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Abstract Despite the increasing interest of the Sustainable Livelihood Framework in the field of international development and in academia and the recent call for the use of mixed-methods approach, there has been little analysis that brings together qualitative and quantitative methods over a large geographical extent. Based on findings from participatory rural appraisals during which participants identified the key assets needed to achieve their livelihoods, this paper argues that common-pool resources (community capitals) should be differentiated from private goods (household capitals) as they operate under different dynamics of decision-making and management. We then create quantitative indicators that can be mapped across a large geographical extent by using data derived from national census and satellite sensors. Spatial patterns and differentials in access to livelihood capitals across the case study are examined and the associations that exist between household capitals, between community capitals, and between both are quantified. The results demonstrate that household physical capital is positively associated with household financial and social capitals but negatively associated with household natural capital, supporting the hypothesis that households trade their natural assets to cope with shocks. It is also shown that proximity to main axes of communication increases access to village amenities but decreases access to natural resources, while remoteness increases household human capital but decreases household physical and financial capitals. Such a cross-scale study adds to the understanding of the question of scale regarding rural livelihoods and community development, which could act as a bridge between the implementation of policy programmes (often targeted at the community level) and their expected outcomes (often targeted at the household level). Highlights • We advocate for the separation of community capitals from household livelihood capitals to characterise rural livelihoods. • We observed spatial variability in access to common-pool resources and private assets affecting livelihoods in rural India. • Workforce availability and access to community natural resources increase with remoteness. • Access to community natural capital is negatively associated with access to community infrastructures. • Households make trade-offs between natural and financial assets to meet their needs and mediate vulnerabilities. [ABSTRACT FROM AUTHOR]

Small island developing States, such as those in the Pacific, are often prone to multiple hazards that have potential to result in disaster and / or restrict development. Hazard data can be limited in resolution or omitted in or near SIDS' coasts, but a growing and improved range of datasets are becoming available. Through an analysis of approximately 100 policy documents on hazards and disaster risk management in Pacific island nations, we found: limited information on hazards and how they manifest to disasters at local levels, thus not fully connecting drivers and subsequent risk; at times a non-specific multi-hazard approach prompting the need to address more specific hazards; and restricted temporal and spatial scales of analysis that potentially limit continuity of actions where mitigation methods evolve. These limitations suggest that appropriate and timely high resolution hazard data is needed from the top-down to underpin the design and development of local disaster risk management plans, simultaneous to local, bottom-up knowledge and interpretation to bring the realities of such hazard data to life. Developing and ensuring openly available hazard data will enable island States to develop more robust, inclusive disaster risk management plans and mitigation policies, plus aid inter-island comparison for communal learning. [ABSTRACT FROM AUTHOR]

Abstract: Social data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed satellite sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed satellite sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GARerr) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GARerr model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution satellite sensor data and methods that can account for such complexities. [Copyright &y& Elsevier]

Summary: This paper investigates a method for the real-time design and execution of a space–time sampling strategy in the context of flood forecasting. Measurements of water level taken by a network of wireless sensors were assimilated into a one-dimensional hydrodynamic model using an ensemble Kalman filter, to create a forecasting model. This research focused on methods for targeting measurements in real-time to be assimilated by the forecasting model, such that the power-limited but flexible sensor network could be used optimally. Two targeting methods were developed. The first targeted measurements systematically over space and time until the forecasting model predicted that the probability of the water level exceeding a pre-defined threshold was less than 5%. The second method targeted measurements based on the expected decrease in forecasted water level error variance at a validation time and location, quickly calculated for various sets of measurements by an ensemble transform Kalman filter. Targeting measurements based on the decrease in forecast error variance was shown to be more efficient than a systematic sampling method. [ABSTRACT FROM AUTHOR]

Abstract: The automated cloud cover assessment (ACCA) algorithm has provided automated estimates of cloud cover for the Landsat ETM+ mission since 2001. However, due to the lack of a band around 1.375μm, cloud edges and transparent clouds such as cirrus cannot be detected. Use of Landsat ETM+ imagery for terrestrial land analysis is further hampered by the relatively long revisit period due to a nadir only viewing sensor. In this study, the ACCA threshold parameters were altered to minimise omission errors in the cloud masks. Object-based analysis was used to reduce the commission errors from the extended cloud filters. The method resulted in the removal of optically thin cirrus cloud and cloud edges which are often missed by other methods in sub-tropical areas. Although not fully automated, the principles of the method developed here provide an opportunity for using otherwise sub-optimal or completely unusable Landsat ETM+ imagery for operational applications. Where specific images are required for particular research goals the method can be used to remove cloud and transparent cloud helping to reduce bias in subsequent land cover classifications. [Copyright &y& Elsevier]

Summary: This paper compares two Monte Carlo sequential data assimilation methods based on the Kalman filter, for estimating the effect of measurements on simulations of state error variance made by a one-dimensional hydrodynamic model. The first method used an ensemble Kalman filter (EnKF) to update state estimates, which were then used as initial conditions for further simulations. The second method used an ensemble transform Kalman filter (ETKF) to quickly estimate the effect of measurement error covariance on forecast error covariance without the need to re-run the simulation model. The ETKF gave an unbiased estimate of EnKF analysed error variance, although differences in the treatment of measurement errors meant the results were not identical. Estimates of forecast error variance could also be made, but their accuracy deteriorated as the time from measurements increased due in part to model non-linearity and the decreasing signal variance. The motivation behind the study was to assess the ability of the ETKF to target possible measurements, as part of an adaptive sampling framework, before they are assimilated by an EnKF-based forecasting model on the River Crouch, Essex, UK. The ETKF was found to be a useful tool for quickly estimating the error covariance expected after assimilating measurements into the hydrodynamic model. It, thus, provided a means of quantifying the ‘usefulness’ (in terms of error variance) of possible sampling schemes. [Copyright &y& Elsevier]

Summary: This paper examines critically the application to a site along the River Crouch, Essex of a river-flow forecasting approach based on a one-dimensional hydraulic flow simulation model updated using real-time data within an ensemble Kalman filtering framework. Given a specified validation location and forecast period the objective of the forecasting model was to estimate water level more accurately with updating than without. The method used to estimate both model state and state uncertainty was evaluated in terms of its forecast accuracy and representation of forecast uncertainty. The ensemble Kalman filter lead to an increase in forecast accuracy of between 50% and 70% depending on location. The hyperparameters of the filter could be calibrated to make estimates of forecast uncertainty at a specific location, where the most data were available. However, the presence of systematic errors in the simulation model and especially measurement data meant that uncertainty estimates were inaccurate at other locations. Although, the major source of uncertainty in this model came from the boundary condition, additional uncertainty within the model domain was required, particularly between channel and floodplain. Changing the temporal sampling rate and spatial density of samples had little effect on the accuracy of forecasts at this site. However, uncertainty was under-estimated when the temporal sampling rate was decreased, indicating that the relative uncertainties prescribed to the simulation model and measurement model were inadequate. [Copyright &y& Elsevier]

The main determinants of agricultural employment are related to households' access to private assets and the influence of inherited social–economic stratification and power relationships. However, despite the recommendations of rural studies which have shown the importance of multilevel approaches to rural poverty, very few studies have explored quantitatively the effects of common-pool resources and household livelihood capitals on agricultural employment. Understanding the influence of access to both common-pool resources and private assets on rural livelihoods can enrich our understanding of the drivers of rural poverty in agrarian societies, which is central to achieving sustainable development pathways. Based on a participatory assessment conducted in rural communities in India, this paper differentiates two levels of livelihood capitals (household capitals and community capitals) and quantifies them using national census data and remotely sensed satellite sensor data. We characterise the effects of these two levels of livelihood capitals on precarious agricultural employment by using multilevel logistic regression. Our study brings a new perspective on livelihood studies and rural economics by demonstrating that common-pool resources and private assets do not have the same effect on agricultural livelihoods. It identifies that a lack of access to human, financial and social capitals at the household level increases the levels of precarious agricultural employment, such as daily-wage agricultural labour. Households located in communities with greater access to collective natural capital are less likely to be agricultural labourers. The statistical models also show that proximity to rural centres and access to financial infrastructures increase the likelihood of being a landless agricultural labourer. These findings suggest that investment in rural infrastructure might increase livelihood vulnerability, if not accompanied by an improvement in the provisioning of complementary rural services, such as access to rural finance, and by the implementation of agricultural tenancy laws to protect smallholders' productive assets. [ABSTRACT FROM AUTHOR]

To better anticipate potential impacts of climate change, diverse information about the future is required, including climate, society and economy, and adaptation and mitigation. To address this need, a global RCP (Representative Concentration Pathways), SSP (Shared Socio-economic Pathways), and SPA (Shared climate Policy Assumptions) (RCP–SSP–SPA) scenario framework has been developed by the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC-AR5). Application of this full global framework at sub-national scales introduces two key challenges: added complexity in capturing the multiple dimensions of change, and issues of scale. Perhaps for this reason, there are few such applications of this new framework. Here, we present an integrated multi-scale hybrid scenario approach that combines both expert-based and participatory methods. The framework has been developed and applied within the DECCMA 1 1 DECCMA ( DEltas , vulnerability and Climate Change: Migration and Adaptation ) project is part of the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA), with financial support from the UK Government's Department for International Development (DFID) and the International Development Research Centre (IDRC), Canada. For more information, visit the project website: http://www.geodata.soton.ac.uk/deccma/ . project with the purpose of exploring migration and adaptation in three deltas across West Africa and South Asia: (i) the Volta delta (Ghana), (ii) the Mahanadi delta (India), and (iii) the Ganges-Brahmaputra-Meghna (GBM) delta (Bangladesh/India). Using a climate scenario that encompasses a wide range of impacts (RCP8.5) combined with three SSP-based socio-economic scenarios (SSP2, SSP3, SSP5), we generate highly divergent and challenging scenario contexts across multiple scales against which robustness of the human and natural systems within the deltas are tested. In addition, we consider four distinct adaptation policy trajectories: Minimum intervention , Economic capacity expansion , System efficiency enhancement , and System restructuring , which describe alternative future bundles of adaptation actions/measures under different socio-economic trajectories. The paper highlights the importance of multi-scale (combined top-down and bottom-up) and participatory (joint expert-stakeholder) scenario methods for addressing uncertainty in adaptation decision-making. The framework facilitates improved integrated assessments of the potential impacts and plausible adaptation policy choices (including migration) under uncertain future changing conditions. The concept, methods, and processes presented are transferable to other sub-national socio-ecological settings with multi-scale challenges. [ABSTRACT FROM AUTHOR]

Summary This article presents results from an investigation of the relationships between welfare and geographic metrics from over 14,000 villages in Assam, India. Geographic metrics accounted for 61% of the variation in the lowest welfare quintile and 57% in the highest welfare quintile. Travel time to market towns, percentage of a village covered with woodland, and percentage of a village covered with winter crop were significantly related to welfare. These results support findings in the literature across a range of different developing countries. Model accuracy is unprecedented considering that the majority of geographic metrics were derived from remotely sensed data. [ABSTRACT FROM AUTHOR]

There is increasing interest in leveraging Earth Observation (EO) and geospatial data to predict and map aspects of socioeconomic conditions to support survey and census activities. This is particularly relevant for the frequent monitoring required to assess progress towards the UNs' Sustainable Development Goals (SDGs). The Sundarban Biosphere Reserve (SBR) is a region of international ecological importance, containing the Indian portion of the world's largest mangrove forest. The region is densely populated and home to over 4.4 million people, many living in chronic poverty with a strong dependence on nature-based rural livelihoods. Such livelihoods are vulnerable to frequent natural hazards including cyclone landfall and storm surges. In this study we examine associations between environmental variables derived from EO and geospatial data with a village level multidimensional poverty metric using random forest machine learning, to provide evidence in support of policy formulation in the field of poverty reduction. We find that environmental variables can predict up to 78% of the relative distribution of the poorest villages within the SBR. Exposure to cyclone hazard was the most important variable for prediction of poverty. The poorest villages were associated with relatively small areas of rural settlement (<∼30%), large areas of agricultural land (>∼50%) and moderate to high cyclone hazard. The poorest villages were also associated with less productive agricultural land than the wealthiest. Analysis suggests villages with access to more diverse livelihood options, and a smaller dependence on agriculture may be more resilient to cyclone hazard. This study contributes to the understanding of poverty-environment dynamics within Low-and middle-income countries and the associations found can inform policy linked to socio-environmental scenarios within the SBR and potentially support monitoring of work towards SDG1 (No Poverty) across the region. • Earth Observation and geospatial data could support monitoring of development. • We examine association between poverty and the environment with machine learning. • Cyclone hazard was the most important variable for prediction of poverty in the SBR. • The poorest villages were predicted with up to 78% accuracy. • Results can inform policy linked to socio-environmental scenarios in the SBR. [ABSTRACT FROM AUTHOR]