Toward a general evaluation model for soil respiration (GEMSR)

Soil respiration is an important component of terrestrial carbon budget. Its accurate evaluation is es- sential to the study of terrestrial carbon source/sink. Studies on soil respiration at present mostly focus on the temporal variations and the controlling factors of soil respiration, but its spatial variations and controlling factors draw less attention. Moreover, the evaluation models for soil respiration at present include only the effects of water and heat factors, while the biological and soil factors controlling soil respiration and their interactions with water and heat factors have not been considered yet. These models are not able to accurately evaluate soil respiration in different vegetation/terrestrial ecosystems at different temporal and spatial scales. Thus, a general evaluation model for soil respiration (GEMSR) including the interacting meteorological (water and heat factors), soil nutrient and biological factors is suggested in this paper, and the basic procedure developing GEMSR and the research tasks of soil respiration in the future are also discussed.     

Evaluation of different models to describe egg and pupal development of Xyleborus fornicatus Eichh, （Coleoptera： Scolytidae）, the shot-hole borer of tea in Sri Lanka

Development data of eggs and pupae ofXyleborusfornicatus Eichh. （Coleoptera： Scolytidae）, the shot-hole borer of tea in Sri Lanka, at constant temperatures were used to evaluate a linear and seven nonlinear models for insect development. Model evaluation was based on fit to data （residual sum of squares and coefficient of determination or coefficient of nonlinear regression）, number of measurable parameters, the biological value of the fitted coefficients and accuracy in the estimation of thresholds. Of the nonlinear models, the Lactin model fitted experimental data well and along with the linear model, can be used to describe the temperature-dependent development of this species.     

Effects of biochar addition on dynamics of soil respiration and temperature sensitivity in a Phyllostachys edulis forest北大核心CSCD

Aims: Recent studies have shown that artificial addition of biochar is an effective way to mitigate atmospheric carbon dioxide concentrations. However, it is still unclear how biochar addition influences soil respiration in Phyllostachys edulis forests of subtropical China. Our objectives were to examine the effects of biochar addition on the dynamics of soil respiration, soil temperature, soil moisture, and the cumulative soil carbon emission, and to determine the relationships of soil respiration with soil temperature and moisture. Methods: We conducted a two-year biochar addition experiment in a subtropical P. edulis forest from 2014.05 to 2016.04. The study site is located in the Miaoshanwu Nature Reserve in Fuyang district of Hangzhou, Zhejiang Province, in southern China. The biochar addition treatments included: control (CK, no biochar addition), low rate of biochar addition (LB, 5 t·hm-2), medium rate of biochar addition (MB, 10 t·hm-2), and high rate of biochar addition (HB, 20 t·hm-2). Soil respiration was measured by using a LI-8100 soil CO2 efflux system. Important findings: Soil respiration was significantly reduced by biochar addition, and exhibited an apparent seasonal pattern, with the maximum occurring in June or July (except LB in one of the replicated stand) and the minimum in January or February. There were significant differences in soil respiration between the CK and the treatments. Annual mean soil respiration rate in the CK, LB, MB and HB were 3.32, 2.66, 3.04 and 3.24 μmol·m-2·s-1, respectively. Compared with CK, soil respiration rate was 2.33%-54.72% lower in the LB, 1.28%-44.21% lower in the MB, and 0.09%-39.22% lower in the HB. The soil moisture content was increased by 0.97%-75.58% in LB, 0.87%-48.18% in MB, and 0.68%-74.73% in HB, respectively, compared with CK. Soil respiration exhibited a significant exponential relationship with soil temperature and a significant linear relationship with combination of soil temperature and moisture at the depth of 5 cm; no significant relationship was found between soil respiration and soil moisture alone. The temperature sensitivity (Q10) value was reduced in LB and HB. Annual accumulative soil carbon emission in the LB, MB and HB was reduced by 7.98%-35.09%, 1.48%-20.63%, and -4.71%-7.68%, respectively. Biochar addition significantly reduced soil carbon emission and soil temperature sensitivity, highlighting its role in mitigating climate change.     

CO2 efflux under different grazing managements on subalpine meadows of Shangri-La, Northwest Yunnan Province, China

Soil carbon stored on the Tibetan Plateau appears to be stable under current temperature, but it may be sensitive to global warming. In addition, different grazing systems may alter carbon emission from subalpine meadow ecosystems in this region. Using a chamber-closed dynamic technique, we measured ecosystem respiration (ER) and soil respiration (SR) rates with an infrared gas analyzer on a perennial grazing meadow (PM) and a seasonal grazing meadow (SM) of Shangri-La in the Hengduan Mountain area. Both PM and SM showed strong unimodal seasonal variations, with the highest rates in July and the lowest in January. Significant diurnal variations in respiration were also observed on PM, affected mainly by air and soil temperatures, with the highest rates at 14:00 and the lowest before dawn. Both ER and SR rates were higher on PM than on SM from June to October, suggesting that the higher grazing pressure on PM increased respiration rates on subalpine meadows. The exponential model F = ae^{bT<,/sup> of soil temperature (T) explained the variation in respiration better than the model of soil moisture (W) (R2 = 0.50–0.78, P < 0.0001), while the multiple model F = aebT<,/sup>Wc gave better simulations than did single-factor models (R2 = 0.56–0.89, P < 0.0001). Soil respiration was the major component of ER, accounting for 63.0%–92.7% and 47.5%–96.4% of ER on PM and SM, respectively. Aboveground plant respiration varied with grass growth. During the peak growing season, total ecosystem respiration may be dominated by this above-ground component. Long-term (annual) Q₁₀ values were about twice as large as short-term (one day) Q₁₀. Q₁₀ at different time scales may be controlled by different ecological processes. The SM had a lower long-term Q₁₀ than did the PM, suggesting that under increased temperature, soil carbon may be more stable with reduced grazing pressure.}     

Longevity of animals under reactive oxygen species stress and disease susceptibility due to global warming

The world is projected to experience an approximate doubling of atmospheric CO_2 concentration in the next decades. Rise in atmospheric CO_2 level as one of the most important reasons is expected to contribute to raise the mean global temperature 1.4 ℃-5.8 ℃ by that time. A survey from 128 countries speculates that global warming is primarily due to increase in atmospheric CO_2 level that is produced mainly by anthropogenic activities. Exposure of animals to high environmental temperatures is mostly accompanied by unwanted acceleration of certain biochemical pathways in their cells. One of such examples is augmentation in generation of reactive oxygen species(ROS) and subsequent increase in oxidation of lipids, proteins and nucleic acids by ROS. Increase in oxidation of biomolecules leads to a state called as oxidative stress(OS). Finally, the increase in OS condition induces abnormality in physiology of animals under elevated temperature. Exposure of animals to rise in habitat temperature is found to boost the metabolism of animals and a very strong and positive correlation exists between metabolism and levels of ROS and OS. Continuous induction of OS is negatively correlated with survivability and longevity and positively correlated with ageing in animals. Thus, it can be predicted that continuous exposure of animals to acute or gradual rise in habitat temperature due to global warming may induce OS, reduced survivability and longevity in animals in general and poikilotherms in particular. A positive correlation between metabolism and temperature in general and altered O_2 consumption at elevated temperature in particular could also increase the risk of experiencing OS in homeotherms. Effects of global warming on longevity of animals through increased risk of protein misfolding and disease susceptibility due to OS as the cause or effects or both also cannot be ignored. Therefore, understanding the physiological impacts of global warming in relation to longevity of animals will become very crucial challenge to biologists of the present millennium.     

Ecological dynamic model of grassland and its practical verification

Based on the physico-biophysical considerations, mathematical analysis and some approximate formulations generally adopted in meteorology and ecology, an ecological dynamic model of grassland is developed. The model consists of three interactive variables, i.e. the bio-mass of living grass, the biomass of wilted grass, and the soil wetness. The major biophysical processes are represented in parameterization formulas, and the model parameters can be determined inversely by using the observational climatological and ecological data. Some major parameters are adjusted by this method to fit the data (although incomplete) in the Inner Mongolia grassland, and other secondary parameters are estimated through sensitivity studies. The model results are well agreed with reality, e.g., (i) the maintenance of grassland requires a minimum amount of annual precipitation (approximately 300 mm); (ii) there is a significant relationship between the annual precipitation and the biomass of living grass; and (iii) the overgrazing will eventually result in desertification. A specific emphasis is put on the shading effect of the wilted grass accumulated on the soil surface. It effectively reduces the soil surface temperature and the evaporation, hence benefits the maintenance of grassland and the reduction of water loss in the soil.     

Climate-influenced boreotropical survival and rampant introgressions explain the thriving of New World grapes in the north temperate zone

The north temperate region was characterized by a warm climate and a rich thermophilic flora before the Eocene, but early diversifications of the temperate biome under global climate change and biome shift remain uncertain. Moreover, it is becoming clear that hybridization/introgression is an important driving force of speciation in plant diversity. Here, we applied analyses from biogeography and phylogenetic networks to account for both introgression and incomplete lineage sorting based on geno...     

Application and comparison of remote sensing GPP models with multi-site data in China北大核心CSCD

Aims Estimation of gross primary productivity (GPP) of vegetation at the global and regional scales is important for understanding the carbon cycle of terrestrial ecosystems. Due to the heterogeneous nature of land surface, measurements at the site level cannot be directly up-scaled to the regional scale. Remote sensing has been widely used as a tool for up-saling GPP by integrating the land surface observations with spatial vegetation patterns. Although there have been many models based on light use efficiency and remote sensing data for simulating terrestrial ecosystem GPP, those models depend much on meteorological data; use of different sources of meteorological datasets often results in divergent outputs, leading to uncertainties in the simulation results. In this study, we examines the feasibility of using two GPP models driven by remote sensing data for estimating regional GPP across different vegetation types. Methods Two GPP models were tested in this study, including the Temperature and Greenness Model (TG) and the Vegetation Index Model (VI), based on remote sensing data and flux data from the China flux network (ChinaFLUX) for different vegatation types for the period 2003-2005. The study sites consist of eight ecological stations located in Xilingol (grassland), Changbaishan (mixed broadleaf-conifer forest), Haibei (shrubland), Yucheng (cropland), Damxung (alpine meadow), Qianyanzhou (evergreen needle-leaved forest), Dinghushan (evergreen broad-leaved forest), and Xishuangbanna (evergreen broad-leaved forest), respectively. Important findings All the remote sensing parameters employed by the TG and VI models had good relationships with the observed GPP, with the values of coefficient of determination, R2, exceeding 0.67 for majority of the study sites. However, the root mean square errors (RMSEs) varied greatly among the study sites: the RMSE of TG ranged from 0.29 to 6.40 g·m-2·d-1, and that of VI ranged from 0.31 to 7.09 g·m-2·d-1, respectively. The photosynthetic conversion coefficients m and a can be up-scaled to a regional scale based on their relationships with the annual average nighttime land surface temperature (LST), with 79% variations in m and 58% of variations in a being explainable in the up-scaling. The correlations between the simulated outputs of both TG and VI and the measured values were mostly high, with the values of correlation coefficient, r, ranging from 0.06 in the TG model and 0.13 in the VI model at the Xishuangbanna site, to 0.94 in the TG model and 0.89 in the VI model at the Haibei site. In general, the TG model performed better than the VI model, especially at sites with high elevation and that are mainly limited by temperature. Both models had potential to be applied at a regional scale in China.     

Impact of adaptive immune response and cellular infection on delayed virus dynamics with multi-stages of infected cells

In this investigation,we propose and analyze a virus dynamics model with multi-stages of infected cells.The model incorporates the effect of both humoral and cell-mediated immune responses.We consider two modes of transmissions,virus-to-cell and cell-to-cell.Multiple intracellular discrete-time delays have been integrated into the model.The incidence rate of infection as well as the generation and removal rates of all compartments are described by general nonlinear functions.Wc derive five threshold parameters which determine the existence of the equilibria of the model under consideration.A set of conditions on the general functions has been established which is sufficient to investigate the global stability of the five equilibria of the model.The global asymptotic stability of all equilibria is proven by utilizing Lyapunov function and LaSalle’s invariance principle.The theoretical results are illustrated by numerical simulations of the model with specific forms of the general functions.     

A numerical study on impact of crop canopy on mesoscale climate

The impact of well watered mesoscale wheat planted on the mesoscale boundary layer structures of midlatitude arid area has been investigated by using a mesoscale biophysical meteorological model. The investigation indicates that mesoscale perturbations in temperature and specific humidity over crop area from the adjacent dry, bare soil, caused by the transpiration from the crop canopy and evaporation from underlying humid soil, result in a horizontal pressure gradient. A mesoscale circulation is forced by the pressure perturbation with a wind speed of about 5 m/s directing from the crop canopy to the bare soil in the lower boundary layer. In the daytime, the boundary layer structure over a complex terrain is determined by the interactions between upslope flow circulations and the circulations mentioned above when wheat crop canopies are located on plain and plateau. The impact of crop canopy scale on this thermally forced mesoscale circulation is also investigated.     

Optimal Formation Reconfiguration Control of Multiple UCAVs Using Improved Particle Swarm Optimization

Optimal formation reconfiguration control of multiple Uninhabited Combat Air Vehicles （UCAVs） is a complicated global optimum problem. Particle Swarm Optimization （PSO） is a population based stochastic optimization technique inspired by social behaviour of bird flocking or fish schooling. PSO can achieve better results in a faster, cheaper way compared with other bio-inspired computational methods, and there are few parameters to adjust in PSO. In this paper, we propose an improved PSO model for solving the optimal formation reconfiguration control problem for multiple UCAVs. Firstly, the Control Parameterization and Time Diseretization （CPTD） method is designed in detail. Then, the mutation strategy and a special mutation-escape operator are adopted in the improved PSO model to make particles explore the search space more efficiently. The proposed strategy can produce a large speed value dynamically according to the variation of the speed, which makes the algorithm explore the local and global minima thoroughly at the same time. Series experimental results demonstrate the feasibility and effectiveness of the proposed method in solving the optimal formation reconfiguration control problem for multiple UCAVs.     

Dynamical behaviors of the generalized hematopoiesis model with discontinuous harvesting terms

This paper is concerned with the generalized hematopoiesis model with discontinuous harvesting terms.Under the framework of Filippov solution,by means of the differential inclusions and the topological degree theory in set-valued analysis,we have established the existence of the bounded positive periodic solutions for the addressed models.After that,based on the nonsmooth analysis theory w让 h Lyapunov-like approach,we employ a novel argument and derive some new criteria on the uniqueness,global exponential stability of the addressed models and convergence of the corresponding autonomous case of the addressed models.Our results extend previous works on hematopoiesis model to the discontinuous harvesting terms and some corresponding results in the literature can be enriched and extended.In addition,typical examples with numerical simulations are given to illustrate the feasibility and validity of obtained results.     

Resource, biological community and soil functional stability dynamics at the soil–litter interface

The interface between decaying plant residues and soil is a focus for soil ecological processes because of resources from the residues diffusing into the soil, and microfauna that proliferate in the adjacent soil. Given that the recovery of soil function following disturbance depends on immigration, colonization and establishment of exotic organisms from adjacent un-disturbed habitats, and the availability of bio-available resources, we hypothesized that the soil–litter interface could contribute to soil functional stability. In laboratory pot trials, soil was separated into two parts by a mesh bag with the inner section amended, or not amended, with rice straw; an outer layer of unamended soil, adjacent to the litter (1.5 cm thick, either heated or not), provided a soil–litter interface. This enabled us to examine the dynamics of dissolved organic carbon (DOC), mineral nitrogen, microbial biomass carbon (MBC), nematode assemblages and functional stability during 35 days incubation. Either 1 mm or 5 μm meshes were used, which allowed nematodes to migrate (SR1) or not (SR5) through the mesh to the soil–litter interface; thus also enabling us to evaluate the role of nematodes in soil functional stability. Higher DOC and MBC but lower mineral nitrogen concentrations were found at the soil–litter interface. Heating increased the availability of soil resources such as mineral nitrogen and DOC, but decreased the MBC and total nematode abundance in the soil. The soil–litter interface was characterized by a higher abundance of nematodes, particularly microbivores, regardless of mesh aperture or disturbance. The difference in nematode abundance between SR1 and SR5 indicated that nematode propagation, due to resource diffusion and nematode migration through the mesh, contributed to the changing numbers of microbivorous nematodes depending on incubation time. The soil functional stability was calculated as a relative change in the functioning of short-term barley decomposition. Soil functional resistance, defined as the instantaneous effect of disturbance on decomposition measured on the first day, was highest in the SR5 treatment. However, soil functional resilience, defined as the recovery of soil function over the whole incubation period (35d), was highest in the SR1 treatment, which is most probably attributed to the functioning of microbivorous nematodes. Our results suggest that small-scale spatial heterogeneity, due to organic residue decomposition, can help maintain soil functions following disturbance.     

urce, biological community and soil functional stability dynamics at the soil–litter interface

The interface between decaying plant residues and soil is a focus for soil ecological processes because of resources from the residues diffusing into the soil, and microfauna that proliferate in the adjacent soil. Given that the recovery of soil function following disturbance depends on immigration, colonization and establishment of exotic organisms from adjacent un-disturbed habitats, and the availability of bio-available resources, we hypothesized that the soil–litter interface could contribute to soil functional stability. In laboratory pot trials, soil was separated into two parts by a mesh bag with the inner section amended, or not amended, with rice straw; an outer layer of unamended soil, adjacent to the litter (1.5 cm thick, either heated or not), provided a soil–litter interface. This enabled us to examine the dynamics of dissolved organic carbon (DOC), mineral nitrogen, microbial biomass carbon (MBC), nematode assemblages and functional stability during 35 days incubation. Either 1 mm or 5 μm meshes were used, which allowed nematodes to migrate (SR1) or not (SR5) through the mesh to the soil–litter interface; thus also enabling us to evaluate the role of nematodes in soil functional stability. Higher DOC and MBC but lower mineral nitrogen concentrations were found at the soil–litter interface. Heating increased the availability of soil resources such as mineral nitrogen and DOC, but decreased the MBC and total nematode abundance in the soil. The soil–litter interface was characterized by a higher abundance of nematodes, particularly microbivores, regardless of mesh aperture or disturbance. The difference in nematode abundance between SR1 and SR5 indicated that nematode propagation, due to resource diffusion and nematode migration through the mesh, contributed to the changing numbers of microbivorous nematodes depending on incubation time. The soil functional stability was calculated as a relative change in the functioning of short-term barley decomposition. Soil functional resistance, defined as the instantaneous effect of disturbance on decomposition measured on the first day, was highest in the SR5 treatment. However, soil functional resilience, defined as the recovery of soil function over the whole incubation period (35d), was highest in the SR1 treatment, which is most probably attributed to the functioning of microbivorous nematodes. Our results suggest that small-scale spatial heterogeneity, due to organic residue decomposition, can help maintain soil functions following disturbance.     

Advance in a terrestrial biogeochemical model—DNDC model

Global climate change is one of the most important issues of contemporary environmental safety. Quantifying regional or global greenhouse gas (GHG) emissions and searching for appropriate mitigation measures have become a relatively hot issue in international global climate change studies. The high temporal and spatial variability of GHG emissions from soils makes their field measurement at regional or national scales impractical. To develop emission factors for a wide range of management practices such as those given in the Intergovernmental Panel on Climate Change Tier I methodology are often considered as a convenient technique to estimate emissions, but these can result in substantial errors when applied to specific geographical regions. Accordingly, considering the complexity of greenhouse gas production in soils, process-based models are required to quantify and predict the GHG emissions, and also interpret the intricate relationships among the gas emissions, the environmental factors and the ecological drivers. Several detailed biogeochemical process-based models of GHG emissions have been developed and accepted in recent years to provide regional scale estimate of GHG emissions and assess the mitigation measures. Among these the DNDC (Denitrification–Decomposition) model, as a process-based biogeochemical model, is capable of predicting the soil fluxes of all three terrestrial greenhouse gases: nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄), as well as other important environmental and economic indicators such as crop production, ammonia (NH₃) volatilisation and nitrate NO₃^- leaching. Originally developed as a tool to simulate GHG emissions generated from agro-ecosystem, DNDC has since been expanded to include ecosystems such as rice paddies, grazed pastures, forests, and wetlands, and the model has attracted worldwide attention to simulate carbon and nitrogen biogeochemical cycles occurring in global ecosystems. This paper introduces the scientific basis underlying the modeling of greenhouse gas emissions from terrestrial soils, brings together the worldwide research undertaken on a wide range of ecosystems to test and verify, improve and modify, and apply the DNDC model to estimate GHG emissions from these systems, and furtherly sums up the advantages and disadvantages of the model for providing a reference for the application and development of the model. Most studies showed that there was a good agreement between the simulated and observed values of CO₂, CH₄ and N₂O emissions from arable, forest and grassland fields at different geographical locations over the world. However, some discrepancies still existed between observed and simulated seasonal patterns of CH₄ and N₂O emissions. Moreover, the DNDC model was mainly tested against experimental data on GHG emissions, but there were a few validations on NO₃^- leaching, soil water dynamics, NH₃ volatilisation which could greatly impact the GHG emissions. With the high development of society and economy, China had been facing a huge challenge between food production and environmental protection. Therefore, it was an urgent task to search optimal measures for optimizing land resource use, increasing crop productivity and reducing adverse environmental impacts. Process-based biogeochemical modeling, as with DNDC, can help in identifying optimal strategies to meet the needs. In future, the DNDC model need to not only improve the capability of predicting the GHG emissions, but also the accuracy of simulating the NO₃^- leaching and soil water dynamics for quantifying the non-point source pollution through modifying the parameters of the model or combining with other models, as SWAT model. The DNDC model will play more and more important role in future studies on global change.     

Advance in a terrestrial biogeochemical model—DNDC model

Global climate change is one of the most important issues of contemporary environmental safety. Quantifying regional or global greenhouse gas (GHG) emissions and searching for appropriate mitigation measures have become a relatively hot issue in international global climate change studies. The high temporal and spatial variability of GHG emissions from soils makes their field measurement at regional or national scales impractical. To develop emission factors for a wide range of management practices such as those given in the Intergovernmental Panel on Climate Change Tier I methodology are often considered as a convenient technique to estimate emissions, but these can result in substantial errors when applied to specific geographical regions. Accordingly, considering the complexity of greenhouse gas production in soils, process-based models are required to quantify and predict the GHG emissions, and also interpret the intricate relationships among the gas emissions, the environmental factors and the ecological drivers. Several detailed biogeochemical process-based models of GHG emissions have been developed and accepted in recent years to provide regional scale estimate of GHG emissions and assess the mitigation measures. Among these the DNDC (Denitrification–Decomposition) model, as a process-based biogeochemical model, is capable of predicting the soil fluxes of all three terrestrial greenhouse gases: nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄), as well as other important environmental and economic indicators such as crop production, ammonia (NH₃) volatilisation and nitrate NO₃^- leaching. Originally developed as a tool to simulate GHG emissions generated from agro-ecosystem, DNDC has since been expanded to include ecosystems such as rice paddies, grazed pastures, forests, and wetlands, and the model has attracted worldwide attention to simulate carbon and nitrogen biogeochemical cycles occurring in global ecosystems. This paper introduces the scientific basis underlying the modeling of greenhouse gas emissions from terrestrial soils, brings together the worldwide research undertaken on a wide range of ecosystems to test and verify, improve and modify, and apply the DNDC model to estimate GHG emissions from these systems, and furtherly sums up the advantages and disadvantages of the model for providing a reference for the application and development of the model. Most studies showed that there was a good agreement between the simulated and observed values of CO₂, CH₄ and N₂O emissions from arable, forest and grassland fields at different geographical locations over the world. However, some discrepancies still existed between observed and simulated seasonal patterns of CH₄ and N₂O emissions. Moreover, the DNDC model was mainly tested against experimental data on GHG emissions, but there were a few validations on NO₃^- leaching, soil water dynamics, NH₃ volatilisation which could greatly impact the GHG emissions. With the high development of society and economy, China had been facing a huge challenge between food production and environmental protection. Therefore, it was an urgent task to search optimal measures for optimizing land resource use, increasing crop productivity and reducing adverse environmental impacts. Process-based biogeochemical modeling, as with DNDC, can help in identifying optimal strategies to meet the needs. In future, the DNDC model need to not only improve the capability of predicting the GHG emissions, but also the accuracy of simulating the NO₃^- leaching and soil water dynamics for quantifying the non-point source pollution through modifying the parameters of the model or combining with other models, as SWAT model. The DNDC model will play more and more important role in future studies on global change.     

Soil preferential flow in the dark coniferous forest of Gongga Mountain based on the kinetic wave model with dispersion wave (KDW preferential flow model)

Based on the law of soil water movement in unsaturated zones, the study discusses the effect of preferential flow on the movement of the researched soil through a soil column experiment using homemade experimental apparatus in four successive stages—young, middle-aged, mature and over-mature and combining dye-tracer analyses of the field process. The study proves that the preferential flow occurs in the area, and as indicated by the Reynolds numerical calculation of the preferential flow path in the 4 different successive stages, the preferential flow in the Gongga Mountain forest ecosystem is a transition flow between the laminar flow and the turbulent flow. By applying the kinetic wave model with dispersion wave (KDW preferential flow model) and comparing this model with the field experiment, the study finds that the preferential flow model has good practicability and high credibility. Verifying the KDW preferential flow model through statistic analysis indicates that the model can simulate the water movement in columns very well and the results are better in low rainfall than in high rainfall.     

Soil preferential flow in the dark coniferous forest of Gongga Mountain based on the kinetic wave model with dispersion wave (KDW preferential flow model)

Based on the law of soil water movement in unsaturated zones, the study discusses the effect of preferential flow on the movement of the researched soil through a soil column experiment using homemade experimental apparatus in four successive stages—young, middle-aged, mature and over-mature and combining dye-tracer analyses of the field process. The study proves that the preferential flow occurs in the area, and as indicated by the Reynolds numerical calculation of the preferential flow path in the 4 different successive stages, the preferential flow in the Gongga Mountain forest ecosystem is a transition flow between the laminar flow and the turbulent flow. By applying the kinetic wave model with dispersion wave (KDW preferential flow model) and comparing this model with the field experiment, the study finds that the preferential flow model has good practicability and high credibility. Verifying the KDW preferential flow model through statistic analysis indicates that the model can simulate the water movement in columns very well and the results are better in low rainfall than in high rainfall.     

Below-canopy CO2 flux and its environmental response characteristics in a coniferous and broad-leaved mixed forest in Dinghushan, China

Accurate estimation of below-canopy CO₂ flux (Fcb) in typical forest ecosystems is of great importance to validate terrestrial carbon balance models. Continuous eddy covariance measurements of Fcb were conducted in a coniferous and broad-leaved mixed forest located in Dinghushan Nature Reserve of South China. Using year-round data, Fcb dynamics and its environmental response were analyzed, and the results mainly showed that: (1) Fcb decreased during daytime which indicated that the understory of the forest continued photosynthesis throughout the year; however, understory and soil acted as CO₂ source as a whole. (2) Using soil temperature (Ts) as a dependent variable, all of Van’t Hoff equation, Arrhenius equation and Lloyd-Taylor equation can explain a considerable variation of Fcb. Among those three equations Lloyd-Taylor equation is the best to reflect the relationship between soil respiration and temperature for its ability in revealing the variation of Q₁₀ with temperature. (3) Fcb derived from Lloyd-Taylor equation is utterly determined by Ts, while Fcb derived from the multiplicative model is driven by Ts and soil moisture (Ms). The multiplicative model can reflect the synthetic effect of Ts and Ms; therefore it explains more Fcb variations than Lloyd-Taylor equation does. (4)Fcb derived from the multiplicative model was higher than that from Lloyd-Taylor equation when Ms was relatively high; on the contrary, Fcb derived from the multiplicative model was lower than that from Lloyd-Taylor equation when Ms was low, indicating that Ms might be a main factor affecting Fcb when the ecosystem is stressed by low-moisture. (5) Annual Fcb of the forest in 2003 was estimated as (787.4±296.8) gCm^-2a^-1, which was 17% lower than soil respiration measured by statistic chamber method. CO₂ flux measured by eddy covariance is often underestimated, and further study therefore calls for emphasis on methods quantifying Fcb components of respiration of soil, as well as respiration and photosynthesis of understory vegetations.