Baozhang Chen

Modeling and Scaling Coupled Energy, Water, and Carbon Fluxes Based on Remote Sensing: An Application to Canada's Landmass

Abstract Land surface models (LSMs) need to be coupled with atmospheric general circulation models (GCMs) to adequately simulate the exchanges of energy, water, and carbon between the atmosphere and terrestrial surfaces. The heterogeneity of the land surface and its interaction with temporally and spatially varying meteorological conditions result in nonlinear effects on fluxes of energy, water, and carbon, making it challenging to scale these fluxes accurately. The issue of up-scaling remains one of the critical unsolved problems in the parameterization of subgrid-scale fluxes in coupled LSM and GCM models. A new distributed LSM, the Ecosystem–Atmosphere Simulation Scheme (EASS) was developed and coupled with the atmospheric Global Environmental Multiscale model (GEM) to simulate energy, water, and carbon fluxes over Canada’s landmass through the use of remote sensing and ancillary data. Two approaches (lumped case and distributed case) for handling subgrid heterogeneity were used to evaluate the effect ...

Comparison of regional carbon flux estimates from CO2 concentration measurements and remote sensing based footprint integration

regional GPP from mixing ratio measurements, we also compared the estimates of regional GPP with estimates made using eddy covariance (EC) flux measurements, although their respective source areas are different. They had similar seasonal patterns, but the regional estimates were consistently smaller than the local EC flux derived GPP throughout the growing season in 2003. These estimates of annual regional GPP were 649–664 g C m � 2 for 2003 while the EC-derived annual GPP was 819–847 g C m � 2 . The annual difference was about 20–25%. The EC flux footprint of the tower was relatively homogeneous old black spruce while the concentration footprint, which was a few orders of magnitude larger than the flux footprint, covered boreal evergreen and deciduous broadleaf forests, grassland, cropland, and lakes. Nonforested land occupied about 10–50% of the concentration footprint depending on wind direction and speed and was less productive than the black spruce forest. The discrepancies between regional and local GPP estimates reflected the differences in underlying land surfaces represented by the different footprint areas.

Antiquity of the earliest cultivated rice in central China and its implications

Until now, most of the early rice remains in China were found in the middle to lower reaches of the Yangtze River drainage. Recently, rice remains earlier than 8000 B.P. were found from Jiahu site (8942-7801 B.P.) in Wuyang County of Henan Province, central China. This is the earliest cultivated rice found at this latitude (33°37’N), which is far outside the current distribution of wild rice species. The discovery is of great implications. It suggests that central China may be one of the centers of early rice domestication.

Deriving daily carbon fluxes from hourly CO2 mixing ratios measured on the WLEF tall tower: An upscaling methodology

[1] The temporal variation of the CO 2 mixing ratio in the atmosphere at a given height results from several processes, including photosynthesis and respiration of the underlying ecosystems, the vertical mixing of the atmosphere near the surface and in the planetary boundary layer (PBL), and entrainment of the air above the PBL. Theoretically, if all atmospheric processes are modeled accurately, we can estimate the magnitude of ecosystem photosynthesis and respiration from the variations in the measured CO 2 mixing ratio. Through analyzing the CO 2 concentration measured at several heights (30 m, 122 m, and 396 m) on the Wisconsin tall tower, we demonstrate that it is possible to derive the daily carbon flux resulting from CO 2 uptake from hourly CO 2 mixing ratio data. At 30 m, the concentration-derived daily gross primary productivity (GPP) is well correlated with measured daily GPP derived from flux measurements (r 2 = 0.70), but the former was 20% larger than the latter. The correlation increased considerably for 10-day averages (r2 = 0.87). As the variations at lower heights have larger diurnal CO 2 amplitudes, the concentration-derived GPP is more accurate at lower heights. The footprint distance of CO 2 concentration during the daytime under the influence of the mixed layer is estimated to be of the order of 10 km, or a footprint area of 10 3 -10 4 km 2 , which is much larger than that of CO 2 fluxes measured using eddy covariance methods (typically 1 km 2 ). The difference in these footprint areas may partly explain the differences between these two flux estimates at the Wisconsin tower. These differences also signify the importance of retrieving flux information from the mixing ratio as it provides a means to upscale from local sites to a region.

Evaluating Parameter Adjustment in the MODIS Gross Primary Production Algorithm Based on Eddy Covariance Tower Measurements

How well parameterization will improve gross primary production (GPP) estimation using the MODerate-resolution Imaging Spectroradiometer (MODIS) algorithm has been rarely investigated. We adjusted the parameters in the algorithm for 21 selected eddy-covariance flux towers which represented nine typical plant functional types (PFTs). We then compared these estimates of the MOD17A2 product, by the MODIS algorithm with default parameters in the Biome Property Look-Up Table, and by a two-leaf Farquhar model. The results indicate that optimizing the maximum light use efficiency (emax) in the algorithm would improve GPP estimation, especially for deciduous vegetation, though it could not compensate the underestimation during summer caused by the one-leaf upscaling strategy. Adding the soil water factor to the algorithm would not significantly affect performance, but it could make the adjusted emax more robust for sites with the same PFT and among different PFTs. Even with adjusted parameters, both one-leaf and two-leaf models would not capture seasonally photosynthetic dynamics, thereby we suggest that further improvement in GPP estimaiton is required by taking into consideration seasonal variations of the key parameters and variables.

Understanding of Coupled Terrestrial Carbon, Nitrogen and Water Dynamics—An Overview

Coupled terrestrial carbon (C), nitrogen (N) and hydrological processes play a crucial role in the climate system, providing both positive and negative feedbacks to climate change. In this review we summarize published research results to gain an increased understanding of the dynamics between vegetation and atmosphere processes. A variety of methods, including monitoring (e.g., eddy covariance flux tower, remote sensing, etc.) and modeling (i.e., ecosystem, hydrology and atmospheric inversion modeling) the terrestrial carbon and water budgeting, are evaluated and compared. We highlight two major research areas where additional research could be focused: (i) Conceptually, the hydrological and biogeochemical processes are closely linked, however, the coupling processes between terrestrial C, N and hydrological processes are far from well understood; and (ii) there are significant uncertainties in estimates of the components of the C balance, especially at landscape and regional scales. To address these two questions, a synthetic research framework is needed which includes both bottom-up and top-down approaches integrating scalable (footprint and ecosystem) models and a spatially nested hierarchy of observations which include multispectral remote sensing, inventories, existing regional clusters of eddy-covariance flux towers and CO2 mixing ratio towers and chambers.

Simulating dynamics of δ13C of CO2 in the planetary boundary layer over a boreal forest region: covariation between surface fluxes and atmospheric mixing

Stable isotopes of CO2 contain unique information on the biological and physical processes that exchange CO2 between terrestrial ecosystems and the atmosphere. Ecosystem exchange of carbon isotopes with the atmosphere is correlated diurnally and seasonally with the planetary boundary layer (PBL) dynamics. The strength of this kind of covariation affects the vertical gradient of δ13C and thus the global δ13C distribution pattern. We need to understand the various processes involved in transport/diffusion of carbon isotope ratio in the PBL and between the PBL and the biosphere and the troposphere. In this study, we employ a one-dimensional vertical diffusion/transport atmospheric model (VDS), coupled to an ecosystem isotope model (BEPS-EASS) to simulate dynamics of 13CO2 in the PBL over a boreal forest region in the vicinity of the Fraserdale (FRD) tower (49°52’29.9”N, 81°34’12.3”W) in northern Ontario, Canada. The data from intensive campaigns during the growing season in 1999 at this site are used for model validation in the surface layer. The model performance, overall, is satisfactory in simulating the measured data over the whole course of the growing season.We examine the interaction of the biosphere and the atmosphere through the PBL with respect to δ13C on diurnal and seasonal scales. The simulated annual mean vertical gradient of δ13C in the PBL in the vicinity of the FRD tower was about 0.25‰ in 1999. The δ13C vertical gradient exhibited strong diurnal (29%) and seasonal (71%) variations that do not exactly mimic those of CO2. Most of the vertical gradient (96.5% ±) resulted from covariation between ecosystem exchange of carbon isotopes and the PBL dynamics, while the rest (3.5%±) was contributed by isotopic disequilibrium between respiration and photosynthesis. This disequilibrium effect on δ13C of CO2 dynamics in PBL, moreover, was confined to the near surface layers (less than 350 m).

Changes in the Land Surface Energy Budget in Eastern China over the Past Three Decades: Contributions of Land-Cover Change and Climate Change

AbstractSensible heat flux (H), latent heat flux (LE), and net radiation (NR) are important surface energy components that directly influence climate systems. In this study, the changes in the surface energy and their contributions from global climate change and/or land-cover change over eastern China during the past nearly 30 years were investigated and assessed using a process-based land surface model [the Ecosystem–Atmosphere Simulation Scheme (EASS)]. The modeled results show that climate change contributed more to the changes of land surface energy fluxes than land-cover change, with their contribution ratio reaching 4:1 or even higher. Annual average temperature increased before 2000 and reversed thereafter; annual total precipitation continually decreased, and incident solar radiation continually increased over the past nearly 30 years. These climatic changes could lead to increased NR, H, and LE, assuming land cover remained unchanged during the past nearly 30 years. Among these meteorological var...

A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data

Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a logistic function to fit the leaf area index (LAI) curve. We applied the resulting asymptotic lines and the curvature extrema to derive the vegetation phenophases of germination, green-up, maturity, senescence, defoliation and dormancy. The new proposed S-curve function has been tested in a specific area (Shangdong Province, China), characterized by a specific pattern in leaf area index (LAI) time course due to the dominant presence of crops. The function has not yet received any global testing. The identified phenophases were validated against measurement stations in Shandong Province. (i) From the site-scale comparison, we find that the detected phenophases using the S-curve (SC) algorithm are more consistent with the observations than using the logistic (LC) algorithm and the asymmetric Gaussian (AG) algorithm, especially for the germination and dormancy. The phenological recognition rates (PRRs) of the SC algorithm are obviously higher than those of two other algorithms. The S-curve function fits the LAI curve much better than the logistic function and asymmetric Gaussian function; (ii) The retrieval results of the SC algorithm are reliable and in close proximity to the green-up observed data whether using the AVHRR LAI or the improved MODIS LAI. Three inversion algorithms shows the retrieval results based on AVHRR LAI are all later than based on improved MODIS LAI. The bias statistics reveal that the retrieval results based on the AVHRR LAI datasets are more reasonable than based on the improved MODIS LAI datasets. Overall, the S-curve algorithm has the advantage of deriving vegetation phenophases across time and space as compared to the LC algorithm and the AG algorithm. With the SC algorithm, the vegetation phenophases can be extracted more effectively.

A comprehensive estimate of recent carbon sinks in China using both top-down and bottom-up approaches

Atmospheric inversions use measurements of atmospheric CO2 gradients to constrain regional surface fluxes. Current inversions indicate a net terrestrial CO2 sink in China between 0.16 and 0.35 PgC/yr. The uncertainty of these estimates is as large as the mean because the atmospheric network historically contained only one high altitude station in China. Here, we revisit the calculation of the terrestrial CO2 flux in China, excluding emissions from fossil fuel burning and cement production, by using two inversions with three new CO2 monitoring stations in China as well as aircraft observations over Asia. We estimate a net terrestrial CO2 uptake of 0.39–0.51 PgC/yr with a mean of 0.45 PgC/yr in 2006–2009. After considering the lateral transport of carbon in air and water and international trade, the annual mean carbon sink is adjusted to 0.35 PgC/yr. To evaluate this top-down estimate, we constructed an independent bottom-up estimate based on ecosystem data, and giving a net land sink of 0.33 PgC/yr. This demonstrates closure between the top-down and bottom-up estimates. Both top-down and bottom-up estimates give a higher carbon sink than previous estimates made for the 1980s and 1990s, suggesting a trend towards increased uptake by land ecosystems in China.