• Energy Research

West Africa, already highly influenced by the negative effects of climate change, is additionally characterized by rapid population growth, endemic poverty, and insecurity. This is affecting the natural capital of its ecosystems and the services they provide. Natural capital accounting (NCA) provides the fundamental evidence base required for informing economics and environmental decisions, thus strengthening the conservation and management of natural resources. The objective of this study is to showcase the development and evaluation of a semi-automated NCA platform (Sys4ENCA) designed to support decision making in the context of protected areas management in a multi-level example in western Africa. The accounting results highlight that simulations at the broader scale using national public data show that the natural capital of ecosystems in western Africa depends strongly on the mean climate and its variability. Evaluating regional datasets, the simulation with the platform shows that pressure on land in combination with weak governance reduces the capability of the ecosystem to deliver the required services in a sustainable manner, i.e., in the eastern part of the Bafing-Falémé landscape, where mining and intensive agriculture are fueling loss of natural capital. The results of Tier-3 accounting using local datasets enhanced the spatial variability and highlighted additional hotspots of degradation compared to the regional results, i.e., the prospective construction of a hydro-electricity dam (Koukoutamba) in the southern part of the Moyen-Bafing National Park located in the Bafing-Falémé landscape. The Sys4ENCA platform, combined with a multi-level approach, showed itself to be a valuable tool to facilitate protected area management as it provides not only consolidated information at a local scale but also the broader context and external pressures, i.e., climate change and demand for land. Given its automatized nature, the platform reduces human errors and increases the efficiency, speed, and harmonisation of computation over long timeframes and spatial scales.

Abstract. We present a new approach to the validation of modelled forest Net Primary Productivity (NPP), using empirical data on the mean annual increment, or MAI, in above-ground forest stock. The dynamic biomass model BETHY/DLR is used to estimate the NPP of forest areas in Germany, driven by remote sensing data from VEGETATION, meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF), and additional tree coverage information from the MODIS Vegetation Continuous Field (VCF). The output of BETHY/DLR, Gross Primary Productivity (GPP), is converted to NPP by subtracting the cumulative plant maintenance and growth respiration, and then validated against MAI data derived from German forestry inventories. Validation is conducted for 2000 and 2001 by converting modelled NPP to stem volume at a regional level. Our analysis shows that the presented method fills an important gap in methods for validating modelled NPP against empirically derived data. In addition, we examine theoretical energy potentials calculated from the modelled and validated NPP, assuming sustainable forest management and using species-specific tree heating values. Such estimated forest biomass energy potentials play an important role in the sustainable energy debate.

Aims: An Arctic Vegetation Classification (AVC) is needed to address issues related to rapid Arctic-wide changes to climate, land-use, and biodiversity. Location: The 7.1 million km2 Arctic tundra biome. Approach and conclusions: The purpose, scope and conceptual framework for an Arctic Vegetation Archive (AVA) and Classification (AVC) were developed during numerous workshops starting in 1992. The AVA and AVC are modeled after the European vegetation archive (EVA) and classification (EVC). The AVA will use Turboveg for data management. The AVC will use a Braun-Blanquet (Br.-Bl.) classification approach. There are approximately 31,000 Arctic plots that could be included in the AVA. An Alaska AVA (AVA-AK, 24 datasets, 3026 plots) is a prototype for archives in other parts of the Arctic. The plan is to eventually merge data from other regions of the Arctic into a single Turboveg v3 database. We present the pros and cons of using the Br.-Bl. classification approach compared to the EcoVeg (US) and Biogeoclimatic Ecological Classification (Canada) approaches. The main advantages are that the Br.-Bl. approach already has been widely used in all regions of the Arctic, and many described, well-accepted vegetation classes have a pan-Arctic distribution. A crosswalk comparison of Dryas octopetala communities described according to the EcoVeg and the Braun-Blanquet approaches indicates that the non-parallel hierarchies of the two approaches make crosswalks difficult above the plantcommunity level. A preliminary Arctic prodromus contains a list of typical Arctic habitat types with associated described syntaxa from Europe, Greenland, western North America, and Alaska. Numerical clustering methods are used to provide an overview of the variability of habitat types across the range of datasets and to determine their relationship to previously described Braun-Blanquet syntaxa. We emphasize the need for continued maintenance of the Pan-Arctic Species List, and additional plot data to fully sample the variability across bioclimatic subzones, phytogeographic regions, and habitats in the Arctic. This will require standardized methods of plot-data collection, inclusion of physiogonomic information in the numeric analysis approaches to create formal definitions for vegetation units, and new methods of data sharing between the AVA and national vegetation- plot databases.

Many areas of the Arctic are simultaneously affected by rapid climate change and rapid industrial development.These areas are likely to increase in number and size as sea ice melts and abundant Arctic natural resources become more accessible. Documenting the changes that have already occurred is essential to inform management approaches to minimize the impacts of future activities. Here, we determine the cumulative geoecological effects of 62 years (1949-2011) of infrastructure- and climate-related changes in the Prudhoe Bay Oilfield, the oldest and most extensive industrial complex in the Arctic, and an area with extensive ice-rich permafrost that is extraordinarily sensitive to climate change. We demonstrate that thermokarst has recently affected broad areas of the entire region, and that a sudden increase in the area affected began shortly after 1990 corresponding to a rapid rise in regional summer air temperatures and related permafrost temperatures. We also present a conceptual model that describes how infrastructure-related factors, including road dust and roadside flooding are contributing to more extensive thermokarst in areas adjacent to roads and gravel pads. We mapped the historical infrastructure changes for the Alaska North Slope oilfields for 10 dates from the initial oil discovery in 1968-2011. By 2010, over 34% of the intensively mapped area was affected by oil development. In addition, between 1990 and 2001, coincident with strong atmospheric warming during the 1990s, 19% of the remaining natural landscapes (excluding areas covered by infrastructure, lakes and river floodplains) exhibited expansion of thermokarst features resulting in more abundant small ponds, greater microrelief, more active lakeshore erosion and increased landscape and habitat heterogeneity. This transition to a new geoecological regime will have impacts to wildlife habitat, local residents and industry.

Satellites provide the only practical source of data for estimating biomass of large and remote areas such as the Alaskan Arctic. Researchers have found that the normalized difference vegetation index (NDVI) correlates well with biomass sampled on the ground. However, errors in NDVI and biomass estimates due to bidirectional reflectance distribution function (BRDF) effects are not well reported in the literature. Sun-sensor-object geometries and sensor band-width affect the BRDF, and formulas relating NDVI to ground-sampled biomass vary between projects. We examined the effects of these different variables on five studies that estimated above-ground tundra biomass of two common arctic vegetation types that dominate the Alaska tundra, moist acidic tussock tundra (MAT) and moist non-acidic tundra (MNT). We found that biomass estimates were up to 33% (excluding extremes) more sensitive than NDVI to BRDF effects. Variation between the sensors resulted in differences in NDVI of under 3% over all viewing geometries, and wider bands were more stable in their biomass estimates than narrow bands. MAT was more sensitive than MNT to BRDF effects due to irregularities in surface reflectance created by the tussocks. Finally, we found that studies that sampled only a narrow range of biomass and NDVI produced equations that were more difficult to correct for BRDF effects.

Satellite-derived remote-sensing products are providing a modern circumpolar perspective of Arctic vegetation and its changes, but this new view is dependent on a long heritage of ground-based observations in the Arctic. Several products of the Conservation of Arctic Flora and Fauna are key to our current understanding. We review aspects of the PanArctic Flora, the Circumpolar Arctic Vegetation Map, the Arctic Biodiversity Assessment, and the Arctic Vegetation Archive (AVA) as they relate to efforts to describe and map the vegetation, plant biomass, and biodiversity of the Arctic at circumpolar, regional, landscape and plot scales. Cornerstones for all these tools are ground-based plant-species and plant-community surveys. The AVA is in progress and will store plot-based vegetation observations in a public-accessible database for vegetation classification, modeling, diversity studies, and other applications. We present the current status of the Alaska Arctic Vegetation Archive (AVA-AK), as a regional example for the panarctic archive, and with a roadmap for a coordinated international approach to survey, archive and classify Arctic vegetation. We note the need for more consistent standards of plot-based observations, and make several recommendations to improve the linkage between plot-based observations biodiversity studies and satellite-based observations of Arctic vegetation.