Sustainability of terrestrial carbon sequestration: A case study in Duke Forest with inversion approach

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95430/1/gbc891.pd

Evaluation and improvement of model algorithms for predicting belowground carbon allocation in forests

Rapidly rising concentrations of atmospheric carbon dioxide (CO 2) influence forest productivity by stimulating plant growth. It can also modify carbon partitioning patterns, altering the global carbon cycle. Nitrogen and carbon cycles are tightly linked; with changes in nitrogen availability affecting ecosystem carbon allocation by shifting carbon to roots for nitrogen uptake. This paper discusses a modification to the PnET-CN model (Aber et al. 1997) developed to shift plant carbon allocation belowground in response to nitrogen limitation. According to functional equilibrium models of plant carbon allocation, a nitrogen control mechanism alters belowground carbon estimates by increasing carbon allocation to fine roots when nitrogen resources are low. Testing of the modified mechanism with data from three free-air CO 2 enrichment (FACE) forests supported the mechanism by allocating more carbon to fine roots. Application of the model with data from five northeastern forests, under a variety of global climate change scenarios, also supported the modified mechanism with an increase in soil carbon storage

Carbon fluxes to the soil in a mature temperate forest assessed by 13C isotope tracing

Photosynthetic carbon uptake and respiratory C release from soil are major components of the global carbon balance. The use of 13C depleted CO2 (δ13C = −30‰) in a free air CO2 enrichment experiment in a mature deciduous forest permitted us to trace the carbon transfer from tree crowns to the rhizosphere of 100-120years old trees. During the first season of CO2 enrichment the CO2 released from soil originated substantially from concurrent assimilation. The small contribution of recent carbon in fine roots suggests a much slower fine root turnover than is often assumed.13C abundance in soil air correlated best with temperature data taken from 4 to 10days before air sampling time and is thus rapidly available for root and rhizosphere respiration. The spatial variability of δ13C in soil air showed relationships to above ground tree types such as conifers versus broad-leaved trees. Considering the complexity and strong overlap of roots from different individuals in a forest, this finding opens an exciting new possibility of associating respiration with different species. What might be seen as signal noise does in fact contain valuable information on the spatial heterogeneity of tree-soil interactio

UNDERSTANDING ECOSYSTEM CARBON DYNAMICS BY MODELING APPROACHES

Ecosystem models are a useful tool to explore ecological processes and their responses to climate change. The basic structures of current ecosystem C cycle models are similar and robust, but their uncertainties are high, especially when coupled with water and nutrient cycles and disturbance effects. In this dissertation, I studied three issues in ecosystem C cycle modeling: interactions between water and C processes, information contribution of theoretical basis (model structure) vs. observations (data), and ecosystem C storage capacity at disequilibrium state due to effects of disturbances. These three issues represent the basic theoretical problems in the development and application of ecosystem models: 1) how the representations of interactions among ecological processes affect the simulation of ecosystem C cycle? 2) Once a model is built up, how much information can be brought in by model calibration? 3) For large spatial C cycle modeling, how will the paradigm of ecosystem states affect our C cycle modeling?In the first study, we evaluated the effects of soil hydrological properties on the interactions of water and carbon dynamics of a grassland ecosystem in response to altered precipitation amount and frequency, increased temperature, elevated atmospheric CO2 with changes in soil available water capacity (AWC). A process-based terrestrial ecosystem (TECO) model was used to simulate responses of soil moisture, evaporation, transpiration, runoff, net primary production (NPP), ecosystem respiration (Rh), and net ecosystem production (NEP) to changes in precipitation amounts and intensity, temperature, and CO2 concentration along a soil texture gradient. Simulation results showed that soil AWC altered partitioning of precipitation among runoff, evaporation, and transpiration, and consequently regulated ecosystem responses to global environmental changes. Fractions of precipitation that were used for evaporation and transpiration increased with soil AWC but decreased for runoff. High AWC could greatly buffer water stress during long drought periods, particularly after a large rainfall event. NPP, Rh, and NEP usually increased with AWC under ambient and 50% increased precipitation scenarios but increased from 7% to 7.5% of AWC followed by declines under the halved precipitation amount. Warming and CO2 effects on soil moisture, evapotranspiration, and runoff were magnified by soil AWC. CO2 effect on NPP, Rh, and NEP increased with soil AWC. Our results indicate that variations in soil texture may be one of the major causes underlying variable responses of ecosystems to global changes observed from different experiments. These results also imply that the interactions between C and water processes can be some soil texture.In the second study, I evaluated the information contribution of model and observations to model predictions by a data assimilation approach. Eight sets of ten-year data (foliage, woody, and fine root biomass, litter fall, forest floor carbon (C), microbial C, soil C, and soil respiration) collected from Duke Forest were assimilated into a Terrestrial ECOsystem model (TECO) using a Monte Carlo Markov Chain approach. The relative information contribution was measured by the Shannon information index calculated from probability density functions (PDF) of carbon pool sizes. Our results showed that the information contribution of the model to constrain carbon dynamics increased with time whereas the data contribution declined. The eight data sets contributed more than the model to constrain C dynamics in foliage and fine root pools over the 100-year forecasts. The model, however, contributed more than the data sets to constrain the litter, fast soil organic matter (SOM), and passive SOM pools. For the two major C pools, woody biomass and slow SOM, the model contributed less information in the first few decades and then more in the following decades than the data. The knowledge on relative information contributions of model vs. data is useful for model development, uncertainty analysis, future data collection, and evaluation of ecological forecasting.In the third study, I integrated the temporal patterns of C storage and spatial patterns of ecosystem states to develope a model to analytically describe relationships between ecosystem carbon storage and NPP, C residence time, and disturbance intervals and severity. The model represents a disequilibrium perspective for examining C storage dynamics in light of the impacts of disturbances and improves our predictive understanding of regional C dynamics. The carbon cycle at the scale of the ecosystem is almost always in dynamic disequilibrium with most ecosystems accumulating carbon at various stages of recovery with intermittent disturbances that release large amounts of carbon. This disequilibrium perspective is critical for scaling of site-level observations to estimate regional and global carbon sinks, for modeling studies on carbon-climate feedbacks, and for design of field experiments and observation networks.These studies showed that current ecosystem C modeling protocols, i.e., a Farquhar model based canopy model simulating C input to the system and a compartmentalized C pool model simulating C allocation, transfer, and decomposition, work well in simulating the short-term patterns of ecosystem C dynamics, but have high uncertainties in simulating the interactions of multiple processes and are very sensitive to some parameters and boundary conditions. Data assimilation is an effective method to combine information from models and data and improve model parameterization and accuracy of predictions and reduce model uncertainties. However, once a model structure is given, optimizing parameters by data assimilation approaches can only find out the best agreement with observations within the space defined by the given model. The theoretical understanding of ecosystem dynamics is central to ecosystem modeling studies. As illustrated by our disturbance model (the third study), new theories and paradigms can fundamentally changes the way in which ecosystems are represented in models

Quantifying terrestrial ecosystem carbon dynamics with mechanistically-based biogeochemistry models and in situ and remotely sensed data

Terrestrial ecosystem plays a critical role in the global carbon cycle and climate system. Therefore, it is important to accurately quantify the carbon dynamics of terrestrial ecosystem under future climatic change condition. This dissertation evaluates the regional carbon dynamics by using upscaling approach, mechanistically-based biogeochemistry models and in situ and remotely sensed data. The upscaling studies based on FLUXNET network has provided us the spatial and temporal pattern of the carbon fluxes but it fails to consider the atmospheric CO2 effect given its important physiological role in carbon assimilation. In the second chapter, we consider the effect of atmospheric CO2 using an artificial neural network (ANN) approach to upscale the AmeriFlux tower of net ecosystem exchange (NEE) and the derived gross primary productivity (GPP) to the conterminous United States. We found that atmospheric CO 2effect on GPP/NEE exhibited a great spatial and seasonal variability. Further analysis suggested that air temperature played an important role in determining the atmospheric CO2 effects on carbon fluxes. In addition, the simulation that did not consider atmospheric CO2 failed to detect ecosystem responses to droughts in part of the US in 2006. The study suggested that the spatially and temporally varied atmospheric CO2 concentrations should be factored into carbon quantification when scaling eddy flux data to a region. The process-based ecosystem models are useful tools to predicting future change in the terrestrial ecosystem. However, they suffer the great uncertainty induced by model structure and parameters. The carbon isotope (13C) discrimination by terrestrial plants, involves the biophysical and biogeochemistry processes and exhibits seasonal and spatial variations, which may provide additional constraints on model parameters. In the third chapter, we found that using foliar 13C composition data, model parameters were constrained to a relatively narrow space and the site-level model simulations were slightly better than that without the foliar 13C constraint. The model extrapolations with three stomatal schemes all showed that the estimation uncertainties of regional carbon fluxes were reduced by about 40%. In addition, tree ring data have great potentials in addressing the forest response to climatic changes compared with mechanistic model simulations, eddy flux measurement and manipulative experiments. In the fourth chapter, we collected the tree ring isotopic carbon data at 12 boreal forest sites to develop a linear regression model, and the model was extrapolated to the whole boreal region to obtain the water use efficiency (WUE) and GPP spatial and temporal variation from 1948 to 2010. Our results demonstrated that most of boreal regions except parts of Alaska showed a significant increasing WUE trend during the study period and the increasing magnitude was much higher than estimations from other land surface models. Our predicted GPP by the WUE definition algorithm was comparable with site observation, while for the revised light use efficiency algorithm, GPP estimation was higher than site observation as well as land surface model estimates. In addition, the increasing GPP trends estimated by two algorithms were similar with land surface model simulations

A process-based model of conifer forest structure and function with special emphasis on leaf lifespan

We describe the University of Sheffield Conifer Model (USCM), a process-based approach for simulating conifer forest carbon, nitrogen, and water fluxes by up-scaling widely applicable relationships between leaf lifespan and function. The USCM is designed to predict and analyze the biogeochemistry and biophysics of conifer forests that dominated the ice-free high-latitude regions under the high pCO2 “greenhouse” world 290–50 Myr ago. It will be of use in future research investigating controls on the contrasting distribution of ancient evergreen and deciduous forests between hemispheres, and their differential feedbacks on polar climate through the exchange of energy and materials with the atmosphere. Emphasis is placed on leaf lifespan because this trait can be determined from the anatomical characteristics of fossil conifer woods and influences a range of ecosystem processes. Extensive testing of simulated net primary production and partitioning, leaf area index, evapotranspiration, nitrogen uptake, and land surface energy partitioning showed close agreement with observations from sites across a wide climatic gradient. This indicates the generic utility of our model, and adequate representation of the key processes involved in forest function using only information on leaf lifespan, climate, and soils

Sources of variation in simulated ecosystem carbon storage capacity from the 5th Climate Model Intercomparison Project (CMIP5)

Ecosystem carbon (C) storage strongly regulates climate-C cycle feedback and is largely determined by both C residence time and C input from net primary productivity (NPP). However, spatial patterns of ecosystem C storage and its variation have not been well quantified in earth system models (ESMs), which is essential to predict future climate change and guide model development. We intended to evaluate spatial patterns of ecosystem C storage capacity simulated by ESMs as part of the 5th Climate Model Intercomparison Project (CMIP5) and explore the sources of multi-model variation from mean residence time (MRT) and/or C inputs. Five ESMs were evaluated, including C inputs (NPP and [gross primary productivity] GPP), outputs (autotrophic/heterotrophic respiration) and pools (vegetation, litter and soil C). ESMs reasonably simulated the NPP and NPP/GPP ratio compared with Moderate Resolution Imaging Spectroradiometer (MODIS) estimates except NorESM. However, all of the models significantly underestimated ecosystem MRT, resulting in underestimation of ecosystem C storage capacity. CCSM predicted the lowest ecosystem C storage capacity (~10 kg C m−2) with the lowest MRT values (14 yr), while MIROC-ESM estimated the highest ecosystem C storage capacity (~36 kg C m−2) with the longest MRT (44 yr). Ecosystem C storage capacity varied considerably among models, with larger variation at high latitudes and in Australia, mainly resulting from the differences in the MRTs across models. Our results indicate that additional research is needed to improve post-photosynthesis C-cycle modelling, especially at high latitudes, so that ecosystem C residence time and storage capacity can be appropriately simulated

THE EFFECTS OF FUTURE GLOBAL CHANGE ON ARBUSCULAR MYCORRHIZAL FUNGI AND SOIL CARBON: USING URBANIZATION AS A SURROGATE FOR FUTURE CONDITIONS IN FIELD STUDIES

Carbon, fixed photosynthetically by plants, cycles through plant, microbial biomass, soil, and atmospheric carbon pools. The effects of global change on this cycling will impact future levels of atmospheric carbon dioxide, but are poorly understood. In urban areas, temperature and carbon dioxide concentrations are often elevated to levels that simulate near-future climate changes. These elevations are not sudden, uniform step increases but are gradual and variable; as such urbanization may provide a means to simulate the effects of near-future climate changes. The dissertation research encompasses two studies utilizing urban macroclimate to study the effects of future climate change. In the first study, plots containing a common imported soil and seed bank were established at three locations along a 50 km urban-to-rural transect. In these plots, plant community development, temperature, carbon dioxide concentrations, and other factors had been monitored for five years. Subsequently, arbuscular mycorrhizal fungal structures in bulk soil were quantified. These fungi receive carbon directly from plant roots, grow into bulk soil, and can transfer immobile soil minerals to their plant hosts. In contrast to expectations, fewer fungal structures were found closer to the urban side of the transect. The second study was an observational study of soil carbon in minimally managed, long-undisturbed soils located at varying distances from urban areas. In sampling sites at 62 golf courses, similar communities of cool-season grasses had been undisturbed for at least 25 years. At each site, total and active soil carbon and many potential explanatory factors were measured and examined with multiple regression analysis. Contrary to expectations, soil carbon was positively correlated with warmer February-only mean daily minimum soil temperatures, suggesting that winter temperatures are more important than mean annual temperature for soil C storage in temperate grassland. Other correlations, including positive correlations with soil cation exchange capacity, soil lead levels, and tropospheric ozone exposure during the peak ozone season, were also detected. Potential mechanisms for the detected relationships are explored. The results of both experiments demonstrate that commonly-held expectations based on single-factor global change experiments or models are not always borne out in complex natural systems

Characteristics of free air carbon dioxide enrichment of a northern temperate mature forest

Tausz, M ORCiD: 0000-0001-8205-8561In 2017, the Birmingham Institute of Forest Research (BIFoR) began to conduct Free Air Carbon Dioxide Enrichment (FACE) within a mature broadleaf deciduous forest situated in the United Kingdom. BIFoR FACE employs large scale infrastructure, in the form of lattice towers, forming 'arrays' which encircle a forest plot of ~30 m diameter. BIFoR FACE consists of three treatment arrays to elevate local CO2 concentrations (e[CO2 ]) by +150 μmol mol-1 . In practice, acceptable operational enrichment (ambient [CO2 ] + e[CO2 ]) is ± 20% of the set-point 1-minute average target. There are a further three arrays that replicate the infrastructure and deliver ambient air as paired controls for the treatment arrays. For the first growing season with e[CO2 ] (April to November 2017), [CO2 ] measurements in treatment and control arrays show that the target concentration was successfully delivered, i.e.: +147 ± 21 μmol mol-1 (mean ± SD) or 98 ± 14% of set-point enrichment target. e[CO2 ] treatment was accomplished for 97.7% of the scheduled operation time, with the remaining time lost due to engineering faults (0.6% of the time), CO2 supply issues (0.6%), or adverse weather conditions (1.1%). CO2 demand in the facility was driven predominantly by wind speed and the formation of the deciduous canopy. Deviations greater than 10% from the ambient baseline CO2 occurred  80 μmol mol-1 (i.e., > 53% of the treatment increment) into control arrays accounted for < 0.1% of the enrichment period. The median [CO2 ] values in reconstructed 3-dimensional [CO2 ] fields show enrichment somewhat lower than the target but still well above ambient. The data presented here provide confidence in the facility setup and can be used to guide future next-generation forest FACE facilities built into tall and complex forest stands. This article is protected by copyright. All rights reserved

Isoprene and monoterpene fluxes from central amazonian rainforest inferred from tower-based and airborne measurements, and implications on the atmospheric chemistry and the local carbon budget

We estimated the isoprene and monoterpene source strengths of a pristine tropical forest north of Manaus in the central Amazon Basin using three different micrometeorological flux measurement approaches. During the early dry season campaign of the Cooperative LBA Airborne Regional Experiment (LBA-CLAIRE-2001), a tower-based surface layer gradient (SLG) technique was applied simultaneously with a relaxed eddy accumulation (REA) system. Airborne measurements of vertical profiles within and above the convective boundary layer (CBL) were used to estimate fluxes on a landscape scale by application of the mixed layer gradient (MLG) technique. The mean daytime fluxes of organic carbon measured by REA were 2.1 mg C m^−2 h^−1 for isoprene, 0.20 mg C m^−2 h^−1 for α-pinene, and 0.39 mg C m^−2 h^−1 for the sum of monoterpenes. These values are in reasonable agreement with fluxes determined with the SLG approach, which exhibited a higher scatter, as expected for the complex terrain investigated. The observed VOC fluxes are in good agreement with simulations using a single-column chemistry and climate model (SCM).\ud \ud In contrast, the model-derived mixing ratios of VOCs were by far higher than observed, indicating that chemical processes may not be adequately represented in the model. The observed vertical gradients of isoprene and its primary degradation products methyl vinyl ketone (MVK) and methacrolein (MACR) suggest that the oxidation capacity in the tropical CBL is much higher than previously assumed. A simple chemical kinetics model was used to infer OH radical concentrations from the vertical gradients of (MVK+MACR)/isoprene. The estimated range of OH concentrations during the daytime was 3–8×10^6 molecules cm^−3, i.e., an order of magnitude higher than is estimated for the tropical CBL by current state-of-the-art atmospheric chemistry and transport models. The remarkably high OH concentrations were also supported by results of a simple budget analysis, based on the flux-to-lifetime relationship of isoprene within the CBL. Furthermore, VOC fluxes determined with the airborne MLG approach were only in reasonable agreement with those of the tower-based REA and SLG approaches after correction for chemical decay by OH radicals, applying a best estimate OH concentration of 5.5×10^6 molecules cm^−3. The SCM model calculations support relatively high OH concentration estimates after specifically being constrained by the mixing ratios of chemical constituents observed during the campaign.\ud \ud The relevance of the VOC fluxes for the local carbon budget of the tropical rainforest site during the measurements campaign was assessed by comparison with the concurrent CO2 fluxes, estimated by three different methods (eddy correlation, Lagrangian dispersion, and mass budget approach). Depending on the CO2 flux estimate, 1–6% or more of the carbon gained by net ecosystem productivity appeared to be re-emitted through VOC emissions