Delta regions are probably the most vulnerable type of coastal environment and their ecosystem services face multiple stresses in the coming decades. These stresses include, amongst others, local drivers due to land subsidence, population growth and urbanisation within the deltas, regional drivers due to changes in catchment management (e.g. upstream land use and dam construction), and global climate change impacts such as sea-level rise.The ecosystem services of river deltas support high population densities, estimated at over 500 million people globally, with particular concentrations in Southern and Eastern Asia and Africa. A large proportion of these people experience extremes of poverty and are severely exposed to vulnerability from environmental and ecological stress and degradation. In areas close to or below the poverty boundary, both subsistence and cash elements of the economy tend to rely disproportionately heavily on ecosystem services which underpin livelihoods.Understanding how to sustainably manage the ecosystem services in delta regions and thus improve health and reduce poverty and vulnerability requires consideration of all these stresses and their complex interaction. This proposal aims to develop methods to understand and characterise the key drivers of change in ecosystem services that affect the environment and economic status in the world's populous deltas. This will be done through analysis of the evolving role of ecosystem services, exploring the implications of changes for the livelihoods of delta residents, and developing management and policy options that will be beneficial now and in the future in the face of the large uncertainties of the next few decades and beyond.The extensive coastal fringe of the Ganges-Brahmaputra-Meghna Delta within Bangladesh has been selected as the pilot study area for this work. This is because Bangladesh is almost entirely located on one of the world's largest and most dynamic deltas. It is characterised by densely populated coastal lowlands with significant poverty, supported to a large extent by natural ecosystems such as the Sunderbahns (the largest mangrove forest in the world). It is under severe development pressure due to many growing cities, eg Khulna and the capital, Dhaka.At present the importance of ecosystems services to poverty and livelihoods is poorly understood. This is due to due to the complexity of interactions between physical drivers, environmental pressures and the human responses to stresses and the resultant impacts on ecosystems. Government policy rarely takes up the ecosystems services perspective and as a result an holistic overview of their value is often overlooked.This project aims to address this gap by providing policy makers with the knowledge and tools to enable them to evaluate the effects of policy decisions on people's livelihoods. This will be done by creating a holistic approach to formally evaluating ecosystems services and poverty in the context of changes such as subsidence and sea level rise, land degradation and population pressure in delta regions. This will be tested and applied in coastal Bangladesh and tested conceptually in other populous deltas.

China's Premier Wen Jiabao recently commented that the sluggish development of agriculture and the slow increase of farmers' incomes constituted the nation's major problems and challenges - a view that is repeated in many developing countries. Alleviating poverty and raising standards of well-being among the rural poor is often seen in terms of inequalities in individual opportunities, land tenure and market imbalances. But increasingly scientists are arguing that rural development must also proceed alongside proper management of the natural environment. Without this, the possible negative consequences for food supply, water quality, biodiversity and other aspects of the environment that we all depend upon, so-called ecological services, are severe. There are countless examples of how agricultural development has caused signficant and often irreversible damage to the natural fabric that supports society. The challenge is how to develop, while ensuring that the decisions made now will lead to sustainable use of the land for decades to come? Conventionally, computer models have provided guidance about future consequences of human activities and climate change on key environmental conditons. But there is increasing criticism that the models do not handle well the possibility that the natural environment can change in unpredictable ways. We know that natural environments can change in complex ways, as with flooding and forest fire, but when humans are involved these changes can be even more unpredictable - and many of the current models do not deal with this well. There is the danger that existing models are providing a false clarity of the future. Our research addresses this problem in a novel way. We argue that contemporary rural landscsapes are the product of their history, and that we can learn much from analysing how the mixture of human actions, climate and ecology has effectively 'evolved' to the state that we see today. This is no idle thought. Many studies have shown that the time taken for ecological processes to change is often over relatively long timescales. For example, pollution of rivers and lakes by sewage and fertlizers can take several decades from the start of the pollution to the whole water system reacting in terms of fish losses or build-up of poisonous algae. Sometimes, ecosytems can withstand a good deal of stress from human activities, but when they finally give way the result can be very damaging. In the lower Yangtze river basin, where the research is set, history describes a catalogue of human catastrophes wrought by flood, famine and poor agricultural practice. Even today, there is widespread rural poverty across many agricultural settings, and many environmental problems. There is accelerating soil erosion on the hilly lands; deteriorating water quality in irrigation channels, rivers and lakes; the ever-present threat of flooding; coastal erosion from rising sea-levels; pressure to produce more food for the rising city populations at a time when the rural population is declining and getting older. We will compile records for local indigenous knowledge, socio-economic data and ecological change for the lower Yangtze basin as a whole and for four selected counties, two of which we have already worked in, for upto the last 200 years or so. These data will be set up within a newly developed application for Google Earth so that we can easily show politicians, administrators, advisors, and farmers the changes that the environment has already experienced and how it might change in the future. We will analyse the trends mathematically and statistically in order to evaluate the sustainability of the current form of agricultural management. We will meet with academics, agencies and rural communities to discuss the implications of the results, how the results compare with their own perceptions of change, and what might be the best alternative futures to aim for.

Microplastics (MPs) have been found in virtually all environments: soil, living things, water and air. The past five years have included a small number of investigations over long time scales (up to a year for some) and across wide ranging locations. One common finding is that MP numbers and types vary greatly in different environments. There is now concern regarding the possible impacts of MPs in terms of public and other organism health. MPs are of the size range range of 1 micron to 5 mm, which ranges from the size of a small bacterium to a sesame seed at most, inhalable, and common in food chains/diet. They come in different shapes and polymer types, depending on the plastics that are derived from eg. polyester or polypropylene, which are common in textiles or packaging. Recently, they have been identified inside people's lungs, blood and bowels, and questions arise as to whether they cause or exacerbate lung or bowel conditions like chronic cough or irritable bowel disease. In marine organisms they are also associated with growth and inflammation type impacts. Current air quality measures and monitoring completely overlook this contaminant type, which is likely to become a public health issue. Current measurements of the types of particles and gases in routine air monitoring also fail to completely explain high levels of specific cough and inflammation type disease incidences in many cities. This study firstly aims to establish a means to measure MPs in the air, which would represent a new air quality measure methodology. The approach we suggest 'slip streams' the current pollen monitoring methods available worldwide, making it accessible for those who do not have access to specialist and expensive equipment. The method proposed does however have certain robust elements included and these are to ensure that the agencies who conduct air quality measurements can use the data produced. The second aspect of this work is to develop an automated identification and counting technology approach so that the monitoring can be completed in future using low cost, non-specialist equipment and expertise. Ideally, the method will be available for reliable and reproducible use around the world. This will be achieved by a combination of manipulation of existing available datasets on MPs found in the air (from our past three years of studies), and trials with colleagues located across four continents who work with different pollen sampling approaches. One novel approach we will use will be to make a set of MP 'reference strips' that can be posted to users and used an as internal calibration when taking images for analysis. There are parallels between established pollen monitoring and trying to set up something similar for MPs, that we can exploit. Pollen come in a comparable range of shapes and sizes (ranging from 5-100+ microns) to MPs. They are a trigger for health impacts and, as such, are routinely and robustly monitored such that datasets can be shared and compared internationally as well as communicated to the public. The same parameters can apply for MPs. The second aspect, the auto-identification and counting would represent a significant step forward for both pollen and MP monitoring, that could see wider benefits still. The trials we conduct will run in parallel with the current practice for MP sampling, to add further cross comparison but also to anchor any new approach to what is currently deemed as acceptable in the wider community of research scientists working in this area.

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.

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.