Indonesia: Java Flood One Flooding represents the most frequently occurring hydrometeorological hazard in Indonesia, contributing to around 31% of all disaster events recorded by the National Disaster Management Agency. Impacts from fluvial flooding in Jakarta alone are estimated to cost around USD 321 million per year. Due to Java's topography, climate, dense urbanisation and inadequate infrastructure, improvements to structural flood defences alone are unlikely to prevent flooding in these areas in the future. Instead, resilience needs to be built through the combined efforts of flood management information systems and greater public awareness.  Improved solutions to this problem would have a significant economic and social impact in the region as well as addressing multiple Sustainable Development Goals including: Goal 6: "Ensure availability and sustainable management of water and sanitation for all." Goal 11: "Make cities and human settlements inclusive, safe, resilient and sustainable." and in particular, Goal 11.5: "By 2030, decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations." Social benefits are difficult to estimate but the economic benefit of flood awareness alone is of the order of USD 400 for every USD 1 invested.  The objective of this project is therefore to create a set of medium term flood forecast (MRFF) tools for the urban centres of Jakarta, Bandung and Surakarta on the island of Java, Indonesia. The skills and development associated with this project will be strongly embedded within Bandung Institute of Technology (ITB) and will depend on free software packages and cost effective data acquisition solutions such that ITB will be able to independently develop similar MRFF tools for other cities in Indonesia following the cessation of the NERC funding. It is envisaged that developed workflows and methodologies can also be extended to flood affected ODA countries beyond Indonesia in the future.  Previous work has mostly focused on Jakarta and has been limited to static return period flood risk maps using standard rainfall runoff modelling methods. We plan to improve on existing work by applying a dynamic stochastic meteorological model to provide medium term flood forecasts. So as to overcome excessive computational times associated with flood inundation models, we plan to wrap everything up within a probabilistic emulation framework. Flooding in the areas of concern is made worse by continuous subsidence and anthropogenic topographic changes. Therefore, we also plan to implement repeat photogrammetry surveys with drones, to capture and characterise these important effects. Crucially, there will be focus group meetings with local flood affected communities to help inform the development of internet/social media flood warning dissemination tools.  The research is designed using participatory approaches to enrol potential end users into co-developing outcomes and outputs to improve flood risk communication. The project will develop and run a series of training workshops and public engagement sessions to launch and cascade the new flood risk communication tools and additional social and economic benefits by developing outputs in the study areas. These sessions will be co-delivered by the UK and Indonesian project teams.  We will be working with a range of Project Partners to deliver impacts including government departments such as BNBP/BPBD (Indonesia's Disaster Management Agency), IAHRI (Indonesian Agro-climate and Hydrological Research Institute), PT Reasuransi MAIPARK and local NGOs such as Jaga Balai and Ciliwung Medeka. These latter organisations represent local community groups who have set up their own flood warning system using real-time and historical visual observations. The Newton Fund builds research and innovation partnerships with developing countries across the world to promote the economic development and social welfare of the partner countries.