Papers by Marc van den Homberg
. Tropical cyclones (TCs) produce strong winds and heavy rains accompanied by consecutive events ... more . Tropical cyclones (TCs) produce strong winds and heavy rains accompanied by consecutive events such as landslides and storm surges, resulting in losses of lives and livelihoods particularly in regions where socioeconomic vulnerability is high. To proactively mitigate the impacts of TCs, humanitarian actors implement anticipatory action. In this work, we build upon such an existing anticipatory action for the Philippines, which uses an impact-based forecasting model for housing damage based on XGBoost to release funding and trigger early action. We improve it in three ways. First, we perform a correlation and selection analysis, to understand if Philippines-specific features can be left out or replaced with features from open global data sources. Secondly, we transform the target variable (percentage of completely damaged houses) and not yet grid-based global features to a 0.1 degrees grid resolution by de-aggregation using Google Building Footprint data. Thirdly, we evaluate XGBoost regression models using different combinations of global and local features at both grid and municipality spatial level. We introduce a two-stage model to first predict if the damage is above 10 % and then use a regression model trained on either all or on only high damage data. All experiments use data from 39 typhoons that impacted the Philippines between 2006–2020. Due to the scarcity and skewness of the training data, specific attention is paid to data stratification, sampling and validation techniques. We demonstrate that employing only the global features does not significantly influence model performance. Despite excluding local data on physical vulnerability and storm surge susceptibility, the two-stage model improves upon the municipality-based model with local features. When applied for anticipatory action our two-stage model would show a higher True Positive rate, a lower False Negative rate and furthermore an improved False Positive rate, implying that fewer resources would be wasted in anticipatory action. We conclude that relying on globally available data sources and working at grid level holds the potential to render a machine learning-based impact model generalisable and transferable to locations outside of the Philippines impacted by TCs. Also, a grid-based model increases the resolution of the predictions, which may allow for a more targeted implementation of anticipatory action. However, it should be noted that an impact-based forecasting model can only be as good as the forecast skill of the TC forecast that goes into it. Future research will focus on replicating to and testing the approach in other TC-prone countries. Ultimately, a transferable model will facilitate the scaling up of anticipatory action for TCs.
bioRxiv (Cold Spring Harbor Laboratory), Feb 23, 2021
Epidemics are among the most costly and destructive natural hazards globally. To reduce the impac... more Epidemics are among the most costly and destructive natural hazards globally. To reduce the impacts of infectious disease outbreaks, the development of a risk index for infectious diseases can be effective, by shifting infectious disease control from emergency response to early detection and prevention. In this study, we introduce a methodology to construct and validate an epidemic risk index using only open data, with a specific focus on scalability. The external validation of our risk index makes use of distance sampling to correct for underreporting of infections, which is often a major source of biases, based on geographical accessibility to health facilities. We apply this methodology to assess the risk of dengue in the Philippines. The results show that the computed dengue risk correlates well with standard epidemiological metrics, i.e. dengue incidence (p = 0.002). Here, dengue risk constitutes of the two dimensions susceptibility and exposure. Susceptibility was particularly associated with dengue incidence (p = 0.047) and dengue case fatality rate (CFR) (p = February 18, 2021 1/26 .
Lecture notes in civil engineering, 2024
<p>The disaster risk community has notably shifted from a response-driven approach ... more <p>The disaster risk community has notably shifted from a response-driven approach to making informed anticipatory action choices through impact-based forecasting (IBF). Algorithms are being developed and improved to increase impact prediction abilities, and to allow automatic triggers to reduce the reliance on human judgement. However, as complexities in modelling algorithms increase, it becomes more difficult for decision makers to interpret and explain the results. This reduces the accountability and transparency, and can lead to lower adoption of the models. Therefore, humanitarian decision-makers can benefit from a mechanism to evaluate different IBF approaches, which has not yet been developed. Through a case study of anticipatory action for tropical cyclones in the Philippines, we evaluated two very different approaches to IBF: (1) a statistical trigger model that uses a machine learning algorithm with several predictor variables, and (2) an elementary trigger model that combines damage curves and weighted overlay of vulnerability indicators, to predict the impact and prioritize areas for intervention. The models were evaluated based on their performance for damage prediction and their sensitivity to different risk indicators for Typhoon Kammuri (2019) in the Philippines. The study also proposed a way of characterising the explainability specific to an IBF model, and that gives clarity on which elements, and why, influence the results, done via a model card. To facilitate this process a prototype interactive decision portal was built, which shows decision makers the sensitivity of the results to variations in input parameters. The results show that in relative terms the elementary model performed better and would have allowed to maximise impact reduction through early action, suggesting that, for this particular case, complex was not necessarily a better choice. However, the uncertainty in both models due to limitations in the initial hazard forecast indicates that multiple models need to be evaluated for practical cases that cover different characteristics of the hazard and socio-vulnerable situations. For this, the evaluation framework we developed can be expanded across operational IBF projects.</p>
Climate services have a well-recognised potential for empowering decision makers in taking climat... more Climate services have a well-recognised potential for empowering decision makers in taking climate smart decisions; across sectors, public agencies, policy makers, and including citizens. This potential is, however, often not fully realised as the uptake of climate services may be hampered by a range of barriers, including the lack of understanding of the needs of users, and the poor recognition of the knowledge users themselves have. Research shows, however, that the users climate services intend to serve often have a well-developed knowledge of the climate systems around them based on their observation and experience. In a recently initiated H2020 research project, Innovating Climate Services through Integration of local and Scientific Knowledge (I-CISK, https://icisk.eu) we recognise that integrating multiple knowledges through co-creation of climate services with users, can contribute to closing the usability gap, despite the challenges to these knowledges as a result of demographic, climatic and environmental change. Here we present an introductory review of the current state of the art in the integration of local knowledge in climate services. This review does not aim to comprehensively address the very broad and multiple dimensions of local knowledge, but rather gives a perspective of current approaches in science and practice to the integration of local and scientific knowledge. We first explore what we consider as local knowledge within the scope of this review, which will also be used as a reference to inform our further research on local knowledge within the context of its integration in climate services in the I-CISK project. We then review how local knowledge is used in climate services, and introduce a basic typology of how local knowledge and scientific knowledge are considered and/or integrated within climate services. Finally, we provide a reflection on the challenges and directions of local and scientific knowledge integration in climate services, and give a brief outlook on how these challenges will be addressed in the I-CISK project.
Routledge eBooks, Mar 16, 2023
Increased flooding frequency and intensity threaten vulnerable populations’ lives and livelihoods... more Increased flooding frequency and intensity threaten vulnerable populations’ lives and livelihoods worldwide. Fitting into the preparedness and mitigation phases of the Disaster Risk Management framework used by humanitarian and conservation organisations, Nature-Based Solutions (NBS) have been advanced as effective alternatives to traditional grey infrastructures in order to mitigate flooding impacts. By reproducing natural processes, NBS have shown to provide multiple environmental, social, and economic benefits in addition to their technical performance in mitigating floods. However, a framework to systematically assess these co-benefits is not readily available, which is an obstacle to the effective implementation of NBS on a larger scale. This paper develops such a framework using a Systematic Literature Review (SLR) based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method. The framework includes a set of descriptors to characterize and analyse NBS consistently. These include:Type of NBS; Type of protection area (coastal, urban, rural, mountainous, riverine); Provided environmental/technical/social/economic benefits; Location of applicability; Scale of implementation; Inclusion of stakeholders’ preferences for NBS implementation. The SLR is shaped using a combination of scholarly literature (via Web of Science) and grey literature from reputable organizations in the NBS domain and beyond, including the WWF Nature-based Solutions Accelerator, the United Nations Office for Disaster Reduction, the Disaster Risk Management Knowledge Centre, and the Geneva Environment Network. The resulting framework can support decision-making and facilitates the deployment of sustainable infrastructure. The Red Cross Red Crescent Movement and WWF will test the framework in a case study in the Zambezi river basin in Zambia.
The Philippines is one of the countries most at risk to natural disasters. Amongst these disaster... more The Philippines is one of the countries most at risk to natural disasters. Amongst these disasters, typhoons and its associated landslides, storm surges and floods have caused the largest impact. Due to increased typhoon intensity, the country’s high population density in coastal areas and rising mean sea levels, the coastal flood risk in the Philippines is only expected to increase. The 510 initiative of the Netherlands Red Cross uses an Impact Based Forecasting (IBF) model based on machine learning to anticipate the impact of an incoming typhoon to set early action into motion. The IBF model underperformed in regions that are susceptible to storm surges. Most notably, it showed a poor performance for Super-Typhoon Haiyan (2013), which caused storm surges to reach up to over five meters high. The goal of this research is to evaluate how the IBF model can be improved by applying a fast hydrodynamic modelling approach that can forecast storm surges and coastal flooding associated with typhoons. First, the accuracy of the Global Tide and Surge Model (GTSM) in simulating Haiyan’s coastal water levels was examined. GTSM was forced with two different meteorological datasets: a gridded climate reanalysis dataset, ERA5, and observed track data combined with Holland’s parametric windfield model. Second, GTSM’s water levels were used as input for a hydrodynamic inundation model to simulate the flood depth and extent in San Pedro Bay, which was subjected to a widespread coastal flood during Haiyan. This was explored both with and without the inclusion of wave setup. Our results show that Haiyan’s flood cannot adequately be indicated using the ERA5 reanalysis dataset as meteorological forcing, as it underestimated Haiyan’s extreme wind speeds with ~60 m/s. By applying the Holland parametric wind field model, more accurate flood predictions and storm surge simulations can be made. Additionally, GTSM’s temporal resolution influences the models performance substantially. By increasing the 1 hour resolution to a 30 minute resolution the prediction of the overall flood extent improved by 16%. In future research we recommend examining the applicability of the Global Tide and Surge Model when using a higher spatial resolution to help better represent local processes. Additionally, exploring the accuracy for other typhoons that struck the Philippines and the applicability in operational setting using forecasted track data can contribute to further improving forecast-based early action systems in anticipating coastal flood occurrences.
Zenodo (CERN European Organization for Nuclear Research), Oct 19, 2021
Epidemics are among the most costly and destructive natural hazards globally. To reduce the impac... more Epidemics are among the most costly and destructive natural hazards globally. To reduce the impacts of infectious disease outbreaks, the development of a risk index for infectious diseases can be effective, by shifting infectious disease control from emergency response to early detection and prevention. In this study, we introduce a methodology to construct and validate an epidemic risk index using only open data, with a specific focus on scalability. The external validation of our risk index makes use of distance sampling to correct for underreporting of infections, which is often a major source of biases, based on geographical accessibility to health facilities. We apply this methodology to assess the risk of dengue in the Philippines. The results show that the computed dengue risk correlates well with standard epidemiological metrics, i.e. dengue incidence (p = 0.002). Here, dengue risk constitutes of the two dimensions susceptibility and exposure. Susceptibility was particularly associated with dengue incidence (p = 0.047) and dengue case fatality rate (CFR) (p = 0.029). Exposure had lower correlations to dengue incidence (p = 0.211) and CFR (p = 0.163). Highest risk indices were seen in the south of the country, mainly among regions with relatively high susceptibility to dengue outbreaks. Our findings reflect that the modelled epidemic risk index is a strong indication of sub-national dengue disease patterns and has therefore proven suitability for disease risk assessments in the absence of timely epidemiological data. The presented methodology enables the construction of a practical, evidence-based tool to support public health and humanitarian decision-making processes with simple, understandable metrics. The index overcomes the main limitations of existing indices in terms of construction and actionability. Author summary Why Was This Study Done? – Epidemics are among the most costly and destructive natural hazards occurring globally; currently, the response to epidemics is still focused on reaction rather than prevention or preparedness. – The development of an epidemic risk index can support identifying high-risk areas and can guide prioritization of preventive action and humanitarian response. – While several frameworks for epidemic risk assessment exist, they suffer from several limitations, which resulted in limited uptake by local health actors - such as governments and humanitarian relief workers - in their decision-making processes What Did the Researchers Do and Find? – In this study, we present a methodology to develop epidemic risk indices, which overcomes the major limitations of previous work: strict data requirements, insufficient geographical granularity, validation against epidemiological data. – We take as a case study dengue in the Philippines and develop an epidemic risk index; we correct dengue incidence for underreporting based on accessibility to healthcare and show that it correlates well with the risk index (Pearson correlation coefficient 0.69, p-value 0.002). What Do These Findings Mean? – Our methodology enables the development of disease-specific epidemic risk indices at a sub-national level, even in countries with limited data availability; these indices can guide local actors in programming prevention and response activities. – Our findings on the case study show that the epidemic risk index is a strong indicator of sub-national dengue disease patterns and is therefore suitable for disease risk assessments in the absence of timely and complete epidemiological data.
<p>Climate Services (CS) are crucial in empowering citizens, stakeh... more <p>Climate Services (CS) are crucial in empowering citizens, stakeholders and decision-makers in defining resilient pathways to adapt to climate change and extreme events. Whilst recent decades have seen significant advances in the science that underpins CS; from sub-seasonal, seasonal through to climate scale predictions; there are several barriers to the uptake of CS and realising of the full opportunity of their value-proposition. Challenges include incorporating the social and behavioural factors, and the local knowledge and customs of climate services users; the poorly developed understanding of the multi-temporal and multi-scalar dimension of climate-related impacts and actions; the translation of CS-provided data into actionable information; and, the consideration of reinforcing or balancing feedback loops associated to users’ decisions.</p><p>The ambition of the recently initiated EU-H2020 I-CISK research & innovation project in addressing these challenges, is to instigate a step-change to co-producing CS through a social and behaviourally informed approach. The trans-disciplinary framework the research sets out to develop recognises that climate relevant decisions consider multiple knowledges; innovating CS through integrating local knowledge, perceptions and preferences of users with scientific climate data and predictions.</p><p>In this contribution we reflect on initial steps in setting up seven living labs in climate hotspots in Europe and Africa. Instrumental to the research, we will work from these living labs with multi-actor platforms that span multiple sectors to co-design, co-create, co-implement, and co-evaluate pre-operational CS to address climate change and extremes (droughts, floods and heatwaves). We present the vision and plans of the I-CISK project, and explore links, contributions and collaborations with existing projects and networks within the community of CS research and practice. </p>
<p>Due to its geographical location, the Philippines is highly exposed to T... more <p>Due to its geographical location, the Philippines is highly exposed to Tropical Cyclones (TC). Every year at least one TC will make landfall and cause significant humanitarian impact and economic loss. To reduce the humanitarian impact of TC, the Philippine Red Cross with the German Red Cross and 510, an initiative of The Netherlands Red Cross, designed and implemented a Forecast Based Financing (FbF) system. The early actions in the FbF system are pre-identified and will be triggered when an impact-based forecasting model indicates a pre-defined danger level will be exceeded. This research develops and evaluates multiple ML algorithms for classification and regression with a lead time of 120 to 72hrs before TC landfall. The algorithms are trained on around 40 historical typhoon events and xx predictors on the hazard, vulnerability, coping capacity, and exposure are used. The classification model predicts if 10% of buildings in a municipality are completely damaged or not. The regression model gives the percentage of buildings that are completely damaged in a municipality. The RandomForest algorithm outperformed other algorithms for both classification and regression for both training and validation datasets. The ML models performed better than a baseline model (a wind-damage curve per building type) for the historical typhoon events. The Philippine Red Cross has been using the ML model since 2019, whereby actual forecast information from ECWMF replaces the historical hazard information at landfall. However, the ML impact-based forecasting model cannot be better than the hazard information that goes into it. Those typhoons that rapidly intensify cannot be captured at the cutoff of 72 hrs lead time (the minimum time required to start up early actions). But for the other typhoons, ML is very beneficial as a trigger tool for activating early actions and can support the reduction of the impact of typhoons on vulnerable communities.</p>
<p>We introduce a methodology to assess and forecast the risk of mosquito-b... more <p>We introduce a methodology to assess and forecast the risk of mosquito-borne diseases using open hydrological and socio-economic data, with a specific focus on scalability, i.e. applicability to countries where limited data is available. We apply this methodology to assess and forecast the risk of dengue in the Philippines. We embedded this model into a full Early-Warning Early-Action system, which includes a web portal to convey the information to disaster managers and a set of pre-defined preventive actions, to mitigate the impact of potential outbreaks. This system has been developed in collaboration with the Philippines Red Cross, which is now adopting it.</p>
Stakeholders in disaster risk management are faced with the challenge to adapt their risk reducti... more Stakeholders in disaster risk management are faced with the challenge to adapt their risk reduction policies and emergency plans to cascading and compounding events, but often lack the tools to account for the cross-sectoral impacts and dynamic nature of the risks involved. The EU Horizon Europe PARATUS project, which started in October 2022 and will run to October 2026, aims to fill this gap by developing an open-source online platform for dynamic risk assessment that allows to analyze and evaluate multi-hazard impact chains, dynamic risk reduction measures, and disaster response scenarios in the light of systemic vulnerabilities and uncertainties. These services will be co-developed within a transdisciplinary consortium of 19 partners, consisting of research organizations, NGOs, SMEs, first and second responders, and local and regional authorities. To gain a deeper understanding of multi-hazard impact chains, PARATUS conducts forensic analysis of historical disaster events, based on a database of learning case studies, augments historical disaster databases with hazard interactions and sectorial impacts, and exploits remote sensing data with artificial intelligence methods. Building on these insights, PARATUS will then develop new exposure and vulnerability analysis methods that enable systemic risk assessment across sectors (e.g. humanitarian, transportation, communication) and geographic settings (e.g. islands, mountains, megacities). These methods will be used to analyze risk changes across space and time and to develop new scenarios and risk mitigation options together with stakeholders, using innovative serious games and social simulations.The methods developed in PARATUS have been applied in four application case studies. The first one is related to Small Island Developing States (SIDS) in the Caribbean. This case study considers the cross-border impacts of tropical storms, tsunamis, volcanic eruptions, and space weather, and focuses on the development of impact-based forecasting, directed at humanitarian response planning, the telecommunication sector, and tourism. The second case study deals with the local and regional economic impact of hazardous events such as extreme wind, floods, rockfall, mudflow, landslides, and snow avalanches on cross-border transportation in the Alps. The third case study relates to the multi-hazard impact of large earthquakes in the Bucharest Metropolitan Region and focuses on systemic vulnerabilities of the city and emergency response. The fourth application case study is the Megacity of Istanbul which is prone to earthquake hazard chains, such as liquefaction, landslides, and tsunami, as well as to hydrometeorological hazards (extreme temperatures, fires, and flooding). Population growth rates, urban expansion speed, composition, and integration of new migrants (native, foreign, and refugees from countries like Syria and Afghanistan) contribute to the increasing disaster risk. The project results will be hosted on two stakeholder hubs related to crisis management and humanitarian relief, and provide stakeholders with a set of tools for risk reduction planning in dynamic multi-hazard environments. The service-oriented approach with active stakeholder involvement will maximize the uptake and impact of the project, and help to increase Europe’s resilience to compounding disasters.
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Papers by Marc van den Homberg