A model of stomatal conductance was developed to relate plant transpiration rate to photosynthetic active radiation (PAR), vapour pressure deficit and soil water potential. Parameters of the model include sensitivity of osmotic potential of guard cells to photosynthetic active radiation, elastic modulus of guard cell structure, soil-to-leaf conductance and osmotic potential of guard cells at zero PAR. The model was applied to field observations on three functional types that include 11 species in subtropical southern China. Non-linear statistical regression was used to obtain parameters of the model. The result indicated that the model was capable of predicting stomatal conductance of all the 11 species and three functional types under wide ranges of environmental conditions. Major conclusions included that coniferous trees and shrubs were more tolerant for and resistant to soil water stress than broad-leaf trees due to their lower osmotic potential, lignified guard cell walls, and sunken and suspended guard cell structure under subsidiary epidermal cells. Mid-day depression in transpiration and photosynthesis of pines may be explained by decreased stomatal conductance under a large vapour pressure deficit. Stomatal conductance of pine trees was more strongly affected by vapour pressure deficit than that of other species because of their small soil-to-leaf conductance, which is explainable in terms of xylem tracheids in conifer trees. Tracheids transport water by means of small pit-pairs in their side walls, and are much less efficient than the endperforated vessel members in broad-leaf xylem systems. These conclusions remain hypothetical until direct measurements of these parameters are available.

A stomatal conductance model and a photosynthesis model were applied to field measurements of transpiration and photosynthesis of seven tree species growing in subtropical southern China. Parameter values of drought resistance and tolerance and biochemical assimilation capacity were obtained by means of nonlinear statistical regression, and were used to quantify species succession. The analysis indicated that the models adequately described the ecophysiological behavior of the trees under various environmental conditions. We found a general pattern of decreased drought resistance and tolerance, but increased biochemical assimilation capacity from pines to heliophilus broadleaf trees to mesophilus broadleaf trees. Succession was explained on the basis of these physiological characteristics together with positive feedbacks caused by changes in soil physical properties. The ecophysiological explanation of succession implies that: (1) fitness of a species for a particular succession stage at a particular location can be measured by stomatal behavior and biochemical assimilation capacity under local climate and soil conditions; (2) selection of species for a particular location at a particular succession stage can be guided by the parameter values provided in this study; and (3) succession may be accelerated by selecting trees with large root systems and large soil–root conductances that facilitate soil hydraulic redistribution of water.

A computer simulation model of regional vegetation dynamics was applied to the terrestrial ecosystems of China to study the responses of vegetation to elevated CO2 and global climatic change. The primary production processes were coupled with vegetation structure in the model. The model was parameterized and partially validated in light of a large number of field observations made throughout China on primary productivity, 10 years of monthly meteorological data, 5 years of monthly normalized differential vegetation index observed by NOAA-11 satellite, and digital vegetation and terrain maps. Eight different climatic scenarios, set by perturbations from the present climate, 100% in atmospheric CO2 concentration, 2 C in monthly mean temperature, and 20% in monthly precipitation, were applied to analyze the sensitivity of the Chinese terrestrial ecosystems to climatic change. Simulation results were obtained for each of the climatic scenarios with the model running toward equilibrium solutions at a time step of 1 month. Preliminary validation indicated that the model was capable of simulating the net primary productivity of most vegetation classes and the potential vegetation structure in China under present climatic conditions. The simulations for the altered climatic scenarios predicted that grasslands, shrubs, and conifer forests are more sensitive to environmental changes than evergreen broadleaf forests in warm, wet southeast China and desert vegetation in cold, arid northwest China. For less than 150% of changes in vegetation structure under altered climatic conditions, about three quarters of the changes in net primary productivity of individual vegetation classes were shown to be attributed to the changes in the corresponding distribution area.

Digital Ecological Model (DEM) is a platform developed with Java. It consists of six components: DEMGIS, DEMTSA, DEMSTA, DEMMOD, DEMVIEW, and DEMAPPLET. DEMGIS features major functions of geographic information system (GIS), such as building digital elevation model, managing geo-referenced database, translating vector data into raster data, and generating geographic graphs with different projections. DEMTSA is used to interpolate the scattered climatic data into raster data, by means of trend surface analysis (TSA) method and interpolation method. As a plug-in for GIS, DEMSTA provides some widely used statistic methods. DEMMOD is a platform for building process-based landscape model. It provides a visual interface-Visual Programming Interface of Digital Ecological Model (DEMVPI) for ecologists to 'write' and record the models in an interpretation language-Ecological Description Language of Digital Ecological Model (DEMEDL). Ecological Model Interpreter of Digital Ecological Model (DEMEMI) is responsible for compiling the programs written in DEMEDL, running the model and displaying the results. DEMVIEW is a tool for viewing and editing some geographic graphs. DEMAPPLET can link a Java applet with geo-referenced database and display the simulation results on the Internet. All the codes of DEM were compiled into Java application programs, and some of the programs are available on the Internet as Java applets. As a case study, amended Penman's method was used to calculate the potential evapotranspiration and aridity index of China, under present situation and three prescribed climate scenarios, which include raising mean temperature by 1.5, 3.0 and 4.5℃, and raising precipitation by 10%, to assess the potential impacts of global climate change on China water condition.

The spatiotemporal variations of vegetation biomass of the ecological transect in northeast China were simulated. State variables of the model included green biomass and nongreen biomass of 12 vegetation categories and water contents of three soil layers. The simulated monthly green biomass was converted into NDVI, or Normalized Differential Vegetation Index of AVHRR (Advanced Very High Resolution Radiometry). A comparison between the modeled and the observed NDVI was made at 10' spatial resolution. Atmospheric CO2 concentration and monthly precipitation were used as two driving variables for global change simulation. Effects of precipitation increments on percentage sunshine, relative humidity, radiation, evapotranspiration, and eventually soil water and plant growth, were considered. Two levels of CO2 concentration (present, doubled) and seven levels of precipitation increments (0, 0.05, 0.1, 0.15, 0.2, 0.25, and 0.30) were prescribed for a total of 14 simulation runs. A steady-state solution was obtained for each simulation run. The results of simulation showed that with the present climate conditions, doubling atmospheric CO2 concentration led approximately to a 20.3% increase in green biomass, 11.0% increase in nongreen biomass, 19.0% increase in green NPP, 12.8% increase in nongreen NPP, and 14.9% increase in overall average NPP at steady state. These increases go, respectively, to 32.9, 13.9, 30.0, 20.1, and 23.4% when a 30% precipitation increase was superimposed on the doubled CO2 concentration.