Carbon sequestration strategies highlight tree plantations without considering their full environmental consequences. We combined field research, synthesis of more than 600 observations, and climate and economic modeling to document substantial losses in stream flow, and increased soil salinization and acidification, with afforestation. Plantations decreased stream flow by 227 millimeters per year globally (52%), with 13% of streams drying completely for at least 1 year. Regional modeling of U.S. plantation scenarios suggests that climate feedbacks are unlikely to offset such water losses and could exacerbate them. Plantations can help control groundwater recharge and upwelling but reduce stream flow and salinize and acidify some soils.
Systematic, operational, long-term observations of the terrestrial carbon cycle (including its interactions with water, energy and nutrient cycles and ecosystem dynamics) are important for the prediction and management of climate, water resources, food resources, biodiversity and desertification. To contribute to these goals, a terrestrial carbon observing system requires the synthesis of several kinds of observation into terrestrial biosphere models encompassing the coupled cycles of carbon, water, energy and nutrients. Relevant observations include atmospheric composition (concentrations of CO 2 and other gases); remote sensing; flux and process measurements from intensive study sites; in situ vegetation and soil monitoring; weather, climate and hydrological data; and contemporary and historical data on land use, land use change and disturbance (grazing, harvest, clearing, fire). A review of model-data synthesis tools for terrestrial carbon observation identifies 'nonsequential' and 'sequential' approaches as major categories, differing according to whether data are treated all at once or sequentially. The structure underlying both approaches is reviewed, highlighting several basic commonalities in formalism and data requirements.An essential commonality is that for all model-data synthesis problems, both nonsequential and sequential, data uncertainties are as important as data values themselves and have a comparable role in determining the outcome.Given the importance of data uncertainties, there is an urgent need for soundly based uncertainty characterizations for the main kinds of data used in terrestrial carbon observation. The first requirement is a specification of the main properties of the error covariance matrix.As a step towards this goal, semi-quantitative estimates are made of the main properties of the error covariance matrix for four kinds of data essential for terrestrial carbon observation: remote sensing of land surface properties, atmospheric composition measurements, direct flux measurements, and measurements of carbon stores.
Mining poses significant and potentially underestimated risks to tropical forests worldwide. In Brazil’s Amazon, mining drives deforestation far beyond operational lease boundaries, yet the full extent of these impacts is unknown and thus neglected in environmental licensing. Here we quantify mining-induced deforestation and investigate the aspects of mining operations, which most likely contribute. We find mining significantly increased Amazon forest loss up to 70 km beyond mining lease boundaries, causing 11,670 km2 of deforestation between 2005 and 2015. This extent represents 9% of all Amazon forest loss during this time and 12 times more deforestation than occurred within mining leases alone. Pathways leading to such impacts include mining infrastructure establishment, urban expansion to support a growing workforce, and development of mineral commodity supply chains. Mining-induced deforestation is not unique to Brazil; to mitigate adverse impacts of mining and conserve tropical forests globally, environmental assessments and licensing must considered both on- and off-lease sources of deforestation.
[1] The turnover time of terrestrial carbon was estimated using a multiobjective parameterization method that combined data sets of plant production, biomass, litter and soil-C observations in the calibration of a C-cycle model for the Australian continent (VAST1.1; Vegetation and Soil carbon Transfer). The method employed a genetic algorithm to minimize model-data deviations and maximize consistency between estimated model parameters and all available data. Based on the parameterization, the turnover time of biosphere C for Australia was estimated to be 78 years which is longer than global C-turnover estimates (of 26-60 years) due entirely to slower turnover of C in the upper 20 cm of soil. Turnover times of litter and deeper soil-C were similar to global values. By splitting total C in the upper 20 cm between labile and nonlabile fractions (based on published data) the turnover time of the labile pool was at least 44 years which is still longer than global estimates (9-25 years). Longer C-turnover in Australian surface soils was attributed to (1) limited soil moisture slowing decomposition more than net primary production, (2) frequent fires leading to a large fraction of nonlabile charcoal C in soil, and (3) strong adsorbing capacity for organic-C in these highly weathered soils. It was found that >89% of the C flux to the atmosphere from decomposition of organic matter originated from fine litter, coarse woody debris and the upper 20 cm of soil in all biomes.
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