Climate change will pose new challenges to conserving Earth's natural ecosystems, due to incremental changes in temperature and weather patterns, and to increased frequency and intensity of extreme climate events. Addressing these challenges will require pragmatic conservation actions informed by sitespecific understanding of susceptibility to climate change and capacity of societies to cope with and adapt to change. Depending on a location's environmental susceptibility and social adaptive capacity, appropriate conservation actions will require some combination of: (1) large-scale protection of ecosystems; (2) actively transforming and adapting social-ecological systems; (3) building the capacity of communities to cope with change; and (4) government assistance focused on de-coupling communities from dependence on natural resources. We apply a novel analytical framework to examine conservation actions in five western Indian Ocean countries, where climate-mediated disturbance has impacted coral reefs and where adaptive capacity differs markedly. We find that current conservation strategies do not reflect adaptive capacity and are, therefore, ill prepared for climate change. We provide a vision for conservation policies that considers social adaptive capacity that copes with complexities of climate change better than the singular emphasis on government control and the creation of no-take areas.

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BackgroundThe decline of coral reefs globally underscores the need for a spatial assessment of their exposure to multiple environmental stressors to estimate vulnerability and evaluate potential counter-measures.Methodology/Principal FindingsThis study combined global spatial gradients of coral exposure to radiation stress factors (temperature, UV light and doldrums), stress-reinforcing factors (sedimentation and eutrophication), and stress-reducing factors (temperature variability and tidal amplitude) to produce a global map of coral exposure and identify areas where exposure depends on factors that can be locally managed. A systems analytical approach was used to define interactions between radiation stress variables, stress reinforcing variables and stress reducing variables. Fuzzy logic and spatial ordinations were employed to quantify coral exposure to these stressors. Globally, corals are exposed to radiation and reinforcing stress, albeit with high spatial variability within regions. Based on ordination of exposure grades, regions group into two clusters. The first cluster was composed of severely exposed regions with high radiation and low reducing stress scores (South East Asia, Micronesia, Eastern Pacific and the central Indian Ocean) or alternatively high reinforcing stress scores (the Middle East and the Western Australia). The second cluster was composed of moderately to highly exposed regions with moderate to high scores in both radiation and reducing factors (Caribbean, Great Barrier Reef (GBR), Central Pacific, Polynesia and the western Indian Ocean) where the GBR was strongly associated with reinforcing stress.Conclusions/SignificanceDespite radiation stress being the most dominant stressor, the exposure of coral reefs could be reduced by locally managing chronic human impacts that act to reinforce radiation stress. Future research and management efforts should focus on incorporating the factors that mitigate the effect of coral stressors until long-term carbon reductions are achieved through global negotiations.

Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77% to 81%. This was supported with local expert knowledge which accurately classified 79% of communities deemed to be highly OPEN ACCESSWater 2015, 7 3532 susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized.

Priorities for conservation, management, and associated activities will differ based on the interplaybetween nearness of ecosystems to full recovery from a disturbance (pristineness), susceptibility to climate change (environmental susceptibility [ES]), and capacity of human communities to cope with and adapt to change (social adaptive capacity [AC]

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Land-use information is required for a number of purposes such as to address food security issues, to ensure the sustainable use of natural resources and to support decisions regarding food trade and crop insurance. Suitable land-use maps often either do not exist or are not readily available. This article presents a novel method to compile spatial and temporal land-use data sets using multi-temporal remote sensing in combination with existing data sources. Satellite Pour l'Observation de la Terre (SPOT)-Vegetation 10-day composite normalized difference vegetation index (NDVI) images (1998-2002) at 1 km2 resolution for a part of the Nizamabad district, Andhra Pradesh, India, were linked with available crop calendars and information about cropping patterns. The NDVI images were used to stratify the study area into map units represented by 11 distinct NDVI classes. These were then related to an existing land-cover map compiled from high resolution Indian Remote Sensing (IRS)-images (Liss-III on IRS-1C), reported crop areas by sub-district and practised crop calendar information. This resulted in an improved map containing baseline information on both land cover and land use. It is concluded that each defined NDVI class represents a varying but distinct mix of land-cover classes and that the existing land-cover map consists of too many detailed 'year-specific' features. Four groups of the NDVI classes present in agricultural areas match well with four categories of practised crop calendars. Differences within a group of NDVI classes reveal area specific variations in cropping intensities. The remaining groups of NDVI classes represent other land-cover complexes. The method illustrated in this article has the potential to be incorporated into remote sensing and Geographical Information System (GIS)-based drought monitoring systems