Disaster losses and damages data

ARIEL 1:Heeey there! Yawnnnn Welcome to EGreenNews! Ariel here, with my AI bestie Ariel and booth are computer generated avatars made in a computer, can you believe that? Today: Disaster losses and damages data: Reviewing existing applications and use cases. Anywayss, buckle up! ARIEL 2: Mmmhmm! Leans in Did you know that having good data on disaster losses and damages is super important for getting ready, responding effectively, and rebuilding after a disaster? It even helps us understand why disasters happen and how to reduce the risks! Wild, right? ARIEL 1: Sooo... just knowing how much damage a hurricane caused isn't enough? We need to keep track of all that stuff? Like, seriously? ARIEL 2: Ooooh! Absolutely! The article we just read explains that this data is essential for everything from figuring out how much money we need for disaster preparedness to planning how to rebuild communities in a way that's safer for the future. It even helps us see if things are getting better or worse over time. ARIEL 1: Hmm, I guess if you don't know what you lost, it's hard to plan for the future. Mmmmaybe all countries are really good at collecting this data? ARIEL 2: Naaaahhh, unfortunately, not all countries collect and use disaster data in a systematic way. There are big differences in how they do it, what they cover, and who's in charge of the systems. This makes it hard to manage risks effectively on a global scale. ARIEL 1: That doesn't sound good. So what's being done to fix this? Geez Louise! ARIEL 2: Yaaas, queen! The UN Office for Disaster Risk Reduction, the UN Development Programme, and the World Meteorological Organization are working together to create a better disaster tracking system. It's aimed at helping countries upgrade their own national systems for tracking hazardous events and the losses and damages they cause. ARIEL 1: So they're trying to create a more standardized way for everyone to collect this important information? ARIEL 2: Exactly! And this article and the case studies it mentions explore how we're currently using disaster data and how better data collection could lead to smarter decisions and more effective action. ARIEL 1: Okay, so how is this data actually being used right now? What are the main ways it's helpful? ARIEL 2: Well, the article lists five key application areas. First, it's crucial for **understanding risks**. By looking at past losses and damages, we can make hidden vulnerabilities more visible and get a better picture of the true cost of disasters. It helps us see trends over time and improve our risk models. ARIEL 1: So it helps us figure out what the real dangers are and who is most at risk? ARIEL 2: Precisely! The second area is **preparedness, early warning, and early action**. This data helps us design and monitor early warning systems and take action before a disaster even hits, making those systems more effective and impact-based. ARIEL 1: So knowing what kind of damage past events caused can help us prepare better for the next one? ARIEL 2: Absolutely! The third area is **disaster risk reduction financing**. You can't really plan a budget for disaster preparedness or recovery without knowing what you've lost in the past and what's at stake in the future. This data is essential for governments and even insurance companies. ARIEL 1: So it helps us put our money where it's needed most? ARIEL 2: Exactly! The fourth application is **risk-informed planning and development**. Good quality disaster data can inform how we plan our cities, build infrastructure, manage resources, and even deliver essential services like healthcare and education in a way that's more resilient to future disasters. ARIEL 1: So it helps us build back better and plan for a safer future in all sorts of ways? ARIEL 2: You got it! And the fifth area is **reporting, benchmarking, and progress monitoring**. By keeping consistent records of losses and damages, we can track our progress in reducing the impact of disasters over time and report on how we're meeting goals like the Sendai Framework and the Sustainable Development Goals. ARIEL 1: So it's also about holding ourselves accountable and seeing if our efforts are actually making a difference? ARIEL 2: Exactly! The article even gives some case studies. For example, in Sri Lanka, they're combining disaster loss data with national survey data to get a really detailed picture of who is most vulnerable. And in the Philippines, they're using disaster data to create impact-based forecasting models for typhoons, so they can take early action to reduce the humanitarian impact. Reviewing existing applications and use cases Data on disaster losses and damages is an essential basis for preparedness, effective response, recovery and reconstruction, the quantification and economic valuation of losses and damages, for understanding root causes and shifts in exposure and vulnerability, and to assess the risk of cascading events and impacts across multiple hazards. Not all countries, however, collect and use disaster data systematically and there are major disparities across countries regarding methods, coverage and system governance, creating barriers to effective risk management. The United Nations Office for Disaster Risk Reduction (UNDRR), United Nations Development Programme (UNDP) and World Meteorological Organization (WMO) are jointly addressing this gap and developing an enhanced disaster tracking system for hazardous events and related losses and damages aimed at national users interested in upgrading and institutionalizing national disaster tracking systems. The report and life repository of case studies below explore current data applications and ways that stronger data collection and management could support better decision-making and informed action. Application areas for disaster losses and damages data 1. Understanding risks 2. Preparedness, early warning and early action 3. Disaster risk reduction financing 4. Risk-informed planning and development 5. Reporting, benchmarking and progress monitoring Related and further reading Application areas for disaster losses and damages data 1. Understanding risks While still a comparatively young science, hazard modelling has accelerated dramatically in recent years, in terms of the number of models developed, research budgets and expertise, granularity, quality and coverage. Impact information, vulnerability models and integrated assessments have not followed. As a result, the different drivers of vulnerability and, therefore, of underlying risk, remain hidden in plain sight. Disaster losses and damages data can make such drivers and hidden vulnerabilities visible. In addition, where solid foundations of exposure, vulnerability and capacity baseline data exist, they provide critical information on the cost of loss, i.e. the ratio of loss compared to total exposure. Disaster data must be collected consistently and over time for them to provide evidence of trends and inform longer-term planning. The importance of baseline information on exposure and vulnerability for risk modelling cannot be underestimated. While high quality is desirable, even modest improvements in increasing coverage, consistency and frequency of updating can go a long way. Combined with disaster losses and damages data information on pre-existing vulnerabilities becomes a powerful contribution to risk modelling and analysis. Silhouettes of fishermen in the sea, Sri Lanka Case study: Understanding multidimensional vulnerability in Sri Lanka: combining disaster losses and damages data with national survey data In Sri Lanka, data collected as part of national citizen surveys provide a clear picture of the multiple dimensions of vulnerability and could directly inform hotspot, vulnerability and risk analysis (UNDP, Oxford Poverty and Human Development Initiative [OPHI] and Government of Sri Lanka, 2023 (c)). Analysed through the lens of the Multidimensional Vulnerability Index (MDVI) (UNDP, 2023 (a)), data from the surveys clearly highlight pre-existing vulnerabilities Combined with long-term records on the impact of disasters from their Desinventar-based national disaster losses and damages database, a disaggregated analysis makes apparent differences on vulnerability dimensions and their drivers providing useful insights for more in depth risk analysis. Recommendations to enhance the usability of disaster losses and damages data for risk analysis and modeling Disaster losses and damages data can provide a “real time” window on ongoing risk accumulation identifying new risk patterns and trends that should feedback into recalibrated risk information. Disaster data can illustrate changing patterns and trends that can be interpreted through qualitative analysis of the underlying risk drivers to explain the changes. Historical disaster data cannot replace risk analysis but can provide additional or substitute information to improve risk modelling for both short-term forecasting and longer-term climate impact assessments. Historic data is particularly useful for capturing frequently occurring, localized and small-scale events, such as local landslides or flash floods – often called extensive disaster events – where global risk models have limitations. Risk assessments are only useful if they are communicated in a manner that is meaningful to decision makers. While historical losses alone are not a good guide to the future, presenting data on actual (realized) losses and damages alongside more complex risk analysis can help in communicating risk effectively. Data on disaster losses and damage can contribute to improved modelling of existing as well as emerging or newly accelerating risks. To improve its usability, it is recommended to: Strengthen disaggregated data collection and analysis both in terms of hazard type, geography, and sectoral impacts, as well as in regard to data sex, age, disability status, income levels, and other dimensions of differentiated exposure and vulnerability. Enable georeferenced impact data collection to support the development of more accurate and replicable risk models. 2. Preparedness, early warning and early action Hazardous event and disaster losses and damages data is critical in the design, development and monitoring of early warning systems and early action. By informing key components of multi hazard early warning systems (MHEWS) – as proposed in 1997 and later adopted by WMO, the United Nations and national governments - these systems and related anticipatory and early action can become impact-based. Typhoon Haiyan survivor, Tacloban, Philippines Case study: Impact-based forecasting for anticipatory action to typhoons in the Philippines To reduce the humanitarian impact of TC, both the Philippines Red Cross and United Nations OCHA Philippines have designed an agency-specific protocol, respectively in 2019 and 2021, which can be used to trigger early actions and release funding based on an impact-based forecasting model. Building on the Netherlands Red Cross 510 model, early actions (such as distributing house-strengthening kits) are pre-identified and triggered when the impact-based forecasting model indicates a pre-defined danger level is exceeded (with a lead time of 120 to 72 hours before landfall). The machine learning model consists of a classification and regression component and is trained on over 60 historical events. Recommendations to enhance the usability of disaster losses and damages data for preparedness, early warning and early action Record losses and damages recorded along with the associated characteristics of the hazardous event (physical phenomena) to be able to link impacts to vulnerability, exposure and specific hazard intensity, characteristics and cascading events. Enhance spatial resolution of damage records to enable training of machine learning models that could enhance resolution of predictions and allow impact-based forecasting model to achieve a higher performance. Collect sector, geographic and population groups disaggregated impact information to be able to understand common disruptions to livelihood systems and services associated with recurrent hazards and to device and monitor effectiveness of early/ anticipatory actions. 3. Disaster risk reduction financing Disaster losses and damages data are the backbone of any financing strategy and plan for preparedness, response, recovery or risk reduction. Without knowing what has been lost in the past and what is at stake in the present and future, the case for investment in risk management and even for contingency planning remains weak (UNDRR, 2013). National ministries of finance, regional financing institutions and multilateral development banks, as well as private-sector finance and insurance companies, all require data to underpin budgets, financing plans and funding proposals for priority sectors or systems Strategies for disaster risk reduction financing can look very different, depending on scale, risk context and financing sources. However, as states are insurers of last resort in disasters, it is increasingly important that they have ownership of the data that informs disaster financing strategies and lead the development of financing instruments, including insurance (Radu, 2022). Insurance mechanisms Methods from the insurance sector have been replicated across the public sector, from modelling approaches to estimations of disaster losses. A critical gap that both the private and public sectors face in developing financing strategies for disaster risk management, however, is the question of indirect and downstream costs, for example in the form of business disruption, cascading costs from power outages or disruption in water supply, etc. The photo shows la Candelaria, a historic neighborhood in downtown Bogota, Colombia, with old colourful buildings. Case study: Using disaster data to calibrate parametric insurance in Manizales, Colombia. In Manizales, Colombia a disaster database registered a total of 1,149 local landslides, between March 2003 and August 2021. These events were classified according to the severity of their impacts on a D-Index using a scale from 1 - 10. A parameter called C5Max, was then established for a critical level of rainfall over 5 days, captured in selected meteorological stations, that could trigger landslides. The level of critical rainfall could then be associated with the severity of landslide impact. This enabled the prediction of expected landslide impacts once a given rainfall threshold was surpassed. In Manizales this was used for the development and calibration of a parametric insurance scheme to cover emergency response. However, the same approach could also be used in impact-based early warning. DRR Financing strategies Whereas the insurance industry usually employs fully developed risk estimation methods, including actuarial data from past disaster impact assessments, many public-sector institutions lack the resources and experience to undertake analysis based on systematic assessments of past events (UNDRR, 2023 (b)). As a result, many national disaster risk reduction financing strategies and risk management budgets rely on a weak evidence base and only a few use disaster loss data collected in the past as a critical input into their assessments (Radu, 2022; UNDRR, 2015; Climate Adapt, n.d.). Instead, estimates of financing needs often use financing commitments or humanitarian spending in previous disasters, rather than records of actual losses. Arugam Bay, Eastern Province, Sri Lanka Case study: Sri Lanka – disaster losses and damages data to identify financing needs in the agriculture sector In Sri Lanka, analysis of historical disaster losses in the infrastructure sector helped identify risk and potential financing gaps in the irrigation sector (see Figure 13a). The calculation of these historical costs provides the basis for modelled estimates of costs associated with damage from future disasters and the potential financing gap the Government of Sri Lanka may face (see Figure 13b). Recommendations to enhance applicability of disaster losses and damages data for disaster risk reduction financing Improve the collection of sector-specific asset and service system (e.g. water distribution or electricity generation) disaggregated and georeferenced data to enhance the understanding on how specific parameters of hazardous events (e.g. water level, flow speeds, stagnation time) cause damage and dysfunction to different structures to better enable sector-specific catastrophic insurance products Ensure losses and damages data is recorded in a way that private vs public sector effects are accounted separately, understanding which losses are incurred by individuals, households and private sector versus those borne by public sector will be particularly helpful when devising risk reduction financing strategies for productive and infrastructure sectors. Disaggregated historical damage data solid baseline data on sector exposure, i.e. inventories of assets and production processes beyond the basic exposure data on buildings and people would enhance the evidence base to develop catastrophic insurance products. 4. Risk-informed planning and development Disaster losses and damages data that is of good quality, geographical and temporal coverage, and consistency of metrics and indicators, can inform and enhance local assessments for sector-specific preparedness, response and recovery planning and beyond, risk-informed development and sector planning. Particularly relevant sectors in this regard are health and education, urban planning (including building and zoning regulations), agriculture and natural resources management, and basic infrastructure and services (transport, energy, waste, and drinking water). High-quality disaster losses and damages data with good geographical and temporal coverage and consistent metrics and indicators, can inform and enhance local assessments for sector-specific preparedness, response and recovery planning, as well as risk-informed development and sector planning. The floating village on the water of Tonle Sap Lake in Cambodia. Case study: Planning resilient roads in Cambodia The Government of Cambodia has recognized that the transportation sector, vital for the country’s economic development, is regularly and severely affected by disaster impacts. Road damage and destruction from disasters is systematically collected and recorded and stored in the Cambodia Disaster Loss and Damage Information System (CamDi), national database managed by the National Committee on Disaster Management (NCDM). Baseline data is collected with details on all roads and related infrastructure and recorded together with disaster loss data, allowing for lost cost assessments, seasonal analysis, and analysis by region or specific location and by road or infrastructure type. Recommendations to enhance the usability of disaster losses and damages for risk-informed development Sector and geographic disaggregated data recording and management would further enable the application of disaster losses and damages data for risk-informed policies, plans, budgets and actions Consistent and institutionalized tracking of losses and damages with engagement of whole-of-government entities and following agreed definitions, metrics and standard would enable creating relevant time series of historic impact data required for enhancing relevance and applicability of data for risk-informed planning. Application of disaster losses and damages data for risk-informed planning at multiple levels should be complemented by monitoring and evaluation frameworks and mechanisms that utilize same data elements to measure progress against targets and milestones. 5. Reporting, benchmarking and progress monitoring Monitoring progress on resilience building Progress on climate change adaptation and action on losses and damages can be efficiently monitored, among other things, by maintaining consistent and granular impact records. Reducing losses and damage from hazardous events over time is the ultimate measurement of progress and the Sendai Framework specifies several indicators that all require disaster losses and damages quantification. Similarly, reporting against the Sustainable Development Goals (SDGs) requires disaster-related data, as 25 targets relate directly to disaster risk and to reducing the negative impacts of disasters). The ongoing development of indicators to monitor the Global Goal on Adaptation targets contained in the United Arab Emirates (UAE) Framework for Global Climate Resilience will also benefit from the enhanced disaster tracking system, enabling monitoring of the reduction in losses and increase in resilience across several sectors (United Nations Framework Convention on Climate Change [UNFCCC], 2023 (a)). Other national frameworks and processes, such as for Nationally Determined Contributions and National Adaptation Plans or Biennial Transparency Reports, can also draw on the enhanced tracking system to strengthen coherence in reporting (UNFCCC, n.d. (a); UNEP, n.d.; UNFCCC, n.d. (b)). ARIEL 1: That sounds like really smart use of data! ARIEL 2: It is! And in Colombia, they used disaster data on landslides to calibrate a parametric insurance scheme for emergency response. And Sri Lanka is also using this data to figure out financing needs in their agriculture sector. ARIEL 1: So it's being used in all sorts of innovative ways to help people and communities? ARIEL 2: Absolutely! The key takeaway is that while we've made progress, we need to keep improving how we collect, manage, and use this crucial data to build a more resilient world. ARIEL 1: Sooo confusing, right? Learn more @EGreenNews! What surprised you more - that not all countries systematically collect disaster data or the diverse ways that this data can actually be used? ARIEL 2: And before we leave, lets give a big Shoutout to the people at EGreenNews, including its founder, Hugi Hernandez for promoting transparency 24×7! Mmm, who knows, maybe you can find them on the web or linkedin. But anyways, please,always remember to be good with yourself. So bye for now, aand we hope we see you next time! ARIEL 1: So its great to be here with you ariel and thanks for having me, ciao ciao!