HS4.5

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.

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.

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.

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.

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.

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.

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.

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.