists a certain out-of-service rate in the VII+ degree zone. In the VIII degree and IX degree zones, we believe that the number of people who were strongly affected by the earthquake and turned off their smartphones at 1:00 pm was small. Thus, the final results of the out-of-service rate in VIII degree and IX degree zones are 48.40% and 78.11%. The reduction in the number of smartphones after the earthquake may be affected by multiple factors, but an out-of-service base station is the most important one. Therefore, the above results can objectively reflect the base station out-of-service rate in different intensity zones.

https://doi.org/10.1371/journal.pone.0298236.t001

https://doi.org/10.1371/journal.pone.0298236.t002

With the same method, we tracked back to the population heat map data of earthquakes with a magnitude of 6.0 or above in mainland China since 2017 in the database, analyzed and processed the communication location big data in the zones of VII degree or above during multiple post-earthquake time periods in the course of earthquake emergency response, used the spatial change of terminal data to infer the damage of communication base stations and the response of people, and summarized the regression modeling so that we can spatially infer the distribution range of the hardest hit areas. Based on the change trend of terminal location data, the research proceeded with the extraction of seismic damage information to correct the empirical isoseismal line so as to make up for the lack of first-hand field data of the existing rapid assessment system. Finally, a rapid assessment method of the seismic influence field based on communication big data is established, and an operable software system is formed, which can provide technical support for earthquake emergency rescues. At present, the related research results have been popularized and applied in many regions.

https://doi.org/10.1371/journal.pone.0298236.g012

Conclusions

With good timeliness and high data accuracy, smartphone population heat map data can be applied to the actual practice of earthquake emergency response. Taking the Luding Ms 6.8 earthquake as an example, we have obtained the simulation data of the population distribution in the earthquake area in real time with the support of smartphone location data. Having been tested by dozens of large and small earthquakes, the data model has become more mature. Based on the cross-corroboration of multiple validation channels, we find that the data obtained at this time is consistent with the actual situation. In the hours after an earthquake, smartphone location data can provide strong support for the government’s disaster relief work when the actual disaster situation is still uncertain. The application of this method in the emergency management field is also widely recognized. Compared with traditional modes of disaster information acquisition, smartphone location data has obvious advantages in terms of cost, accuracy, efficiency, and other aspects. With high timeliness and good continuity of data, as well as the absence of additional investment in hardware equipment and organization of large-scale field investigations, this method can realize the rapid acquisition of the location change rule of communication terminal population in a disaster area after an earthquake. By analyzing the characteristics of spatial–temporal changes in smartphone location big data before and after the earthquake, we can infer the strength and distribution range of earthquake intensity. In addition, unlike the “black box period” in previous earthquakes, this work enables us to provide the government with a sufficient and reliable information basis for disaster relief within one hour after the earthquake, even when a more accurate and complete picture of the disaster situation is not available. This approach can greatly complement the shortcomings of the existing rapid seismic assessment systems and enables the rapid obtainment of highly credible disaster information in a timely manner when a sudden and destructive earthquake occurs ( Table 3 ).

https://doi.org/10.1371/journal.pone.0298236.t003

An out-of-service base station will cause a large amount of mobile terminal location data to disappear after an earthquake. During this earthquake, the magnitude reached 6.8, the maximum intensity of the epicenter was IX degree, and the hardest hit area experienced power interruptions and out-of-service base stations, which directly led to a cliff-like drop in the mobile terminal location data we obtained after the earthquake. The closer to the epicenter, the more obvious this phenomenon was. After the communication and power lines were repaired, the acquisition of smartphone location data began to recover gradually. Determining the base station out-of-service rate plays an important indicative role in estimating the seismic intensity of the hardest hit area in the first moments. This further helps us to obtain the seismic intensity, isoseismal line, and other critical information in the affected areas. With the support of communication big data, we can correct the empirical isoseismal lines based on the seismic damage information extracted from the model. In addition, we can also verify the locations that cannot be accurately assessed and dynamically modify the isoseismal lines to gradually improve the assessment accuracy. By combining empirical models and automatic computer processing technologies, we can obtain the earthquake disaster information in a faster manner. In the meantime, we still need to discover the characteristics of seismic damage in different regions and of different magnitudes, and on this basis, we can establish the basis of determining seismic intensity according to the performance of smartphone location data in different situations. This will ultimately further improve the method of obtaining the seismic intensity influence field based on mobile Internet data and enhance our earthquake response capability.

In practice, we have also found some shortcomings in using mobile terminals to judge disaster situations. Even in modern society, the utilization rate of smartphones is still closely related to factors such as age, geographic location and economic status. Large-intensity earthquakes usually occur in mountainous and sparsely populated areas. In the disaster area of this Luding earthquake, there was also an extreme situation where the population in the mountainous area in the northwest of the disaster area was extremely small and there was a clear gap in population composition, as most of the population were aged over 60 or children, with low smartphone usage rates, which may have led to underestimated location data. In contrast, Moxi Town and Detou Town near the epicenter were relatively densely populated. As Moxi Town is a scenic location, most of the population here were tourists, with a high smartphone usage rate, which may have led to overestimating the pre-earthquake data to a certain extent if a unified model was used. Therefore, in order to estimate and judge disaster situations more accurately, it is necessary to customize a suitable regional population model to take these differential factors into account. In the Luding Ms 6.8 earthquake, the research team applied different population models to adapt to the special conditions in Moxi Town and the northwestern mountainous areas. This has fully demonstrated the importance of flexibility and customization strategies. This approach can not only make up for the data bias caused by uneven smartphone usage rate but also help improve the accuracy of disaster assessment.

Smartphone-based location big data has great application potential in future earthquake emergency management. This technology can not only realize the real-time estimation of population distribution during an earthquake but also achieve in-depth analysis based on its rich attribute information, such as tracking the places of origin of the population, the population movement vector, traffic jams, etc., which could not be accomplished in past earthquake emergency responses. The mining of smartphone data can derive richer applications to better serve earthquake emergency responses. The application of mobile location big data sources can replace the traditional field investigation of seismic damages. We can calculate the spatial distribution of a disaster based on the disaster information reflected in big data so as to obtain the simulated results of disaster distribution, which will greatly improve the efficiency of seismic damage information acquisition.

Supporting information

S1 file. calculation on out-of-service rate based on mobile terminal location data in this earthquakes..

https://doi.org/10.1371/journal.pone.0298236.s001

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Study: Heavy snowfall and rain may contribute to some earthquakes

The results suggest that climate may influence seismic activity

Massachusetts Institute of Technology

When scientists look for an earthquake’s cause, their search often starts underground. As centuries of seismic studies have made clear, it’s the collision of tectonic plates and the movement of subsurface faults and fissures that primarily trigger a temblor. 

But MIT scientists have now found that certain weather events may also play a role in setting off some quakes. 

In a study appearing today in  Science Advances , the researchers report that episodes of heavy snowfall and rain likely contributed to a swarm of earthquakes over the past several years in northern Japan. The study is the first to show that climate conditions could initiate some quakes. 

“We see that snowfall and other environmental loading at the surface impacts the stress state underground, and the timing of intense precipitation events is well-correlated with the start of this earthquake swarm,” says study author William Frank, an assistant professor in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “So, climate obviously has an impact on the response of the solid earth, and part of that response is earthquakes.”

The new study focuses on a series of ongoing earthquakes in Japan’s Noto Peninsula. The team discovered that seismic activity in the region is surprisingly synchronized with certain changes in underground pressure, and that those changes are influenced by seasonal patterns of snowfall and precipitation. The scientists suspect that this new connection between quakes and climate may not be unique to Japan and could play a role in shaking up other parts of the world. 

Looking to the future, they predict that the climate’s influence on earthquakes could be more pronounced with global warming.  

“If we’re going into a climate that’s changing, with more extreme precipitation events, and we expect a redistribution of water in the atmosphere, oceans, and continents, that will change how the Earth’s crust is loaded,” Frank adds. “That will have an impact for sure, and it’s a link we could further explore.”

The study’s lead author is former MIT research associate Qing-Yu Wang (now at Grenoble Alpes University), and also includes EAPS postdoc Xin Cui, Yang Lu of the University of Vienna, Takashi Hirose of Tohoku University, and Kazushige Obara of the University of Tokyo.

Seismic speed

Since late 2020, hundreds of small earthquakes have shaken up Japan’s Noto Peninsula — a finger of land that curves north from the country’s main island into the Sea of Japan. Unlike a typical earthquake sequence, which begins as a main shock that gives way to a series of aftershocks before dying out, Noto’s seismic activity is an “earthquake swarm” — a pattern of multiple, ongoing quakes with no obvious main shock, or seismic trigger. 

The MIT team, along with their colleagues in Japan, aimed to spot any patterns in the swarm that would explain the persistent quakes. They started by looking through the Japanese Meteorological Agency’s catalog of earthquakes that provides data on seismic activity throughout the country over time. They focused on quakes in the Noto Peninsula over the last 11 years, during which the region has experienced episodic earthquake activity, including the most recent swarm. 

With seismic data from the catalog, the team counted the number of seismic events that occurred in the region over time, and found that the timing of quakes prior to 2020 appeared sporadic and unrelated, compared to late 2020, when earthquakes grew more intense and clustered in time, signaling the start of the swarm, with quakes that are correlated in some way. 

The scientists then looked to a second dataset of seismic measurements taken by monitoring stations over the same 11-year period. Each station continuously records any displacement, or local shaking that occurs. The shaking from one station to another can give scientists an idea of how fast a seismic wave travels between stations. This “seismic velocity” is related to the structure of the Earth through which the seismic wave is traveling. Wang used the station measurements to calculate the seismic velocity between every station in and around Noto over the last 11 years. 

The researchers generated an evolving picture of seismic velocity beneath the Noto Peninsula and observed a surprising pattern: In 2020, around when the earthquake swarm is thought to have begun, changes in seismic velocity appeared to be synchronized with the seasons. 

“We then had to explain why we were observing this seasonal variation,” Frank says.

Snow pressure

The team wondered whether environmental changes from season to season could influence the underlying structure of the Earth in a way that would set off an earthquake swarm. Specifically, they looked at how seasonal precipitation would affect the underground “pore fluid pressure” — the amount of pressure that fluids in the Earth’s cracks and fissures exert within the bedrock. 

“When it rains or snows, that adds weight, which increases pore pressure, which allows seismic waves to travel through slower,” Frank explains. “When all that weight is removed, through evaporation or runoff, all of a sudden, that pore pressure decreases and seismic waves are faster.” 

Wang and Cui developed a hydromechanical model of the Noto Peninsula to simulate the underlying pore pressure over the last 11 years in response to seasonal changes in precipitation. They fed into the model meteorological data from this same period, including measurements of daily snow, rainfall, and sea-level changes. From their model, they were able to track changes in excess pore pressure beneath the Noto Peninsula, before and during the earthquake swarm. They then compared this timeline of evolving pore pressure with their evolving picture of seismic velocity. 

“We had seismic velocity observations, and we had the model of excess pore pressure, and when we overlapped them, we saw they just fit extremely well,” Frank says. 

In particular, they found that when they included snowfall data, and especially, extreme snowfall events, the fit between the model and observations was stronger than if they only considered rainfall and other events. In other words, the ongoing earthquake swarm that Noto residents have been experiencing can be explained in part by seasonal precipitation, and particularly, heavy snowfall events. 

“We can see that the timing of these earthquakes lines up extremely well with multiple times where we see intense snowfall,” Frank says. “It’s well-correlated with earthquake activity. And we think there’s a physical link between the two.”

The researchers suspect that heavy snowfall and similar extreme precipitation could play a role in earthquakes elsewhere, though they emphasize that the primary trigger will always originate underground. 

“When we first want to understand how earthquakes work, we look to plate tectonics, because that is and will always be the number one reason why an earthquake happens,” Frank says. “But, what are the other things that could affect when and how an earthquake happens? That’s when you start to go to second-order controlling factors, and the climate is obviously one of those.”

This research was supported, in part, by the National Science Foundation.

Written by  Jennifer Chu, MIT News

Science Advances

10.1126/sciadv.ado1469

Article Title

“Untangling the environmental and tectonic drivers of the Noto (Japan) earthquake swarm”

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Can heavy snowfall trigger earthquakes? A new study suggests a link

A new theory suggests that heavy snowfall could be a factor in triggering swarms of earthquakes — evidence that what’s happening on and above the Earth’s surface may play a role in events underground.

That’s according to a study published Wednesday in the journal Science Advances , which points to a potential link between heavy snowfall on Japan’s Noto Peninsula and thousands of quakes measured there since late 2020.

It’s one of the first studies to link changes in weather or climate to earthquake activity.

"Those big snowfall events seem to correlate well with the start of these big earthquake swarms," said William Frank, an author of the study and an assistant professor of Earth, atmospheric and planetary sciences at the Massachusetts Institute of Technology. "We shouldn’t forget the climate itself can also play a role in changing the stress state at depth where earthquakes are happening."

The study does not say that changes in climate or weather are directly causing earthquakes. Instead, it suggests that the rate of earthquakes in a given area could increase or decrease because of changes in how water is moving beneath the surface and how much pressure the weight of the snow exerts.

David Shelly, a research geophysicist with the United States Geological Society, said the study raises interesting questions, but more research is needed to validate its findings.

“What they’re showing is that seismicity seems to initiate and ramp up following these times of snowfall. It’s an intriguing observation, I’m not sure it’s conclusive,” he said.

Still, Shelly expects other seismologists to be keenly interested in the research.

“The sequence is a subject of a lot of interest in the community. This is the first paper that suggests there might be an environmental factor,” Shelly said.

The study evaluated thousands of earthquakes on Japan’s Noto Peninsula, which is located about 190 miles northwest of Tokyo on the Sea of Japan. In 2021, after a heavy snow, the rate of earthquakes in the area rose substantially — with hundreds recorded each day.

“They were seeing a factor-10 increase in the number of earthquakes in this region compared to what was happening previously,” Frank said.

The biggest in the swarm was a magnitude-7.5 earthquake on New Year’s Day of this year. More than 240 deaths were linked to this mainshock event, according to the Japanese Red Cross Society .

The timing of the swarm was also unusual compared to a typical sequence of aftershocks, according to Frank.

“They have this statistical signature of earthquakes being driven by something else,” he said.

He and the other study authors compared the pattern of earthquakes in the Noto Peninsula with a model of pressure within pores under the Earth’s surface. The model accounts for above-ground factors that change the pressure within the rock underneath — such as seasonal sea-level changes, fluctuations in atmospheric pressure and heavy rain or snow events.

The results indicated that the weight of the snowpack on the Noto Peninsula increased the pressure in those pores. Pressure changes from adding and removing weight as snow accumulates and then melts can destabilize pre-existing faults, the researchers think.

“If you have enough snow, you’ll actually be pushing the earth down and if you take the snow off, it will bounce back up,” Frank said.

Some previous research has also suggested that environmental factors can play a role in initiating earthquakes. In a 2019 study , Shelly and his colleagues found that spring snowmelt flowing into cracks in the earth near a caldera in the area around Mammoth Lakes, California, likely triggered a swarm of earthquakes.

The snowmelt recharged groundwater rapidly, and the pressure changes appeared to trigger activity on relatively shallow faults. The researchers found that seismic activity in the region was historically about 37 times more likely during wet periods than dry.

Research like this has piqued scientists’ curiosity about whether climate change could have a small influence on earthquake behavior.

“There are more studies that show a connection in some cases between the timing of earthquakes and these processes happening on the surface,” Shelly said. “What I understand of climate change forecasts is that there’s more extreme weather, more periods of prolonged drought and extreme wet periods. That might make these effects more obvious.”

This article was originally published on NBCNews.com

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Groundwater salinization risk in coastal regions triggered by earthquake-induced saltwater intrusion

• Original Paper
• Open access
• Published: 09 May 2024

Cite this article

You have full access to this open access article

• Alban Kuriqi   ORCID: orcid.org/0000-0001-7464-8377 1 &
• Ismail Abd-Elaty 2  

Anthropogenic factors such as over-pumping and natural events such as earthquakes impact coastal aquifers by reducing freshwater recharge, aquifer water budgets, and increasing saltwater intrusion (SWI). This study investigates the impact of hydrodynamic forces induced by earthquakes on SWI in one hypothetical case, namely, the Henry problem, and a real case of the Biscayne aquifer located in Florida, USA. The analysis was carried out using the analytical solution of estimating the earthquake’s induced hydrodynamic pressure and applying the SEAWAT code to investigate the SWI for the base case and three scenarios, namely for the horizontal acceleration (α h ) by 0.10 g, 0.20 g, and 0.30 g. The results show that earthquakes might considerably increase the SWI in coastal aquifers. Moreover, the rise in salinity across expansive land areas significantly threatens agricultural productivity and jeopardizes food security. Namely, in the case of Biscayne aquifer, salinity was increased by 12.10%, 21.90%, and 45.70% for the horizontal seismic acceleration of 0.1 g, 0.20 g, and 0.30 g, respectively. Hence, the conclusions drawn from this study underscore the need for carefull consideration of earthquake impacts in future planning and water management strategies for coastal regions. This proactive approach is crucial to preemptively address and mitigate the groundwater salinization hazard associated with SWI fluctuations due to earthquakes.

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1 Introduction

Natural seismic activities such as earthquakes are prevalent worldwide, frequently occurring phenomena, especially in the boundary areas between tectonic plates (Galvez et al. 2021 ). High-magnitude earthquakes not only result in substantial casualties and property losses but also bring about significant alterations to the natural environment (Doocy et al. 2013 ). These changes encompass liquefaction, modifications in stream and spring flows, and variations in groundwater characteristics, including geochemistry, temperature, and turbidity. Moreover, shifts in groundwater heads within wells and the occurrence of mud volcano eruptions, extensively documented for centuries, further highlight the manifold impacts of such seismic events on the Earth’s dynamics (De Boer and Sanders 2021 ; Oluwafemi et al. 2018 ).

Nonetheless, there is a limited body of research documenting alterations in groundwater temperature and other physical-chemical parameters following an earthquake, especially in costal aquifers (Lee et al. 2021a ; Miyakoshi et al. 2020 ; Shi and Wang 2014a ; Tronin et al. 2002 ). Changes in groundwater temperature and permeability have been observed exclusively after significant earthquakes. Specifically, coseismic alterations stem from shifts in aquifer permeability, resulting from the unclogging of fractures induced by seismic waves (Shi and Wang 2014b ). Dynamic responses primarily explain most hydrologic risks associated with seismic activities (Albini et al. 2013 ; De Boer and Sanders 2021 ). Earthquakes impact groundwater through the interaction of seismic forces with saturated porous media and the subsequent transfer of stress from solid grains to interstitial fluids (Medici et al. 2021 ; Medici and West 2021 ). In many cases alterations in groundwater within aquifers and shifts in surface water and groundwater quantities, as illustrated in Fig.  1  are linked to earthquakes.

Schematic presentation of the earthquake impact on soil and hydraulic response; + and - signs indicate the type of response (positive and negative), adapted after Mays ( 2010 )

Changing water levels following an earthquake are classified as step-like changes, sustained changes, and oscillations (Lee et al. 2021b ; Shi and Wang 2015 ). The near-field groundwater levels usually show a step-like change during an earthquake (Shi et al. 2013 ). Step-like changes are explained by the elastic—static strain or the undrained consolidation at a few source dimensions of earthquake epicenters (Galvez et al. 2021 ; Sukkarak et al. 2021 ). The observed sustained alterations in water levels in the far field, coupled with shifts in aquifer permeability induced by seismic waves, have emerged as plausible mechanisms for this response (Frind 1982 ; Panthi et al. 2022 ). Moreover, sustained changes are observed when the epicenter distance is more than one ruptured fault length (Shi and Wang 2014b ). The enduring impacts are associated with lasting or temporary alterations in rock properties, manifesting in undrained contraction, liquefaction, or modifications in aquifer porosity and permeability (Sakr 1999 ).

Groundwater level oscillations in wells, induced by surface waves from a distant seismic event, result in the expansion and compression of aquifers, giving rise to resonant motion in high-transmissivity aquifers (Zhao et al. 2021 ; Zheng et al. 2021 ). After the 1999 Chi-Chi earthquake in Taiwan, there was a notable rise in groundwater levels in most near-field wells, attributable to aquifer expansions (Lai et al. 2021 ).

Furthermore, in the aftermath of the 2004 Sumatra earthquake, even at a distance of over 5000 km in Japan, approximately 50% of the monitoring wells registered fluctuations in groundwater levels that aligned with the recorded seismic strain (Ammon et al. 2005 ; Wang and Liu 2006 ).

The global earthquake model (GEM) project compiled a historical catalog documenting non-instrumentally recorded earthquakes from 1000 to 1903 (Crowley et al. 2013 ). This catalog identifies earthquakes of magnitude seven or higher, encompassing approximately 825 events (Albini et al. 2013 ). The assessment relied on thorough archival research and historical evaluation. Figure  2 provides a comprehensive overview of the potential seismic impact across different regions, offering insights into the expected peak ground acceleration (PGA) levels with a statistical context for a half-century timeframe.

A global-scale seismic hazard map showing the PGA for 10% exceedance probability in 50 years, after Nikitas et al. ( 2019 )

Groundwater in coastal aquifers is vital for various purposes, including water supply. Nevertheless, SWI impairs fresh groundwater and increases salinity (Abd-Elaty et al. 2022a ). Assessment of the water quality indicators is essential for integrated water resource management and future sustainable development (Abd-Elaty et al. 2019 ). Groundwater is vital as ample groundwater storage supports the water supply during drought (Bear and Cheng 2010 ). Saltwater intrusion poses a serious challenge in coastal areas globally (Alfarrah and Walraevens 2018 ). Saltwater intrusion arises from human activities and natural events, including elevated sea levels, excessive pumping, non-proper surface water runoff management, non-sustainable land use practices, and artificial recharge (Abd-Elaty et al. 2021 , 2022b ; Bear et al. 1999 ).

The surface hydrologic system exhibits noteworthy responses to earthquake events, with particularly notable changes in stream flow that persist until the next rainfall event occurs (Muir-Wood and King 1993 ). The peak hydrograph can occur a day to several weeks after the earthquake (Matsumoto et al. 2003 ). Also, the peak flow in rivers typically occurs during periods of high precipitation or snowmelt when water inputs into the river system are at their highest (Kohout 1964 ). Shi et al. ( 2013 ) demonstrated that stress-induced alterations during earthquakes lead to the opening and closing of saturated micro-cracks in rocks. These dynamic changes result in fluctuations, causing either a decrease or increase in groundwater discharge into streams. Wong and Wang ( 2007 ) reported an increase in stream flow at Sespe Creek, California, following several earthquakes. Nevertheless, their analysis did not account for basin contraction or dilatation resulting from coseismic strain.

Hence, the observed alterations in the hydrologic system are attributed to the earthquake-induced dynamic strain. Nespoli et al. ( 2018 ) utilized the TOUGH2 code to examine the fluctuations in groundwater levels linked to the initial ML5.9 mainshock of the 2012 seismic sequence in Emilia, Italy. Furthermore, the findings align with the observations recorded in deep and shallow wells.

Our current understanding and literature review show a notable lack of research examining the influence of dynamic forces, particularly earthquakes, on saltwater intrusion. Thus, this study contributes to the existing body of knowledge by addressing a significant gap in the literature, exploring the hitherto understudied impact of seismic activity on saltwater intrusion. The findings enhance our understanding of the complex interactions between earthquakes and coastal aquifers, providing valuable insights for water resource management and disaster preparedness.

This study primarily aimed to examine the impact of earthquakes on SWI in coastal aquifers, focusing on Henry’s problem. Additionally, the research employs the real-world case of the Biscayne aquifer in Florida, USA, to investigate the seismic effects on SWI. The rest of the paper is organized as follows: Sect. 2 details the methodology dataset and provides brief information on the modeling process; Sect. 3 presents the key findings derived from the study; Sect. 4 delves into the significance of the findings and policy implications; and finally, Sect. 5 presents the main conclusions drawn from the study.

2 Materials and methods

Figure 3 illustrates the flowchart outlining the investigation methodology for analyzing saltwater intrusion in coastal aquifers considering the impact of earthquake. The diagram encompasses a hypothetical case study of Henry’s problem and applies to the Biscayne aquifer in Florida, USA.

A visual representation outlining the methodological approach employed in this study

The modeling process was executed utilizing the SEAWAT code, incorporating the dynamics of variable groundwater density. In the initial phase of our proposed methodology, comprehensive data acquisition took place, encompassing hydrogeological and hydrological data crucial for simulating the intricate nature of variable-density groundwater. This groundwork laid the foundation for the subsequent steps in our analysis.

The second phase involved the setup and calibration of our numerical model, followed by its application to two selected case studies for simulating saltwater intrusion. Rigorous calibration ensures the accuracy and reliability of our model in representing real-world conditions, enhancing its predictive capabilities.

Moving forward, the third phase introduced an analytical solution for estimating hydrodynamic pressure, a critical factor in understanding the dynamics of SWI. Furthermore, we explored various scenarios to assess the impact of earthquakes on saltwater intrusion, providing valuable insights into potential vulnerabilities and risks associated with seismic activity.

Finally, our research culminates in the proposition of practical and sustainable recommendations to mitigate the adverse effects of earthquakes on saltwater intrusion. By combining advanced modeling techniques, comprehensive data analysis, and strategic mitigation strategies, we contribute to a more resilient and sustainable management of groundwater resources in the face of dynamic environmental challenges.

2.1 Numerical model

The finite difference of SEAWAT V.4.00.05 code was used in this study, an upgraded version of the previous codes (Guo and Bennett 1998 ; Guo and Langevin 2002 ) and SEAWAT 2000 (Langevin et al. 2003 ). This is a coupled version of MODFLOW (Harbaugh et al. 2000 ) and MT3DMS (Zheng 2006 ; Zheng and Wang 1999 ) designed to simulate three-dimensional, variable-density groundwater flow and multispecies transport.

The variable-density flow (VDF) process is used in SEAWAT code to solve the groundwater flow, according to Guo and Langevin ( 2002 ). While the integrated MT3D (IMT) process solved the following form of the advection-dispersion equation and is described by Zheng and Wang ( 1999 ).

2.2 Hypothetical case study of Henry’s problem

SEAWAT code is calibrated and then used in a hypothetical case study, Henry’s problem (Henry 1959 , 1964 ), to investigate the SWI. Henry’s problem domain is 2 m (horizontal), 1 m (vertical), and 10 cm wide. Figure  4 a presents the definition sketch of case study parameters using Henry’s problem. It shows the problem domain and boundary conditions.

The prevailing model outcomes for groundwater heads are visually presented in Fig.  4 b, encapsulating the foundational scenario of our study. This graphical representation offers a succinct portrayal, facilitating a comprehensive understanding of our system’s spatial distribution and magnitude of groundwater heads. Simultaneously, Fig.  4 c unveils the salinity results derived from the SEAWAT code, showcasing a remarkable concordance with other established models employed to address Henry’s problem.

Notably, our findings align closely with the seminal works of Frind ( 1982 ), Henry ( 1964 b), and Segol ( 1994 ). The observed congruence substantiates the reliability and robustness of our model, as its outcomes harmonize with those of well-recognized hydrological studies. The results of equi-concentration lines 17,000 ppm reached 82.75 cm from the seaside, respectively.

Henry’s problem results for a ) domain and boundary conditions, b ) velocity distribution and hydraulic head, and c ) salinity distribution

2.3 The Biscayne aquifer case study

The application of numerical simulation through the SEAWAT code was extended to the Biscayne aquifer in Florida, USA. This endeavor aimed to delve into the nuanced dynamics of SWI under the influence of seismic forces. Figure  5 a represents the aquifer domain under consideration in the picturesque yet hydrologically significant at the Coconut Grove area.

The delineated aquifer domain, illustrated in Fig.  5 b, is a rectangular expanse spanning 8 km, reaching a depth of 40 m below mean sea level and extending its width to 300 m. This carefully defined domain is crucial for our simulation, capturing the essential hydrogeological features and dimensions necessary for a comprehensive analysis of SWI dynamics under seismic accelartion.

Biscayne aquifer, Florida, a ) principal aquifers of Florida exposed at or near the land surface (Upchurch et al. 2019 ; after Alarcon et al. 2022 ) and b ) location of the study case, after Langevin ( 2001 )

Kohout ( 1964 ) investigated the groundwater flow using monitoring wells in the Cutler Ridge area (Fig.  5 a; near Miami) in south-eastern Florida. The research findings unveil a significant aspect of hydrological dynamics, indicating that approximately 12.5% of the groundwater discharge into Biscayne Bay undergoes circulation through the aquifer. Moreover, this circulation is notably concentrated within 130 m of the shoreline (Alarcon et al. 2022 ). These findings underscores the groundwater movement’s localized and impactful nature in the studied area, particularly along the coastal fringe. The 12.5% circulation through the aquifer contributes to the intricate balance of the Biscayne Bay ecosystem and emphasizes the critical role of nearshore hydrogeological processes.

This insight holds implications for our understanding of the spatial distribution of groundwater discharge, shedding light on the hydrological connectivity between the aquifer system and Biscayne Bay. The Biscayne aquifer has been a focal point in numerous studies examining SWI concerns. Notably, Lee and Cheng ( 1974 ); Langevin ( 2001 ); Abd-Elaty et al. ( 2022a ) have contributed to this body of research. Their work advanced the understanding of SWI dynamics by employing steady-state 2-D finite element models.

By focusing on this specific geographical context, we not only enhance the accuracy of our simulation but also contribute valuable insights into the potential impact of seismic events on the delicate balance of freshwater and saltwater in the Biscayne aquifer. This localized exploration provides a nuanced perspective on the interplay between earthquake forces and aquifer behavior, adding depth to our understanding of SWI dynamics in the unique hydrogeological setting of Coconut Grove, Florida.

2.4 Aquifer geometry and boundary conditions

Figures 6 a and 6 b provide a schematic representation of the study area, illustrating the geometry and the prescribed model boundary conditions within the aquifer. These visual depictions serve as essential references, offering a clear and concise overview of the spatial configuration and environmental constraints considered in our analysis. The aquifer’s detailed data and information were deduced from the available literature and reported by Langevin ( 2001 ). A 2D model was used for the Biscayne aquifer with a model domain including 50 layers and 80 columns.

Boundary conditions of Biscayne aquifer for a ) hydraulic head and b ) saline water concentration

In a single row, each cell dimension is defined as 100 m x 1 m x 1 m, corresponding to (Δx x Δz x Δy) dimensions, respectively. Drawing from existing literature, specific conditions are imposed on the side where 0 ≤ x ≤ 2100 m: a designated head (i.e., seawater level) is set, characterized by seawater concentrations of 35000 ppm, and a constant head positioned 0.22 m above mean sea level (AMSL). Conversely, on the land side where x = -5900 m, the concentration is equated to freshwater by 0 ppm and the freshwater recharge by 15 m³/day/m (Langevin 2001 ).

2.5 Aquifer hydraulic properties

The hydraulic parameters for the hypothetical case study of Henry’s problem and real study area of Coconut Grove are used in the model simulation. Also, it have been investigated and reported by Langevin ( 2001 ) and are presented in Table  1 .

This table outlines various boundary conditions, hydraulic parameters, and solution methods for a model simulating groundwater dynamics, particularly in the context of Henry’s problem and the Biscayne aquifer. The following are key points summarizing the most important parameters:

Flow characteristics

Inland freshwater Flux (Q in ) values quantify the rate of freshwater movement, distinguishing between Henry’s problem and the Biscayne aquifer.

Saltwater head (h s ) values delineate the head of the saltwater interface, a critical parameter for modeling seawater intrusion (Table  1 ).

Concentration profiles

Seaside saltwater and landside fresh water concentration values (C s and C f ) define the conditions for salinity, providing insights into the distribution of saltwater and freshwater at different boundaries (Table  1 ).

Hydraulic properties

Horizontal and vertical hydraulic conductivity (K h and K v ) values describe the aquifer’s permeability, influencing groundwater flow.

Also, the aquifer porosity (n) affects the volume of void spaces in the aquifer, impacting fluid storage, the specific storage is volume of water per volume of aquifer released under a unit decline in hydraulic head. The variable-density parameters used in the coupled system of flow and transport equations are longitudinal and transverse dispersivities (α L , α T ), and molecular diffusion coefficient (D*) characterize fluid movement and dispersion (Table  1 ).

Density and recharge

Freshwater and saltwater density values (ρ f and ρ s ) are crucial for calculating buoyancy effects. Vertical and horizontal hydraulic recharge values provide insights into water input and potential replenishment and changing the fluid density (Table  1 ).

Modeling approach

The chosen implicit finite-difference solver with upstream-weighting (GCG) and the specified grid parameters (i.e., number of columns, rows, and initial time step) detail the numerical methodology used for simulation (Table  1 ).

In summary, the table synthesizes critical parameters for modeling groundwater behavior, facilitating a comprehensive understanding of the hydrogeological system in Henry’s problem and the Biscayne aquifer. These details are crucial for accurately simulating, predicting, and managing groundwater flow and salinity dynamics.

The calibration of the Biscayne Aquifer model was carefully conducted, aligning with field data obtained from 21 observation wells dedicated to monitoring the concentration of total dissolved solids. These observations were derived from published data by Langevin ( 2001 ), utilizing a network of 21 monitoring wells, as illustrated in Fig.  7 a.

Results of SEAWAT at Biscayne aquifer for a ) calibration between the field data obtained from Langevin ( 2001 ) and b ) salinity, the vertical distribution of 0.5 isochlor (17,500 ppm)

The calibration process yielded insightful results. The residuals, representing the differences between observed and simulated values, ranged from − 5552 ppm to 388.20 ppm. The mean residual was calculated at 347.05 ppm, providing a central measure of the model’s accuracy. The root mean square (RMS), a comprehensive indicator of model performance, was determined to be 2085.70 ppm. Additionally, the standard error of the estimate was quantified at 459.88 ppm, offering a measure of the precision of the model’s predictions. Notably, the normalized RMS, a percentage expressing the RMS relative to the observed data range, stood at 6.13%. This metric is particularly valuable as it gauges the relative accuracy of the model in the context of the observed data variability (Chai and Draxler 2014 ).

In summary, the calibration process, anchored in a robust dataset and employing multiple observation wells, resulted in a model that effectively captures the intricacies of total dissolved solids concentration in the Biscayne aquifer. The reported metrics provide a comprehensive assessment of the model’s performance, instilling confidence in its predictive capabilities and contributing valuable insights to understanding groundwater dynamics in the studied area.

2.6 Analytical solution of hydrodynamic pressure

The analytical solution employed in this study computes the repercussions of an earthquake on water pressure. This method considers the dynamic and temporary effects of horizontal acceleration exerted on the reservoir. This analytical approach provides a comprehensive understanding of how such accelerations influence and alter water pressure dynamics by delving into the intricate interplay of forces during seismic events. This seismic force leads to a momentary surge in water pressure, estimating the system’s dynamic response to the seismic event. This hydrodynamic force ( Pe ) is given by Zangger’s formula (1952) (Neuman and Wilson 1980 ), Eqs.  1 , 2 , and 3 .

where C m : is the maximum value of pressure coefficient [-], \(\theta\) : is the angle in degree, which the upstream face of the dam makes with the horizontal; k h : the fraction of gravity adopted for horizontal acceleration ( α h ) such as 0.10 or 0.20 [-], g: the acceleration gravity [LT -2 ], γ s : the saline water density [ML -3 ], and h : the hydrostatic saline water head [L].

This section delves into the comprehensive analysis of groundwater dynamics, specifically focusing on variable density, and presents the results derived from the seismic impact on groundwater aquifers. The simulation, a pivotal aspect of our investigation, was executed utilizing SEAWAT code for Henry’s problem and the Biscayne aquifer. This approach enables a nuanced exploration of how earthquakes influence saltwater intrusion in these hydrogeological contexts. The integration of SEAWAT code ensures a robust and sophisticated modeling framework, allowing us to unravel the intricacies of seismic interactions with groundwater systems and enhance our understanding of the potential implications on SWI.

3.1 Effect of the earthquake on SWI on Henry’s problem

The seismic acceleration intensities considered in this study were 0.1 g, 0.2 g, and 0.3 g. The resulting earthquake forces translated into hydrostatic seawater pressures of 7.35 cm, 14.70 cm, and 22.05 cm, respectively, considering the original head of 1 m in Henry’s problem. For an in-depth examination of saltwater intrusion, encompassing hydrostatic seawater pressure and earthquake forces, we incorporated total head values of 1.0735 m, 1.147 m, and 1.2205 m into the model. The outcomes of this analysis, revealing the impact of saltwater intrusion, are depicted in Fig.  8 a and b, and 8 c for the respective total head values of 1.0735 m, 1.147 m, and 1.2205 m.

Henry’s problem results from salinity vertical distribution at earthquake effect for the horizontal seismic acceleration of a ) 0.1 g, b ) 0.2 g, and c ) 0.3 g

These visual representations shows the intricate relationship between seismic forces and hydrostatic pressures in influencing saltwater intrusion, offering valuable insights into the nuanced responses of the aquifer system across different earthquake intensities. Specifically, the intrusion depths in the aquifer reached 92.63 cm, 95.75 cm, and 101.25 cm, compared to the baseline of 82.75 cm for an isochlorine concentration of 17,500 ppm.

These findings highlight a discernible increase in saltwater intrusion within coastal aquifers under the influence of earthquake forces. The observed variations underscore the need for a comprehensive understanding of the interplay between seismic events and hydrogeological systems, providing critical information for effective management and mitigation strategies in vulnerable coastal regions.

3.2 Effect of the earthquake on SWI for Biscayne aquifer

This scenario was examined to delineate the freshwater-seawater interface dynamics under the influence of seismic activity. The aquifer, with a depth of 40 m, experiences varying equivalent hydrostatic seawater pressures of 2.94 m, 5.88 m, and 8.82 m for horizontal accelerations of 0.1 g, 0.20 g, and 0.30 g, respectively. The numerical model effectively incorporates these equivalent heads, visually representing the evolving interface in response to seismic forces (Fig.  9 ). The initial configuration established the groundwater table at the landside and other relevant parameters. In Fig.  9 a–c, the model’s salinity outcomes are depicted, showcasing the isochlorine concentration of 17,500 ppm extending to distances of 4178 m, 4572 m, and 5470 m for horizontal accelerations of 0.10 g, 0.20 g, and 0.3 g, respectively, compare with 3610 m at the base case. The distance is calculated from the shore line (X = 0 m) at the base of the aquifer. Additionally, there is an incremental salt water intrusion of 568 m, 962 m, and 1860 m, respectively.

Salinity results for Biscayne aquifer for the vertical distribution of 0.5 isochlor (17,500 ppm) at earthquake effect for the horizontal acceleration of a ) 0.1 g, b ) 0.2 g, and c ) 0.3 g

These findings underscore the susceptibility of the aquifer to heightened Saltwater Intrusion (SWI) in response to the dynamic forces exerted by seismic events. Figure  10 shows results from Henry’s problem, wherein the base case exhibits a salinity mass of 0.42209 kg. These insights contribute to a comprehensive understanding of the aquifer’s vulnerability and the potential for increased SWI under seismic influence. Furthermore, the salinity mass demonstrated an increase, reaching 0.52716 kg, 0.55598 kg, and 0.55611 kg at horizontal accelerations of 0.10 g, 0.20 g, and 0.30 g, respectively, as compared to the baseline value of 0.42209 kg in the Henry problem. The Henry’s problem salinity increased by 24.90%, 31.70%, and 41.70% compare with the base case.

Salt mass balance in the two case studies for different horizontal seismic accelerations

The base case’s salinity mass was recorded at 891,486 kg in the Biscayne aquifer scenario.

Moreover, the introduction of horizontal accelerations at 0.1 g, 0.20 g, and 0.30 g resulted in an elevation of the equivalent hydrostatic seawater pressure to 2.94 m, 5.88 m, and 8.82 m, respectively. Consequently, the salinity mass escalated to 999,342 kg, 1,086,460 kg, and 1,298,470 kg, signifying a notable impact of dynamic earthquake acceleration on groundwater levels within coastal aquifers and the consequent influence on aquifer salinity (Fig.  10 ). The Biscayne aquifer salinity was increased by 12.10%, 21.90%, and 45.70% compare with the baseline case.

4 Discussion

The global concern on the saltwater intrusion in coastal regions is compounded by various factors, including over-pumping driven by population growth, scarce rainfall, rising sea levels due to climate change, and seismic activities. Despite extensive documentation of anthropogenic influences on SWI, there is a notable gap in research particulary regarding the impact of earthquakes on SWI and its associated environmental consequences. The intersection of seismic forces with SWI represents an intricate and understudied aspect, necessitating a deeper exploration to comprehensively understand and address the multifaceted challenges posed by the dynamic interplay of geological and anthropogenic factors in coastal aquifers (Stein et al. 2019 ).

Henry ( 1964 ) was among the pioneering researchers who discovered that earthquakes in southern California induced phase shifts in water levels attributable to tidal strain. Moreover, the dynamic force of seismic activities was observed to enhance aquifer permeability. Notably, this augmentation in permeability might be temporary, influenced by potential biogeochemical processes that could reseal or obstruct fractures generated during seismic events. The duration and sustainability of this permeability increase are contingent upon the geological characteristics of the aquifer formation (Matsumoto et al. 2003 ; Reese and Cunningham 2000 ). The sedimentary geological formations are generally more prone to SWI than mantle-originated ophiolites (Maury and Balaji 2015 ). Hence, the extent of geological formation heterogeneity plays a pivotal role on SWI. Low levels of heterogeneity seem to exert minimal impact on the thickness of mixing zones, distinguishing between freshwater and saltwater.

Conversely, when aquifer properties exhibit significant variations in heterogeneity, there is a profound influence on the dynamic behavior of SWI. The intricate interplay of heterogeneity in geological formations emerges as a critical factor, demonstrating that the magnitude of these variations significantly shapes the dynamics of saltwater intrusion within the aquifer system. Shi and Wang ( 2015 ) scrutinized alterations in groundwater levels post the Wenchuan earthquake, employing data from three wells located approximately 300 km away from the epicenter. Their findings emphasized that permeability variations predominantly contributed to shifts in groundwater levels across the three wells. The seismic activities induced hydrostatic pressure changes, showcasing a direct correlation with water-level fluctuations.

Additionally, in the aftermath of a seismic event, the geological structure of the aquifer may incur damage, intensifying SWI. Consequently, substantial pumping from shallow freshwater layers can act as a catalyst, prompting saltwater movement from deeper zones and facilitating upward migration into the unconfined freshwater zone (Abd-Elaty et al. 2022c ). This intricate interplay underscores the interconnected dynamics between seismic forces, aquifer structure, and human activities impacting the delicate balance of groundwater salinity (Sakr 1999 ; Werner et al. 2013 ).

In contrast, our study’s results reveal a noteworthy distinction: In Henry’s problem, the mass salt concentration attains a saturation point despite escalating horizontal acceleration. Surprisingly, this saturation phenomenon is not replicated in the Biscayne aquifer, where the mass salt concentration continues to increase alongside horizontal acceleration. This discrepancy underscores the unique response of different aquifer systems to seismic forces.

Given the increasing frequency of earthquakes globally, often resulting in significant human casualties, it becomes imperative to assess their environmental consequences, particularly regarding SWI. Our findings indicate that understanding the varied impacts of seismic activities on aquifers is crucial for informed environmental management and sustainable groundwater resource utilization.

Therefore, in that context, it is essential to develop adaptation and manage the impact of seismic activities, earthquakes on SWI, and related environmental aspects such as the salinization of groundwater and land (Szota et al. 2015 ). In this regard, several measures have proved effective against SWI (Abd-Elaty et al. 2022c ). First, monitoring coastal groundwater systems holds paramount importance in unraveling the intricacies of SWI. Precise measurements of groundwater levels become instrumental in capturing accurate data regarding depth variations and salinity fluctuations. In essence, continuous monitoring of coastal aquifers is a foundational step toward understanding SWI dynamics comprehensively (Soltane et al. 2022 ; Werner et al. 2013 ).

Findings resulting from this study underscore the significant impact of seismic activities on groundwater levels and aquifer salinity in coastal areas. Earthquake-induced forces can lead to increased saltwater intrusion, which has implications for the management and sustainability of coastal aquifers. These results call for careful consideration when assessing the potential consequences of seismic events on groundwater resources.

5 Conclusions

Coastal water resources,vital for future development, face looming threats of salinization spurred by seismic activities, especially earthquakes. This study investigates the hypothetical case of Henry’s problem and the Biscayne aquifer in Florida, USA, aiming to discern the seismic impact on SWI in coastal regions. The simulation considers three scenarios: horizontal acceleration ( α h ) by 0.10 g, 0.20 g, and 0.30 g, where the SWI reached 92.63 cm, 95.75 cm, and 101.25 cm compared with 82.75 cm at the base case for the equi-concentration line 17,500 for Henry’s problem.

For the Biscayne aquifer, the equivalent hydrostatic seawater pressure reached 2.94 m, 5.88 m, and 8.82 m for a horizontal acceleration of 0.1 g, 0.20 g, and 0.30 g, respectively, with the intrusion of isochlorine 17,500 ppm reaching 4178 m, 4572 m, and 5740 m compared with 3610 m from shoreline measured at the bottom of the aquifer. The horizontal seismic accelerations increased saltwater intrusion by 568 m, 962 m, and 1860 m, respectively compare to the base case. In addition, increasing the saline water heads due to seismic forces will affect the water and land’s salinity and reduce crop productivity. By shedding light on the intricate dynamics between seismic forces and coastal aquifers, this study provides pivotal insights for strategic water resource management. The findings emphasize the necessity of seismic-aware planning, especially in coastal regions, to fortify future development against the challenges posed by earthquake-induced salinization. Thus, future research should explore the cyclical effect of seismic acceleration on SWI and develop cost-effective strategies to mitigate or potentially prevent deep inland SWI.

Data availability

Not applicable.

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Acknowledgements

The authors express their sincere appreciation to the Department of Water and Water Structures Engineering, Faculty of Engineering, Zagazig University Egypt, for the instrument and software facilities. Alban Kuriqi is grateful for the Foundation for Science and Technology’s support through funding UIDB/04625/2020 from the research unit CERIS (DOI: 10.54499/UIDB/04625/2020).

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Kuriqi, A., Abd-Elaty, I. Groundwater salinization risk in coastal regions triggered by earthquake-induced saltwater intrusion. Stoch Environ Res Risk Assess (2024). https://doi.org/10.1007/s00477-024-02734-y

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