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

The uncertainty analysis of the MODIS GPP product in global maize croplands

Gross primary productivity (GPP) is very important in the global carbon cycle. Currently, the newly released estimates of 8-day GPP at 500 m spatial resolution (Collection 6) are provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Science Team for the global land surface via the improved light use efficiency (LUE) model. However, few studies have evaluated its performance. In this study, the MODIS GPP products (GPPMOD) were compared with the observed GPP (GPPEC) values from site-level eddy covariance measurements over seven maize flux sites in different areas around the world. The results indicate that the annual GPPMOD was underestimated by 6%‒58% across sites. Nevertheless, after incorporating the parameters of the calibrated LUE, the measurements of meteorological variables and the reconstructed Fractional Photosynthetic Active Radiation (FPAR) into the GPPMOD algorithm in steps, the accuracies of GPPMOD estimates were improved greatly, albeit to varying degrees. The differences between the GPPMOD and the GPPEC were primarily due to the magnitude of LUE and FPAR. The underestimate of maize cropland LUE was a widespread problem which exerted the largest impact on the GPPMOD algorithm. In American and European sites, the performance of the FPAR exhibited distinct differences in capturing vegetation GPP during the growing season due to the canopy heterogeneity. In addition, at the DE-Kli site, the GPPMOD abruptly produced extreme low values during the growing season because of the contaminated FPAR from a continuous rainy season. After correcting the noise of the FPAR, the accuracy of the GPPMOD was improved by approximately 14%. Therefore, it is crucial to further improve the accuracy of global GPPMOD, especially for the maize crop ecosystem, to maintain food security and better understand global carbon cycle

Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2 and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment

Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2 and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment

Contrasting responses of water use efficiency to drought across global terrestrial ecosystems

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/Drought is an intermittent disturbance of the water cycle that profoundly affects the terrestrial carbon cycle. However, the response of the coupled water and carbon cycles to drought and the underlying mechanisms remain unclear. Here we provide the first global synthesis of the drought effect on ecosystem water use efficiency (WUE = gross primary production (GPP)/evapotranspiration (ET)). Using two observational WUE datasets (i.e., eddy-covariance measurements at 95 sites (526 site-years) and global gridded diagnostic modelling based on existing observation and a data-adaptive machine learning approach), we find a contrasting response of WUE to drought between arid (WUE increases with drought) and semi-arid/sub-humid ecosystems (WUE decreases with drought), which is attributed to different sensitivities of ecosystem processes to changes in hydro-climatic conditions. WUE variability in arid ecosystems is primarily controlled by physical processes (i.e., evaporation), whereas WUE variability in semi-arid/sub-humid regions is mostly regulated by biological processes (i.e., assimilation). We also find that shifts in hydro-climatic conditions over years would intensify the drought effect on WUE. Our findings suggest that future drought events, when coupled with an increase in climate variability, will bring further threats to semi-arid/sub-humid ecosystems and potentially result in biome reorganization, starting with low-productivity and high water-sensitivity grassland

Synthesis of Satellite Microwave Observations for Monitoring Global Land-Atmosphere CO2 Exchange

This dissertation describes the estimation, error quantification, and incorporation of land surface information from microwave satellite remote sensing for modeling global ecosystem land-atmosphere net CO2 exchange. Retrieval algorithms were developed for estimating soil moisture, surface water, surface temperature, and vegetation phenology from microwave imagery timeseries. Soil moisture retrievals were merged with model-based soil moisture estimates and incorporated into a light-use efficiency model for vegetation productivity coupled to a soil decomposition model. Results, including state and uncertainty estimates, were evaluated with a global eddy covariance flux tower network and other independent global model- and remote-sensing based products

Modeling the coupled exchange of water and CO2 over croplands

Croplands are a managed type of vegetation, with a carbon storage that is highly optimized for food production. For instance, their sowing dates are chosen by the farmers, their genetic potential is bred for high grain yields, and their on-field competition with other species is reduced to the minimum. As a result of human intervention, croplands are a major land cover type (roughly one fifth of the land area over Europe) and they experience a short growing season during which they exchange carbon and water intensively with the atmosphere. Their growth significantly affects the seasonal amplitude of CO2 mole fractions over the globe, interact with extreme weather events such as droughts and heat waves, and impact surface hydrology due to their water consumption. However, and in spite of their relevance, terrestrial biosphere models used in carbon cycle and atmospheric research often assume the phenology of croplands to be similar to the one of grasslands, and they also ignore the impact of crop management. This oversimplification is the motivation for this thesis. We focus on understanding and modeling the key surface and atmospheric processes that shape the cropland water and CO2 exchange, and the resulting impact on the CO2 mole fractions of the atmosphere overhead. We study these processes from the daily to the seasonal scale, for croplands of the mid-latitudes. In the end, we come with recommendations and a new modeling framework to represent the cropland CO2 and water exchange in the Earth System, weather and climate models