Gaining user insights into the research-to-operational elements of Impact-based Forecasting (IbF) from within the SHEAR programme : summary of findings

Impact based Forecasting (IbF) is an expanding and evolving area of research within National Meteorological and Hydrological Services (NMHSs) and the humanitarian sector, with a broad aim to enhance communication and timely action to reduce losses associated with natural hazards. Although the principles of IbF may seem new to some disciplines, they leverage knowledge built over several years within the risk and emergency management communities (Smith, 2013) and therefore although its application may be newer to some disciplines, many of the principles and practices are based on existing risk theory concepts. However, a key advance of IbF is the pull-through of these concepts into implementable prototypes, tools and services and in order to do this, a growth in interdisciplinary working. The World Meteorological Organisation (WMO), as well as global Non-Governmental Organisations (e.g. Red Cross Red Crescent) strongly advocate for a shift towards IbF and have developed supporting guidelines (WMO, 2015a; Red Cross Climate Centre, 2020; WMO, 2021) to enhance implementation of such techniques across the globe. In doing this the WMO have distinguished two main types of IbF, subjective and objective. A subjective IbF relies on expert interpretation to provide the impact-based elements to a forecast or warning, whilst an objective IbF utilises vulnerability and exposure datasets, together with hazard information to calculate the risk and/or impacts. It is noted however, that risk assessments almost always utilise a combination of both subjective and objective methods. There are a wide range of dependencies on how an IbF system might evolve, and it is these dependencies which have introduced variety into the approaches and methods used to generate impact-based forecasts and warnings. This variability is also driven by different interpretations of what IbF should provide. Some stakeholders desire to have information on the number of assets or people that might be affected; however, most IbF warnings systems currently provide categorical risk forecasts (i.e. very low, low, medium and high) with supporting generalised impact information. Although the difference between these styles of output may appear subtle it can have significant implications for the development of forecasting and warning applications and the upstream modelling requirements. IbF has rapidly become an umbrella term under which a plethora of methods are being tried and different disciplines engaged. This broad scope is beneficial for research as it enables blue-sky thinking, transdisciplinary research opportunities and ideally, sustained cooperation and collaboration between a wide range of groups (e.g. stakeholders, researchers, technologists, practitioners, decision-makers). However, these same benefits can pose challenges when moving towards operational implementation, particularly for NMHSs with reduced institutional capacities. It should also be noted that the term IbF is linked to a range of other activities and terminologies, including forecast-based action and forecast-based financing (FbF). The lens through which IbF is viewed therefore influences its role and the value it might provide in meeting the objective ‘to enhance usability by making forecasts and warnings more actionable’. Given the growing scope of IbF and the potential challenges this may have for implementation, this research aims to answer the following questions: (1) Is there a shared understanding of what IbF is across individuals involved in its development? (2) Is there a shared perception of the challenges, barriers and opportunities associated with implementing IbF operationally? To accomplish this aim, practitioners, forecasters and researchers, working within the NERC Science for Humanitarian Emergencies and Resilience (SHEAR) Programme, were invited to provide their perspectives on a range of IbF related topics through a set of semi-structured interviews. This report provides a synthesis of the interviewee transcripts from key informant interviews. In section 2 the methodology is described, while section 3 provides a review of the key findings from the complete set of interviews. The final section (section 4) provides recommendations and concluding remarks

Action-based flood forecasting for triggering humanitarian action

Too often, credible scientific early warning information of increased disaster risk does not result in humanitarian action. With financial resources tilted heavily towards response after a disaster, disaster managers have limited incentive and ability to process complex scientific data, including uncertainties. These incentives are beginning to change, with the advent of several new Forecast-based Financing systems that provide funding based on a forecast of an extreme event. Given the changing landscape, here we demonstrate a method to select and use appropriate forecasts for specific humanitarian disaster prevention actions, even in a data-scarce location. This action-based forecasting methodology takes into account the parameters of each action, such as action lifetime, when verifying a forecast. Forecasts are linked with action based on an understanding of (1) the magnitude of previous flooding events and (2) the willingness to act "in vain" for specific actions. This is applied in the context of the Uganda Red Cross Society Forecast-based Financing pilot project, with forecasts from the Global Flood Awareness System (GloFAS). Using this method, we define the "danger level" of flooding, and we select the probabilistic forecast triggers that are appropriate for specific actions. Results from this methodology can be applied globally across hazards, and fed into a financing system that ensures that automatic, pre-funded early action will be triggered by forecasts