Marc van den Homberg

<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 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.

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 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.  

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, 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 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-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 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.

<p>The Ethiopian agricultural system is predominantly formed by smallholder and rainfed farmers. Their local food systems are greatly reliant on seasonal climate variability. Often, droughts and food insecurity are interlinked and can negatively impact local communities. In addition to climate variability, a number of socio-economic factors such as multiple harvest failures, distance to markets and pre-existing inequalities are well known to impact people’s access to safe, sufficient and affordable food. Anticipatory action to avoid a situation of food security crisis often requires the understanding of how many people can be potentially affected by a shock and how much financing should be invested. </p><p>This study aims to forecast shortages in maize calories, which is defined as the percentage of the population for which not sufficient maize calories are available. Forecast models were developed for agricultural and agro-pastoral livelihood zones in Ethiopia in connection to the unimodal and bimodal rain seasons by using the Fast-and-Frugal Trees Algorithm. To forecast shortage events, five variables were used ranging from socio-economic to physical drivers: 1) soil moisture (Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT)), 2) maize production from the previous season, 3) the Gini index, 4) the main livelihood mode and 5) the travel time to the closest market. The lead time of the model is increased using TAMSAT forecast data to create a wider window for action before harvesting. </p><p>The skill of the model with increased lead-time in relation to the cost of the humanitarian intervention was analysed to examine the cost-effectiveness of forecast-based action. Therefore, the cost of acting early (through a scheme of cash transfer) has been compared to ex-post interventions. To assess the cost-effectiveness of the cash transfer, the prices of a basket of goods before and after harvesting are included in the model with the assumption that prices of staple crops increase when there is scarcity (food insecurity). With these results, the study will explore the practicality of implementing the anticipatory action by looking at the implications of model uncertainty (False Alarms, ‘acting in vain’). Likewise, the possible opportunities and challenges in regards to operationalizing the model will be deliberated. Accordingly, this study hopes to contribute to the use of early warning early action systems by humanitarian agencies in reducing the impacts of natural hazards. </p>

<p>The project “Forecast based Financing for Food Security” (F4S) aims to provide a deeper understanding of how key drivers of food insecurity can be forecasted early enough to enable the trigger of humanitarian action in pilot areas in Ethiopia, Kenya, and Uganda. In combination with the knowledge being produced about early warning and forecasting, F4S also wants to inform early action (e.g. ex-ante cash transfers) that can reduce the risk of food insecurity. F4S has been achieving this goal through three main pillars:  (i) modelling, (ii) local knowledge and (iii) cost-benefits analysis.</p><p>This PICO presentation shares the lessons learnt and results of the F4S project. Moreover, it  hopes to trigger the discussion on how the scientific community together with local stakeholders and communities can co-produce knowledge that is relevant to local action, focussing on three result areas.</p><ul><li>(i) The impact-based forecasting model to understand the key drivers of food insecurity in agricultural, agro-pastoral, and pastoral regions. Simple to more complex Machine Learning algorithms have been developed, applied and benchmarked. These algorithms were used to forecast, 6 to 1 months ahead, key indicators of food insecurity such as the shortage of calories and the transitions in IPC classes. Local knowledge was used to inform the selection of the predictors of the Machine Learning algorithm.</li> <li>(ii) The results of a household survey and individual choice experiments among 600 household members of vulnerable communities. The survey collected local knowledge on early warning (food insecurity triggers) and early actions traditionally taken to lessen food insecurity. The novel choice experiment consisted of giving potential beneficiaries of ex-ante cash transfers the choice between different timings and frequencies of cash transfers for different drought and food security scenarios. The results provided a better understanding of people’s willingness to invest in risk reduction actions and individual preferences on key design elements of cash transfer mechanisms.</li> <li>(iii) The evaluation of the cost-effectiveness of different cash transfer mechanisms that investigates how cash transfer programs can achieve a significant reduction in costs if cash is disbursed prior to the food insecurity occurrence.</li> </ul><p>This knowledge, as produced on the three areas above, is being currently used to improve the design of ex-ante cash programs. In addition to yielding significant cost savings, the project has found that cash transfer programs can be a more dignified solution when disbursed early enough. Cash transfer programs have the potential to increase the range of early action by beneficiaries that ultimately can reduce the risk of food insecurity and possibly malnutrion in vulnerable communities.</p>

<p>To accurately identify the most vulnerable areas to floods, physical (e.g., building material) and social (e.g., education, health, income of households) housing stock information is required. However, in developing countries, this information is often unreliable, unavailable or inaccessible, and manual data collection is time-consuming. This can lead to difficulties for humanitarians or policymakers in implementing appropriate disaster risk reduction and response interventions. Therefore, there is a need for the development of alternative approaches to data collection and analysis. An alternative approach to on-site vulnerability assessment is to extract physical vulnerability characteristics, such as land use type or rooftop material, from satellite or Unmanned Aerial Vehicle (UAV) imagery. However, other social or physical vulnerability information on the household level can often not be extracted from only the remote sensing data. This research develops an approach for integrating multiple data sources into a Geographic Information System to improve the completeness of data on different vulnerability indicators. This approach is applied on the housing stock of the Karonga district in Malawi. An Object-Based Image Analysis on UAV imagery is combined with a machine learning analysis of Mapillary data to enable remote identification of both rooftop ànd wall material. Depth-damage curves were created to describe the impact on the housing stock for different categories of physical vulnerability (such as building material) and levels of inundation. Moreover, local survey data is used for the creation of a social vulnerability index. Combined, the datasets represent the spatial distribution of housing stock vulnerability for multiple flood scenarios. This approach is useful in situations where proactive risk analyses must be carried out or where local-scale interventions, such as building strengthening- or flood awareness projects, have to be implemented. Finally, we give recommendations for scaling the methodology to areas where only lower resolution data is available.</p>

Climate change adaptation and disaster risk management in the Balkans require strong regional cooperation, given that disasters in the Balkans are often cross-border. However, currently information and collaboration gaps occur. This paper proposes to harness the potential of information management, geoinformatics, and big data to bridge these gaps and to create data preparedness as follows. First of all, map regularly regional, national, and local data sets on multi-institutional information needs. Secondly, use new digital and collaborative tools, including geospatial sharing platforms and OpenStreetMap volunteers for sharing, collecting, and using data. Thirdly, build capacity through, for example, courses, exercises, and regional demonstrations. Overall, it is about creating a Data Collaborative for the Balkans, a lightweight way of institutionalizing regional information sharing and creating trust. The NATO Crisis Management and Disaster Response Centre of Excellence can play a pivotal role by convening stakeholders, leveraging military capabilities, and offering an advanced curriculum.

Droughts and changing rainfall patterns due to natural climate variability and climate change, threaten the livelihoods of Malawi's smallholder farmers, who constitute 80% of the population. Provision of seasonal climate forecasts (SCFs) is one means to potentially increase the resilience of rainfed farming to drought by informing farmers in their agricultural decisions. Local knowledge can play an important role in improving the value of SCFs, by making the forecast better-suited to the local environment and decision-making. This study explores whether the contextual relevance of the information provided in SCFs can be improved through the integration of farmers’ local knowledge in three districts in central and southern Malawi. A forecast threshold model is established that uses meteorological indicators before the rainy season as predictors of dry conditions during that season. Local knowledge informs our selection of the meteorological indicators as potential predictors. Verification of forecasts made with this model shows that meteorological indicators based on local knowledge have a predictive value for forecasting dry conditions in the rainy season. The forecast skill differs per location, with increased skill in the Southern Highlands climate zone. In addition, the local knowledge indicators show increased predictive value in forecasting locally relevant dry conditions, in comparison to the currently-used El Niño-Southern Oscillation (ENSO) indicators. We argue that the inclusion of local knowledge in the current drought information system of Malawi may improve the SCFs for farmers. We show that it is possible to capture local knowledge using observed station and climate reanalysis data. Our approach could benefit National Meteorological and Hydrological Services in the development of relevant climate services and support drought-risk reduction by humanitarian actors.Water ResourcesTransport and LogisticsSystem Engineerin

Social vulnerability is a key concept that guides the design, evaluation, and targeting of humanitarian and development programs worldwide. However, vulnerability remains an abstract concept, and many methodologies and assessment tools exist to characterize vulnerability. What is missing is a standardized framework to determine which method is most useful to assess social vulnerability and to determine the sensitivity of different methodologies.In this paper, we make a headway in addressing this gap by comparing two methods for assessing social vulnerability and their sensitivity in a case study for Burkina Faso: 1) the inductive principal component approach (SoVI) and 2) the hierarchical equal weighting approach (INFORM).  Our hypothesis is that the spatio-temporal characterization of social vulnerability is highly sensitive to different methods and the quality of the input data.To test the impact of the different methods, this paper presents a case study of Burkina Faso. Burkina F...

<p>Most people of Malawi are dependent on rainfed agriculture for their livelihoods. This leaves them vulnerable to drought and changing rainfall patterns due to climate change. Farmers have adopted local strategies and knowledge which have evolved over time to help in reducing the overall vulnerability to climate variability shocks. One other option to increase the resilience of rainfed farmers to drought, is providing forecast information on the upcoming rainfall season. Forecast information has the potential to inform farmers in their decisions surrounding agricultural strategies. However, significant challenges remain in the provision of forecast information. Often, the forecast information is not tailored to farmers, resulting in limited uptake of forecast information into their agricultural decision-making.</p> <p>Therefore, this study explores how drought forecast information can be linked to existing farmers strategies and local knowledge on predicting future rainfall patterns. By using participatory approaches, an understanding is created of what requirements drought forecast information should meet to effectively inform farmers in their decision-making. Based on these requirements a sequential threshold model, using meteorological indicators based on farmer’s local knowledge was developed to predict drought indicators (e.g. late onset of rains and dry spells). Additionally, using interviews among stakeholders and a visualisation of the current information flow, further insights on the current drought information system was developed.</p> <p>The results of this research show that local knowledge has a predictive value for forecasting drought indicators. The skill of the forecast differs per location with an increased skill for Southern locations. In addition, the results suggest that local knowledge indicators have an increased predictive value in forecasting the locally relevant drought indicators in comparison the currently used ENSO-related indicators. This research argues that the inclusion of local knowledge could potentially improve the current forecast information by tailoring it to farmer's forecast requirements and context. Therefore, the findings of this research could be insightful and relevant for actors or research fields involved in drought forecasting in relation to user-specific needs. </p>

<p>Climate change, political instability, and the non-sustainable use of water threaten the per capita water resources of dependent societies and severely impact communities during a period of below-average rainfall. To combat the increasing impact of drought disasters, the International Red Cross Red Crescent Movement focuses on Anticipatory Action. The indication of the status quo of droughts is vital in the anticipation of natural disasters. This indication is potentially benefitted by data on the freshwater reserves. Global Water Watch, being developed by Deltares, WRI, and WWF, is the first online platform providing open access, transparent, and near real-time information on the (historic) water dynamics of fresh surface water resources across the globe, ranging from small to large water bodies. The dataset ranges from 1985 to the present and is derived from earth observation data using artificial intelligence on a global scale. In the scope of Human Centred Design, co-design sessions were held with representatives of Red Cross Red Crescent National Societies in Mozambique, Eswatini, and Zimbabwe. The results were analyzed in a persona journey, gap analysis, and product definition. This resulted in the identification of five potential products of Global Water Watch, related to Anticipatory Action as well as responsive action, the traditional disaster management method used by National Societies. The priority in the recommendation was based on the products their effort in development, relative to their impact. Products that are considered low-hanging fruits in development (high impact, low effort) are monitoring surface waters in near-real-time, and the service of providing data in an API. This ensures that the data can be used in the Impact Based Forecasting platform, developed by 510. Over the long run, a reservoir volume monitor in near-real-time is recommended (high impact, high effort). Also, a long-term recommendation is a product that ensures the export of data in a specific format that can be easily read and shared via email and WhatsApp (low impact, low effort). Last, a product that estimates the future volume of reservoirs (high impact, high effort) could be considered. However, it is not sure if the impact is worth the effort, especially in a situation where a reservoir volume monitor in near-real-time might already be in place.  </p>

Food security is commonly measured by means of surveys, requiring substantial time and budget. Open data can possibly serve as a cost-effective alternative to predict food security. In this paper a method is proposed that uses open data related to food insecurity drivers to predict food security in Ethiopia at the subnational level. The method is based on an ordinal classification approach with a random forest as underlying algorithm. The model turned out to have an accuracy of approximately 90%. Although using an ordinal approach increases performance, a negative side-effect is that the model struggled to predict records with the label ‘stressed’ as a target. The basis of this effect lays in how probabilities for classes ranked in the middle are calculated. Further research on adding open data sources on other drivers and on finetuning hyperparameters in the modelling is advised before implementing machine learning to predict food security.

Climate related disasters, such as floods and drought, are becoming increasingly frequent and extreme as a result of climate change. These increasingly frequent and extreme events compound disaster impact and subsequent suffering amongst those affected. Despite these increasing impacts, governments and humanitarian organizations often do not start response operations until after a disaster has taken place. This is still the case, even though disasters can often be predicted with relatively high confidence and a lead time between forecast and event in which humanitarian action to mitigate the impact and suffering of climate based disasters can be taken. It is the aim of IARP to engage with this critical time period to take action based on forecast in Kenya, Uganda and Ethiopia.

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...

The occurrence and intensity of some natural hazards (e.g. hydro-meteorological) increase due to climate change, with growing exposure and socio-economic vulnerability, leading to mounting risks. In response, Disaster Risk Reduction policy and practice emphasize people-centred Early Warning Systems (EWS). Global policies stress the need for including local knowledge and increasing the literature on integrating local and scientific knowledge for EWS. In this paper, we present a review to understand and outline how local and scientific knowledge integration is framed in EWS, namely: (1) existing integration approaches, (2) where in the EWS integration happens, (3) outcomes, (4) challenges, and (5) enablers. The objective is to critically evaluate integration and highlight critical questions about assumptions, goals, outcomes, and processes. In particular, we unpack the impact of power and knowledges as plural. We find a spectrum of integration between knowledges in EWS, mainly with di...

All too often the collection as well as analysis of data for humanitarian response only starts once a disaster hits. This paper proposes a framework to assess Data Preparedness on five dimensions: Data Sets, Data Services and Tooling, Data Governance, Data Literacy, and Networked Organizations for Data. We demonstrate for one dimension, i.e. Data Sets, how it can be quantified. First step is to determine which Data Sets must be collected before a disaster strikes so that as many as possible decision-makers’ information needs are covered. Subsequently, a Data Sets Preparedness Index can be calculated based on Completeness, Recency and Accuracy & Reliability. We tested the index for Malawi and The Philippines and show how it can be used to direct data collection and determine when data analysis for e.g. predicting severity becomes meaningful. The index can be modified for reporting on global policies such as the Sustainable Development Goals.

The global shift within disaster governance from disaster response to preparedness and risk reduction includes the emergency of novel Early Warning Systems such as impact based forecasting and forecast-based financing. In this new paradigm, funds usually reserved for response can be released before a disaster happens when an impact-based forecast—i.e., the expected humanitarian impact as a result of the forecasted weather—reaches a predefined danger level. The development of these impact-based forecasting models are promising, but they also come with significant implementation challenges. This article presents the data-driven impact-based forecasting model as developed by 510, an initiative of the Netherlands Red Cross. It elaborates on how questions on legitimacy, accountability and ownership influenced the implementation of the model within the Philippines with the Philippine Red Cross and the local government as the main stakeholders. The findings imply that the exchange of knowl...

<p>Anticipatory action requires models that can accurately predict the impact of both the primary hazard and its consecutive events. In the Philippines, typhoons trigger 90% of landslides, causing a lot of fatalities and damage to infrastructure and agriculture. The lack of information on past landslides hampers the development of accurate forecasting models of landslide occurrence and impact. An impact-based forecasting model for typhoons currently operational in the Philippines predicts impact due to the high wind speeds associated with typhoons and includes the possible impact due to landslides only via a static landslide susceptibility map. This study expands the impact-based forecasting model of 510, an initiative of the Netherlands Red Cross, with a dynamic landslide component via hybrid modeling for two typhoon events in the Bicol region in the Philippines.</p><p>A hydrometeorological model to forecast landslide occurrences was successfully created, even with the limited data on landslide occurrences and rainfall available. The newly established regional event duration threshold was applied on the case study events with an increased impact boundary of 300 km compared to the typhoon impact boundary of 100 km. The dynamic multi-hazard model showed an improved impact forecast - compared to the model considering solely static input of landslides - both in geographical impact extent and accuracy: the True Positives doubled, whereas the False Negatives reduced by half. A separate landslide forecasting model as an extension of the existing ML model provided additional benefits as the models can be decoupled to optimize the performance and reliability of both models. This study resulted in a prototype of an impact-based multi-hazard or consecutive event model for the Philippines and demonstrated the importance of considering the impact from consecutive hazards.</p><p><strong>Keywords</strong>: Landslide, typhoon, consecutive hazards, impact-based forecasting, rainfall, machine learning, Philippines</p>