HS4.5

HS4 EDI
Reducing the impacts of natural hazards through forecast-based action: from early warning to early action 

The Sendai Framework for Disaster Risk Reduction (SFDRR) and its seventh global target recognizes that increased efforts are required to develop risk-informed and impact-based multi-hazard early warning systems. Despite significant advances in disaster forecasting and warning technology, it remains challenging to produce useful forecasts and warnings that are understood and used to trigger early actions. Overcoming these challenges requires understanding of the reliability of forecast tools and implementation barriers in combination with the development of new risk-informed processes. It also requires a commitment to create and share risk and impact data and to co-produce impact-based forecasting models and services. To deal with the problem of coming into action in response to imperfect forecasts, novel science-based concepts have recently emerged. As an example, Forecast-based Financing and Impact-based Multi-Hazard Early Warning Systems are currently being implemented operationally by both governmental and non-governmental organisations in several countries as a result of increasing international effort by several organizations such as the WMO, World Bank, IFRC and UNDRR to reduce disaster losses and ensuring reaching the objectives of SFDRR. This session aims to showcase lessons learnt and best practices on impact-based multi-hazards early warning system from the perspective of both the knowledge producers and users. It presents novel methods to translate forecast of various climate-related and geohazards into an impact-based forecast. The session addresses the role of humanitarian agencies, scientists and communities at risk in creating standard operating procedures for economically feasible actions and reflects on the influence of forecast uncertainty across different time scales in decision-making. Moreover, it provides an overview of state-of-the-art methods, such as using Artificial Intelligence, big data and space applications, and presents innovative ways of addressing the difficulties in implementing forecast-based actions. We invite submissions on the development and use of operational impact-based forecast systems for early action; developing cost-efficient portfolios of early actions for climate/geo-related impact preparedness such as cash-transfer for droughts, weather-based insurance for floods; assessments on the types and costs of possible forecast-based disaster risk management actions; practical applications of impact forecasts.

Convener: Andrea Ficchì | Co-conveners: Gabriela Guimarães Nobre, Marc van den Homberg, David MacLeodECSECS, Maurine Ambani
Presentations
| Mon, 23 May, 08:30–10:00 (CEST)
 
Room 2.31

Session assets

Session materials

Presentations: Mon, 23 May | Room 2.31

Chairpersons: Andrea Ficchì, Gabriela Guimarães Nobre, Marc van den Homberg
08:30–08:35
08:35–08:40
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EGU22-760
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ECS
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Virtual presentation
David Hoffmann, Beth Ebert, Carla Mooney, and Brian Golding

The weather information value chain provides a framework for characterising the production, communication, and use of information by all stakeholders in an end-to-end warning system. Since the generation of weather warning and climate services has become more complex, both technically and organizationally, the value chain concept has become a popular tool for describing and assessing the production, use and benefits of such services.

The end-to-end warning system for high impact weather brings together hazard monitoring, modelling and forecasting, risk assessment, communication and preparedness activities and systems and processes which support people to take timely action to reduce risks. Weather and associated warning services are typically developed and provided through a multitude of complex and malleable value chains (networks), often established through co-design, co-creation and co-provision.  

A 4-year international project under the WMO World Weather Research Programme that started in November 2020 is using value chain approaches to describe and evaluate the end-to-end warning system for high impact weather. Its aims are

  • To review value chain approaches used to describe weather, warning and climate services to assess and provide guidance on how they can be best applied in a high impact weather warning context that involves multiple users and partnerships;
  • To generate an easily accessible means (an End-to-End Warning Chain Database) for scientists and practitioners involved in researching, designing and evaluating weather-related warning systems to review previous experience of high impact weather events and assess their efficacy using value chain approaches.

We encourage the research and operational community to participate in this project by contributing case studies of high impact events and collaborating in their analysis. Integration of the physical and social sciences in this project will lead to new insights that we hope will ultimately improve the effectiveness of warnings for high impact weather.

How to cite: Hoffmann, D., Ebert, B., Mooney, C., and Golding, B.: Using Value Chain Approaches to Evaluate End-to-End Warning Systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-760, https://doi.org/10.5194/egusphere-egu22-760, 2022.

08:40–08:45
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EGU22-9001
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Virtual presentation
Joshua Ngaina, Niccolo Lombardi, Dunja Dujanovic, Nora Guerten, Catherine Jones, Luca Parodi, Sergio Innocente, Brenda Lazarus, Quraishia Merzouk, and Siphokazi Moloinyane

Slow-onset disasters build up gradually over time, often at the confluence of different hazards, and progressively erode livelihoods, especially among most vulnerable people. The aim of the paper is to summarize FAO’s conceptual and programmatic approach for anticipating and mitigating the impact of slow-onset hazards on the most vulnerable people depending on agriculture for their livelihoods and food security. In order to protect diverse livelihood groups at the right time before such sequenced impacts materialize, the phased approach to Anticipatory Action (AA) seeks to facilitate the identification of multiple windows of opportunity for anticipatory action along the crisis timeline of the slow-onset hazards. The five steps process include (1) determining who is at risk and when, (ii) which actions can be taken to mitigate hazard impacts, and when, (iii) how much time is needed to implement the actions selected, (iv) what kind of early warning information is available at the critical points in time identified and (v) bringing all the information together to define the action phases and the cut-off points beyond which an intervention cannot be considered ‘anticipatory’ anymore.  Since 2016, FAO has supported extensive country-level work on AA against several slow-onset hazards such as drought (e.g. in Kenya, Madagascar, Afghanistan, Philippines, Pakistan, and Sudan, among others), cold waves dzud (Mongolia), pests and diseases (e.g. desert locusts in the Greater Horn of Africa Region and Yemen), Rift valley fever in Kenya and the secondary consequences of COVID-19 (e.g. in Afghanistan, Bangladesh, the Democratic Republic of the Congo, Haiti, Kenya, Senegal, Sierra Leone, the Syrian Arab Republic, and Zimbabwe). Drawing on FAO’s experiences gathered in implementing AA and the technical expertise built over decades of supporting agriculture-based livelihoods, this paper recommends a phased approach to AA for slow-onset hazards as it reduces uncertainties associated with early warning information, improves the targeting of AA interventions, and helps adapt the selection of AA options to the evolving hazard context.

How to cite: Ngaina, J., Lombardi, N., Dujanovic, D., Guerten, N., Jones, C., Parodi, L., Innocente, S., Lazarus, B., Merzouk, Q., and Moloinyane, S.: Windows of opportunity for Anticipatory Action along the crisis timeline for slow-onset hazards: a phased approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9001, https://doi.org/10.5194/egusphere-egu22-9001, 2022.

08:45–08:50
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EGU22-12660
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ECS
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Virtual presentation
Emie Klein Holkenborg, Hessel Winsemius, and Marc van den Homberg

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.  

How to cite: Klein Holkenborg, E., Winsemius, H., and van den Homberg, M.: Co-designing Global Water Watch for Anticipatory Action , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12660, https://doi.org/10.5194/egusphere-egu22-12660, 2022.

08:50–08:55
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EGU22-4480
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On-site presentation
Will Veness, Wouter Buytaert, and Adrian Butler

Drought Early Warning Systems (DEWSs) require data on spatial drought intensity and exposure to highlight the most-affected areas for early interventions. This data also provides evidence of drought severity to trigger early financing mechanisms. However, existing DEWSs are dependent on satellite-based parameters, which have a course spatial resolution and high measurement uncertainty. As a result, these indicators do not provide a reliable proxy for local groundwater availability during hydrological drought. This research explores groundwater monitoring for providing an alternative, direct index of groundwater availability for DEWSs, considering the increasing affordability and feasibility of monitoring due to advancements in modern sensors. Using in-situ observations collected from abstraction wells in Maroodi Jeex, Somaliland, a lumped parameter groundwater model has been calibrated that can forecast local groundwater levels during drought, by inputting seasonal and mid-range weather forecasts. The model can also simulate well water levels if the sensor is removed after 1 year, enabling an ongoing, locally calibrated groundwater index without the need for sensor maintenance. This suggests that national-scale groundwater monitoring in Somaliland is technically feasible, and it raises further research questions regarding how such a system can be funded, governed and maintained, as well as how this groundwater information would be practically used in the drought early warning early action process to inform management and financing decisions.

How to cite: Veness, W., Buytaert, W., and Butler, A.: Localised Drought Early Warning using In-situ Groundwater Sensors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4480, https://doi.org/10.5194/egusphere-egu22-4480, 2022.

08:55–09:00
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EGU22-2129
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ECS
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On-site presentation
Dr. George Otieno, Dave MacLeod, Martin C Todd, Emma Visman, Richard Graham, Shamton Waruru, Abebe Tadege, and Khalid Hassaballah

Skillful weather and climate forecasts, if utilized effectively, have the potential to improve preparedness and disaster risk reduction. Forecast-based Action (FbA) is a framework for aiding decisions on preparedness in advance of weather/climate hazards, through use of forecasts. Here, we present a summary of research results and pilot project work within the Arid and Semi-Arid Land (ASAL) areas of Kenya conducted under the Towards Forecast-based Preparedness and Action (ForPAc) project. We also present opportunities for scaling up FbA  across the Greater Horn of Africa region through leveraging on connected projects and initiatives like Down2Earth.  Skill assessment of a pool of weather/climate models has established the most skilful multi-model combinations for monthly-seasonal timescale.  Co-production initiatives between forecast users and producers established the forecast variables best aligned with Kenya’s existing Drought Early Warning Systems (DEWS); Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI) and soil moisture, as well as optimum forecast delivery time required by the DEWS processes. Our analysis shows that rainfall forecasts have skill across ‘seamless’ sub-seasonal to seasonal lead times, offering the potential to improve the anticipatory actions within the DEWS of Kitui county of Kenya. Working with multiple stake-holders from across local and national government, humanitarian agencies, forecasting services and climate researchers, we have explored the potential for a more anticipatory, proactive DEWS using forecast information. The Down2Earth project, which aims at translating climate information for adaptation and climate-resilience across decision-making levels is leveraging on gains of ForPAc by advancing FbA approaches within the rural communities of Kenya, Somalia and Ethiopia. To facilitate the institutionalization of FbA, we have developed a regional roadmap to guide implementation within National, regional and international humanitarian actors.   

How to cite: Otieno, Dr. G., MacLeod, D., Todd, M. C., Visman, E., Graham, R., Waruru, S., Tadege, A., and Hassaballah, K.: Climate Predictions in a Forecast based Action (FbA) pilot within the Greater Horn of Africa; Experiences from ForPAc and Down2Earth Projects, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2129, https://doi.org/10.5194/egusphere-egu22-2129, 2022.

09:00–09:05
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EGU22-6618
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Virtual presentation
Seppo Pulkkinen, Tero Niemi, Annakaisa von Lerber, Miikka Leinonen, and Tiia Renlund

Convective storms and long-lasting mesoscale convective systems have the potential to cause heavy rainfall, flooding, hail, wind gusts and lightning that can result in significant damage to property and loss of lives. Accurate prediction of the location or severity of such storms (e.g. in the sub-kilometer resolution for the next hour) to assist the decision-making of civil protection authorities is beyond the capabilities of the current numerical weather prediction models. Thus, weather radar and machine learning-based methods provide an important tool to predict such events and their impacts in advance. Identifying a storm cell or system as an “object” from a radar image provides a natural way for associating different meteorological attributes of a storm with its impacts. In the TAMIR project funded by the EU Civil Protection Mechanism, we have implemented this by combining a cell tracking system with a machine learning model. The hazard levels of storms are estimated from their distance and time delay to the associated emergency reports obtained from the PRONTO database provided by the Finnish civil protection authorities. Using several meteorological attributes related to severe weather (e.g. lightning flash, hail and wind observations and indicators of convective potential), a random forest model was trained for predicting the storm hazard level. This was done by using a large sample of data during summer months between 2013-2020. The model for predicting the hazard level was verified by cross-validation. A Kalman filter-based methodology was applied for probabilistic nowcasting of future storm locations, which was combined with the model for hazard level prediction. Finally, the hazard nowcasts were combined with different exposure layers to translate them into prediction of impacts caused by convective storms. In the presentation, we demonstrate the added value of the implemented hazard and impact nowcast products with case studies. The products have also been evaluated by the Finnish civil protection authorities during the test period June-September 2021 with largely positive feedback. While the feasibility of the proposed methodology is demonstrated in Finland, discussion about its transferability to other parts of the world is also given.

How to cite: Pulkkinen, S., Niemi, T., von Lerber, A., Leinonen, M., and Renlund, T.: Machine learning tools for predicting multi-hazards caused by convective storms - the TAMIR project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6618, https://doi.org/10.5194/egusphere-egu22-6618, 2022.

09:05–09:10
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EGU22-3341
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ECS
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Virtual presentation
Douglas Mulangwa, Andrea Ficchì, Philip Nyenje, Jotham Sempewo, Linda Speight, Hannah Cloke, Shaun Harrigan, Benon Zaake, and Liz Stephens

This study aims to evaluate the comparative suitability of a global hydrological forecasting and monitoring system, the Copernicus-Emergency Management Service - Global Flood Awareness System (GloFAS), and a local catchment-based model (GR4J) as possible alternative or complementary flood forecasting tools in Uganda. Local stakeholders and end-users in Uganda need to understand whether flood forecasts from ready-to-use global systems can be relied on as one of the available tools to inform flood preparedness actions or whether other easy-to-set-up local hydrological models can provide more reliable information at the catchment scale or some advantages in particular regions. While GloFAS provides probabilistic extended-range forecasts, it has only been calibrated at a few locations in Africa and remains uncalibrated at most locations in Uganda and eastern Africa. A simpler catchment-based model can be calibrated more easily by local national authorities using observed hydrological data. This research investigates whether the reanalysis data from GloFAS can perform satisfactorily in Uganda with respect to the simulation of a lumped catchment-based model (GR4J) using the same meteorological inputs across Uganda.

Results are presented for four Ugandan catchments with different morphological and hydrological characteristics. An evaluation of both GloFAS reanalysis (GloFAS-ERA5) and extended-range (re-)forecasts has been carried out against observed streamflow data, analysing performance statistics including the Kling-Gupta Efficiency (KGE) for the reanalysis, and the False Alarm Ratio and Probability of Detection for forecasts at short lead times (< 15 days). The GR4J model simulations were run using the ERA5 meteorological reanalysis as input. In both calibration and validation mode, on average, the calibrated GR4J model provides better KGE scores than GloFAS, especially for the smaller catchments (< 2000 km2). However, GloFAS performance is relatively good for the two largest basins (>2200 km2) and is acceptable with respect to a mean flow benchmark for all catchments, except the smallest (500 km2). Our results suggest that in small- to medium-size basins in Uganda, a simple lumped catchment-based model may outperform GloFAS, but even without calibration GloFAS performs satisfactorily in larger basins. Thus, GloFAS can be relied on as interim solution for flood forecasting in Uganda, especially for larger river catchments. An evaluation of the accuracy of the rainfall reanalysis (ERA5) with respect to local rainfall observations showed significant differences in biases and correlation of rainfall input data across catchments and this can explain the different performance of the hydrological models across Uganda. Finally, the importance of assessing and calibrating flood forecasting models with action-relevant scores to support humanitarian actions is highlighted by analysing the discrepancies between traditional general scores (as the KGE) with other more specific flood event-based scores.

How to cite: Mulangwa, D., Ficchì, A., Nyenje, P., Sempewo, J., Speight, L., Cloke, H., Harrigan, S., Zaake, B., and Stephens, L.: Comparative Suitability of the Global Flood Awareness System and a Catchment-based Model to Simulate Floods in Uganda, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3341, https://doi.org/10.5194/egusphere-egu22-3341, 2022.

09:10–09:15
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EGU22-8204
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Virtual presentation
John Bevington, Heather Forbes, Kay Shelton, Richard Smith, Elizabeth Wood, Paul Maisey, and Sophie Ludlam

Flood Foresight is JBA’s strategic flood forecasting system, providing flood inundation and depth estimates at 30m resolution up to 10-days ahead of fluvial flood events.  The system is globally scalable and recent projects have seen the Forecasting module provide forecast flood footprints in Democratic Republic of Congo, Myanmar and the Indus River basin in Pakistan.  Data produced by these systems are being used for a variety of purposes including informing humanitarian anticipatory actions, parametric insurance and disaster risk financing.  This presentation will explore the use of Flood Foresight in Democratic Republic of Congo and Pakistan for the purposes of humanitarian disaster risk financing and demonstrate the benefits to this user community in otherwise data sparse regions. 

Disaster Risk Financing (DRF) programmes are being developed for the Democratic Republic of Congo and Pakistan and are designed to allow civil society actors in country to proactively manage disaster risks. By quantifying risks in advance of disasters, pre-positioning funds, and releasing them according to pre-agreed plans, the user community are better placed to enable early disaster relief actions to help reduce the human and economic costs of disasters.  In both Democratic Republic of Congo and Pakistan, JBA were tasked with developing an operational fluvial flood forecasting model which can, at lead times of 0 – 10 days ahead, predict the number of people who will be inundated by fluvial flooding.

For forecasting population impacts, JBA’s Flood Foresight system couples the Copernicus Global Flood Awareness System (GloFAS) with the Flood Foresight technology to generate daily probabilistic forecasts of flood inundation extents and depths.  From the maps generated, the system then generates estimates of the population at risk.  This fully automated early warning system is providing humanitarian organisations with daily forecasts of flood conditions to inform rapid financing for anticipatory actions designed to reduce overall humanitarian impact.  To help inform the definition of risk and subsequently set appropriate financing triggers, a probabilistic flood risk assessment was also developed using JBA’s Global Flood Model, providing national, province and territory level risk profiles of population affected by fluvial flooding.

How to cite: Bevington, J., Forbes, H., Shelton, K., Smith, R., Wood, E., Maisey, P., and Ludlam, S.: Disaster Risk Financing systems for fluvial flood risk in Democratic Republic of Congo and Pakistan, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8204, https://doi.org/10.5194/egusphere-egu22-8204, 2022.

09:15–09:20
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EGU22-12673
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On-site presentation
Marc van den Homberg, Andrea Ficchi, Phuoc Phung, Sidiky Sangare, Abdouramane Gado Djibo, and Cheikh Kane

Riverine floods are one of Mali's most devastating and frequently occurring disasters. However, so far, actions linked to it are mainly post-disaster ones. For this reason, the Mali Red Cross has recently established with partners a Forecast-based Financing mechanism that triggers early actions to reduce the impacts of floods once a predefined trigger is reached. Given the lack of forecasts at the national scale, the current trigger model is based only on real-time observations from the hydrological monitoring network of the National Directorate of Hydraulics (DNH): if the observed upstream water level exceeds the 5-year return period, an action is triggered to prepare for floods downstream, four days ahead, taking into account the delays in the propagation of the flood. Global flood forecasting systems can possibly complement this local flood monitoring model, especially in large transboundary river basins. This research aims to investigate the riverine flood forecast skill of the Global Flood Awareness System (GloFAS version 3.1, part of the Copernicus Emergency Management Service) in the Niger river basin by evaluating reforecast data against two reference datasets: river flow observations and impact data. The False Alarm Ratio (FAR) and the Probability of Detection (POD) have been calculated for all available extended-range reforecasts (lead times up to 46 days) over a 20-year period and for 15 river gauge station locations. For the skill assessment of GloFAS against river flow observations, most river gauge stations with enough observed data (8 out of 15) show good and robust skill scores for all lead times up to 10 days. For the skill assessment based on impact data, even though at some stations the POD is good, the FAR is too high. A preliminary conclusion is that setting trigger levels for longer lead times (up to 10 days) - to complement the existing monitoring system with a four-day lead time - can be done only for those locations where enough historical observed data is available. Using impact data to set triggers is currently hampered by limitations of the impact dataset, such as no precise event dates and locations.

How to cite: van den Homberg, M., Ficchi, A., Phung, P., Sangare, S., Gado Djibo, A., and Kane, C.: Assessing the riverine flood forecast skill of GloFAS with streamflow observations and impact data: a case study for Mali, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12673, https://doi.org/10.5194/egusphere-egu22-12673, 2022.

09:20–09:25
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EGU22-9300
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ECS
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Virtual presentation
Faith Mitheu, Elisabeth Stephens, Elena Tarnavsky, Andrea Ficchi, Rosalind Cornforth, and Celia Petty

As the world faces an uncertain future due to climate variability, environmental and climate change, and an increase in extreme hydrometeorological events, investing in early warning early action mechanisms can be an effective way to prepare and adapt to changes and extremes and reduce any impending impacts. Such an investment will require an understanding of the information needs of the users/user-groups, and in particular, the communities at risk, to ensure the design of tailored anticipatory actions, as well as an evaluation of how forecasts perform in detecting these extreme events and their impacts. This helps to ensure that flood-risk preparedness actions are better contextualised and not taken in vain. Community-led approaches for anticipatory action planning are based on the engagement with the communities at risk and can be an effective way of ensuring that: 1) the information needs of the specific user-groups are identified and integrated with the development of preparedness actions and plans; 2) data on loss and damages to lives and livelihoods can be used to demonstrate how reliable the forecasts are in informing early actions at the community level; and 3) gaps and challenges that hinder effective use of early warning information are identified across user-groups to help improve on the design and dissemination of early warning information. In this talk, we bring together information collected at the community and disaster management levels together with a recent evaluation of flood forecasts using impact (loss and damage) reports at a district level, to show how community-led approaches can help towards improving early warning mechanisms. By integrating global hydro-meteorological forecasts with information on crop calendars and impact reports collected from farmers and local communities, an enhanced impact-based flood early warning system focusing on crop impacts, as well as the natural hazard, is developed for a flood-prone district in Uganda (Katakwi).

How to cite: Mitheu, F., Stephens, E., Tarnavsky, E., Ficchi, A., Cornforth, R., and Petty, C.: Towards a community-led approach to improve the design of early warning systems and anticipatory action for flood risk preparedness, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9300, https://doi.org/10.5194/egusphere-egu22-9300, 2022.

09:25–09:30
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EGU22-12707
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Highlight
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Virtual presentation
Eleanor Hansford, Calum Baugh, Christel Prudhomme, Marc Berenguer, Shinju Park, Annakaisa von Lerber, Anna Berruezo, Victor González, Juan Colonese, Corentin Carton de Wiart, Seppo Pulkkinen, and Tero Niemi

As part of TAMIR, a European Commission Civil Protection Preparedness project (ID. 874435), probabilistic operational pan-European flash flood impact forecasts with lead times from 0 to 120 hours have been developed by combining flash flood hazard forecasts with exposure data. Working with civil protection agencies, the aim is to develop forecasts which clearly identify the areas most at risk of serious impacts and therefore may require their intervention. Firstly, the project engaged with these agencies to identify their requirements of flash flood impact forecasts and which elements of exposure and important to them when assessing impacts. Accordingly, pan-European exposure data for population and critical infrastructure (health, education, transport, and energy generation facilities) were sourced from several open source datasets (HARCI-EU, OSM, GHS). These exposure data were (if necessary) regridded and cropped to the spatial domain, transformed to reduce skewness, and rescaled between 1 and 2 to give the datasets common units. The five exposure types were then added together and re-scaled, to produce a combined exposure layer with values ranging from 1 (low exposure) to 2 (high exposure). Flash flood hazard forecasts were created in a previous project by blending hourly ensemble precipitation nowcasts with ensemble numerical weather predictions (NWP) from the ECMWF IFS (Integrated Forecast System). These forecasts are created once per hour and have a lead time of up to 5 days. The flash flood impact forecasts were created by combining the hazard forecasts and exposure data on a two-dimensional impact matrix. Both axes of matrix are split into 3 categories (low, medium, high). For exposure, the ranges for each category were chosen based on the distribution of the data. For hazard, the low, medium, and high categories indicate where the forecast probability shows a 5%-50%, 50%-80%, and 80%+ likelihood of exceeding the 5-year return period threshold.

Once developed, the impact forecasts were applied to 6 case studies of single flash flooding events across Europe chosen by the civil protection agencies, and the results presented to them. This helped evaluate the impact forecasts and enabled end users to provide feedback for further improvement. Results indicated the impact forecasts provided considerable added value compared to the hazard forecasts, by identifying targeted areas where serious impacts were observed. In the final stages of the project, the methods and products described here will be implemented in the European Flood Awareness System (EFAS) platform as a quasi-operational experimental product, and made available to the wider scientific community in the form of a Web Map Service Time (WMS-T) layer. Overall, this presentation focuses on the creation and communication of the exposure data and subsequent impact forecasts. Additionally, it outlines the evaluation of the impact forecasts, and the benefits obtained from engaging end users throughout the process. Finally, it highlights some of the challenges of using pan-European data and a continental scale forecast system to provide impact forecasts useful at the smaller scales required by decision makers.

How to cite: Hansford, E., Baugh, C., Prudhomme, C., Berenguer, M., Park, S., von Lerber, A., Berruezo, A., González, V., Colonese, J., Carton de Wiart, C., Pulkkinen, S., and Niemi, T.: Beyond hazard: Combining pan-European exposure data with flash flood hazard forecasts to create impact forecasts for civil protection agencies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12707, https://doi.org/10.5194/egusphere-egu22-12707, 2022.

09:30–09:35
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EGU22-10916
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ECS
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Virtual presentation
Akshay Singhal, Ashwin Raman, and Sanjeev Jha

Each year India witnesses numerous casualties, economic losses and vast displacement of people due to extreme rainfall events (EREs). One of the reasons for such losses is that the weather warnings associated with the EREs are not properly communicated to the general public. It is essential that the expected impacts are communicated well in advance so that appropriate remedial actions can be taken and losses can be minimized. Several national and regional rainfall forecasting agencies have started issuing risk-based warnings which includes the potential impacts arising due to the EREs. This framework of providing forecast information based on the potential impacts of a hazard is called Impact-Based Forecasting (IBF). In this study, we develop a framework for generating the impact-based forecasts and associated warning matrices for the districts of eastern Uttar Pradesh, India, by integrating the rainfall forecasts and the socio-economic characteristics of the region. The region is densely populated, has relatively poor socio-economic conditions and is prone to frequent EREs. We take into account various sectors such as population, economy and agriculture where maximum impacts are expected to take place. Moreover, we identify the vulnerable districts based on the frequency of the number of extreme rainfall forecasts in the past four years (2017-2020) and the nature of socio-economic conditions. The vulnerable districts are categorized in three categories (low, medium and high) based on the expected impacts. For each of the vulnerable districts, sample IBFs and warning matrices are generated. IBFs inform about the possible impacts different sectors in each district may face on a given day due to the forecasted ERE. On the other hand, warning matrices provide updated information regarding the category of risk for the district a few days in advance. The study is significant since it follows a methodological framework to generate impact-based forecasts and warnings which includes analysis of rainfall forecasts, identification of possible impacts and suggestion of appropriate mitigation actions.

How to cite: Singhal, A., Raman, A., and Jha, S.: Towards use of extreme rainfall forecast and socio-economic data to generate Impact-based forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10916, https://doi.org/10.5194/egusphere-egu22-10916, 2022.

09:35–09:40
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EGU22-12917
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Presentation form not yet defined
Aklilu Teklesadik and Marc van den Homberg

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.

How to cite: Teklesadik, A. and van den Homberg, M.: Forecasting impacts of tropical cyclones with machine learning : A case study in the Philippines , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12917, https://doi.org/10.5194/egusphere-egu22-12917, 2022.

09:40–09:45
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EGU22-2648
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ECS
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On-site presentation
Jacopo Margutti, Marc van den Homberg, Fleur Hierink, and Nicolas Ray

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.

How to cite: Margutti, J., van den Homberg, M., Hierink, F., and Ray, N.: Assessing and Forecasting Dengue Risk with Hydrological Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2648, https://doi.org/10.5194/egusphere-egu22-2648, 2022.

09:45–09:55
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EGU22-5901
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solicited
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Highlight
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On-site presentation
Liz Stephens, Faith Mitheu, Linda Speight, Sazzad Hossain, Hannah Cloke, and Stefania Giodini

Forecast-based action within the humanitarian community supports at-risk communities when a forecast indicates a potentially imminent disaster. Within the Red Cross Red Crescent Movement the development of an Early Action Protocol enables access to pre-agreed funds and avoids indecision when faced with an uncertain forecast. To ensure value for money, this protocol must demonstrate that the forecast is good enough for the decisions being made. But how can we be confident that forecasts are good enough if we don’t have any observations? How do we evaluate an impact-based forecast? And how do we communicate these limitations to all stakeholders? In this talk I will discuss some of the challenges we have faced, and some solutions.

How to cite: Stephens, L., Mitheu, F., Speight, L., Hossain, S., Cloke, H., and Giodini, S.: How do we ensure that the humanitarian use of forecasts is robust?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5901, https://doi.org/10.5194/egusphere-egu22-5901, 2022.

09:55–10:00