That is the central argument of a major new perspective paper published in the journal Natural Hazards and Earth System Sciences. An interdisciplinary team led by Philip J. Ward of the Institute for Environmental Studies at Vrije Universiteit Amsterdam, together with Marleen C. de Ruiter, Kees Boersma, Jasper Verschuur, Nicole van Maanen and more than a dozen colleagues spanning climate risk, public health, behavioural science, crisis management and disaster governance, sets out five core challenges that stand between today’s fragmented risk research and what the United Nations has called Comprehensive Disaster and Climate Risk Management, or CRM. The paper, published as an invited highlight contribution, arrives in the wake of the Sendai Framework’s Mid-Term Review, which concluded bluntly that science is falling short in understanding complex multi-risks.

The authors’ starting point is a conceptual shift that has gathered momentum over the past decade. Risk, as the UN Office for Disaster Risk Reduction defines it, is a function of hazard, exposure and vulnerability. Traditional assessments treated these ingredients as static, studying each hazard in isolation. Researchers now recognise that hazards interact, triggering, amplifying or compounding one another, and that exposure and vulnerability themselves evolve over time, sometimes for decades. Slow-onset crises such as droughts and pandemics erode financial resources, education, health and social networks, while rapid-onset events like floods and earthquakes displace people into new exposures. A person displaced by flooding may encounter vector-borne disease; a community enduring an outbreak may face heightened exposure because its drinking water infrastructure has been damaged.

The links between climatic hazards and infectious disease are particularly striking. Flooding and high temperatures can expand breeding grounds for mosquitoes, amplifying malaria and dengue transmission, while floodwaters spread the bacteria responsible for cholera. A systematic review cited in the paper found that 58 percent of all known human infectious diseases, including Zika, Chikungunya and Ebola, have been aggravated by climatic hazards through more than 1,000 unique transmission pathways. Yet disaster risk models almost never include biological hazards, and health systems rarely plan for geophysical and meteorological shocks, leaving what the authors describe as double or even triple health burdens when physical injury, disease and mental health impacts coincide.

The first of the five challenges is observational: we lack comprehensive, high-quality data on how societies are impacted by, prepare for, and respond to multi-risks. Health scientists curate disease data, hydrologists curate flood and drought data, and seismologists curate earthquake data, each in disciplinary silos with incompatible standards. Spatial and temporal scales rarely match, so urban heat islands, flash floods and informal-settlement vulnerabilities fall below the resolution of global datasets. Vulnerability is captured in episodic snapshots such as censuses that quickly go stale, while impact databases like EM-DAT record only aggregated, largely tangible losses, overlooking mental health, social cohesion and long-term path dependencies. In many low- and middle-income countries, the sustainability of health information systems itself is threatened by budget cuts to external funding.

The second challenge follows directly: our understanding of why people, communities and institutions prepare, or fail to prepare, for complex multi-risks remains incomplete. Classic theories such as Expected Utility Theory, Prospect Theory, the Theory of Planned Behaviour and Protection Motivation Theory were largely built for single hazards and one-time decisions. In a multi-hazard world, households facing frequent floods and rare earthquakes may misallocate scarce resources, or conversely, experience with one risk may raise awareness of another. Systemic risks transmitted through food prices, supply chains and disease outbreaks compound the problem, because people perceive remotely materialising risks differently from local ones. Most empirical studies capture a single snapshot, so feedback loops between protective behaviour and declining risk perception remain theoretically hypothesised but empirically unverified.

Third, the capacity to simulate how current and future risk could be reduced is hampered by these observational and behavioural gaps. A review of roughly 70 large-scale risk models found that only two included dynamic vulnerability. Agent-based models and system-dynamics approaches have advanced for single hazards such as floods, droughts and outbreak response, but little work extends them to multi-risk settings. Validation is another bottleneck: disasters are inherently local processes, yet validation data arrive as coarse aggregates, leaving low-probability, high-impact events, which climate change may intensify, dangerously under-constrained. The authors call for simulations that couple hazards with dynamic exposure, vulnerability and human decision-making across societal groups, enabled by high-performance computing and richer empirical foundations.

Fourth, governance structures often reinforce the very silos that plague data and models. Mandates overlap or leave gaps when risks interact, political and budgetary cycles favour short-term action, and operational institutions are organised in isolated pillars that slow decision-making. Public participation, where it exists, is frequently semi-formal and structurally weak. The authors advocate poly-centric and net-centric governance approaches: adaptive, networked arrangements that distribute authority vertically and horizontally, connect state and non-state actors, and support collective sensemaking, with digital infrastructures for data interoperability and innovations such as citizen climate assemblies as potential test beds for deeper engagement.

The fifth challenge concerns knowledge exchange at the science, policy and practice interface. The multi-risk problem is, in the authors’ words, super wicked: complex, evolving and without definitive solutions. During the Covid-19 pandemic in the Netherlands, biomedical advice dominated early scientific guidance while social and economic perspectives were added later in parallel structures, illustrating how advisory architectures can entrench compartmentalisation. Research from Western, Educated, Industrialized, Rich and Democratic contexts travels more readily into policy than locally grounded and tacit knowledge, reinforcing epistemic injustices and blind spots about marginalised groups. Knowledge translation is often treated as linear dissemination rather than dialogue and co-production, and even co-located disciplines fail to integrate without deliberate frameworks and boundary objects such as participatory maps, outbreak scenarios and serious games.

Looking forward, the team sketches an ambitious agenda built on five pillars: a Disaster Risk Observatory integrating satellite imagery, census data, surveys and disaster forensics into a living system that tracks risk dynamics in near real time; a new generation of risk-behaviour theories incorporating learning, feedbacks and systemic interdependence; integrative modelling frameworks, notably agent-based, that are optimised locally but scalable globally; poly-centric governance for sharing actionable multi-risk data and models; and systematic study of the science-policy-practice interface itself, supported by knowledge brokers who span disciplinary and administrative boundaries. Crucially, the authors stress that the five challenges are mutually reinforcing, each compounding the others into a persistent actionability gap. Closing it, they argue, will require funding mechanisms and research efforts that reach well beyond the traditional one-to-four-year project cycle, engaging meaningfully with the Sendai Framework, the WHO Health Emergency and Disaster Risk Management Framework, and the global climate agenda.

Subject of Research: Comprehensive disaster and climate risk management and the scientific challenges of multi-risk assessment

Article Title: Invited Perspectives: Science for Comprehensive Disaster and Climate Risk Management

Article References: Ward, P. J., de Ruiter, M. C., Boersma, K., Cesuroglu, T., Clark, N., Comes, T., Dahal, A., Fransen, S., Verschuur, J., Anthonj, C., Balakrishnan, S., de Bruijn, J., Hendriks, E., Mena Fluhmann, R., Perlaviciute, G., Steg, L., van den Homberg, M. J. C., van Westen, C., Atun, F., … van Maanen, N. (2026). Invited Perspectives: Science for Comprehensive Disaster and Climate Risk Management. Natural Hazards and Earth System Sciences, 26(9), 4589-4609. https://doi.org/10.5194/nhess-26-4589-2026

Image Credits: AI Generated

DOI: 10.5194/nhess-26-4589-2026

Keywords: disaster risk management, climate risk, multi-hazard, cascading disasters, infectious disease, vulnerability dynamics, agent-based modelling, disaster governance, Sendai Framework, risk perception, knowledge exchange, Haiti earthquake

The award, part of NSF’s Fire Science Innovations through Research and Education program, runs from September 2026 through August 2029 and brings together a multidisciplinary team from the University at Buffalo, the University of California, Berkeley, Northeastern University and the U.S. Department of Energy’s Pacific Northwest National Laboratory. The study, titled Collaborative Research: FIRE-WUI: REKINDLE: Resilient Engineering through Knowledge Integration for NetworkeD Lifelines and Ecosystem, will focus on communities in the wildland-urban interface, the zone where housing, power lines and other human development meet or intermingle with forests, grasslands and other natural vegetation. These are precisely the places where the destructive potential of wildfire and its aftermath is growing fastest.

As wildfires become more frequent and severe across the American West and beyond, wildland-urban interface communities face escalating risks that extend well beyond the immediate destruction of the fire itself. Burned landscapes lose the vegetation and soil structure that once absorbed rainfall, making them dramatically more prone to debris flows and flash floods during subsequent storms. At the same time, fires and their secondary hazards can inflict significant disruptions on lifeline infrastructure, including power grids, water systems and transportation networks. Current approaches to wildfire resilience planning, however, typically consider these interconnected risks in isolation, which makes it difficult to see how they interact with one another and how people actually respond when multiple hazards strike in sequence.

REKINDLE is designed to close that gap by combining artificial intelligence, infrastructure modeling and social science to assess wildfire-related risks together and to identify concrete ways to bolster public safety and community resilience. Sayanti Mukherjee, PhD, the project’s lead principal investigator and an associate professor in UB’s Department of Industrial and Systems Engineering, explained the central problem the team hopes to solve. People are familiar with wildfire risk, she noted, but what is often overlooked are the additional hazards and cascading effects that can follow. A region affected by wildfire can become more vulnerable to subsequent hazards like debris flows and flash floods, and when those events cut off roads or interrupt power or water service, the impacts can quickly compound for communities already struggling to recover.

Mukherjee, who is also an affiliate faculty member with UB’s Department of Electrical and Computer Engineering and the Institute for Artificial Intelligence and Data Science, is joined by co-principal investigator Susan Spierre Clark, PhD, of UB’s Department of Environment and Sustainability. The broader investigative team includes Auroop R. Ganguly, PhD, of Northeastern University; Marta González, PhD, and Anna Serra-Llobet, PhD, of UC Berkeley; and Andre Coleman, PhD, and Sam Chatterjee, PhD, of Pacific Northwest National Laboratory. This mix of expertise in engineering, environmental science, data science and social systems reflects the project’s core premise: that understanding cascading wildfire disasters requires simultaneously modeling the physical hazards, the engineered networks they damage and the human decisions those damage cascades trigger.

Through REKINDLE, researchers will investigate how wildfires and the hazards they set in motion can compromise key infrastructure, producing disruptions that ripple through the essential services people depend on for daily life and well-being. Clark emphasized that the team wants to understand not only how infrastructure systems are affected, but how those disruptions shape household decisions and community response. Bringing those pieces together, she said, can help communities build resilience, restore critical infrastructure and recover faster from future disasters. The framing matters because infrastructure failures are rarely just technical problems; a closed road can prevent evacuation, a lost power connection can disable water pumping, and a prolonged outage can determine whether a family can remain in or return to a damaged neighborhood.

Artificial intelligence will play a central role in the technical core of the project. The researchers plan to build a physics-informed, geospatial-AI model that fuses Earth observation data with wildfire expertise to generate maps showing where wildfires and related hazards such as debris flows and flash floods are most likely to occur. By anchoring machine learning in physical processes, the team aims to produce hazard predictions that are both data-driven and scientifically interpretable, an increasingly important consideration for tools intended to inform emergency management decisions. In parallel, the researchers will deploy machine learning models to trace how failures could propagate across interconnected power, water and transportation systems, capturing the dependency structures that turn a localized fire damage into a region-wide service disruption.

The human dimension of the cascade will receive equal attention. Using household surveys, focus groups and simulation games, the research team will examine how infrastructure failures affect families’ livelihoods and their ability to recover from cascading disasters. These social science methods are intended to reveal how households weigh risks, make evacuation and recovery decisions, and cope when multiple services fail at once. The team will then use these findings to help determine where investments in stronger infrastructure could deliver the greatest protective impact before a fire occurs, and how recovery efforts should be prioritized in the aftermath. Mukherjee noted that deciding where to invest resources, whether through hardening infrastructure or increasing community engagement and outreach, will be an important part of the project, and that AI and optimization can help assess different strategies and pinpoint where investments would have the most value. That kind of insight, she said, can support emergency managers, infrastructure operators and environmental planners in making more informed decisions for their communities.

To keep the work grounded in real conditions, the researchers will build and test REKINDLE through case studies in the Los Angeles and Montecito regions of California, both of which have experienced major wildfires and devastating post-fire debris flows in recent years. Montecito in particular has become a sobering reference point for cascading wildfire hazards, having seen destructive debris flows sweep through burned terrain after intense rainfall. The research team will collaborate with government agencies, utility providers and community stakeholders throughout the project to help ensure that the tools they develop reflect the practical needs of the people and institutions that will ultimately use them, from county emergency planners deciding where to pre-position resources to utilities weighing which assets to reinforce first.

The project also carries a substantial education and workforce development mission. Researchers will create wildfire-focused educational materials for students ranging from middle school through graduate school, introducing a new generation to the science of wildfire resilience and the related hazards that follow fires. Undergraduate and graduate students will have opportunities to gain hands-on experience through internships with industry partners and national laboratories, building career pathways in a field where demand for expertise continues to grow. The team will make its educational resources and research data publicly available so that other researchers, practitioners and communities can build on the work. If REKINDLE succeeds, its integrated view of fire, flood, infrastructure and human response could reshape how at-risk communities plan for the full arc of a wildfire disaster, not just the day the flames arrive.

Subject of Research: AI-based modeling of cascading wildfire hazards and infrastructure resilience in wildland-urban interface communities

Article Title: University at Buffalo to lead $2 million National Science Foundation project on wildfire resilience

Article References: University at Buffalo to lead $2 million National Science Foundation project on wildfire resilience. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: wildfire, cascading disasters, artificial intelligence, infrastructure resilience, wildland-urban interface, debris flows, flash floods, National Science Foundation, University at Buffalo, geospatial AI, community resilience, lifeline infrastructure