The Science and Policy of the Verified Carbon Standard Methodology for Tidal Wetland and Seagrass Restoration

The restoration of tidal wetland and seagrass systems has the potential for significant greenhouse gas benefits, but project-level accounting procedures have not been available at an international scale. In this paper, we describe the Verified Carbon Standard Methodology for Tidal Wetland and Seagrass Restoration, which provides greenhouse gas accounting procedures for marsh, mangrove, tidal forested wetland, and seagrasses systems across a diversity of geomorphic conditions and restoration techniques. We discuss and critique the essential science and policy elements of the methodology and underlying knowledge gaps. We developed a method for estimating mineral-protected (recalcitrant) allochthonous carbon in tidal wetland systems using field-collected soils data and literature-derived default values of the recalcitrant carbon that accompanies mineral deposition. We provided default values for methane emissions from polyhaline soils but did not provide default values for freshwater, oligohaline, and mesohaline soils due to high variability of emissions in these systems. Additional topics covered are soil carbon sequestration default values, soil carbon fate following erosion, avoided losses in organic and mineral soils, nitrous oxide emissions, soil profile sampling methods, sample size, prescribed fire, additionality, and leakage. Knowledge gaps that limit the application of the methodology include the estimation of CH₄ emissions from fresh and brackish tidal wetlands, lack of validation of our approach for the estimation of recalcitrant allochthonous carbon, understanding of carbon oxidation rates following drainage of mineral tidal wetland soils, estimation of the effects of prescribed fire on soil carbon stocks, and the analysis of additionality for projects outside of the USA.     

The trade-off between gender,energy and climate change in Africa: the case of Niger Republic

GeoJournal - This article examines the role of gender in climate change adaptation and energy access in Africa. Drawing on the energy situation in Niger Republic, it argues that redressing...     

Improvements in the integration of remote sensing and rock slope modelling

Natural Hazards - Forests are always dangerous to fire in China’s forested regions, and forest fire hazard has long been a serious issue in China. Zhejiang province is one of the key forest...     

Can bottom-up procedures improve the performance of stream classifications?

Top-down methods for defining stream classifications are based on a conceptual model or expert-defined rules, whereas bottom-up methods use biological training data and statistical modelling. We compared the performance of six classification methods for explaining the taxonomic composition of invertebrate and fish assemblages recorded at 327 and 511 sites, respectively, distributed throughout France. Classification 1 and 2 were top-down classifications; The European Water Framework System A (WFDa,) and the French Hydro-ecoregions (HER 2). Four bottom-up classification procedures of increasing complexity were defined based on 11 variables that included watershed characteristics describing climate, topography, and geology, and site characteristics including elevation, bed slope and temperature. Classification 3 was defined using matrix correlation (MC) to select a combination of variable categories that produced the best discrimination of the observed taxonomic composition. Classification 4 and 5 were defined by clustering the sites based on their taxonomic data and then using linear discriminant analysis (LDA) and Random forests (RF) to discriminate the clusters based on the environmental variables. Classification 6 was defined using generalized dissimilarity modelling (GDM). Our hypothesis was that the bottom-up classifications would perform better because they flexibly accommodate complex relationships between compositional and environmental variation. We tested the classifications using the classification strength statistic (CS). The RF-based classification fitted the taxonomic patterns better than GDM or LDA and these latter classifications generally fitted better than the MC, WFDa or HER classifications. Cross validation analysis showed that differences in predictive CS (i.e. the CS statistics produced from sites not used in defining the classifications) were often significant. However, these differences were generally small. Gains in predictive performance of classifications appear to be small relative to the increase in complexity in the manner in which environmental variables are combined to define classes.