Xiaolu Zhou

Regional drought-induced reduction in the biomass carbon sink of Canada's boreal forests

The boreal forests, identified as a critical “tipping element” of the Earths climate system, play a critical role in the global carbon budget. Recent findings have suggested that terrestrial carbon sinks in northern high-latitude regions are weakening, but there has been little observational evidence to support the idea of a reduction of carbon sinks in northern terrestrial ecosystems. Here, we estimated changes in the biomass carbon sink of natural stands throughout Canadas boreal forests using data from long-term forest permanent sampling plots. We found that in recent decades, the rate of biomass change decreased significantly in western Canada (Alberta, Saskatchewan, and Manitoba), but there was no significant trend for eastern Canada (Ontario and Quebec). Our results revealed that recent climate change, and especially drought-induced water stress, is the dominant cause of the observed reduction in the biomass carbon sink, suggesting that western Canadas boreal forests may become net carbon sources if the climate change–induced droughts continue to intensify.

Assessing the generality and accuracy of the TRIPLEX model using in situ data of boreal forests in central Canada

Abstract TRIPLEX1.0 is a hybrid model that integrates three well-established process models including 3-PG, TREEDYN3.0 and CENTURY4.0. We have conducted calibrations using eight sites to determine and generalize parameters of the TRIPLEX. We also performed model validation using 66 independent data sets to examine the model accuracy and the generality of its application. Simulations were conducted for plots with large sample size from the boreal ecosystem atmosphere study (BOREAS) program, including the northern study area (NSA) near Thompson, Manitoba (55.7° N, 97.8° W) and the southern study area (SSA) near Prince Albert, Saskatchewan (53.7° N, 105.1° W). The calibrations and simulations emphasized on generating average parameters and initial statuses for applying a complex model in a broad region where site detailed information such as photosynthetic capacity, soil carbon, nutrient, soil water, and tree growth is not always available. A suggestion was presented regarding adjusting the sensitive parameter by estimating tree growth rate corresponding to different site conditions. The study actually presented a reasonable and balanced parameter generalization procedure that did not lead to a significant reduction of model accuracy, but did increase the model practicability. The comparison of observations and simulations produced a good agreement for tree density, mean tree height, DBH, soil carbon, above-ground and total biomass, net primary productivity (above-ground) and soil nitrogen in both short- and long-term simulation. Results presented here imply that the set of parameters generalized and suggested in this study can be used as basic referenced values, in which TRIPLEX can be applied to simulate the general site conditions of boreal forest ecosystems.

Developing carbon-based ecological indicators to monitor sustainability of Ontario's forests

With 2% of the world’s forests and 17% of Canada’s forested land, Ontario plays a major role in maintaining Canada’s forests and managing them sustainably. Ontario is developing a set of criteria and indicators of sustainable forest management (SFM) to aid in conservation and sustainable management of its temperate and boreal (BO) forests. The criteria and indicators are intended to provide a framework for describing and assessing processes of SFM at a regional scale; and to improve the information available to the public and decision-makers. This paper describes three ecological indicators, evaluated using a carbon (C) budget model, a forest inventory database, and disturbance records to assess long-term sustainability of Ontario’s forest ecosystems based on the environmental conditions of the past 70 years. Results suggest that total net primary productivity (NPP) of Ontario’s forest ecosystems increased from 1925 to 1975 and then decreased between 1975 and 1990; Ontario’s forest ecosystems acted as a C sink between 1920 and 1980, and a C source from 1981 to 1990, mainly due to decreased average forest age and NPP caused by increased ecosystem disturbance (e.g. fire, insect and disease infestations, harvesting) since 1975. Current estimates from this analysis suggest that there is significant potential for Ontario’s forests to function as C sinks by reducing ecosystem disturbances and increasing growth and storage of C in the young forests throughout the province.

Effects of increased nitrogen deposition and rotation length on long-term productivity of Cunninghamia lanceolata plantation in southern China.

Cunninghamia lanceolata (Lamb.) Hook. has been widely planted in subtropical China to meet increasing timber demands, leading to short-rotation practices that deplete soil nutrients. However, increased nitrogen (N) deposition offsets soil N depletion. While long-term experimental data investigating the coupled effects related to short rotation practices and increasing N deposition are scarce, applying model simulations may yield insights. In this study, the CenW3.1 model was validated and parameterized using data from pure C. lanceolata plantations. The model was then used to simulate various changes in long-term productivity. Results indicated that responses of productivity of C. lanceolata plantation to increased N deposition were more related to stand age than N addition, depending on the proportion and age of growing forests. Our results have also shown a rapid peak in growth and N dynamics. The peak is reached sooner and is higher under higher level of N deposition. Short rotation lengths had a greater effect on productivity and N dynamics than high N deposition levels. Productivity and N dynamics decreased as the rotation length decreased. Total productivity levels suggest that a 30-year rotation length maximizes productivity at the 4.9 kg N ha−1 year−1 deposition level. For a specific rotation length, higher N deposition levels resulted in greater overall ecosystem C and N storage, but this positive correlation tendency gradually slowed down with increasing N deposition levels. More pronounced differences in N deposition levels occurred as rotation length decreased. To sustain C. lanceolata plantation productivity without offsite detrimental N effects, the appropriate rotation length is about 20–30 years for N deposition levels below 50 kg N ha−1 year−1 and about 15–20 years for N deposition levels above 50 kg N ha−1 year−1. These results highlight the importance of assessing N effects on carbon management and the long-term productivity of forest ecosystems.

Towards a paradigm for open and free sharing of scientific data on global change science in China

Abstract Despite great progress in data sharing that has been made in China in recent decades, cultural, policy, and technological challenges have prevented Chinese researchers from maximizing the availability of their data to the global change science community. To achieve full and open exchange and sharing of scientific data, Chinese research funding agencies need to recognize that preservation of, and access to, digital data are central to their mission, and must support these tasks accordingly. The Chinese government also needs to develop better mechanisms, incentives, and rewards, while scientists need to change their behavior and culture to recognize the need to maximize the usefulness of their data to society as well as to other researchers. The Chinese research community and individual researchers should think globally and act personally to promote a paradigm of open, free, and timely data sharing, and to increase the effectiveness of knowledge development.

Development and Evaluation of Models for the Relationship between Tree Height and Diameter at Breast Height for Chinese-Fir Plantations in Subtropical China.

Tree diameter at breast height (dbh) and height are the most important variables used in forest inventory and management as well as forest carbon-stock estimation. In order to identify the key stand variables that influence the tree height-dbh relationship and to develop and validate a suit of models for predicting tree height, data from 5961 tree samples aged from 6 years to 53 years and collected from 80 Chinese-fir plantation plots were used to fit 39 models, including 33 nonlinear models and 6 linear models, were developed and evaluated into two groups. The results showed that composite models performed better in height estimate than one-independent-variable models. Nonlinear composite Model 34 and linear composite Model 6 were recommended for predicting tree height in Chinese fir plantations with a dbh range between 4 cm and 40 cm when the dbh data for each tree and the quadratic mean dbh of the stand (Dq) and mean height of the stand (Hm) were available. Moreover, Hm could be estimated by using the formula Hm=11.707×ln(Dq)-18.032. Clearly, Dq was the primary stand variable that influenced the height-dbh relationship. The parameters of the models varied according to stand age and site. The inappropriate application of provincial or regional height-dbh models for predicting small tree height at local scale may result in larger uncertainties. The method and the recommended models developed in this study were statistically reliable for applications in growth and yield estimation for even-aged Chinese-fir plantation in Huitong and Changsha. The models could be extended to other regions and to other tree species only after verification in subtropical China.

Long-term changes in tree basal area across the boreal zone, Canada

Abstract: Permanent forest plots (PSP) were used to investigate long-term basal-area tree growth rates across the boreal forests in Canada. The objectives were to discern whether or not these rates i) are similar across the boreal zone and ii) correlate to change in climate from 1970 to 2010. The results show that rates vary by region, with decreasing growth rates for about 60% of individual trees in western Canada (Alberta, Saskatchewan, Manitoba) but increasing rates for about 70% of individual trees in eastern Canada (Ontario and Quebec). These changes are interpreted from an overall carbon sequestration perspective and within the context of available precipitation and air temperature data and an annual climate moisture index. This study provides long-term plot-based evidence for the ecological variability and regional differences in tree growth detected by satellite-based remote-sensing and tree-ring studies in Canadas boreal forests.

Modeling Global Soil Carbon and Soil Microbial Carbon by Integrating Microbial Processes into the Ecosystem Process Model TRIPLEX‐GHG

Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process-based model TRIPLEX-GHG was developed by coupling it with the new MEND (Microbial-ENzyme-mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX-MICROBE) shows considerable improvement over the previous version (TRIPLEX-GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well-regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated by Xu et al. [2014]. We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC) and mineral-associated organic carbon (MOC). However, our work represents the first step towards a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles.

Application of the ecosystem model and Markov Chain Monte Carlo for parameter estimation and productivity prediction

It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process-based ecological models and data in cohesive, systematic ways. In process-based model applications, inherent spatial and temporal heterogeneities found within terrestrial ecosystems may lead to the uncertainties of model predictions. To reduce simulation uncertainties due to inaccurate model parameters, the Markov Chain Monte Carlo (MCMC) method was applied in this study to improve the estimations of four key parameters used in the process-based ecosystem model of TRIPLEX-FLUX. These four key parameters include a maximum photosynthetic carboxylation rate of 25°C (Vmax), an electron transport (Jmax) light-saturated rate within the photosynthetic carbon reduction cycle of leaves, a coefficient of stomatal conductance (m), and a reference respiration rate of 10°C (R10). Seven forest flux tower sites located across North America were used to investigate and facilitate understanding of the daily variation in model parameters for three deciduous forests, three evergreen temperate forests, and one evergreen boreal forest. Eddy covariance CO2 exchange measurements were assimilated to optimize the parameters in the year 2006. After parameter optimization and adjustment took place, net ecosystem production prediction significantly improved (by approximately 25%) compared to the CO2 flux measurements taken at the seven forest ecosystem sites. Results suggest that greater seasonal variability occurs in broadleaf forests in respect to the selected parameters than in needleleaf forests. This study also demonstrated that the model-data fusion approach by incorporating MCMC method is able to better estimate parameters and improve simulation accuracy for different ecosystems located across North America.