Uncertainty in REDD+ carbon accounting: a survey of experts involved in REDD+ reporting.

BACKGROUND: Reducing Emissions from Deforestation and forest Degradation (REDD+) is a program established under the United Nations Framework Convention on Climate Change (UNFCCC) to reduce carbon emissions from forests in developing countries. REDD+ uses an incentive-based approach whereby participating countries are paid to reduce forest carbon loss and increase carbon storage. Country-level carbon accounting is challenging, and estimates of uncertainty in emission reductions are increasingly required in REDD+ reports. This requirement is hard to meet if countries lack the necessary resources, tools, and capabilities. Some REDD+ programs adjust their payments for the uncertainty reported, which presents a perverse incentive because uncertainties are larger if more sources of uncertainty are reported. We surveyed people involved in REDD+ reporting to assess current capacities and barriers to improving estimates of uncertainty. RESULTS: Representatives from 27 countries (44% of REDD+ countries at the time of survey implementation) responded to the survey. Nearly all respondents thought it important to include uncertainty in REDD+ reports, but most felt that the uncertainty reporting by their countries was inadequate. Our independent assessment of reports by these countries to the UNFCCC supported this opinion: Most countries reported uncertainty in activity data (91%) but not in emission factors (4-14%). Few countries use more advanced approaches to estimate uncertainty, such as Monte Carlo and Bayesian techniques, and many respondents indicated that they lack expertise, knowledge, or technical assistance. Other barriers include lack of financial resources and appropriate data. Despite these limitations, nearly all respondents indicated a strong desire to improve estimates of uncertainty in REDD+ reports. CONCLUSIONS: The survey indicated that people involved in REDD+ reporting think it highly important to improve estimates of uncertainty in forest carbon accounting. To meet this challenge, it is essential to understand the obstacles countries face in quantifying uncertainty so we can identify where best to allocate efforts and funds. Investments in training and resources are clearly needed to better quantify uncertainty and would likely have successful outcomes given the strong desire for improvement. Tracking the efficacy of programs implemented to improve estimates of uncertainty would be useful for making further refinements.

Multidate, multisensor remote sensing reveals high density of carbon-rich mountain peatlands in the páramo of Ecuador.

Tropical peatlands store a significant portion of the global soil carbon (C) pool. However, tropical mountain peatlands contain extensive peat soils that have yet to be mapped or included in global C estimates. This lack of data hinders our ability to inform policy and apply sustainable management practices to these peatlands that are experiencing unprecedented high rates of land use and land cover change. Rapid large-scale mapping activities are urgently needed to quantify tropical wetland extent and rate of degradation. We tested a combination of multidate, multisensor radar and optical imagery (Landsat TM/PALSAR/RADARSAT-1/TPI image stack) for detecting peatlands in a 2715 km2 area in the high elevation mountains of the Ecuadorian páramo. The map was combined with an extensive soil coring data set to produce the first estimate of regional peatland soil C storage in the páramo. Our map displayed a high coverage of peatlands (614 km2 ) containing an estimated 128.2 ± 9.1 Tg of peatland belowground soil C within the mapping area. Scaling-up to the country level, páramo peatlands likely represent less than 1% of the total land area of Ecuador but could contain as much as ~23% of the above- and belowground vegetation C stocks in Ecuadorian forests. These mapping approaches provide an essential methodological improvement applicable to mountain peatlands across the globe, facilitating mapping efforts in support of effective policy and sustainable management, including national and global C accounting and C management efforts.

Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system.

BACKGROUND: Forest Inventory and Analysis (FIA) data may be a valuable component of a LIDAR-based carbon monitoring system, but integration of the two observation systems is not without challenges. To explore integration methods, two wall-to-wall LIDAR-derived biomass maps were compared to FIA data at both the plot and county levels in Anne Arundel and Howard Counties in Maryland. Allometric model-related errors were also considered. RESULTS: In areas of medium to dense biomass, the FIA data were valuable for evaluating map accuracy by comparing plot biomass to pixel values. However, at plots that were defined as "nonforest", FIA plots had limited value because tree data was not collected even though trees may be present. When the FIA data were combined with a previous inventory that included sampling of nonforest plots, 21 to 27% of the total biomass of all trees was accounted for in nonforest conditions, resulting in a more accurate benchmark for comparing to total biomass derived from the LIDAR maps. Allometric model error was relatively small, but there was as much as 31% difference in mean biomass based on local diameter-based equations compared to regional volume-based equations, suggesting that the choice of allometric model is important. CONCLUSIONS: To be successfully integrated with LIDAR, FIA sampling would need to be enhanced to include measurements of all trees in a landscape, not just those on land defined as "forest". Improved GPS accuracy of plot locations, intensifying data collection in small areas with few FIA plots, and other enhancements are also recommended.

Chronic water stress reduces tree growth and the carbon sink of deciduous hardwood forests.

Predicted decreases in water availability across the temperate forest biome have the potential to offset gains in carbon (C) uptake from phenology trends, rising atmospheric CO2 , and nitrogen deposition. While it is well established that severe droughts reduce the C sink of forests by inducing tree mortality, the impacts of mild but chronic water stress on forest phenology and physiology are largely unknown. We quantified the C consequences of chronic water stress using a 13-year record of tree growth (n = 200 trees), soil moisture, and ecosystem C balance at the Morgan-Monroe State Forest (MMSF) in Indiana, and a regional 11-year record of tree growth (n > 300 000 trees) and water availability for the 20 most dominant deciduous broadleaf tree species across the eastern and midwestern USA. We show that despite ~26 more days of C assimilation by trees at the MMSF, increasing water stress decreased the number of days of wood production by ~42 days over the same period, reducing the annual accrual of C in woody biomass by 41%. Across the deciduous forest region, water stress induced similar declines in tree growth, particularly for water-demanding 'mesophytic' tree species. Given the current replacement of water-stress adapted 'xerophytic' tree species by mesophytic tree species, we estimate that chronic water stress has the potential to decrease the C sink of deciduous forests by up to 17% (0.04 Pg C yr(-1) ) in the coming decades. This reduction in the C sink due to mesophication and chronic water stress is equivalent to an additional 1-3 days of global C emissions from fossil fuel burning each year. Collectively, our results indicate that regional declines in water availability may offset the growth-enhancing effects of other global changes and reduce the extent to which forests ameliorate climate warming.