The Hub will reduce disaster risk for the poor in tomorrow's cities. The failure to integrate disaster risk resilience into urban planning and decision-making is a persistent intractable challenge that condemns hundreds of millions of the World's poor to continued cyclical destruction of their lives and livelihoods. It presents a major barrier to the delivery of the Sustainable Development Goals in expanding urban systems. Science and technology can help, but only against complex multi-hazard context of urban life and the social and cultural background to decision-making in developing countries. Science-informed urbanisation, co-produced and properly integrated with decision support for city authorities, offers the possibility of risk-sensitive development for millions of the global poor. This is a major opportunity - some 60% of the area expected to be urban by 2030 is yet to be built. Our aim is to catalyse a transition from crisis management to risk-informed planning in four partner cities and globally through collaborating International governance organisations. The Hub, co-designed with local and international stakeholders from the start, will deliver this agenda through integrated research across four urban systems - Istanbul, Kathmandu, Nairobi and Quito - chosen for their multi-hazard exposure, and variety of urban form, development status and governance. Trusted core partnerships from previous Global Challenge Research Fund, Newton Fund and UK Research Council projects provide solid foundations on which city based research projects have been built around identified, existing, policy interventions to provide research solutions to specific current development problems. We have developed innovative, strategic research and impact funds and capable management processes constantly to monitor progress and to reinforce successful research directions and impact pathways. In each urban system, the Hub will reduce risk for 1-4 million people by (1) Co-producing forensic examinations of risk root causes, drivers of vulnerability and trend analysis of decision-making culture for key, historic multi-hazard events. (2) Combining quantitative, multi-hazard intensity, exposure and vulnerability analysis using advances in earth observation, citizen science, low cost sensors and high-resolution surveys with institutional and power analysis to allow multi-hazard risk assessment to interface with urban planning culture and engineering. (3) Convene diverse stakeholder groups-communities, schools, municipalities private enterprise, national agencies- around new understanding of multi-hazard urban disaster risk stimulating engagement and innovation in making risk-sensitive development choices to help meet the SDGs and Sendai Framework. Impact will occur both within and beyond the life of the Hub and will raise the visibility of cities in global risk analysis and policy making. City Partnerships, integrating city authorities, researchers, community leaders and the private sector, will develop and own initiatives including high-resolution validated models of multi-hazard risk to reflect individual experience and inform urban development planning, tools and methods for monitoring, evaluation and audit of disaster risk, and recommendations for planning policy to mitigate risks in future development. City partnerships will collaborate with national and regional city networks, policy champions and UN agencies using research outputs to structure city and community plans responding to the Sendai Framework and targeted SDG indicators, and build methods and capacity for reporting and wider critique of the SDG and Sendai reporting process. Legacy will be enabled through the ownership of risk assessment and resilience building tools by city and international partners who will identify need, own, modify and deploy tools beyond the life of the Hub.

Humanitarian agencies are able to use weather (and other) forecasts to act in anticipation of humanitarian crises. For example, when a heatwave or hurricane is forecast, supplies can be moved into position early and emergency supplies positioned or pre-distributed. This reduces the overall impact and the cost of responding to the disaster. However, financing in advance of a disaster requires a high level of confidence in the forecast, to avoid the possibility of misallocated or wasted resources. Many forecasts are currently available but not all are accompanied by an assessment of the forecast quality. For example, it may be that the forecast is over-confident, predicting an event more times than it is actually observed, or it could be under confident, failing to predict events which do then occur. We propose to develop and demonstrate a general method of measuring and displaying the information content of forecasts, using a novel idea which is based on existing research and freely available data. This will allow humanitarian agencies to act confidently in anticipation of humanitarian crises when there is sufficient information in the forecast, and to implement forecast-based financing schemes such as insurance or anticipatory funding allocation only when there is known to be confidence that the scheme will be effective.

Whether on television, newspapers, the internet or first-hand we have all seen the damage that floods, droughts and other weather hazards can have on people's lives and their livelihoods. It is a sad fact that such hazard events disproportionately impact developing countries and poor people. However it is also increasingly evident that acting before a disaster occurs can save lives. For example, frontline humanitarian organisations and government agencies can themselves prepare by getting supplies and staff in readiness. More importantly, agencies can directly help the population prepare so that the impacts of a hazard are actually much reduced. Such actions depend on the lead time of a forecast but can range for example from distributing money, drought-resistant seeds, animal fodder to communities to ensuring evacuation procedures are followed. Acting before an event means they can also do this at a lower cost than the traditional 'late' post disaster humanitarian response. As a result there is growing momentum within the humanitarian system to move beyond the current 'begging bowl' funding model of post-disaster appeals, towards obtaining and distributing humanitarian funds before a disaster occurs. This change can enable humanitarians to mobilise more collaboratively, more predictably, and importantly in anticipation of crises. For this to occur requires trustworthy forecasts of hazards like storms, floods and droughts, and credible information on the condition of the people and systems exposed to them. Forecast based financing and Disaster Risk financing initiatives, utilise information to anticipate potential disasters and set pre-agreed triggers for the release of disaster prevention finance. The advantage of this approach is that it is data-driven and objective. It thereby circumvents long debates around potentially conflicting early warning signs which tend to paralyse humanitarian action. It puts in place a robust predictable process to release funding or initiate action before a disaster occurs. Humanitarian agencies working on developing these systems face a problem, however. They are not scientists nor social scientists; but they need to use information from both realms of research to trigger the systems and have confidence in this information. They also must be accountable to the people that the system looks to support and the donors that finance it. The START Network Drought financing facility (DFF) and the Weithungerhilfe (WHH) Madagascar Forecast based financing project are both at this juncture of selection and development of scientific data to apply to these initiatives. The DFF having begun the design with a Global Parametric model and have a prototype model that requires testing and evaluation, whereas the WHH Madagascar Forecast based financing project is starting out from the beginning. However, currently no process, independent honest broker, or method to provide an independent review of the scientific (science and social science) credibility of these systems exists in an operational context. This is a stumbling point in the adoption of these ground breaking initiatives by other organisations. This project looks to meet the needs of humanitarian agencies. In particular it will provide "scientific due diligence" to the forecast and action components of these proactive schemes and hence ensure that the information going into them is as trustworthy as possible. It will assess a suit of global drought models in regard to their uncertainty and ability to depict emerging food security crisis. Global data products will be explored alongside data on the ground of drought and food security events in the three test sites associated of Pakistan, Zimbabwe and Madagascar. It will help the humanitarian practitioners understand the limitations of the science for decision making and the fundamental risk of acting proactively when acting with forecast and monitoring information.

Flooding is the deadliest and most costly natural hazard on the planet, affecting societies across the globe. Nearly one billion people are exposed to the risk of flooding in their lifetimes and around 300 million are impacted by floods in any given year. The impacts on individuals and societies are extreme: each year there are over 6,000 fatalities and economic losses exceed US$60 billion. These problems will become much worse in the future. There is now clear consensus that climate change will, in many parts of the globe, cause substantial increases in the frequency of occurrence of extreme rainfall events, which in turn will generate increases in peak flood flows and therefore flood vast areas of land. Meanwhile, societal exposure to this hazard is compounded still further as a result of population growth and encroachment of people and key infrastructure onto floodplains. Faced with this pressing challenge, reliable tools are required to predict how flood hazard and exposure will change in the future. Existing state-of-the-art Global Flood Models (GFMs) are used to simulate the probability of flooding across the Earth, but unfortunately they are highly constrained by two fundamental limitations. First, current GFMs represent the topography and roughness of river channels and floodplains in highly simplified ways, and their relatively low resolution inadequately represents the natural connectivity between channels and floodplains. This restricts severely their ability to predict flood inundation extent and frequency, how it varies in space, and how it depends on flood magnitude. The second limitation is that current GFMs treat rivers and their floodplains essentially as 'static pipes' that remain unchanged over time. In reality, river channels evolve through processes of erosion and sedimentation, driven by the impacts of diverse environmental changes (e.g., climate and land use change, dam construction), and leading to changes in channel flow conveyance capacity and floodplain connectivity. Until GFMs are able to account for these changes they will remain fundamentally unsuitable for predicting the evolution of future flood hazard, understanding its underlying causes, or quantifying associated uncertainties. To address these issues we will develop an entirely new generation of Global Flood Models by: (i) using Big Data sets and novel methods to enhance substantially their representation of channel and floodplain morphology and roughness, thereby making GFMs more morphologically aware; (ii) including new approaches to representing the evolution of channel morphology and channel-floodplain connectivity; and (iii) combining these developments with tools for projecting changes in catchment flow and sediment supply regimes over the 21st century. These advances will enable us to deliver new understanding on how the feedbacks between climate, hydrology, and channel morphodynamics drive changes in flood conveyance and future flooding. Moreover, we will also connect our next generation GFM with innovative population models that are based on the integration of satellite, survey, cell phone and census data. We will apply the coupled model system under a range of future climate, environmental and societal change scenarios, enabling us to fully interrogate and assess the extent to which people are exposed, and dynamically respond, to evolving flood hazard and risk. Overall, the project will deliver a fundamental change in the quantification, mapping and prediction of the interactions between channel-floodplain morphology and connectivity, and flood hazard across the world's river basins. We will share models and data on open source platforms. Project outcomes will be embedded with scientists, global numerical modelling groups, policy-makers, humanitarian agencies, river basin stakeholders, communities prone to regular or extreme flooding, the general public and school children.