In tropical areas, pioneer occupation fronts steer the rapid expansion of deforestation, contributing to carbon emissions. Up-to-date carbon emission estimates covering the long-term development of such frontiers depend on the availability of high spatial–temporal resolution data. In this paper, we provide a detailed assessment of carbon losses from deforestation and potential forest degradation from fragmentation for one expanding frontier in the Brazilian Amazon. We focused on one of the Amazonia’s hot-spots of forest loss, the BR-163 highway that connects the high productivity agricultural landscapes in Mato Grosso with the exporting harbors of the Amazon. We used multi-decadal (1984–2012) Landsat-based time series on forested and non-forested area in combination with a carbon book-keeping model. We show a 36% reduction in 1984s biomass carbon stocks, which led to the emission of 611.5 TgCO2 between 1985 and 1998 (43.6 TgCO2 year−1) and 959.8 TgCO2 over 1999–2012 (68.5 TgCO2 year−1). Overall, fragmentation-related carbon losses represented 1.88% of total emissions by 2012, with an increasing relevance since 2004. We compared the Brazilian Space Agency deforestation assessment (PRODES) with our data and found that small deforestation polygons not captured by PRODES had increasing importance on estimated deforestation carbon losses since 2000. The comparative analysis improved the understanding of data-source-related uncertainties on carbon estimates and indicated disagreement areas between datasets that could be subject of future research. Furthermore, spatially explicit, annual deforestation and emission estimates like the ones derived from this study are important for setting regional baselines for REDD+ or similar payment for ecosystem services frameworks.
The spatial variability of annual and seasonal precipitation in the conterminous land of Spain has been evaluated by using correlation decay distance analysis (CDD). The CDD analysis essentially explores how the correlation between neighbouring stations varies according to distance. We analysed CDD independently for the decades 1956–1965, 1966–1975, 1976–1985, 1986–1995, and 1996–2005 using only those stations with no missing values for each decade. To this end, 972, 1,174, 1,242, 773 and 695 complete series were used for each decade, respectively. In particular, for each station and decade, we calculated the threshold distance at which the common variance between target (i) and neighbour series is higher than 50 % (r2 = 0.5) to evaluate whether current density of the climate data set captures the spatial variability of precipitation within the study area. Results indicate that, at an annual scale, neighbouring stations with 50 % of common variance are restricted on average to about 105 km, but this distance can vary from 28 to 251 km within the study area. The lowest variability is located to the SW and in winter, while the higher spatial variability is found to the north, in the Cantabrian area, and to the east, in the Mediterranean and Pyrenees, during summer. Our results suggest that current density of climate stations (those operating in 2005) is good enough to study precipitation variability at an annual scale for winter, spring and autumn, but not enough for summer. 相似文献
The Ria of Huelva estuary, in SW Spain, is known to be one of the most heavy metal contaminated estuaries in the world. River contribution to the estuary of dissolved Cu, Zn, Mn, Cr, Ni, Cd, and As were analysed for the period 1988-2001. The obtained mean values show that this contribution, both because of the magnitude of total metals (895.1 kg/h), composition, toxicity (8.7 kg/h of As+Cd+Pb) and persistence, is an incomparable case in heavy metal contamination of estuaries. The amount and typology of heavy metal discharge to the Ria of Huelva are related to freshwater flow (and, consequently, to rainfall); as a result, two different types of heavy metal discharge can be distinguished in the estuary: during low water (50% of the days), with only 19.3 kg/h of heavy metals, and during high water or flood (17% of the days), where daily maximum discharge of 72,475 kg of heavy metals were recorded, from which 1481 kg were of As, 470 kg of Pb, and 170 kg of Cd. In the most frequent situation (77% of the days), the Odiel River discharges from 90% to 100% of the freshwater received by the estuary. Despite this, the high concentration of heavy metals in the Tinto River water causes this river to discharge into the Ria of Huelva 12.5% of fluvial total dissolved metal load received by the estuary. 相似文献
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