David Lallemant

• PhD
• Professor (Associate) at Nanyang Technological University

Climate-induced hazards exert uneven impacts on communities. However, conventional risk models rarely consider these disparities, which are critical for informing risk reduction decisions. Instead, they quantify risk solely based on the value of assets at risk, without accounting for how communities are differentially exposed and vulnerable to part...

Cost functions such as mean square error are often used in environmental model calibration. These treat observations as independent and equally important even though model residuals exhibit spatial dependence and additional observations near existing points do not provide as much information on the system as those elsewhere. To address this issue,...

Coastal currents significantly impact port activities, coastal landform morphodynamics, and ecosystem functioning. It is therefore necessary to understand the physical characteristics and natural variability of these currents within coastal settings. Traditional methods, such as harmonic analysis, assume stationarity of tide-driven currents and may...

Modern research often involves the collection or analysis of data and the use of specialized computer algorithms. Traditional text articles thus provide only partial documentation of a research study. Readers have limited ability to reproduce or utilize work if the source data are not available or if it relies on an algorithm that is described, but...

In Maritime Continent, the shift of intertropical convergence zone (ITCZ) location directly regulates the distribution of black carbon and hence affects public health in the region, but the mechanism and human health impacts have not yet been comprehensively revealed. Here we used multiple reanalysis datasets to investigate the long-term shift of s...

The recent destruction of thousands of homes by lava flows from La Palma volcano, Canary Islands, and Nyiragongo volcano, Democratic Republic of Congo, serves as a reminder of the devastating impact that lava flows can have on communities living in volcanically active regions. Damage to buildings and infrastructure can have widespread and long-last...

Coastal currents significantly impact port activities, coastal landform morphodynamics and ecosystem functioning. It is therefore necessary to understand the physical characteristics and natural variability of these currents within coastal settings. Traditional methods such as harmonic analysis assume stationarity of tide-driven currents and thus m...

Global climate change and sea level rise are increasing the risks of flooding for coastal communities. Probabilistic coastal flood risk analysis at regional or global scales requires flood models with relatively low data requirements and low computational costs. Bathtub inundation models, which compute flood depth as the difference between water le...

Classical approaches to flood hazard are obtained by the concatenation of a recurrence model for the events (i.e., an extreme river discharge) and an inundation model that propagates the discharge into a flood extent. The classical approach, however, uses “best‐fit” models that do not include uncertainty from incomplete knowledge or limited data av...

Extreme events such as natural and economic disasters leave lasting impacts on society and motivate the analysis of extremes from data. While classical statistical tools based on Gaussian distributions focus on average behaviour and can lead to persistent biases when estimating extremes, extreme value theory (EVT) provides the mathematical foundati...

Societal efforts to understand and mitigate threats posed by hazards are often informed by complex disaster risk models. Despite research demonstrating the disproportionate effects of disasters on vulnerable groups, current risk modeling approaches lack robust methods to account for such equity concerns. Consequently, efforts to develop evidence-ba...

Data-driven models for seismic damage and loss assessment of buildings have become more common in recent years due to the availability of large repositories of recorded and synthetic ground motions coupled with structural response simulation data. This paper explores the benefits of using bivariate and multivariate fragility functions to estimate e...

The availability of large repositories of recorded and synthetic ground motions coupled with structural response simulation data in recent years has led to the increased popularity of data-driven models for seismic damage and loss assessment of buildings. This paper explores the benefits of using bivariate fragility functions to estimate earthquake...

Classical approaches to flood hazard are obtained by the concatenation of a recurrence model for the events (i.e. an extreme river discharge) and an inundation model that propagates the discharge into a flood extent. The traditional approach, however, uses ‘best-fit‘ models that do not include uncertainty from incomplete knowledge or limited data a...

Computational simulators of complex physical processes, such as inundations, require a robust characterization of the uncertainties involved to be useful for flood hazard and risk analysis. While flood extent data, as obtained from synthetic aperture radar (SAR) imagery, have become widely available, no methodologies have been implemented that can...

The recent destruction of thousands of homes by lava flows from Cumbre Vieja, La Palma, Spain and Nyiragongo volcano, Democratic Republic of Congo, serves as a reminder of the devastating impact that lava flows can have on communities living in volcanically active regions. Damage to buildings and infrastructure in particular can have widespread and...

Following a disaster, crucial decisions about recovery resources often prioritize immediate damage, partly due to a lack of detailed information on who will struggle to recover in the long term. Here, we develop a data-driven approach to provide rapid estimates of non-recovery, or areas with the potential to fall behind during recovery, by relating...

Asia has the fastest growing population and economy, but it is also the most disaster‐prone region in the world. Resilience to disaster impacts from natural hazards will be key to the long‐term sustainability of this rapidly growing region. The first step to building resilience is to identify the key threats that this region faces. We describe thes...

Purpose Despite decades of social science research into disasters, practice in the field continues to be informed largely from a technical perspective. The outcome is often a perpetuation of vulnerability, as narrowly defined technical interventions fail to address or recognize the ethical, historical, political and structural complexities of real-...

Computational simulators of complex physical processes, such as inundations, require a robust characterization of the uncertainties involved to be useful for flood hazard and risk analysis. While flood extent data, as obtained from synthetic aperture radar (SAR) imagery, has become widely available, no methodologies have been implemented that can r...

Extreme events such as natural and economic disasters leave lasting impacts on society and motivate the analysis of extremes from data. While classical statistical tools based on Gaussian distributions focus on average behaviour and can lead to persistent biases when estimating extremes, extreme value theory (EVT) provides the mathematical foundati...

Cataloguing damage and its correlation with hazard intensity is one of the key components needed to robustly assess future risk and plan for mitigation as it provides important empirical data. Damage assessments following volcanic eruptions have been conducted for buildings and other structures following hazards such as tephra fall, pyroclastic den...

Weeks after a disaster, crucial response and recovery decisions require information on the locations and scale of building damage. Geostatistical data integration methods estimate post-disaster damage by calibrating engineering forecasts or remote sensing-derived proxies with limited field measurements. These methods are meant to adapt to building...

This is a contributing paper to the UN Office for Disaster Risk Reduction Global Assessment Report 2022. Also downloadable at: https://hdl.handle.net/10356/153502 ------------------ The goal of Disaster Risk Management (DRM) is to ensure that society continues to function, thrive, and recover quickly despite shocks arising from natural or human ac...

In the aftermath of a disaster, news and research attention is focused almost entirely on catastrophic narratives and the various drivers that may have led to the disaster. Learning from failure is essential to preventing future disasters. However, hyperfixation on the catastrophe obscures potential successes at the local scale, which could serve a...

The technical expertise of the engineering discipline is a dominant input into the information systems and products shaping our knowledge of disaster and climate-change crises. Despite decades of social science research into disasters, policy and practice in the field continues to be informed largely from a technical and data driven perspective. Th...

Devastating disasters that are predicted but ignored are known as Black Elephants—a cross between a Black Swan event and the proverbial elephant in the room. It’s time we acknowledged the looming natural hazard risks that no one wants to talk about.

Undervaluing the protections natural ecosystems provide against flooding has detrimental impacts for society, particularly given the increase in flood hazard in the context of climate and land-use changes. Against this backdrop, we develop a framework to quantify these natural protections, even in settings with limited available data. By applying t...

The Adaptation and Resilience in ASEAN report is part of an overarching project developed in collaboration with the COP26 Universities Network and the British High Commission to Singapore. The COP26 Universities Network is a growing group of over 80 UK-based universities working together to help deliver an ambitious outcome at COP26 and beyond. In...

Probabilistic loss assessments from natural hazards require the quantification of structural vulnerability. Building damage data can be used to estimate fragility curves to obtain realistic descriptions of the relationship between a hazard intensity measure and the probability of exceeding certain damage grades. Fragility curves based on the lognor...

After an earthquake, many responding organizations need to understand the scale and distribution of building damage to react effectively. However, their building damage information needs and information use remain poorly understood, limiting the efficacy of information production, sharing, and research. To clarify those needs, we conducted a two-pa...

This paper presents the advantages and opportunities for rapid preliminary intervention screening to enhance inclusion of green infrastructures in regional scale stormwater management. Stormwater flooding is widely recognised as a significant and worsening natural hazard across the globe; however, current management approaches aimed at the site sca...

Modern tsunami events have highlighted the vulnerability of port structures to these high-impact but infrequent occurrences. However, port planning rarely includes adaptation measures to address tsunami hazards. The 2011 Tohoku tsunami presented us with an opportunity to characterise the vulnerability of port industries to tsunami impacts. Here, we...

ICTs such as mapping platforms, algorithms, and databases are a central component of how society responds to the threats posed by disasters. However, these systems have come under increasing criticism in recent years for prioritizing technical disciplines over insights from the humanities and social science and failing to adequately incorporate the...

Following a disaster, crucial decisions about recovery resources often focus on immediate impact, partly due to a lack of detailed information on who will struggle to recover. Here we perform an analysis of surveyed data on reconstruction and secondary data commonly available after a disaster to estimate a metric of non-recovery or the probability...

International disaster databases and catalogs provide a baseline for researchers, governments, communities, and organizations to understand the risk of a particular place, analyze broader trends in disaster risk, and justify investments in mitigation. Perhaps because Singapore is routinely identified as one of the safest countries in the world, Sin...

In volcanology, remote sensing provides a global observation framework that is becoming increasingly valuable at multiple spatio-temporal scales. Satellite observations have improved monitoring for even the most remote volcanoes, provided a large-scale context to extrapolate field-based observations, and is now routinely used to characterize and mo...

Disaster risk research’s reliance on past events has proved inadequate when it comes to extreme events. This shortcoming stems from limited records (for example, due to the vast differences in timescales between geological processes and human records) and the dynamic nature of all three components of risk–drivers of change in hazard (e.g., climate...

Modern tsunami events have highlighted the vulnerability of port structures to these high-impact but infrequent occurrences. However, port planning rarely includes adaptation measures to address tsunami hazards. The 2011 Tohoku tsunami presented us with an opportunity to characterise the vulnerability of port industries to tsunami impacts. Here, we...

While thousands of lives are saved through effective earthquake risk reduction programs, very few opportunities exist to celebrate such successes. First, if the tangible benefits of such interventions are only felt in the advent of a large earthquake, there is often a large time-delay between the intervention and its benefits. Second, large earthqu...

Information systems increasingly shape our knowledge of crises such as disasters and climate change. While these tools improve our capacity to understand, prepare for, and mitigate such challenges, critical questions are being raised about how their design shapes public imagination of these problems and delimits potential solutions. Prior work in h...

Increasing amounts of data, together with more computing power and better machine learning algorithms to analyse the data, are causing changes in almost every aspect of our lives. This trend is expected to continue as more data keep becoming available, computing power keeps improving and machine learning algorithms keep improving as well. Flood ris...

While unprecedented amounts of building damage data are now produced after earthquakes, stakeholders do not have a systematic method to synthesize and evaluate damage information, thus leaving many datasets unused. We propose a Geospa-tial Data Integration Framework (G-DIF) that employs regression kriging to combine a sparse sample of accurate fiel...

Disasters continue to present tremendous obstacles to sustained development progress and the wellbeing of communities around the world. Key to mitigating the long-term impacts of disasters is the ability to rapidly respond and recover in ways that build resilience and protect hard-fought development gains. However, the information systems needed to...

Ballistic projectiles are the most frequently lethal volcanic hazard close to the vent. Recent eruptions of Ontake in 2014 and Kusatsu-Shirane in 2018 showed that un-reinforced, timber-framed buildings - those typically considered highly vulnerable to the dangerous penetration of ballistics - provided life-saving shelter from ballistic impact. Mode...

Increasing amounts of data, together with more computing power and better machine learning algorithms to analyse the data are causing changes in almost every aspect of our lives. This trend is expected to continue as more data becomes available, computing power increases and machine learning algorithms improve. Flood risk and impact assessments are...

This is an exploration of the predictive performance of a tephra depth inversion model using spatial statistics. The inversion model estimates the best eruption source parameters of an observed tephra deposit using nonlinear optimisation. The optimisation performs regression with an assumption that observed points are independent of each other. The...

The typical process of engineering risk analysis assumes a static state of vulnerability through the lifespan of the structure. However many civil engineering systems change states over time causing significant impact on their vulnerability. Such dynamic changes may involve an increase in vulnerability driven by deterioration processes (e.g. corros...

Civil infrastructure systems and communities are continually subjected to changes in environmental and urban settings, evolving expectations and preferences of the public, tightening budgets, and unpredictable political circumstances over their service lives. Conventional decision methods, which determine optimal strategies based on stationary risk...

The seismic risk of cities is constantly changing as cities themselves evolve in time. Key to making informed policy decisions to promote resilient cities is the ability to futurecast the risk of cities based on such potential policy decisions. This work demonstrates a flexible stochastic framework for analysing the potential seismic risk trajector...

Adaptive decision-making (ADM) is a structured process of learning, improving understanding, and ultimately adapting management decisions in a systematic and efficient way, aimed at reducing uncertainties over the course of the management timeframe. This approach holds a great potential for dealing with the challenges faced by civil infrastructure...

In the wake of large earthquake disasters, governments, international agencies, and large nongovernmental organizations scramble to conduct impact and damage assessments that help them understand the nature and scale of the emergency in order to orchestrate a complex series of emergency, response, and recovery activities. Using the Gorkha earthquak...

The relationship between the earthquake performance of an inventory of buildings and the seismic resilience of a residential community is examined, by quantifying the immediate post-earthquake reduction and recovery of the shelter-in-place housing capacity. The effect of several mitigation strategies that involve replacing portions of the existing...

This study proposes a framework for incorporating time-dependent fragility into large-scale risk assessment models, focusing on incremental building expansion as a significant driver of changes in vulnerability. In rapidly urbanizing areas in developing countries, the pay-as-you-go process of informal building construction and staged expansion is t...

This paper describes statistical procedures for characterizing and accounting for uncertainty in earthquake fragility models. Both fully analytical and non-parametric bootstrap methods are used to describe the conditional probability distribution of damage exceedance given an intensity measure. This enables the development of confidence intervals f...

Key to making informed policy decisions to promote resilient and sustainable future cities is the ability to predict their risk as it relates to dynamic changes in our urban environments, reflecting increases in population, specific urban growth patterns and evolving vulnerability. Yet current seismic risk assessment practice characterizes the comp...

Infographic showing global population growth and urbanisation.

This paper describes statistical procedures for developing earthquake damage fragility functions. Although fragility curves abound in earthquake engineering and risk assessment literature, the focus has generally been on the methods for obtaining the damage data (i.e., the analysis of structures), and little emphasis is placed on the process for fi...

A framework is presented for incorporating probabilistic building performance limit states in the assessment of community resilience to earthquakes. The limit states are defined on the basis of their implications to post-earthquake functionality and recovery. They include damage triggering inspection, occupiable damage with loss of functionality, u...

Recovering from a disaster is a deeply human event – it requires us to reach deep inside of ourselves and bring to others the best of who we can be. It’s painful, tiring, rewarding and meaningful. The responsibility can be heavy and at times you may feel alone. This is your companion through chaos that will connect you with over 100 other people wh...

Fragility functions that define the probabilistic relationship between structural damage and ground motion intensity are an integral part of performance-based earthquake engineering or seismic risk analysis. This paper introduces three approaches based on kernel smoothing methods for developing analytical and empirical fragility functions. A kernel...

The M7.0 earthquake in Haiti in 2010 was one of the most devastating in recorded history. Originally thought to have occurred along the Enriquillo-Plantain Garden Fault (EPGF), geologic, geodedic and seismologic studies following the earthquake have identified that the earthquake source was actually on the Leogane plain. This observation is a sober...

One of the main challenges to promoting disaster risk reduction practices following a disaster is that these (a) rarely take proper account of the multitude of risks that households are exposed to and arbitrating between, (b) are rarely framed within a definition of “acceptable risk”, and (c) rarely acknowledge that decisions about risk are usually...

Current seismic risk assessment practice characterizes the components of risk—hazard, exposure and vulnerability—in a static state. This paper provides a conceptual framework for an urban risk assessment model that accounts for spatial and temporal dynamics of urban environments. Specifically, it incorporates spatial and temporal variations in expo...

This study investigates methods for modeling the distribution of post-earthquake damage among categorical damage states. Specifically, it is demonstrated that the beta distribution is a good model for characterizing the complete probability distribution of damage states conditioned on ground-motion intensity. Based on extensive post-earthquake dama...

This paper proposes a framework to understand and model the drivers of new risk creation, with a particular focus on dynamic urban environments. Such a framework will help policy makers to understand and predict risk as it relates to dynamic changes in urban environments—such as increases in population, specific urban growth patterns over an evolvi...

This study proposes a new approach for rapid post-disaster damage assessment, based on the integration of remote-sensing and limited field-based damage assessment data. Data from the 2010 earthquake in Haiti is used to investigate geostatistical tools for effective damage estimation. Spatial auto-correlation and cross-correlation of limited field-b...

The paper provides an account of how three key relief organizations worked together after the devastating Haiti earthquake to produce the first damage assessment based mainly on the use of remotely-sensed imagery. This assessment was jointly conducted by the World Bank (WB), the United Nations Institute for Training and Research (UNITAR) Operationa...

The paper provides an account of how three key relief organizations worked together after the devastating Haiti earthquake to produce the first damage assessment based mainly on the use of remotely-sensed imagery. This assessment was jointly conducted by the World Bank (WB), the United Nations Institute for Training and Research (UNITAR) Operationa...