Development of a protocol for monitoring status and trends in forest soil carbon at a national level

The national Forest Health Monitoring (FHM) program requires protocols for monitoring soil carbon contents. In a pilot study, 30 FHM plots loblolly shortleaf (Pinus taeda L./Pinus echinata Mill.) pine forests across Georgia were sampled by horizon and by depth increments. For total soil carbon, approximately 40% of the variance was between plots, 40% between subplots and 20% within subplots. Results by depth differed from those obtained by horizon primarily due to the rapid changes in carbon content from the top to the bottom of the A horizon. Published soil survey information overestimated bulk densities for these forest sites. The measurement of forest floor depths as a substitute to sampling did not provide reliable estimates of forest floor carbon. Precision of replicate samples was approximately 10-30% for field duplicates and 5-10% for laboratory duplicates. Based on national indicator evaluation criteria, sampling by depth using bulk density core samplers has been recommended for national implementation. Additional procedures are needed when sampling organic soils or soils with a high percentage of large rock fragments.     

Modeling and spatially distributing forest net primary production at the regional scale

Forest, agricultural, rangeland, wetland, and urban landscapes have different rates of carbon sequestration and total carbon sequestration potential under alternative management options. Changes in the proportion and spatial distribution of land use could enhance or degrade that area's ability to sequester carbon in terrestrial ecosystems. As the ecosystems within a landscape change due to natural or anthropogenic processes, they may go from being a carbon sink to a carbon source or vice versa. Satellite image analysis has been tested for timely and accurate measurement of spatially explicit land use change and is well suited for use in inventory and monitoring of terrestrial carbon. The coupling of Landsat Thematic Mapper (TM) data with a physiologically based forest productivity model (PnET-II) and historic climatic data provides an opportunity to enhance field plot-based forest inventory and monitoring methodologies. We use periodic forest inventory data from the U.S. Department of Agriculture (USDA) Forest Service's Forest Inventory and Analysis (FIA) Program to obtain estimates of forest area and type and to generate estimates of carbon storage for evergreen, deciduous, and mixed-forest classes. The area information is used in an accuracy assessment of remotely sensed forest cover at the regional scale. The map display of modeled net primary production (NPP) shows a range of forest carbon storage potentials and their spatial relationship to other landscape features across the southern United States. This methodology addresses the potential for measuring and projecting forest carbon sequestration in the terrestrial biosphere of the southern United States.     

Modeling and Spatially Distributing Forest Net Primary Production at the Regional Scale

Abstract

Forest, agricultural, rangeland, wetland, and urban landscapes have different rates of carbon sequestration and total carbon sequestration potential under alternative management options. Changes in the proportion and spatial distribution of land use could enhance or degrade that area’s ability to sequester carbon in terrestrial ecosystems. As the ecosystems within a landscape change due to natural or anthropogenic processes, they may go from being a carbon sink to a carbon source or vice versa. Satellite image analysis has been tested for timely and accurate measurement of spatially explicit land use change and is well suited for use in inventory and monitoring of terrestrial carbon. The coupling of Landsat Thematic Mapper (TM) data with a physiologically based forest productivity model (PnET-II) and historic climatic data provides an opportunity to enhance field plot-based forest inventory and monitoring methodologies. We use periodic forest inventory data from the U.S. Department of Agriculture (USDA) Forest Service’s Forest Inventory and Analysis (FIA) Program to obtain estimates of forest area and type and to generate estimates of carbon storage for evergreen, deciduous, and mixed-forest classes. The area information is used in an accuracy assessment of remotely sensed forest cover at the regional scale. The map display of modeled net primary production (NPP) shows a range of forest carbon storage potentials and their spatial relationship to other landscape features across the southern United States. This methodology addresses the potential for measuring and projecting forest carbon sequestration in the terrestrial biosphere of the southern United States.     

Methodology for estimating soil carbon for the forest carbon budget model of the United States, 2001

The largest carbon (C) pool in United States forests is the soil C pool. We present methodology and soil C pool estimates used in the FORCARB model, which estimates and projects forest carbon budgets for the United States. The methodology balances knowledge, uncertainties, and ease of use. The estimates are calculated using the USDA Natural Resources Conservation Service STATSGO database, with soil dynamics following assumptions based on results of site-specific studies, and area estimates from the USDA Forest Service. Forest Inventory and Analysis data and national-level land cover data sets. Harvesting is assumed to have no effect on soil C. Land use change and forest type transitions affect soil C. We apply the methodology to the southeastern region of the United States as a case study.     

Carbon storage in Amazonia during the last glacial maximum: secondary data and uncertainties

The Amazonian forest is, due to its great size, carbon storage capacity and present-day variability in carbon uptake and release, an important component of the global carbon cycle. Paleo-environmental reconstruction is difficult for Amazonia due to the scarcity of primary palynological data and the mis-interpretation of some secondary data. Studies of lacustrine sediment records have shown that Amazonia has known periods in which the climate was drier than it is today. However, not all geomorphological features such as dunes, and slope erosion, which are thought to indicate rainforest regression, date from the time of the Late Glacial Maximum (LGM) and these features do not necessarily correspond to episodes of forest regression. There is also uncertainty concerning LGM carbon storage due to rainforest soils and biomass estimates. Soil carbon content may decrease moderately during the LGM, whereas rainforest biomass may change considerably in response to changes in the global environment. Biomass per unit area in Amazonia has probably been reduced by the cumulative effects of low CO2 concentration, a drier climate and lower temperatures. As few paleo-vegetation data are available, there is considerable uncertainty concerning the amount of carbon stored in Amazonia during the LGM, which may have corresponded to 44-94% of the carbon currently stored in biomass and soils.     

Computer simulations of terrestrial carbon and atmospheric interactions

Terrestrial carbon modelling shows that the Goudriaan and Ketner and Esser simulations fit historical data well, but the results are sensitive to the decomposition rate coefficient of old sediment carbon. Modification of this rate constant over time, weighted by emission increases or linear increases, changes the model results to fit historic ice core data. Very old sediment carbon decomposition has an effect on the model postdictions only when the rate constant is 10 times greater than that predicted from sediment studies. Future estimates show that a maximum change from agriculture to forest has a small effect on abating emission increases. Controlling emission rates at 5.1 x 10(15) g C/a will result in almost a 50% increase in atmospheric CO(2) in 200 years, and reducing emission rates to 1960 levels (approximately 2.5 x 10(15) g C/a) immediately will still result in an increase in atmospheric CO(2).     

Hurricane impacts on US forest carbon sequestration

Recent focus has been given to US forests as a sink for increases in atmospheric carbon dioxide. Current estimates of US forest carbon sequestration average approximately 20 Tg (i.e. 10(12) g) year. However, predictions of forest carbon sequestration often do not include the influence of hurricanes on forest carbon storage. Intense hurricanes occur two out of three years across the eastern US. A single storm can convert the equivalent of 10% of the total annual carbon sequestrated by US forests into dead and downed biomass. Given that forests require at least 15 years to recover from a severe storm, a large amount of forest carbon is lost either directly (through biomass destruction) or indirectly (through lost carbon sequestration capacity) due to hurricanes. Only 15% of the total carbon in destroyed timber is salvaged following a major hurricane. The remainder of the carbon is left to decompose and eventually return to the atmosphere. Short-term increases in forest productivity due to increased nutrient inputs from detritus are not fully compensated by reduced stem stocking, and the recovery time needed to recover leaf area. Therefore, hurricanes are a significant factor in reducing short-term carbon storage in US forests.     

Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks

Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively.     

Regional estimation of current and future forest biomass

The 90,674 wildland fires that burned 2.9 million ha at an estimated suppression cost of $1.6 billion in the United States during the 2000 fire season demonstrated that forest fuel loading has become a hazard to life, property, and ecosystem health as a result of past fire exclusion policies and practices. The fire regime at any given location in these regions is a result of complex interactions between forest biomass, topography, ignitions, and weather. Forest structure and biomass are important aspects in determining current and future fire regimes. Efforts to quantify live and dead forest biomass at the local to regional scale has been hindered by the uncertainty surrounding the measurement and modeling of forest ecosystem processes and fluxes. The interaction of elevated CO2 with climate, soil nutrients, and other forest management factors that affect forest growth and fuel loading will play a major role in determining future forest stand growth and the distribution of species across the southern United States. The use of satellite image analysis has been tested for timely and accurate measurement of spatially explicit land use change and is well suited for use in inventory and monitoring of forest carbon. The incorporation of Landsat Thematic Mapper data coupled with a physiologically based productivity model (PnET), soil water holding capacity, and historic and projected climatic data provides an opportunity to enhance field plot based forest inventory and monitoring methodologies. We use periodic forest inventory data from the USDA Forest Service's Forest Inventory and Analysis (FIA) project to obtain estimates of forest area and type to generate estimates of carbon storage for evergreen, deciduous, and mixed forest classes for use in an assessment of remotely sensed forest cover at the regional scale for the southern United States. The displays of net primary productivity (NPP) generated from the PnET model show areas of high and low forest carbon storage potential and their spatial relationship to other landscape features for the southern United States. At the regional scale, predicted annual NPP in 1992 ranged from 836 to 2181 g/m2/year for evergreen forests and 769-2634 g/m2/year for deciduous forests with a regional mean for all forest land of 1448 g/m2/year. Prediction of annual NPP in 2050 ranged from 913 to 2076 g/m2/year for evergreen forest types to 1214-2376 g/m2/year for deciduous forest types with a regional mean for all forest land of 1659 g/m2/year. The changes in forest productivity from 1992 to 2050 are shown to display potential areas of increased or decreased forest biomass. This methodology addresses the need for spatially quantifying forest carbon in the terrestrial biosphere to assess forest productivity and wildland fire fuels.     

Homegardens as a Multi-functional Land-Use Strategy in Sri Lanka with Focus on Carbon Sequestration

This paper explores the concept of homegardens and their potential functions as strategic elements in land-use planning, and adaptation and mitigation to climate change in Sri Lanka. The ancient and locally adapted agroforestry system of homegardens is presently estimated to occupy nearly 15 % of the land area in Sri Lanka and is described in the scientific literature to offer several ecosystem services to its users; such as climate regulation, protection against natural hazards, enhanced land productivity and biological diversity, increased crop diversity and food security for rural poor and hence reduced vulnerability to climate change. Our results, based on a limited sample size, indicate that the homegardens also store significant amount of carbon, with above ground biomass carbon stocks in dry zone homegardens (n = 8) ranging from 10 to 55 megagrams of carbon per hectare (Mg C ha⁻¹) with a mean value of 35 Mg C ha⁻¹, whereas carbon stocks in wet zone homegardens (n = 4) range from 48 to 145 Mg C ha⁻¹ with a mean value of 87 Mg C ha⁻¹. This implies that homegardens may contain a significant fraction of the total above ground biomass carbon stock in the terrestrial system in Sri Lanka, and from our estimates its share has increased from almost one-sixth in 1992 to nearly one-fifth in 2010. In the light of current discussions on reducing emissions from deforestation and forest degradation (REDD+), the concept of homegardens in Sri Lanka provides interesting aspects to the debate and future research in terms of forest definitions, setting reference levels, and general sustainability.     

A functional perspective on the analysis of land use and land cover data in ecology

Assessments of large-scale changes in habitat are a priority for management and conservation. Traditional approaches use land use and land cover data (LULC) that focus mostly on “structural” properties of landscapes, rather than “functional” properties related to specific ecological processes. Here, we contend that designing functional analyses of LULC can provide important and complementary information to traditional, structural analyses. We substantiate this perspective with an example of functional changes in habitat due to industrial anthropogenic footprints in Alberta’s boreal forest, where there has been little overall forest loss (~ 6% structural change), but high levels of functional change (up to 93% functional change) for species’ habitat, biodiversity, and wildfire ignition. We discuss the methods needed to achieve functional LULC analyses, when they are most appropriate to add to structural assessments, and conclude by providing recommendations for analyses of LULC in a future of increasingly high-resolution, dynamic remote sensing data.Electronic supplementary materialThe online version of this article (10.1007/s13280-020-01434-5) contains supplementary material, which is available to authorized users.     

A simple empirical model to predict forest insecticide ground-level deposition from a compendium of field data

Deposit data from 205 aerial forest insecticide applications conducted in field trials by the Canadian Forest Service, Great Lakes Forestry Centre over a 15-year period are summarized. Deposit measurements were taken under "worst case" scenarios in the sense that direct applications were made over water bodies, and ground samplers were intentionally placed in open or cleared areas of forest. The median % deposit on shoreline collectors (32 separate applications) was 5.7%, on mid-stream collectors (44 separate applications) was 6.2%, and on forest floor collectors (129 separate applications) was 4.9%. Forest floor deposit was most closely associated with application rate and droplet size (r = 0.624, p < 0.001 and r = 0.662, p = 0.011, respectively) but these variables combined only explained 44% of the variation in deposit. Data from all three collector types were grouped by 10% deposit increments and combined to provide a data set from all deposition scenarios. A negative exponential model was fitted to the proportion of these combined sites regressed on % deposit in 10% increments and plotted as a deposit probability distribution curve (p < 0.001, r2 = 0.992). The probability distribution curve indicated that 5-10% deposit would be expected about 57-91% of the time, whereas 50% deposit or greater would be expected about 2% of the time or less. In a probabilistic risk assessment for aerially applied insecticides in a conifer-dominated forest environment, the probability distribution curve based on empirical data presented here can be used to refine the characterization of exposure scenarios from which effects estimates can be derived.     

Improvements to lawn and garden equipment emissions estimates for Baltimore, Maryland

Lawn and garden equipment are a significant source of emissions of volatile organic compounds (VOCs) and other pollutants in suburban and urban areas. Emission estimates for this source category are typically prepared using default equipment populations and activity data contained in emissions models such as the U.S. Environmental Protection Agency's (EPA) NONROAD model or the California Air Resources Board's (CARB) OFFROAD model. Although such default data may represent national or state averages, these data are unlikely to reflect regional or local differences in equipment usage patterns because of variations in climate, lot sizes, and other variables. To assess potential errors in lawn and garden equipment emission estimates produced by the NONROAD model and to demonstrate methods that can be used by local planning agencies to improve those emission estimates, this study used bottom-up data collection techniques in the Baltimore metropolitan area to develop local equipment population, activity, and temporal data for lawn and garden equipment in the area. Results of this study show that emission estimates of VOCs, particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen oxides (NO(x)) for the Baltimore area that are based on local data collected through surveys of residential and commercial lawn and garden equipment users are 24-56% lower than estimates produced using NONROAD default data, largely because of a difference in equipment populations for high-usage commercial applications. Survey-derived emission estimates of PM and VOCs are 24 and 26% lower than NONROAD default estimates, respectively, whereas survey-derived emission estimates for CO, CO2, and NO(x) are more than 40% lower than NONROAD default estimates. In addition, study results show that the temporal allocation factors applied to residential lawn and garden equipment in the NONROAD model underestimated weekend activity levels by 30% compared with survey-derived temporal profiles.     

Effects of nitrogen with and without acidified sulphur on an ectomycorrhizal community in a Sitka spruce (Picea sitchensis Bong. Carr) forest

This preliminary study investigated the effects of enhanced nitrogen (NH4NO3 at 48 kg ha(-1) y(-1)), sulphur (Na2SO4 at 50 kg ha(-1) y(-1)), acidified nitrogen and sulphur (H2SO4 + NH4NO3) at pre-stated doses (pH 2.5), and acidified nitrogen and sulphur deposition at double these doses on the ectomycorrhizal community associated with a 13-year-old Sitka spruce (Picea sitchensis) forest. Sulphur deposition had little impact on below ground ectomycorrhizal diversity, but stimulated sporocarp production. Nitrogen inputs increased below ground colonisation compared to acidified nitrogen and sulphur, largely due to an increase in Tylospora fibrillosa colonisation. Sporocarp production and ectomycorrhizal root colonisation by Lactarius rufus were reduced in the nitrogen treated plots. These observations suggest that nitrogen deposition to a young plantation may suppress ectomycorrhizal fungi producing large sporocarps. It is proposed that enhanced nitrogen deposition increases ectomycorrhizal nitrogen assimilation, consuming more carbon and leaving less for extrametrical mycelium and sporocarp development.     

Carbon storage in northeast China as estimated from vegetation and soil inventories

We have estimated the stocks of carbon in vegetation and soil in northeast China based on data for 122 plots from the fourth national forest inventory, and for 388 soil profiles from the second national soil survey. The techniques of Geographic Information System (GIS) have been used to extrapolate site-specific estimates of vegetation and soil organic carbon to the entire area of northeast China. Our estimate indicates that the amount of carbon in vegetation and soil for the region are 2.81 PgC (10(15) g C) and 26.43 PgC, respectively, and that the area weighted average density of vegetation and soil organic carbon are 22.7 MgC/ha and 212.7 MgC/ha, respectively. The eastern and northern parts of the region show much higher carbon storage than the rest of the region. Substantial spatial variations in vegetation and soil organic carbon across northeast China suggest that regional estimates on carbon stocks and fluxes should take into account these spatial variations. We suggest that the methodology developed can be used for the entire nation of China as well as other regions of the world.     

A simple empirical model to predict forest insecticide ground-level deposition from a compendium of field data

Deposit data from 205 aerial forest insecticide applications conducted in field trials by the Canadian Forest Service, Great Lakes Forestry Centre over a 15-year period are summarized. Deposit measurements were taken under “worst case” scenarios in the sense that direct applications were made over water bodies, and ground samplers were intentionally placed in open or cleared areas of forest. The median % deposit on shoreline collectors (32 separate applications) was 5.7%, on mid-stream collectors (44 separate applications) was 6.2%, and on forest floor collectors (129 separate applications) was 4.9%. Forest floor deposit was most closely associated with application rate and droplet size (r = 0.624, p < 0.001 and r = 0.662, p = 0.011, respectively) but these variables combined only explained 44% of the variation in deposit. Data from all three collector types were grouped by 10% deposit increments and combined to provide a data set from all deposition scenarios. A negative exponential model was fitted to the proportion of these combined sites regressed on % deposit in 10% increments and plotted as a deposit probability distribution curve (p < 0.001, r² = 0.992). The probability distribution curve indicated that 5–10% deposit would be expected about 57–91% of the time, whereas 50% deposit or greater would be expected about 2% of the time or less. In a probabilistic risk assessment for aerially applied insecticides in a conifer-dominated forest environment, the probability distribution curve based on empirical data presented here can be used to refine the characterization of exposure scenarios from which effects estimates can be derived.     

Changes in the grassland-forest boundary at Ta-Ta-Chia long term ecological research (LTER) site detected by stable isotope ratios of soil organic matter

The carbon isotope analysis [delta13C values] of organic samples can be a useful research in ecological studies because delta13C values are indicative of the plant source. This study investigated the changes in plant communities along the grassland-forest boundary in the alpine forest at Ta-Ta-Chia long term ecological research (LTER) site in central Taiwan using carbon isotope data. The aim of this study was focused on the forest fire affected the change of vegetation community. Four pedons from grassland dominated by Miscanthus transmorrisonensis (pedons 1 and 2), transition zone by Tsuga and Yushania nittakeyamensis (pedon 3), and forest zone by Tsuga and nittakeyamensis (pedon 4) were examined. Soil organic matter (SOM) delta13C values in the upper soil horizon were similar to delta13C values of the overlaying vegetation types. This indicates that the boundary between these plant communities remained the same in the past decades. The delta13C values of the grassland SOM ranged from -19.4 per thousand to -24.1 per thousand, showing decrease with soil depth. This suggests that C4 plants (transmorrisonensis) have replaced C3 plants of Tsuga and nittakeyamensis. The delta13C values of the Tsuga forest area (pedon 4) range from -27.0 per thousand to -23.5 per thousand and showed only slight change with soil depth, implying that C3 plants have remained the major species in the forest.     

Contaminant transport in a municipal drinking water supply: a steady-state approach to estimate rate and uncertainty

Contaminant transport is generally considered to be a key factor when assessing and classifying the environmental risk of polluted areas. In the study presented here, a steady-state approach was applied to obtain estimates of the transit time and concentration of the pesticide metabolite BAM (2,6-dichlorobenzoamide) at a site where it is contaminating a municipal drinking water supply. A Monte Carlo simulation technique was used to quantify the uncertainty of the results and to evaluate the sensitivity of the used parameters. The adopted approach yielded an estimated median transit time of 10 y for the BAM transport from the polluted site to the water supply. Soil organic carbon content in the unsaturated zone and the hydraulic conductivity in the saturated zone explained 44% and 23% of the uncertainty in the transit time estimate, respectively. The sensitivity analysis showed that the dilution factor due to regional groundwater flow and the soil organic carbon content at the polluted site explained 53% and 31% of the uncertainty of concentration estimates, respectively. In conclusion, the adopted steady-state approach can be used to obtain reliable first estimates of transit time and concentration, but to improve concentration predictions of degrading contaminants, a dynamic model is probably required.     

Determination of bromacil transport as a function of water and carbon content in soils

This study was conducted to determine the significance of bromacil transport as a function of water and carbon content in soils and to explore the implications of neglecting sorption when making assessments of travel time of bromacil through the vadose zone. Equilibrium batch sorption tests were performed for loamy sand and sandy soil added with four different levels of powdered activated carbon (PAC) content (0, 0.01, 0.05, and 0.1%). Column experiments were also conducted at various water and carbon contents under steady-state flow conditions. The first set of column experiments was conducted in loamy sand containing 1.5% organic carbon under three different water contents (0.23, 0.32, and 0.41) to measure breakthrough curves (BTCs) of bromide and bromacil injected as a square pulse. In the second set of column experiments, BTCs of bromide and bromacil injected as a front were measured in saturated sandy columns at the four different PAC levels given above. Column breakthrough data were analyzed with both equilibrium and nonequilibrium (two-site) convection-dispersion equation (CDE) models to determine transport and sorption parameters under various water and carbon contents. Analysis with batch data indicated that neglect of the partition-related term in the calculation of solute velocity may lead to erroneous estimation of travel time of bromacil, i.e. an overestimation of the solute velocity by a factor of R. The column experiments showed that arrival time of the bromacil peak was larger than that of the bromide peak in soils, indicating that transport of bromacil was retarded relative to bromide in the observed conditions. Extent of bromacil retardation (R) increased with decreasing water content and increasing PAC content, supporting the importance of retardation in the estimation of travel time of bromacil even at small amounts of organic carbon for soils with lower water content.