Terrestrial Carbon Model Research Articles

Exploring Optimal Complexity for Water Stress Representation in Terrestrial Carbon Models: A Hybrid‐Machine Learning Model Approach

AbstractTerrestrial biosphere models offer a comprehensive view of the global carbon cycle by integrating ecological processes across scales, yet they introduce significant uncertainties in climate and biogeochemical projections due to diverse process representations and parameter variations. For instance, different soil water limitation functions lead to wide productivity ranges across models. To address this, we propose the Differentiable Land Model (DifferLand), a novel hybrid machine learning approach replacing unknown water limitation functions in models with neural networks (NNs) to learn from data. Using automatic differentiation, we calibrated the embedded NN and the physical model parameters against daily observations of evapotranspiration, gross primary productivity, ecosystem respiration, and leaf area index across 16 FLUXNET sites. We evaluated six model configurations where NNs simulate increasingly complex soil water and photosynthesis interactions against test data sets to find the optimal structure‐performance tradeoff. Our findings show that a simple hybrid model with a univariate NN effectively captures site‐level water and carbon fluxes on a monthly timescale. Across a global aridity gradient, the magnitude of water stress limitation varies, but its functional form consistently converges to a piecewise linear relationship with saturation at high water levels. While models incorporating more interactions between soil water and meteorological drivers better fit observations at finer time scales, they risk overfitting and equifinality issues. Our study demonstrates that hybrid models have great potential in learning unknown parameterizations and testing ecological hypotheses. Nevertheless, careful structure‐performance tradeoffs are warranted in light of observational constraints to translate the retrieved relationships into robust process understanding.

Reactive iron, not fungal community, drives organic carbon oxidation potential in floodplain soils

Wetlands host ∼20% of terrestrial organic carbon and serve as a major sink for atmospheric carbon. Anoxic soils and sediments accrue soil organic carbon (SOC) partly by hampering the activity of extracellular oxidative enzymes that break down phenolic polymers. Upon aeration, fungal-driven oxidative enzymatic depolymerization and microbial respiration of released monomers ensue. Redox-active metals can simultaneously catalyze abiotic nonspecific oxidation of SOC, notable examples including Mn(III) or Fe(II) through Fenton-like, hydrogen peroxide-catalyzed oxidative radical production. However, the extent of reactive metal contributions to biotic and abiotic SOC degradation is not understood in the context of natural environments with diverse redox chemistry. We tested the relative contributions of fungi, Mn(III) and Fe(II) to phenolic substrate (L-DOPA) oxidation in floodplain soils representing a range of transient redox conditions driven by permanent vs. periodic flooding. Phenol oxidative potential was highest in permanently flooded soils with fewer fungal taxa known for observed (per)oxidase activity and instead correlated with HCl-extractable Fe(II), Fe(total) and Fe(II)/Fe(total), suggesting a specific role of Fe(II). Fe(II) additions enhanced phenol oxidative potential in sterilized and non-sterilized soils in the presence of hydrogen peroxide, indicating abiotic Fe-mediated radical chemistry could significantly enhance wetland SOC oxidative depolymerization throughout redox-active floodplain soils. Fungal community composition did not correlate to phenol oxidative potential overall and only more oxic soils adjacent to the river with diverse fungal communities showed declining oxidative potential after sterilization. Mn(III) addition did not significantly enhance phenol oxidative potential across all soils, although it appeared to drive fungal-mediated oxidative potential in the most aerated floodplain soils. Understanding how metals mediate SOC depolymerization as abiotic oxidants or microbially-harnessed enzyme cofactors and substrates in soils under variable hydrologic controls will improve our ability to represent depolymerization in terrestrial carbon models in wetland and other frequently saturated soils.

Medium Spatial Resolution Mapping of Global Land Cover and Land Cover Change Across Multiple Decades From Landsat

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon “reanalysis” product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower net ecosystem exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit–Wishart ensemble filter (TWEnF) state data assimilation (SDA) framework, a generalization of the common ensemble Kalman filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ∼ 500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and leaf area index (LAI) (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced root mean square error (RMSE) compared to the calibrated forecast. Ultimately, PEcAn's new open-source regional data assimilation framework provides a scalable workflow for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) as well as accelerating terrestrial carbon cycle research.

Evaluating carbon use efficiency in forest types and ecoregions of Turkey

The total amount of carbon deposited by forests is gross primary productivity (GPP), and the net amount remaining after respiration is net primary productivity (NPP). The carbon storage effectiveness of forests is determined by the NPP:GPP ratio. This ratio is carbon use efficiency (CUE), and it is important for understanding the dynamics of carbon exchange between the biosphere and atmosphere. The aim of this study is to evaluate the variation of CUE, which is commonly used as a constant value in terrestrial carbon models, in different forest types and ecoregions of Turkey. The distribution of forest types in different ecoregions of Turkey was determined and, NPP, GPP, and CUE values were calculated for the different forest types in each region in this study. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite image was obtained for NPP and GPP content of forest types. In addition that the CORINE land use classification system was used for the spatial distribution of coniferous, broad-leaved and mixed forests in Turkey. The highest mean NPP (640 gC / m-2 y-1) and GPP (856 gC / m-2 y-1) were found in coniferous forests. The lowest NPP (267 gC / m-2 y-1) and GPP (493 gC / m-2 y-1) were detected in mixed forests in the Southeastern Anatolian Transitional Region. The highest CUE value was found in the Black Sea Climatic Region (0.89). Our results showed that the CUE was not a constant value. Although the mean CUE values among forest types were close to each other, significant differences were found between ecoregions. With the findings obtained, it is recommended that the CUE value should be determined under different environmental conditions

Carbon Dioxide Production in Bedrock Beneath Soils Substantially Contributes to Forest Carbon Cycling

AbstractSoils are widely considered the primary terrestrial organic matter pool mediating carbon transactions with the atmosphere and groundwater. Because soils are both a host and a product of rhizosphere activity, they are thought to mark the location where photosynthetic fixation of carbon dioxide (CO2) is balanced by the oxidation of organic matter. However, in many terrestrial environments, the rhizosphere extends below soils and into fractured bedrock, and it is unknown if the resulting biological and hydrologic dynamics in bedrock have a significant impact on carbon cycling. Here we show substantial production of CO2 in weathered bedrock at 4–8 m below the thin soils (<0.5 m thick) of a Northern California forest using innovative monitoring technology for sampling gases and water in fractured rock. The deep CO2 production supports a persistent upward flux of CO2(g) year‐round from bedrock to soil, constituting between 2% and 29% of the average daily CO2 efflux from the land surface. When water is rapidly transported across the fractured bedrock vadose zone, nearly 50% of the CO2 produced in the bedrock dissolves into water, promoting water‐rock interaction and export of dissolved inorganic carbon (DIC) from the unsaturated zone to groundwater, constituting as much as 80% of the DIC exiting the hillslope. Such CO2 production in weathered bedrock is subject to unique moisture, temperature, biological, and mineralogical conditions which are currently missing from terrestrial carbon models.

Decomposition responses to climate depend on microbial community composition

Bacteria and fungi drive decomposition, a fundamental process in the carbon cycle, yet the importance of microbial community composition for decomposition remains elusive. Here, we used an 18-month reciprocal transplant experiment along a climate gradient in Southern California to disentangle the effects of the microbial community versus the environment on decomposition. Specifically, we tested whether the decomposition response to climate change depends on the microbial community. We inoculated microbial decomposers from each site onto a common, irradiated leaf litter within "microbial cages" that prevent microbial exchange with the environment. We characterized fungal and bacterial composition and abundance over time and investigated the functional consequences through litter mass loss and chemistry. After 12 months, microbial communities altered both decomposition rate and litter chemistry. Further, the functional measurements depended on an interaction between the community and its climate in a manner not predicted by current theory. Moreover, microbial ecologists have traditionally considered fungi to be the primary agents of decomposition and for bacteria to play a minor role. Our results indicate that not only does climate change and transplantation have differential legacy effects among bacteria and fungi, but also that bacterial communities might be less functionally redundant than fungi with regards to decomposition. Thus, it may be time to reevaluate both the role of microbial community composition in its decomposition response to climate and the relative roles of bacterial and fungal communities in decomposition.

Lower land-use emissions responsible for increased net land carbon sink during the slow warming period

The terrestrial carbon sink accelerated during 1998-2012, concurrently with the slow warming period, but the mechanisms behind this acceleration are unclear. Here we analyse recent changes in the net land carbon sink (NLS) and its driving factors, using atmospheric inversions and terrestrial carbon models. We show that the linear trend of NLS during 1998-2012 is about 0.17 +/- 0.05 Pg C yr(-2), which is three times larger than during 1980-1998 (0.05 +/- 0.05 Pg C yr(-2)). According to terrestrial carbon model simulations, the intensification of the NLS cannot be explained by CO2 fertilization or climate change alone. We therefore use a bookkeeping model to explore the contribution of changes in land-use emissions and find that decreasing land-use emissions are the dominant cause of the intensification of the NLS during the slow warming period. This reduction of land-use emissions is due to both decreased tropical forest area loss and increased afforestation in northern temperate regions. The estimate based on atmospheric inversions shows consistently reduced land-use emissions, whereas another bookkeeping model did not reproduce such changes, probably owing to missing the signal of reduced tropical deforestation. These results highlight the importance of better constraining emissions from land-use change to understand recent trends in land carbon sinks.

The role of snow cover affecting boreal-arctic soil freeze–thaw and carbon dynamics

Abstract. Northern Hemisphere permafrost affected land areas contain about twice as much carbon as the global atmosphere. This vast carbon pool is vulnerable to accelerated losses through mobilization and decomposition under projected global warming. Satellite data records spanning the past 3 decades indicate widespread reductions (~ 0.8–1.3 days decade−1) in the mean annual snow cover extent and frozen-season duration across the pan-Arctic domain, coincident with regional climate warming trends. How the soil carbon pool responds to these changes will have a large impact on regional and global climate. Here, we developed a coupled terrestrial carbon and hydrology model framework with a detailed 1-D soil heat transfer representation to investigate the sensitivity of soil organic carbon stocks and soil decomposition to climate warming and changes in snow cover conditions in the pan-Arctic region over the past 3 decades (1982–2010). Our results indicate widespread soil active layer deepening across the pan-Arctic, with a mean decadal trend of 6.6 ± 12.0 (SD) cm, corresponding to widespread warming. Warming promotes vegetation growth and soil heterotrophic respiration particularly within surface soil layers (≤ 0.2 m). The model simulations also show that seasonal snow cover has a large impact on soil temperatures, whereby increases in snow cover promote deeper (≥ 0.5 m) soil layer warming and soil respiration, while inhibiting soil decomposition from surface (≤ 0.2 m) soil layers, especially in colder climate zones (mean annual T ≤ −10 °C). Our results demonstrate the important control of snow cover on northern soil freeze–thaw and soil carbon decomposition processes and the necessity of considering both warming and a change in precipitation and snow cover regimes in characterizing permafrost soil carbon dynamics.

Assimilating the LAI Data to the VEGAS Model Using the Local Ensemble Transform Kalman Filter: An Observing System Simulation Experiment

AbstractInformation on the spatial and temporal patterns of surface carbon flux is crucial to understanding of source/sink mechanisms and projection of future atmospheric CO2 concentrations and climate. This study presents the construction and implementation of a terrestrial carbon cycle data assimilation system based on a dynamic vegetation and terrestrial carbon model Vegetation-Global-Atmosphere-Soil (VEGAS) with an advanced assimilation algorithm, the local ensemble transform Kalman filter (LETKF, hereafter LETKF-VEGAS). An observing system simulation experiment (OSSE) framework was designed to evaluate the reliability of this system, and numerical experiments conducted by the OSSE using leaf area index (LAI) observations suggest that the LETKF-VEGAS can improve the estimations of leaf carbon pool and LAI significantly, with reduced root mean square errors and increased correlation coefficients with true values, as compared to a control run without assimilation. Furthermore, the LETKF-VEGAS has the po...

Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological "common sense" in a model–data fusion framework

Abstract. Many of the key processes represented in global terrestrial carbon models remain largely unconstrained. For instance, plant allocation patterns and residence times of carbon pools are poorly known globally, except perhaps at a few intensively studied sites. As a consequence of data scarcity, carbon models tend to be underdetermined, and so can produce similar net fluxes with very different parameters and internal dynamics. To address these problems, we propose a series of ecological and dynamic constraints (EDCs) on model parameters and initial conditions, as a means to constrain ecosystem variable inter-dependencies in the absence of local data. The EDCs consist of a range of conditions on (a) carbon pool turnover and allocation ratios, (b) steady-state proximity, and (c) growth and decay of model carbon pools. We use a simple ecosystem carbon model in a model–data fusion framework to determine the added value of these constraints in a data-poor context. Based only on leaf area index (LAI) time series and soil carbon data, we estimate net ecosystem exchange (NEE) for (a) 40 synthetic experiments and (b) three AmeriFlux tower sites. For the synthetic experiments, we show that EDCs lead to an overall 34% relative error reduction in model parameters, and a 65% reduction in the 3 yr NEE 90% confidence range. In the application at AmeriFlux sites all NEE estimates were made independently of NEE measurements. Compared to these observations, EDCs resulted in a 69–93% reduction in 3 yr cumulative NEE median biases (–0.26 to +0.08 kg C m−2), in comparison to standard 3 yr median NEE biases (–1.17 to −0.84 kg C m−2). In light of these findings, we advocate the use of EDCs in future model–data fusion analyses of the terrestrial carbon cycle.

Complementarity of flux- and biometric-based data to constrain parameters in a terrestrial carbon model

To improve models for accurate projections, data assimilation, an emerging statistical approach to combine models with data, have recently been developed to probe initial conditions, parameters, data content, response functions and model uncertainties. Quantifying how many information contents are contained in different data streams is essential to predict future states of ecosystems and the climate. This study uses a data assimilation approach to examine the information contents contained in flux- and biometric-based data to constrain parameters in a terrestrial carbon (C) model, which includes canopy photosynthesis and vegetation–soil C transfer submodels. Three assimilation experiments were constructed with either net ecosystem exchange (NEE) data only or biometric data only [including foliage and woody biomass, litterfall, soil organic C (SOC) and soil respiration], or both NEE and biometric data to constrain model parameters by a probabilistic inversion application. The results showed that NEE data mainly constrained parameters associated with gross primary production (GPP) and ecosystem respiration (RE) but were almost invalid for C transfer coefficients, while biometric data were more effective in constraining C transfer coefficients than other parameters. NEE and biometric data constrained about 26% (6) and 30% (7) of a total of 23 parameters, respectively, but their combined application constrained about 61% (14) of all parameters. The complementarity of NEE and biometric data was obvious in constraining most of parameters. The poor constraint by only NEE or biometric data was probably attributable to either the lack of long-term C dynamic data or errors from measurements. Overall, our results suggest that flux- and biometric-based data, containing different processes in ecosystem C dynamics, have different capacities to constrain parameters related to photosynthesis and C transfer coefficients, respectively. Multiple data sources could also reduce uncertainties in parameter estimation if these data sources contain complementary information.

Impact of large‐scale climate extremes on biospheric carbon fluxes: An intercomparison based on MsTMIP data

AbstractUnderstanding the role of climate extremes and their impact on the carbon (C) cycle is increasingly a focus of Earth system science. Climate extremes such as droughts, heat waves, or heavy precipitation events can cause substantial changes in terrestrial C fluxes. On the other hand, extreme changes in C fluxes are often, but not always, driven by extreme climate conditions. Here we present an analysis of how extremes in temperature and precipitation, and extreme changes in terrestrial C fluxes are related to each other in 10 state‐of‐the‐art terrestrial carbon models, all driven by the same climate forcing. We use model outputs from the North American Carbon Program Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). A global‐scale analysis shows that both droughts and heat waves translate into anomalous net releases of CO2 from the land surface via different mechanisms: Droughts largely decrease gross primary production (GPP) and to a lower extent total respiration (TR), while heat waves slightly decrease GPP but increase TR. Cold and wet periods have a smaller opposite effect. Analyzing extremes in C fluxes reveals that extreme changes in GPP and TR are often caused by strong shifts in water availability, but for extremes in TR shifts in temperature are also important. Extremes in net CO2 exchange are equally strongly driven by deviations in temperature and precipitation. Models mostly agree on the sign of the C flux response to climate extremes, but model spread is large. In tropical forests, C cycle extremes are driven by water availability, whereas in boreal forests temperature plays a more important role. Models are particularly uncertain about the C flux response to extreme heat in boreal forests.

Effects of ecological restoration projects on land use and land cover change and its influences on territorial NPP in Xinjiang, China

Since the Chinese government initiated its economic reform in 1978, rapid economic development has spurred land use and land cover change (LULCC) in China, which resulted in many ecological problems such as land degradation and desertification. To address these serious ecological crises, the government launched a series of ecological restoration programs which have caused significant LULCC and a profound impact on the terrestrial ecosystem. This study used net primary productivity (NPP) as an important indicator of the arid and semi-arid ecosystem's productivity to estimate the impacts of the LULCC driven by ecological restoration programs in Xinjiang from 2001 to 2009. The modeling method was based upon the Carnegie–Ames–Stanford Approach (CASA) terrestrial carbon model and Moderate-resolution Imaging Spectroradiometer (MODIS) remote sensing data and meteorological data for modeling simulation. The results demonstrated that the forest area of Xinjiang had the most net increase of 9093km2 in the study period, compared to other land cover types. The most dominant land cover changes during 2001–2009 were from grassland to forest and mutual transformation between grassland and desert. Total NPP of whole area increased by 252.51GgC during the study period. The increase of total NPP in forest was the most obvious among all vegetation types, with a net increase of 1782.88GgCyr−1. It can be concluded that the increase of regional NPP mainly resulted from forest expansion. During 2001–2009, the mean NPP in forest, grassland and desert had a slight decrease, whereas the cropland and crop/natural vegetation mosaic land mean NPP increased fractionally. By using the climate in 2001 to simulate the NPP of Xinjiang in 2009, we explored the influences of land use and cover changes and climate change on regional NPP. Compared to climate change, human activities produced an obvious positive effect in the increase of total NPP, especially for forest land. As a result, ecological restoration programs produced positive impacts on forest expansion and carbon sequestration in Xinjiang.

Earth System Model FGOALS-s2: Coupling a dynamic global vegetation and terrestrial carbon model with the physical climate system model

Earth System Models (ESMs) are fundamental tools for understanding climate-carbon feedback. An ESM version of the Flexible Global Ocean-Atmosphere-Land System model (FGOALS) was recently developed within the IPCC AR5 Coupled Model Intercomparison Project Phase 5 (CMIP5) modeling framework, and we describe the development of this model through the coupling of a dynamic global vegetation and terrestrial carbon model with FGOALS-s2. The performance of the coupled model is evaluated as follows.

The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties

Abstract. The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System (the thin layer that contains and supports life) over the coming decades and centuries. However, Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their terrestrial carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Utilising empirical data to constrain and assess component processes in terrestrial carbon cycle models will be essential to achieving this goal. We used a new model construction method to data-constrain all parameters of all component processes within a global terrestrial carbon model, employing as data constraints a collection of 12 empirical data sets characterising global patterns of carbon stocks and flows. Our goals were to assess the climate dependencies inferred for all component processes, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model and ultimately to identify a methodology by which alternative component models could be compared within the same framework in the future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies, although it was not possible to data-constrain all parameters, indicating some potentially redundant details. Similar climate dependencies were obtained for most processes, whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium plant carbon was predicted especially well (explaining around 70% of the variation in the withheld evaluation data). We discuss the advantages of our approach in relation to advancing our understanding of the carbon cycle and enabling Earth System Models to make better constrained projections.

Improving model parameter estimation using coupling relationships between vegetation production and ecosystem respiration

Data assimilation techniques and inverse analysis have been applied to extract ecological knowledge from ecosystem observations. However, the number of parameters in ecosystem models that can be constrained is limited by conventional inverse analysis. This study aims to increase the number of parameters that can be constrained in parameter inversions by considering the internal relationships among ecosystem processes. Our previous study has reported thermal adaptation of net ecosystem exchange (NEE). Ecosystems tend to transfer from a carbon source to sink when the air temperature exceeds the mean annual temperature, and attain their maximum uptake when the temperature reaches the long-term growing season mean. Because NEE is the difference between gross primary production (GPP) and ecosystem respiration (ER), the adaptation of NEE indirectly indicates the coupling relationship between GPP and ER. Five assimilation experiments were conducted with (1) estimated GPP based on eddy flux measurements, (2) estimated GPP and coupling relationship between GPP and ER, (3) observed NEE measurements, (4) observed NEE measurements and internal relationship between GPP and ER and (5) observed NEE, estimated ER and GPP. The results show that the inversion method, using only estimated GPP based on eddy covariance towers, constrained 4 of 16 parameters in the terrestrial ecosystem carbon model, and the improved method using both GPP data and the internal relationship between GPP and ER allowed us to constrain 10 of 16 parameters. The improved method constrained the parameters for ER without additional ER observations, and accordingly improved the model performance substantially for simulating ER. Overall, our method enhances our ability to extract information from ecosystem observations and potentially reduces uncertainty for simulating carbon dynamics across the regional and global scales.

长三角地区1995-2007年生态资产时空变化

This paper analyses the characteristics of,and drivers for,spatial and temporal change in the ecological assets in the Yangtze River Delta from 1995 to 2007.The ecological assets are simulated by using remote sensing quantitative retrieval and economic evaluation methods.The remote sensing data sets include NOAA/AVHRR-NDVI at a spatial resolution of 1km in 1995(February 1995—January 1996),MODIS\\NDVI in 2000(February 2000—January 2001) and 2007 at a resolution of 250m,land use interpreted by using Landsat TM images in 1995,2000 and 2007 at the scale of 1∶100000,and land cover in 1980S and 2005 at the scale of 1∶250000.The ecological adjustment coefficient is estimated on the basis of the net primary productivity and vegetation coverage in the region,which are simulated by a modified Carnegie Ames Stanford Approach(CASA) terrestrial carbon model and the Pixel Binary Model,respectively.The results show that the overall amount of ecological assets in the Yangtze River Delta declined by 5.1%,changing from 89.1 billion yuan in 1995 to 84.6 billion yuan in 2007.The ecological assets in the region consist of forest,rivers/lakes and cropland,accounting for 48.0%,26.7% and 20.5%,respectively.The total amount of ecological assets for each major city in the region presents a decreasing trend,and there is a spatial heterogeneity.The reduction of ecological assets in the cities of Shanghai,Suzhou,Jiaxing,Huzhou,Shaoxing and Ningbo was substantial,at 798,486,441,427 and 403 million yuan,respectively.However the decreased amount and rate of the ecological assets in other cities such as Nanjing,Zhenjiang,Hangzhou and Taizhou were small,at 57,47,122 and 155 million yuan,respectively.The spatial distribution of ecological assets in the Yangtze River Delta exhibits a declining pattern from the south to the north,increasing slightly in the south and north but decreasing severely in the central area from 1995 to 2007.Such a pattern of change in ecological assets implies that it may reduce the ecological safety in some affected areas,but this will not be an ecological constraint for sustainable development in the Yangtze River Deltaic region as a whole.Land-use change is found to be the dominant factor resulting in the reduction of ecological assets and their dissimilarities in spatial distribution.Change in ecological assets is also mediated by urban expansion,land reformation and reclamation.However the national ecological restoration projects,such as 'Grain to Green','Soil and Water Conservation' and 'Wetland and Barren Hills Rehabilitation',have played an offsetting role in reversing the downward trend of ecological assets in the Yangtze River Delta.There are some uncertainties for evaluating ecological assets based on remote sensing quantitative retrieval and economic evaluation methods.The uncertainties are mainly influenced by three factors: the evaluation factors for ecological assets and the criteria for evaluation;the spatial resolution of remote sensing data and the precision of applied ecological models;climate fluctuations and meteorological data with inherent interpolation errors.

Impacts of China's Three Gorges Dam Project on net primary productivity in the reservoir area

China's Three Gorges Dam Project (TGP) is the world's largest hydroelectric power project, and as a consequence the reservoir area is at risk of ecological degradation. This study uses net primary productivity (NPP) as an important indicator of the reservoir ecosystem's productivity to estimate the impacts of the TGP in the local resettlement region of the Three Gorges Reservoir Area (TGRA) over the 2000–2010 period. The modeling method is based upon the Carnegie Ames Stanford Approach (CASA) terrestrial carbon model and uses Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data for modeling simulation. The results demonstrate that total NPP in the resettlement region decreased by 8.0% (632.8 Gg) from 2000 to 2010. The impact of the TGP on NPP is mainly mediated by land-use change brought about by the large-scale inundation of land and subsequent massive resettlement of both rural and urban residents. Nearby resettlement, land inundation, and relocation of old urban centers and affiliated urban dwellers are responsible for 54.3%, 28.0%, and 5.8% respectively of total NPP reduction in the resettlement region over the study period. The major national ecological projects implemented in the TGRA since 1998 have played a key role in offsetting the negative impacts of the TGP on NPP in the region.

Atypical soil carbon distribution across a tropical steepland forest catena

article i nfo Soil organic carbon (SOC) in a humid subtropical forest in Puerto Rico is higher at ridge locations compared to valleys, and therefore opposite to what is commonly observed in other forested hillslope catenas. To better understand the spatial distribution of SOC in this system, plots previously characterized by topographic position, vegetation type and stand age were related to soil depth and SOC. Additional factors were also investigated, including topographically-related differences in litter dynamics and soil chemistry. To investigate the influence of litter dynamics, the Century soil organic model was parameterized to simulate the effect of substituting valley species for ridge species. Soil chemical controls on C concentrations were investigated with multiple linear regression models using iron, aluminum and clay variables. Deeper soils were associated with indicators of higher landscape stability (older tabonuco stands established on ridges and slopes), while shallower soils persisted in more disturbed areas (younger non-tabonuco stands in valleys and on slopes). Soil depth alone accounted for 77% of the observed difference in the mean 0 to 60 cm SOC between ridge soils (deeper) and valley soils (shallower). The remaining differences in SOC were due to additional factors that lowered C concentrations at valley locations in the 0 to 10 cm pool. Model simulations showed a slight decrease in SOC when lower litter C:N was substituted for higher litter C:N, but the effects of different woody inputs on SOC were unclear. Multiple linear regression models with ammonium oxalate extractable iron and aluminum, dithionite-citrate-extractable iron and aluminum, and clay contents explained as much as 74% of the variation in C concentrations, and indicated that organo-mineral complexation may be more limited in poorly developed valley soils. Thus, topography both directly and indirectly affects SOC pools through a variety of inter-related processes that are often not quantified or captured in terrestrial carbon models.