Simulating the impact of climate change on the growth of Chinese fir plantations in Fujian province, China
© The Author(s). 2017
Received: 23 September 2016
Accepted: 13 September 2017
Published: 6 October 2017
Climate change represents a considerable source of uncertainty with respect to the long-term health and productivity of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantations in southeastern China.
We employed the process-based, stand-level model FORECAST Climate to investigate the potential impact of four alternative climate-change scenarios on the long-term growth and development of Chinese fir plantations in Fujian province, China. The capability of the model to project seasonal patterns of productivity related to variation in temperature and moisture availability was evaluated using 11 years of 8-day composite MODIS remote sensing data.
Simulation results suggest climate change will lead to a modest increase in long-term stemwood biomass production (6.1 to 12.1% after 30 to 60 years). The positive impact of climate change was largely attributable to both a lengthening of the growing season and an increase in nutrient-cycling rates. The increase in atmospheric CO2 concentrations associated with the different emission scenarios led to an increase in water-use efficiency and a small increase in productivity. While the model predicted an overall increase in dry-season moisture stress, it did not predict increased levels of drought-related mortality.
Climate change is expected have positive impact on the growth of Chinese fir in the Fujian region of China. However, the projected increase in plantation productivity associated with climate change may not be realised if the latter also results in enhanced activity of biotic and abiotic disturbance agents.
KeywordsFORECAST Climate Process simulation Climate change Cunninghamia lanceolata Forest productivity Nutrient cycling MODIS
Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is an evergreen conifer species and is one of the most important commercial species in China. Not only is it a valuable timber species useful for construction and furniture manufacturing (Huang 2013; Zhang et al. 2013), but it is also commonly used for pulp and biomass energy production (Nie et al. 1998; Li et al. 2013). Chinese fir has been widely planted throughout subtropical China (Yu 1997; Wu 1984), and according to the results of the 8th National Forest Inventory, the planting area of Chinese fir is about 11.0 million ha, accounted for 15.8% of all plantations in the country (SFA 2014). In addition to its importance as a source of fibre, Chinese fir plantations also play an important role in water and soil conservation, and in climate regulation through their function as carbon sinks (Tian et al. 2002; Wang et al. 2009). Considering their large area of cultivation, and high potential for atmospheric CO2 fixation (Yao et al. 2015), Chinese fir plantations represent a key component of China’s greenhouse gas mitigation strategy.
There is considerable uncertainty surrounding the possible effects of climate change on the long-term health and productivity of Chinese fir plantations throughout China. Deviations from historical temperature and precipitation regimes can influence a wide range of ecological processes associated with forest productivity including decomposition of dead organic matter and nutrient mineralisation rates (Gholz et al. 2000), photosynthetic rates and length of growing season (Boisvenue and Running 2006), and water stress and drought-related mortality (Allen et al. 2010). Moreover, relatively little is known about how climate change may influence the long-term growth and development of Chinese fir plantations in different parts of its range. In general, Chinese fir is adapted to warm and relatively moist climate regimes although it is also considered to be tolerant of periods of moisture stress (Wei et al. 1991). Past studies on the relationship between the growth of Chinese fir and climate factors have primarily focused on the analysis of suitable climate envelopes throughout its distribution (Wei et al. 1991; Guan 1989; Shi 1994; Wu and Hong 1984; Zhang 1995). While such studies are useful, they do not provide adequate information to inform managers how current plantations may respond to shifting climate regimes. Such information is essential to support the development of sustainable and resilient plantation management systems.
Another valuable approach to examining and monitoring the impacts of climate change is through the application of remote-sensing technologies. Satellite imagery and other remotely sensed data have been shown to be effective tools for detecting subtle long-term impacts of climate change on extensive forest areas (e.g. Keenan et al. 2014). Such data, when used in combination with ecosystem models, represent an efficient approach for projecting long-term impacts of climate change and testing model performance.
In this study we employ the process-based, forest management model FORECAST Climate to evaluate the potential impact of alternative climate change scenarios on the long-term growth and development of Chinese fir plantations in Fujian province, China. The FORECAST model (without the climate change component) has been previously applied in subtropical Chinese fir plantations to evaluate the impact of intensive short-rotation management on soil productivity (Bi et al. 2007; Xin et al. 2011). The capability of FORECAST Climate to project seasonal patterns of productivity related to variation in temperature and moisture availability was assessed using 11 years of 8-day composite MODIS remote sensing data. The broader evaluation includes an assessment of the impacts of climate change on key ecosystem processes regulating growth response.
Study area description
Soils within the study area are largely classified as “red earth” under the Chinese classification system (equivalent to Ultisol under USDA soil taxonomy). Soils in this region tend to be acidic, with depths greater than 1 m. The texture is mostly loam and clay, and they tend to be rich in organic matter.
The FORECAST Climate model (Seely et al. 2015) was developed as an extension of the hybrid forest growth model FORECAST (Kimmins et al. 1999) created through the dynamic linkage of FORECAST with the stand-level hydrology model ForWaDy (Seely et al. 1997). The linked model is capable of representing the impact of climate and climate change on forest growth dynamics. Specifically, it includes detailed representations of the relationships between temperature and water availability on growth rates and as well as the effect of soil temperature and moisture contents on decomposition and nutrient cycling. The model also includes a function to represent mortality associated with severe drought events. The following sections include descriptions of the two underlying models and their linkage to form FORECAST Climate.
The FORECAST model
The Forestry and Environmental Change Assessment Tool (FORECAST) is an ecosystem-based, stand-level growth and forest ecosystem management simulator. The model was designed to accommodate a wide variety of harvesting and silvicultural systems in order to compare and contrast their effect upon forest productivity, stand dynamics and a series of biophysical indicators of non-timber values. FORECAST employs a hybrid approach whereby local growth and yield data are used to derive estimates of the rates of key ecosystem processes related to the productivity and resource requirements of selected species. This information is combined with data describing rates of decomposition, nutrient cycling, light competition, and other ecosystem properties to simulate forest growth under changing management conditions.
Decomposition and dead organic matter dynamics are simulated using a method in which specific biomass components are transferred, at the time of litterfall, to one of a series of independent litter types. Decomposition rates used for the main litter types represented in the model are based on the results of extensive field incubation experiments (Camiréet et al. 1991; Prescott et al. 2000; Trofymow et al. 2002). Residual litter mass and associated nutrient content is transferred to active and passive humus pools at the end of the litter decomposition period (when mass remaining is approximately 15 to 20% of original litter mass). Mean residence times for active and passive humus types are typically in the range of 50 and 600 years, respectively. In FORECAST Climate, these decomposition rates are modified through the use of annual indices of temperature and moisture.
FORECAST has been widely used in Canada, Scotland, Norway, China and other countries in the world. It has been applied in variety of forest ecosystems including lodgepole pine forest (Wei et al. 2000; Wei et al. 2003), mixed aspen and white spruce forest (Seely et al. 2002; Welham et al. 2002), Scots pine forest (Blanco et al. 2006), coastal Douglas-fir forest (Blanco et al. 2007; Morris et al. 1997), Korean larch (Sun et al. 2012), and Chinese fir plantations (Bi et al. 2007; Xin et al. 2011). The model has been used in a variety of applications and evaluated against field data for growth, yield, ecophysiological and soil variables. A detailed description of FORECAST is provided in Kimmins et al. (1999).
The forest water dynamics (ForWaDy) model
The ForWaDy model simulates the hydrologic dynamics of a forest stand on a daily time step for a given set of climatic and vegetation conditions. It has been shown to perform well for predicting the effect of forest management on evapotranspiration (Seely et al. 2006) and temporal patterns in soil moisture content under field conditions (Dordel et al. 2011; Titus et al. 2006). The model represents potential evapotranspiration (PET) using an energy balance approach based on a modified version of the Priestley-Taylor equation (Priestley and Taylor 1972). This equation has shown to be effective in predicting evapotranspiration under a wide variety of forest types and conditions (Rao et al. 2011; Stagnitti et al. 1989; Sumner and Jacobs 2005). Net shortwave solar radiation interception is used to drive the PET calculations. It is calculated for each tree and plant species from the light competition submodel built into FORECAST (Kimmins et al. 1999) and surface albedo. ForWaDy includes a representation of the vertical flow of water through canopy and soil layer compartments. Storage and movement of water in and through each soil layer is regulated by physical properties that dictate moisture holding capacity, permanent wilting point moisture content, and infiltration rate.
CanTDemad, i,d = energy-driven transpiration demand for species i (mm) on day d, as a function of leaf area index (LAI), intercepted short-wave radiation, canopy albedo, and canopy resistance,
CanTActual, i,d = actual tree transpiration for species i (mm) on day d, as a function of CanTDemad, i,d , root occupancy, and available soil moisture.
Linking tree growth with hydrology
In the general version of the FORCAST model, forest productivity is simulated based primarily upon light and nutrient availability. FORECAST Climate was designed to incorporate explicit representations of the impact of moisture availability and temperature on forest growth processes to expand the application of the model to address potential climate change. The foundation for the expanded model was established through the creation of a dynamic linkage between the detailed representation of forest biomass growth and structure in FORECAST with the hydrological processes represented in ForWaDy (Seely et al. 2015).
Accounting for climate impacts on ecosystem processes
The impact of climate on tree growth and decomposition processes in FORECAST Climate is focused primarily on their relationship to temperature and water availability. These relationships are represented using curvilinear response functions, simulated on a daily time step and summarised annually. The temperature growth response functions are designed to encapsulate the physiological growth processes governing the response of trees and minor vegetation growth to mean daily temperature. The relative effect of temperature as a limiting factor on tree growth is captured annually through the sum of daily values. The positive effect of a lengthening growing season, for example, may be captured with this approach. The effect of moisture availability on plant growth rates is calculated using daily TDI values, which represent the degree that a given tree species is able to meet its energy-driven transpiration demands. As TDI increases, plants tend to close stomata to conserve water and there is an associated reduction in photosynthetic production (McDowell et al. 2008). The model also includes a representation of the effect of increasing atmospheric CO2 concentration on water use efficiency (Seely et al. 2015).
T Growth,d = The temperature growth index (range 0–1; dimensionless) on day d.
S Growth,d = The water stress growth index (range 0–1; dimensionless) on day d.
T Decomp,d = The temperature decomposition index (range 0–1; dimensionless) on day d.
M Decomp,d = The moisture decomposition index (range 0–1; dimensionless) on day d.
FORECAST climate model calibration
The calibration of FORECAST Climate includes three steps: (1) the calibration of the FORECAST model, (2) the parameterisation of the ForWaDy model and (3) the calibration of the climate response functions in FORECAST Climate. The calibration of the base FORECAST model for Chinese fir in the Fujian region is described in Bi et al. (2007) and Xin et al. (2011) and the main calibration parameters employed in the model are provided in Additional file 1. The calibration of the climate response functions was verified using remotely sensed measures of NPP from the study area.
Parameterisation of the ForWaDy model
Parameter values in the ForWaDy submodel relevant for the simulation of plant available water, transpiration and water stress on a mesic site
Soil texture class
Coarse fragment (%)
Mineral soil depth (cm)
Field capacity moisture content (θ)
Soil water extraction and transpiration
Permanent wilting pointd (%)
Maximum root depthe (cm)
Calibration of climate response functions
Growth response functions
Climate impacts on decomposition
The decomposition of litter and soil organic matter in FORECAST is represented by grouping litter, created through the death of specific biomass components, into different litter types with defined mass loss rates based on litter quality and field studies (Kimmins et al. 1999). In FORECAST Climate, these base litter decomposition rates and their associated nutrient mineralisation rates are adjusted based on mean air temperature and moisture content. The climate-influenced decomposition functions for the study area are shown in Fig. 2c, d. The shape of the temperature-decomposition response curve was based upon a Q10 relationship of 2 (Zhou et al. 2008).
Drought-related mortality rate
Reference climate data
Calculation of climate normals
FORECAST Climate simulates the impact of climate change on growth and decomposition rates using climate normals calculated from reference climate data (Kimmins et al. 1999). Mean values for climate growth and decomposition response indices (GRINormal, and DRINormal) were calculated by running the model in climate calibration mode using the 30 years of daily reference climate data to drive the model without climate feedback on growth. The normal climate indices are subsequently stored for use in climate simulation runs where they are compared against simulated future climate indices to determine relative changes in baseline growth and decomposition rates.
Application of the FORECAST climate model
Model validation using MODIS data
Measurements of net photosynthesis from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA satellites, Terra and Aqua (Zhao et al. 2006), were used to validate the parameterised model. MODIS data were selected for evaluation as previous studies have demonstrated that MODIS estimates of GPP provide a good approximation for ground-based measures of productivity derived from eddy flux measurements in Chinese fir forests located in neighbouring Jiangxi Province (Wang et al. 2014). The data were collected during an 11-year period (2000–2010) with 8-day composite time steps. The MODIS product (MOD 17A2) provides an estimate of GPP and net photosynthesis (PSNnet) based upon direct measures of the absorption of photosynthetically active radiation (PAR) (Zhao et al. 2005).
The MODIS data, available at (http://modis.gsfc.nasa.gov/data/), are formatted as a HDF EOS (Hierarchical Data Format – Earth Observing System) tile with a 1 km × 1 km grid in a sinusoidal projection. A total of 18 tiles with predominant Chinese fir cover were identified within the Shunchang study region (Fig. 1). A relative PSNnet value was calculated for each 8-day period for each tile and combined to produce a study-area average. Relative 8-day composite values were calculated using annual maxima for each tile. The relative values help to isolate the effects of climate on PSNnet as compared to absolute values, which are subject to differences in forest cover and other factors among tiles. Quality control data, included as part of the MOD 17A2 product (Zhao et al. 2005), were used to exclude periods for which there was excessive cloud cover or other error factors.
Daily climate data from the Fujian climate station (2000–2010) were used to drive the validation simulation of the study area with FORECAST Climate. An initial condition of a medium site quality Chinese fir plantation age 20 was established as a starting condition for the model validation exercise. For the purposes of comparison, daily output of the modelled climate growth response index (GRI d ) was averaged for the equivalent 8-day periods used with the MODIS data.
Development of climate change scenarios
Establishment of initial conditions
Prior to conducting a simulation run, it is necessary to establish initial soil conditions that are representative of past management activities. This is achieved by running the model in setup mode to generate an ECOSTATE file that contains values for state variables describing amounts of soil humus, decomposing litter and soil nutrient capital. We created an initial ECOSTATE file to represent a medium site (where top height = 13 m at age 20) within the study area following steps described in Bi et al. (2007).
Simulation of climate change impacts
Following the establishment of an initial ECOSTATE file, the model was used to simulate the long-term impact of climate change on the growth of Chinese fir. The four climate change scenarios described above (HG4.5, HG8.5, CNRM4.5 and CNRM8.5) and a reference climate scenario were used to drive climate simulation runs for three consecutive 30-year rotations starting in 2013. A total of five simulations were conducted.
A two-part sensitivity analysis was conducted to evaluate the sensitivity of the model response to changes in atmospheric CO2 concentrations and to potential changes in growing-season rainfall patterns. The impact of changing CO2 concentrations was assessed by running the climate change scenarios described in the “Development of climate change scenarios” section without the corresponding changes in atmospheric CO2 concentrations (atmospheric CO2 was held at 396 ppm). To evaluate the impact of changes in growing season rainfall patterns, a new set of climate change scenarios were generated in which the frequency of growing season (Mar–Oct) precipitation events were reduced by 35% while maintaining the total monthly rainfall amounts. The scenarios were created using the Statistical Downscaling Model (SDSM v5.2) developed by Wilby and Dawson (2013). These were intended to represent potential increases in rainfall intensity associated with climate warming.
Model evaluation against MODIS data
Effects of climate change on tree growth
The projected impact of the different climate change scenarios on the annual water stress index is illustrated in Fig. 7c. Water stress varies substantially year-to-year in all climate scenarios as a result of interannual variability inherent in the reference data. Scenarios based upon the CNRM-CM5 model show the highest water stress while those based upon the HadGEM2 model tend to be slightly lower than the reference climate. These results are consistent with projected rainfall trends shown in Fig. 5. Consecutive years of elevated water stress were rare in all scenarios (Fig. 7c) and, accordingly, the model projected low levels of drought-related mortality.
Effects of climate change on daily growth response index
Effects of climate change on decomposition rates and nutrient cycling
Results of the sensitivity analysis
Results from the sensitivity analysis described above show that the calculation of water stress in the model is sensitive to both changes in atmospheric CO2 concentration and the frequency and intensity of growing season precipitation events. Removing the effect of CO2 on water use efficiency (by holding CO2 constant) led to average increase of water stress of 22% above the levels observed for the original scenarios. Similarly, decreases in the frequency of growing season precipitation events led to an average increase of 24% in the mean annual water stress index (TDI) relative to the original scenarios. However, despite the increase in water stress, biomass production at rotation only declined slightly (1 to 2%) in the sensitivity analysis runs. The impact of the increased water stress was small because although it increased, water stress was still low enough that it had only a minimal impact on growth and mortality. Detailed results from the sensitivity analysis are provided in Additional file 1.
Projections of future climate regimes
The projection of future climate regimes is invariably a source of uncertainty in modelling studies of climate change impacts on forest health and productivity. The approach taken here was to provide a range of potential patterns of change derived from the combination of two reputable climate models and two possible emission scenarios derived as part of the IPCC AR5 analysis. It should be noted that the prediction of future trends of interannual variability is extremely challenging considering most of the output from global climate models only report trends as changes in annual and monthly means and often only for particular time slices. The direct downscaling approach applied here is based upon the assumption that future patterns of interannual variability will reflect past observations. However, there is mounting evidence that this may not be the case (Wilby and Dawson 2013), particularly with respect to precipitation patterns. The sensitivity analysis conducted herein provided some insight as to potential impact changes in the frequency of and intensity of growing season precipitation events but more work should be done in this area.
Effects of climate change on Chinese fir productivity
Model results suggest that Chinese fir plantations developed in the subtropical climate of Fujian province will show modest increases in productivity (6.1 to 12.1%) over the next 30 to 60 years due primarily to a lengthening of the growing season. These results are consistent with a recent study by Wang et al. (2014), who observed that productivity of Chinese fir in the Fujian region increased during a 10-year warming trend from 2000 to 2010. They suggested this pattern was linked to a prolonging of the photosynthetically active period. This conclusion is supported by a spatial-temporal climate analysis conducted by Liu et al. (2009), who reported an increase in the growing season in southeastern China of 6.9–8.7 days during the period from 1955 to 2000.
While net annual growth rates are projected to increase, FORECAST Climate predicts variably reduced growth during the dry season as a result of increased water stress and greater-than-optimal daytime temperatures depending on the climate model selected (see Fig. 5). Predicted elevations in peak summer temperatures will exceed optimal levels causing reductions in growth during these periods, but the net positive impacts of increased growing season length were greater. Wang (2006) found that the optimal temperature and humidity required during the main period of the Chinese fir growing season were 18–20 °C and 80%, respectively. In addition, he observed that growth of Chinese fir was limited when daily temperature exceeded 28 °C and/or monthly precipitation was less than 50 mm.
Our modelling results suggest that warming associated with climate change will also benefit productivity by increasing rates of litter decomposition and associated nutrient cycling. These results are consistent with those reported by Moore et al. (1999), in a study of litter decomposition rates in Canadian forests in which litter decomposition rates were projected to increase by 4 to 7% above contemporary levels because of warming trends expected with climate change. Evidence of the positive influence of climate enhanced decomposition and nutrient mineralisation rates on productivity has been observed by several authors. For example, Kirwan and Blum (2011) found that organic matter decomposition rates increased by about 20% per degree of warming in salt marsh experiments, which led to the enhanced productivity of marsh vegetation. Further, Melillo et al. (2011), in a 7-year soil warming study, pointed out that the plant carbon storage increased with the support of the additional inorganic nitrogen released by the warming-enhanced decay of soil organic matter.
Projected impacts on mortality rates
While the model predicts an increase in the negative impact of dry season moisture stress on growth rates in some climate change scenarios, simulated stress levels were not high enough in consecutive years to cause increases in drought-related mortality. Several studies have observed increases in drought-related tree mortality attributed to prolonged periods of moisture stress associated with climate change (Breshears et al. 2005; Gitlin et al. 2006; van Mantgem et al. 2009; Allen et al. 2010; O'Grady et al. 2013). The reader should be reminded that the direct downscaling approach employed in the analysis presented here limits the interannual variability of future precipitation as it forces the data to follow historic patterns of variation. If interannual variation were to increase as a result of climate change, it could lead to a significant increase in drought-related mortality, which would, in turn, have a negative impact on estimates of future productivity in Chinese fir plantations. Further research is required to assess the potential of such events.
It also is important to note that FORECAST Climate does not account for the potential impacts of climate change on the activity of other disturbance agents. There is evidence that both biotic (insects and pathogens) and abiotic (fire and wind) disturbance are influenced by climate change (van Mantgem and Stephenson 2007; Mutch and Parsons 1998). O’Grady et al. (2013) pointed out that trees suffering from soil-water deficits are more susceptible to biotic disturbance agents due to significant changes in the energy and carbon balance of the plants. Thus, the predicted increases in Chinese fir plantations associated with climate change may not be realised if changes in climate regime lead to increased mortality from biotic and abiotic disturbance agents.
The process-based, stand-level model FORECAST Climate was applied to examine the potential impacts of alternative climate change scenarios on the long-term growth and development of Chinese fir plantations in Fujian province. Climate change is projected to have a modest positive impact on plantation productivity due to a gradual lengthening of the growing season and an associated increase in nutrient cycling rates. While the model predicted an increase in dry-season water stress under climate change, it did not project an increase in drought-related mortality. Moreover, the model predicted that the increases in atmospheric CO2 concentrations associated with climate change would lead to increases in WUE thereby reducing the development of water stress. However, additional research should be conducted to examine the potential sensitivity of Chinese fir forests in this region to significant changes in the interannual variability in precipitation patterns associated with climate change. It is also important to note that the predicted increases in Chinese fir plantations associated with climate change may not be realised if changes in climate regime also lead to increased mortality from biotic and abiotic disturbance agents. The work presented here demonstrates the value of employing a process-based model with relatively minor calibration requirements to evaluate the potential impacts of climate change on key ecosystem processes and long-term productivity.
This research was conducted as part of the APF Net-funded project “Adaptation of Asia-Pacific Forests to Climate Change” (project # APFNet/2010/PPF/001) founded by the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation.
BS, GW and DZ conceived and designed the study; HK and BS analysed the data; BS, JI, TW and PC contributed reagents/materials/analysis tools; HK and BS wrote the paper. All authors contributed to preparing the manuscript. All authors read and approved the final manuscript.
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Allen, C. D., Macalady, A. K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., Michel, K., Thomas, R., Andreas, B., David, D., Hogg, E. H., Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J. H., Allard, G., Running, S. W., Semerci, A., & Cobb, N. (2010). A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management, 259, 660–684.View ArticleGoogle Scholar
- Bi, J., Blanco, J. A., Seely, B., Kimmins, J. P., Ding, Y., & Welham, C. (2007). Yield decline in Chinese-fir plantations: a simulation investigation with implications for model complexity. Canadian Journal of Forest Research, 37, 1615–1630.View ArticleGoogle Scholar
- Blanco, J. A., Olarieta, J. R., & Kimmins, J. P. (2006). Assessment of long-term sustainability of forest management in Scots pine forests in the Pyrenees: An ecosystem-level simulation approach. In E. Carrera, J. J. de Felipe, B. Sureda, & N. Tollin (Eds.). International Conference on Sustainability Measurement and Modelling. Barcelona: Centre Internacional de Mètodes Numèrics a l'Enginyeria (CIMNE).Google Scholar
- Blanco, J. A., Seely, B., Welham, C., Kimmins, J. P., & Seebacher, T. M. (2007). Testing the performance of a forest ecosystem model (FORECAST) against 29 years of field data in a Pseudotsuga menziesii plantation. Canadian Journal of Forest Research, 37, 1808–1820.View ArticleGoogle Scholar
- Boisvenue, C., & Running, S. W. (2006). Impacts of climate change on natural forest productivity - evidence since the middle of the 20th century. Global Change Biology, 12, 862–882.View ArticleGoogle Scholar
- Breshears, D. D., Cobb, N. S., Rich, P. M., Price, K. P., Allen, C. D., Balice, R. G., Romme, W. H., Kastens, J. H., Floyd, M. L., Belnap, J., Anderson, J. J., Myers, O. B., & Meyer, C. W. (2005). Regional vegetation die-off in response to global-change-type drought. PNAS, 102, 15144–15148.View ArticlePubMedPubMed CentralGoogle Scholar
- Camiré, C., Côté, B., & Brulotte, S. (1991). Decomposistion of roots of black alder and hybrid poplar in short-rotation plantings: Nitrogen and lignin control. Plant and Soil, 138, 123–132.View ArticleGoogle Scholar
- Dordel, J., Seely, B., & Simard, S. W. (2011). Relationships between simulated water stress and mortality and growth rates in underplanted Toona ciliata Roem. in subtropical Argentinean plantations. Ecological Modelling, 222, 3226–3235.View ArticleGoogle Scholar
- Gholz, H. L., Wedin, D. A., Smitherman, S. M., Harmon, M. E., & Parton, W. J. (2000). Long-term dynamics of pine and hardwood litter in contrasting enviroments: toward a global model of decomposition. Global Change Biology, 6, 751–765.View ArticleGoogle Scholar
- Gitlin, A. R., Sthultz, C. M., Bowker, M. A., Stumpf, S., Paxton, K. L., Kennedy, K., Muñoz, A., Bailey, J. K., & Whitham, T. G. (2006). Mortality gradients within and among dominant plant populations as barometers of ecosystem change during extreme drought. Conservation Biology, 20, 1477–1486.View ArticlePubMedGoogle Scholar
- Guan, Y. (1989). A Study of the climate conditions for Chinese fir fast-growing plantations. Chinese Journal of Agrometeorology, 10, 37–41.Google Scholar
- Hogg, E. H., Brandt, J. P., & Michaelian, M. (2008). Impacts of a regional drought on the productivity, dieback, and biomass of western Canadian aspen forests. Canadian Journal of Forest Research, 38, 1373–1384.View ArticleGoogle Scholar
- Huang, J. (2013). Discussion of Chinese fir planting management techniques. Agriculture and Technology, 33, 77–78.Google Scholar
- IPCC. (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press.Google Scholar
- Keenan, T. F., Gray, J., Friedl, M. A., Toomey, M., Bohrer, G., Hollinger, D. Y., Munger, J. W., O’Keefe, J., Schmid, H. P., Wing, I. S., Yang, B., & Richardson, A. D. (2014). Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nature Climate Change, 4, 598–604.View ArticleGoogle Scholar
- Kimmins, J. P., Mailly, D., & Seely, B. (1999). Modelling forest ecosystem net primary production: the hybrid simulation approach used in FORECAST. Ecological Modelling, 122, 195–224.View ArticleGoogle Scholar
- Kirwan, M. L., & Blum, L. K. (2011). Enhanced decomposition offsets enhanced productivity and soil carbon accumulation in coastal wetlands responding to climate change. Biogeosciences, 8, 987–993.View ArticleGoogle Scholar
- Li, J., Chen, X., Zhu, N., Tan, Y., Yan, Y., Gao, Z., & Zhang, Y. (2013). Study on selection of main fuel-wood forest tree species in South China and combustion characteristics of wood pellets of fuel-wood trees. Journal of Central South University of Forest & Technology, 33, 126–129.Google Scholar
- Liu, B., Henderson, M., Zhang, Y., & Xu, M. (2009). Spatiotemporal change in China’s climatic growing season: 1955–2000. Climatic Change, 99, 93–118.View ArticleGoogle Scholar
- McDowell, N., Pockman, W. T., Allen, C. D., Breshears, D. D., Cobb, N., Kolb, T., Plaut, J., Sperry, J., West, A., Williams, D. G., & Yepez, E. A. (2008). Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytologist, 178, 719–739.View ArticlePubMedGoogle Scholar
- Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., & Vuuren, D. P. P. (2011). The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109, 213–241.View ArticleGoogle Scholar
- Melillo, J. M., Butler, S., Johnson, J., Mohan, J., Steudler, P., Lux, H., Burrows, E., Bowles, F., Smith, R., Scott, L., Vario, C., Hill, T., Burton, A., Zhou, Y., & Tang, J. (2011). Soil warming, carbon-nitrogen interctions, and forest carbon budgets. PNAS, 108, 9508–9512.View ArticlePubMedPubMed CentralGoogle Scholar
- Moore, T. R., Trofymow, J. A., Taylor, B., Prescott, C., Camiré, C., Duschene, L., Fyles, J., Kozak, L., Kranabetter, M., Morrison, I., Siltanen, M., Smith, S., Titus, B., Visser, S., Wein, R., & Zoltai, S. (1999). Litter decomposition rates in Canadian forests. Global Change Biology, 5, 75–82.View ArticleGoogle Scholar
- Morris, D. M., Kimmins, J. P., & Duckert, D. R. (1997). The use of soil organic matter as a criterion of the relative sustainability of forest management alternatives: a modelling approach using FORCAST. Forest Ecology and Management, 94, 61–78.View ArticleGoogle Scholar
- Mutch, L. S., & Parsons, D. J. (1998). Mixed conifer forest mortality and establishment before and after prescribed fire in Sequoia National Park, California. Forest Science, 44, 341–355.Google Scholar
- Nie, S., Lin, J., Bian, L., Chen, L., & Lin, S. (1998). Evaluation of pulping properties of Chinese fir wood from plantation at different ages. Journal of Fujian College of Forestry, 18, 87–91.Google Scholar
- O'Grady, A. P., Mitchell, P. J. M., Pinkard, E. A., & Tissue, D. T. (2013). Thirsty roots and hungry leaves: unravelling the roles of carbon and water dynamics in tree mortality. New Phytologist, 200, 294–297.View ArticlePubMedGoogle Scholar
- Peters, G. P., Andrew, R. M., Boden, T., Canadell, J. G., Ciais, P., Le Quéré, C., Marland, G., Raupach, M. R., & Wilson, C. (2012). The challenge to keep global warming below 2 °C. Nature Climate Change, 3, 4–6.View ArticleGoogle Scholar
- Prescott, C. E., Blevins, L. L., & Staley, C. L. (2000). Effects of clear-cutting on decomposition rates of litter and forest floor in forests of British Columbia. Canadian Journal of Forest Research, 30, 1751–1757.View ArticleGoogle Scholar
- Priestley, C. H. B., & Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100, 81–92.View ArticleGoogle Scholar
- Rao, L. Y., Sun, G., Ford, C. R., & Vose, J. M. (2011). Modeling potential evapotranspiration of two forested watersheds in the Southern Appalachians. TASABE, 54, 2067–2078.Google Scholar
- Seely, B., Arp, P., & Kimmins, J. P. (1997). A forest hydrology submodel for simulating the effect of management and climate change on stand water stress. In A. Amaro & M. Tomé (Eds.), Empirical and Process-based Models for Forest, Tree and Stand Growth Simulation (pp. 463–477). Lisboa, Oeiras, Portugal: Edições Salamandra.Google Scholar
- Seely, B., Welham, C., & Elshorbagy, A. (2006). A comparison and needs assessment of hydrological models used to simulate water balance in oil sands reclamation covers. Final Report for the Cumulative Environmental Management Association (CEMA). Belcarra: FORRx Consulting Inc. from http://library.cemaonline.ca/ckan/dataset/2005-0030/resource/2ec51784-d5a2-464c-b9e3-c3aeabc781c2. Accessed 25 Sept 2017.
- Seely, B., Welham, C., & Kimmins, J. P. (2002). Carbon sequestration in a boreal forest ecosystem: results from the ecosystem simulation model, FORECAST. Forest Ecology and Management, 169, 123–135.View ArticleGoogle Scholar
- Seely, B., Welham, C., & Scoullar, K. (2015). Application of a hybrid forest growth model to evaluate climate change impacts on productivity, nutrient cycling and mortality in a montane forest ecosystem. PloS One, 10(8), e0135034.View ArticlePubMedPubMed CentralGoogle Scholar
- Shi, B. (1994). Analysis of the suitable climatic conditons for Chinese fir using fuzzy comprehensive evaluation method. Central South Forest Inventoryand Planning, 1, 8–10.Google Scholar
- Stagnitti, F., Parlange, J. Y., & Rose, C. W. (1989). Hydrology of a small wet catchment. Hydrological Processes, 3, 137–150.View ArticleGoogle Scholar
- State Forestry Administration. (2014). General situation of forest resources in China-the 8th National Forest Inventory. Beijing.Google Scholar
- Sumner, D. M., & Jacobs, J. M. (2005). Utility of Penman–Monteith, Priestley–Taylor, reference evapotranspiration, and pan evaporation methods to estimate pasture evapotranspiration. Journal of Hydrology, 308, 81–104.View ArticleGoogle Scholar
- Sun, Z., Bi, Y., Mo, C., & Cai, T. (2012). Using an ecosystem simulation model FORECAST to evaluate the effects of forest management strategies on long-term productivity of Korean larch plantations. Journal of Beijing Forestry University, 34, 1–6.Google Scholar
- Tian, D., Kang, W., & Wen, S. (2002). Chinese fir plantation ecosystem ecology. Beijing: Science Press.Google Scholar
- Titus, B. D., Prescott, C. E., Maynard, D. G., Mitchell, A. K., Bradley, R. L., Feller, M. C., Beese, W. J. B., Seely, B. A., Benton, R. A., Senyk, J. P., Hawkins, B. J., & Koppenaal, R. (2006). Post-harvest nitrogen cycling in clearcut and alternative silvicultural systems in a montane forest in coastal British Columbia. The Forestry Chronicle, 82, 844–859.View ArticleGoogle Scholar
- Trofymow, J. A., Moore, T. R., Titus, B., Prescott, C., Morrison, I., Siltanen, M., Smith, S., Fyles, J., Wein, R., Camiré, C., Duschene, L., Kozak, L., Kranabetter, M., & Visser, S. (2002). Rates of litter decomposition over 6 years in Canadian forests: influence of litter quality and climate. Canadian Journal of Forest Research, 32, 789–804.View ArticleGoogle Scholar
- van Mantgem, P. J., & Stephenson, N. L. (2007). Apparent climatically induced increase of tree mortality rates in a temperate forest. Ecology Letters, 10, 909–916.View ArticlePubMedGoogle Scholar
- van Mantgem, P. J., Stephenson, N. L., Byne, J. C., Daniels, L. D., Franklin, J. F., Fulé, P. Z., Harmon, M. E., Larson, A. J., Smith, J. M., Taylor, A. H., & Veblen, T. T. (2009). Widespread increase of tree mortality rates in the western United States. Science, 323, 521–524.View ArticlePubMedGoogle Scholar
- Wang, B., Ma, X., Guo, H., Wang, Y., & Leng, L. (2009). Evaluation of the Chinese fir forest ecosystem services value. Scientia Silvae Sinicae, 45, 124–130.Google Scholar
- Wang, C. (2006). Analysis on the relationships between the phenology and growth process of Chinese fir and climate factors in Henan Province. XIANDAINONGYE KEJI, 9(5–6), 16.Google Scholar
- Wang, L., Zhang, Y., Berninger, F., & Duan, B. (2014). Net primary production of Chinese fir plantation ecosystems and its relationship to climate. Biogeosciences, 11, 5595–5606.View ArticleGoogle Scholar
- Wei, G., Deng, Y., Xiao, T., Zhong, L., Liu, J., & Tong, S. (1991). Analysis on the annual growth pulse of young Chinese fir stand and its relevant climate factors. Journal of Sichuan Forestry Science and Technology, 12, 15–22.Google Scholar
- Wei, X., Kimmins, J. P., & Zhou, G. (2003). Disturbances and the sustainability of long-term site productivity in lodgepole pine forests in the central interior of British Columbia—an ecosystem modeling approach. Ecological Modelling, 164, 239–256.View ArticleGoogle Scholar
- Wei, X., Liu, W., Waterhouse, J., & Armleder, M. (2000). Simulations on impacts of different management strategies on long-term site productivity in lodgepole pine forests of the central interior of British Columbia. Forest Ecology and Management, 133, 217–229.View ArticleGoogle Scholar
- Welham, C., Seely, B., & Kimmins, J. P. (2002). The utility of the two-pass harvesting system: an analysis using the ecosystem simulation model FORECAST. Canadian Journal of Forest Research, 32, 1071–1079.View ArticleGoogle Scholar
- Wilby, R. L., & Dawson, C. W. (2013). The statistical downscaling model: insights from one decade of application. International Journal of Climatology, 33, 1707–1719.View ArticleGoogle Scholar
- Wu, J., & Hong, W. (1984). A study of the climatology for Chinese fir. Meteorological Science and Technology, 1, 74–75.Google Scholar
- Wu, Z. (1984). Chinese-fir. Beijing: China Forestry Publishing House.Google Scholar
- Xin, Z., Jiang, H., Jie, C., Wei, X., Juan, B., & Zhou, G. (2011). Simulated nitrogen dynamics for a Cunninghamia lanceolata plantation with selected rotation ages. Journal of Zhejiang A&F University, 28, 855–862.Google Scholar
- Yao, L., Kang, W., Zhao, Z., & He, J. (2015). Carbon fixed characteristics of plant of Chinese fir (Cunninghamia lanceolata) plantation at different growth stages in Huitong. Acta Ecologica Sinica, 35, 1–16.Google Scholar
- Yu, X. (1997). Silviculture of Chinese-fir. Fuzhou: Science and Technology Press of Fujian.Google Scholar
- Zhang, X., Duan, A., & Zhang, J. (2013). Tree biomass estimation of Chinese fir based on Bayesian method. PloS One, 8, 1–7.Google Scholar
- Zhang, X., & Xu, D. (2002). Effects of temperature on the photosynthetic physio-ecology of 18-year-old Chinese fir. Scientia Silvae Sinicae, 38, 27–33.Google Scholar
- Zhang, Y. (1995). A study of the effects of climatic fluctuation on Chinese fir and bamboo ecological environment in subtropical regions in China. Quarterly Journal of Applied Meteorology, 6, 75–82.Google Scholar
- Zhao, M., Heinsch, F. A., Nemani, R. R., & Running, S. W. (2005). Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95, 164–176.View ArticleGoogle Scholar
- Zhao, M., Running, S. W., & Nemani, R. R. (2006). Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses. Journal of Geophysical Research, 111, 2005–2012.Google Scholar
- Zhou, G., Guan, L., Wei, X., Tang, X., Liu, S., Liu, J., Zhang, D., & Yan, J. (2008). Factors influencing leaf litter decomposition: an intersite decomposition experiment across China. Plant and Soil, 311, 61–72.View ArticleGoogle Scholar