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Digital ecological model and case study on China water condition
Ecological Modelling 139 (2001) 235– 252 www.elsevier.com/locate/ecolmodel
Digital ecological model and case study on China water condition Zongbo Shang a,*, Qiong Gao a,b, Dianan Yang a a
Laboratory of Quantitati6e Vegetation Ecology, Institute of Botany, The Chinese Academy of Sciences, Nanzincun 20, Xiangshan, Beijing 100093, People’s Republic of China b MDE Key Lab of En6ironmental Change and Natural Disaster, Institute of Resources Science, Beijing Normal Uni6ersity, Beijing 100875, People’s Republic of China Received 12 May 2000; received in revised form 22 December 2000; accepted 9 January 2001
Abstract 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°C, and raising precipitation by 10%, to assess the potential impacts of global climate change on China water condition. © 2001 Elsevier Science B.V. All rights reserved. Keywords: Digital ecological model; Digital Earth; Water condition; Global climate change
* Corresponding author. Tel.: + 86-10-62591431 ext. 6511; fax: +86-10-82595962. E-mail address: [email protected] (Z. Shang).
1. Introduction During the last two decades, many ecologists devoted their attention to landscape models (Turner and Gardner, 1990), searching for the principles of spatial pattern on ecological pro-
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cesses, and demonstrating the processes and constrained changes across scales. It is just for the last decade that spatial simulation became feasible, profiting from the theoretic progresses in landscape ecology, the fast development of computer technology, and the frequent application of remote sensing (RS) and geographic information system (GIS) in landscape ecology modeling. There has been increasing evidences (Sellers et al., 1986; Graetz, 1990; Cyr et al., 1995; Bergen and Dobson, 1999; Goetz et al., 1999) of interaction between the remote sensing and ecological process communities. Plummer (2000) classified the approaches for using remotely sensed data in ecological process models into some alternative strategies: (1) to provide estimates of variables which were required for driving ecological process models; (2) to test, validate or verify predictions of ecological process models; and (3) to update or adjust ecological process model predictions. With the wide use of GIS in ecological studies (Elston and Buckland, 1993; Congleton et al., 1996; Hill, 1998; Skop and Sørensen, 1998), it become more and more significant for spatial simulation. At the same time, ecological modeling carries through a thorough reform. For instance, applications of object-oriented design (OOD) in the modeling of biological systems are becoming more and more popular (Baveco and Lingeman, 1992; Silvert, 1993; Ferreira, 1995; Downing and Reed, 1996; He et al., 1999; Hong et al., 1999). Ecologists (Gao, 1994; Mooij and Boersma, 1996; Power, 1996; Congleton et al., 1997; Clemen, 1998; Bobba et al., 1999; Gauthier et al., 1999) began building some environmental information systems or frameworks, to provide software tools for ecological simulation study. However, the landscape modeling falls behind greatly with the developments of some high-tech, e.g. information science, computer science, GIS, RS, Global Position System (GPS), and Digital Earth (http://digitalearth.gsf.nasa.gov/ VP19980131.html). Lorek and Sonnenschein (1998) gave the long list of problems in the individual-oriented models e.g. 1. Ecologists must be programmers. 2. Models equal computer programs.
3. There is no separation between models and experiments. 4. Analysis depends on models. 5. There are no methodical supports. 6. There is no interaction and visualization between users and the simulation. 7. It is difficult to communicate between theoretical ecology and field ecology. There are some other problems that always hinder the progress of the landscape model. We can give some instances of them. (1) No model can address issues at all scales, and thus ecologists must build numerous models for different researching scale, at the time that they have not found the excellent techniques for scaling up (or scaling down) their models. (2) Explicit and inexplicit assumptions must be made according to the researching subjects and region, which restricts the model to be used on other subjects or regions. (3) All model programmers have to deal with the large amount of data, which hinder them to focus on the ecological processes. (4) Little software is available for ecologists to make direct use of the remotely sensed data to build their model, or to do some statistical analysis. (5) The great gap between ecological model and GIS prevents ecologists from making efficient use of the powerful tool — GIS to spatially analyze the ecosystems. As there is no model can fit for all scales, it becomes significant to build a programming platform for spatial simulation. We can use it to manage the geo-referenced data, to provide some common statistic methods, to build a platform for programming, to make full use of the remotely sensed data, to integrate the landscape model with GIS perfectly, and to build some programs or display their fruits on the Internet. Digital Ecological Model is a piece of software that was designed and developed for this purpose.
2. Methods To build a process-based landscape model, one always goes through some procedures after prescribing the researching purposes and objects: (1) data processing; (2) model definition; (3) model building, programming and parameterization; (4)
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model validation; and (5) results analysis. Digital Ecological Model is a platform to assistant ecologists to build their landscape model favorably. It is composed of six parts — DEMGIS, DEMTSA, DEMSTA, DEMMOD, DEMVIEW, and DEMAPPLET. Fig. 1 illustrates the basis structure of Digital Ecological Model. DEMGIS is directed to feature main functions of GIS, such as building of digital elevation model, management of geo-referenced database, translation of vector data into raster data, and graphics analysis with different projections. DEMTSA is used to interpolate the climatic data (collected from meteorological observation station) into raster data, with trend surface analysis method (TSA) and interpolation method. As a plug-in for GIS, DEMSTA is a tool for statistic analysis. It has been found to be useful for discovering the principles of ecological processes in a large landscape, and helpful for model definition. DEMMOD is the most impor-
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tant platform for building a process-based landscape model. It can be used to manage the experimental data, climatic data, remotely sensed data and topography data in the model conveniently. Therefore, ecologists could concentrate on the ecological processes, instead of swarms of data, and the model programming. What they should do is to ‘write’ the model with the visual interface — Visual Programming Interface of Digital Ecological Model (DEMVPI) and then the model will be written into Ecological Description Language of Digital Ecological Model (DEMEDL) automatically. DEMMOD is a simple, visual, object-oriented and interpreted platform. It uses Ecological Model Interpreter of Digital Ecological Model (DEMEMI) to interpret and run the ecological model, create the geographic graphs of predefined variables with requested projections, and statistically analyze the simulated results with experimental data. As geo-
Fig. 1. The structure of Digital Ecological Model.
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graphic graphs are always strict for publication, we can use DEMVIEW to edit the graphs. DEMAPPLET can link a Java applet with georeferenced database (such as simulated results or remotely sensed data) to be shown on the Internet. Digital Ecological Model was programmed with the object-oriented language — Java, with the free software — JDK1.2, downloaded from the homepage of Sun Company (http:// java.sun.com/). Application programs and Java applet are the two formats.
2.1. DEMGIS DEMGIS is a software to accomplish major functions of traditional GIS. It can translate vector data (e.g. DXF format drawn with AutoCAD) into raster data. It can build a digital elevation model and graphically analyze the geo-spatial data (raster or vector data) with sorts of projections (e.g. orthographic projection, conical projection, cylindrical projection, Mercator projection, azimuthal projection, and spherical projection). But there are some remarkable differences between DEMGIS and traditional GIS software, as DEMGIS is based on the Representational Model of Geo-referenced Object (RMGO), E(S, T, A)
(1)
where E() is the representation of digital geo-referenced objects; S are spatial coordinates, such as longitude, latitude and elevation; T are temporal coordinates; A are attributes of geo-referenced objects. Both the topographic and the temporal relationships are assigned importance levels to express the attributions of geo-referenced objects. Each object will be impacted by other objects, especially by the adjacent objects, and thus change diversely and show different states.
2.2. DEMTSA Raster climatic data are significant for spatial simulation study. Interpolation is a common method for interpolating the climatic data that come from distantly scattered meteorological observation stations into raster data, which has its benefits for simplicity and convenience, and vice
versa for disability of analyzing spatial regulations of scattered data. Trend surface analysis (Chorley and Haggett, 1968; Gittins, 1968) is a statistical model with superiority on describing the spatial (or spatial-temporal) distribution regulations of certain factors. This technique (Cliff et al., 1975) uses multiple-regression method to separate trend from residual variation. Longitude, latitude and sometimes elevation are always selected as factors for trend surface model (Pan and Zhou, 1984). In a large-scale, the spatial distribution of climatic variables are impacted by large-scale environmental factors, such as latitude, distance from sea, influence of great mountains, plateaus or basins, and atmospheric circulation backgrounds, and local environmental factors, e.g. slope gradient, slope aspect, shading degree, hypsography, vegetative and microclimatic factors. In some ways (Gao and Lu, 1988), longitude, latitude and elevation can be regarded as the dominant factors that effect the climatic characters of an extensive region. But climatic distribution of variable has its non-determinacy, for the spatial-temporal regulations will be fluctuated by random environment. It is found that in a certain extension, with the short of temporal scale, the functions of random events will ‘cover up’ the spatial-temporal regulations greatly. To decrease the function of random events, we integrate the spatial function by time and get the average spatial distribution of climate variables in a certain period. Hypothesizing that for some certain climatic variables, e.g. temperature, precipitation, relative humidity and sunshine fraction, the functions of large-scale environmental factors and local environmental factors can be separated from each other, and we can get, Yp = fp{, u, H}+gp{h, i, V...} +|p
(2)
where Yp is average spatial distribution of climate variables; is latitude; u is longitude; H is elevation; h is slope gradient; i is slope aspect; V is vegetative factor; fp is the function of large-scale environmental factors; gp is the function of local environmental factors; and |p is function of random events. TSA is a special multivariate statistical analysis method, which has superiority for spatial (or spatial–temporal) analysis and separating functions
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of large topography factors from local environmental factors and random events. Pan and Zhou (1984) gave the mathematical theory of TSA. Supposing a dot in a three-dimensional space, with coordinate of (xt, yt, zt) and observation data of wt, we use three-dimensional trend surface models (TSM) to simulate spatial distribution of such dots aggregate, W = GA + m
(3)
where W is matrix of observation data, and m is residual matrix, both are vector-matrix (N ×1); N is the number of observation data; G is vectormatrix (N×q); A is matrix of coefficients (q ×1); and q is determined by s, rank of TSA. (s +1)(s + 2)(s +3) q= 6
(4)
Here we give D, one row vector of G, which has been omitted the subscripts for code of observation dot, D ={1, z, z 2 …, z s, y, yz, yz 2 … yz s − 1 …, y s − 1z, y s, x, xz, xz 2 …, xz s − 1, xy, xyz, …, xyz s − 2 …, xy s − 1, x 2x 2z …, x 2z s − 2, x 2y, x 2yz …, x 2yz s − 3 …, x s − 1y, x s} (5) TSA can be calculated with least squares method or generalized inverse matrix method. As the second method has its superiority for accuracy, and convenience for higher order TSM, it is used here. With Gram–Schmidt’s method of orthogonalization (Pan and Zhou, 1984), we can calculate the Moore –Penrose’s generalized inverse matrix G + of real matrix G, and get the shortest least squares solution A0 of A, A0 = G+Z
(6)
With TSA, we can get the function of largescale topography easily, as we use latitude, longitude and the elevation for the three independent variables, and use climatic variables (e.g. temperature, precipitation, relative humidity, and sunshine fraction) for dependent variables. After getting the TSM, and the residual matrix, we can calculate grid data of certain climatic variables,
Y. i, j = Di, jA0 T
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(7)
where Y. I, j is the simulated result of climatic variables, and A0 T is coefficient vector. The residuals can be regarded as the synthetical functions of local environmental factors and random events, which cannot be explained by largescale topography analysis. Residuals are interpolated into each grid, to get fully information about spatial distribution of climatic variables. The distance from neighbor meteorological stations is used as the impact coefficient, and the interpolation method is expressed as, t
% Vi /di VP =
i=1 t
(8)
% 1/di
i=1
where VP is interpolated result of climatic variable; di is distance from neighbor station; Vi is residual of neighbor station; and t is the number of neighbor stations. By searching at four directions (north, south, west and east), the nearest six stations are selected with at least one station at each direction, and the residuals of these six stations are used for interpolation. By adding the TSM results and the interpolation results, we can get the raster data of each climatic variable.
2.3. DEMSTA DEMSTA provides some widely used statistic methods; e.g. correlation analysis, variance analysis, multiple linear regression, stepwise regression, principal component analysis, discriminatory analysis, fuzzy cluster analysis, and TSA. Both experimental data saved in ‘dbf’ or ‘txt’ format can be used directly with DEMSTA. Furthermore, DEMSTA was designed at the very start as a plug-in for GIS on a personal computer and the Internet. It has been proved to be useful to reveal the principles by statistical analysis on landscapes.
2.4. DEMMOD Landscape model differentiates greatly from the patch model. Both the heterogeneous environmental factors and other objects (especially the
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adjacent objects) will affect the development of the focused object. Gao (1994) gave the differential equation models to describe the process of landscape model,
(ui (uj ( 2uj (uj ( 2uj , Pk, x, y, t =fi uj, , , , (t (x (x (y (y 2 i, j =1, 2, … , n k =1, 2, … , m
(9)
where ui, uj are the state variable vector of dimension n; Pk is the parameter vector of dimension m; x, y denote spatial coordinates and t denotes time. To define the ecological models, we use an Ecological Description Language of Digital Ecological Model (DEMEDL) to describe the mechanisms of most of the ecological processes described by Eq. (9). Furthermore, we built the software named as Visual Programming Interface of Digital Ecological Model (DEMVPI) to record an ecological model in DEMEDL. With this tool, it becomes simple for an ecologist to ‘write’ a landscape model on computer, with his own data and model definition, to describe the ecological processes in his interesting region and scale, to solve the ecological problems and to draw his conclusion. We classify the possible variables in landscape models into some sorts: environmental variables, system variables, state variables, parameters, constants, spatial variables, and temporal variables. In some ways, we can regard the climatic, edaphic and geographical variables as the environmental variables, because they always carry the environmental background. The climatic raster data can be generated from the spot records of meteorological observation stations with DEMTSA. The edaphic and geographical raster data can be transformed from vector data with DEMGIS. Then we can get the raster data of these environmental variables. The remotely sensed data can also be considered as a sort of ‘environmental variable data’, because they describe the integrated information of environment. System variables are some important variables used to create geography map for analysis, or generate the register files for model validation. They should be marked clearly and cautiously, as the DEMMOD will log them at regular intervals. The state vari-
ables represent the variables necessary in the middle progress. Parameters vary in different spatial-temporal ‘cells’, while constants are fixed. The spatial variables describe the spatial position of ‘cells’, e.g. longitude and latitude, or x and y coordinate. The temporal variables describe the period that can be represented with absolute time or the step sequence. DEMMOD provides a special sorts of variables, named ‘artifical variables’. These ‘artifical variables’ can only be generated from the original data, especially the environmental data. But they are necessary in spatial simulation, such as the slope gradient and slope aspect, the exoatmospheric solar radiation and the duration of sunshine and sunset. Furthermore, these ‘artifical variables’ can be calculated with some sound methods. Here, we use the digital elevation model (Ke et al., 1992) to calculate the slope gradient and slope aspect, and used the digital elevation model (Dozier and Outcalt, 1979; Dozier and Frew, 1990) and analytical equations (Fu, 1983; Fu and Weng, 1994) to calculate the exoatmospheric solar radiation and duration of sunshine and sunset. Some expression symbols, conditional expression, cycle expression are provided in DEMMOD also. With the assistants of DEMVPI, ecologists can build a landscape model with simple and convenient operations, just like clicking the mouse or inputting a character or number. The model will be written into DEMEDL and thus a file with extension name as ‘mod’ will be created automatically to record this model. Ecological Model Interpreter of Digital Ecological Model (DEMEMI) is responsible for interpreting the program written in the DEMEDL, compiling, running the model and displaying the result. It can ‘understand’ the model and check mistakes. Then, it will run the model, create the log files for recording system variables, and display the results. As the whole system was written with object-oriented language — Java, and the program has been compiled into ‘classes’, DEMEMI can use the functions of DEMGIS directly to generate a geography map with different projections, or use the functions of DEMSTA to statistically analyze the simulated results with experimental data.
Z. Shang et al. / Ecological Modelling 139 (2001) 235–252 Table 1 Comparison between simulated results with trend surface model and observed data Climate variables
N
R2
Mean temperature Precipitation Mean relative humidity Mean sunshine fraction
25 524 25 236 25 236 24 852
0.9898 0.8765 0.8826 0.9008
2.5. DEMVIEW and DEMAPPLET DEMVIEW is the software to view and edit the created geography graphics with DEMMOD or DEMGIS. DEMAPPLET is used to link the database with a Java applet for displaying the geo-referenced data on the Internet.
3. Application of digital ecological model, a case study A major application (Clemen, 1998) of environmental information systems is the simulation of landscape management and global change scenarios. Predictions for the global greenhouse effect for the mid-21st century gave increases in mean air temperature of between 1.5 and 4.5°C and altered precipitation (Houghton et al., 1990). Tang et al. (2000) estimated that the climate under doubled atmospheric CO2 concentration with three sound GCMs: HadCM2, CGCM1 (The first version of the Canadian Global Coupled Model) and ECHAM4. The results show that air temperature will increase for about 2.5, 3.5 and 2.6°C, and the total precipitation will increase for 10.4, 4.7 and 10.4%, respectively, in China. As a case study, we give three prescribed climate scenarios, which include raising mean temperature by 1.5, 3.0 and 4.5°C, and raising precipitation by 10%. A spatial model was built with DEM to calculate the spatial distribution of precipitation, potential evapotranspiration, and aridity index under the present situation and these three prescribed climate scenarios, to assess the potential impact of global climate change on China water condition.
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3.1. Pretreatment Large-scale relief maps and topography raster data were used to build digital elevation models of China with DEMGIS. As TSA is fit for large scale, China area was divided into eight subareas — (1) Northeastern China; (2) Northern China; (3) Middle China; (4) Southern China; (5) Southeastern China; (6) Inner Mongolia; (7) Northwestern China; and (8) Tibet plateau. To avoid the difference of climatic variables’ spatial distribution among these subareas, each subarea was overlapped and included some area of neighboring subareas. With DEMTSA and monthly meteorological data of 954 meteorological stations of China, from 1951 to 1980, three rank’s trend surface models (TSM) were calculated to analyze the spatial distribution regulations of four climatic variables (i.e. mean air temperature, precipitation, mean relative humidity, and mean sunshine fraction) of 12 months for these eight subareas. Table 1 shows the comparison between simulated results and observed data of these four climatic variables. Fig. 2 shows the spatial distribution of annual precipitation. With TSM and interpolation method, we built the raster database of these four climate variables of 12 month. In the simulation study, we found that TSA is not suitable for spatial analysis on wind velocity, so the raster data of mean wind velocity were calculated with interpolation methods only.
3.2. Mathematics description Amended Penman’s equations (Monteith, 1965; Frere and Pruitt, 1979) were widely used to calculate the potential evapotranspiration, for its sound physics theory. Xie et al. (1991) developed an amended Penman’s method for China, ETP =
!
"
P0 D n × x× QA a+ b P k N
− |T 4(0.56− 0.079 ea) 0.1+0.9 + 0.26(ea − ed)(1+ c× U2) /
n N
n
P0 D × +1 P k (10)
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Fig. 2. Spatial distribution of annual precipitation (mm) in China.
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where ETP is potential evapotranspiration; P0 and P are air pressure on the sea level and at the research spot; D is curve slope gradient of saturated vapor pressure’s change with temperature; k is psychrometric constant; Qa is exoatmospheric solar radiation; ea is saturated vapor pressure, and ed is water vapor pressure; U2 is wind velocity at height of 2m; | is Stefen-Boltzman constant; n is amending coefficient; a, b and c are coefficients. Aridity index (Gao and Lu, 1988) has been used as an index for arid-humid climate analysis. Here the aridity index is expressed as, AI = log10
ETP R
(11)
where AI is aridity index; ETP is potential evapotranspiration; and R is annual precipitation. With the assistants of DEMVPI, we built a spatial model to estimate potential evapotranspiration and aridity index. The model was written in DEMEDL with DEMMOD automatically. Then, we used DEMEMI to run the model, log the results and create geographic graphs.
3.3. Simulation results The monthly climatic data of 905 meteorological observation stations during the period of 1951– 1980 were used to calculate the potential evapo-
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transpiration. Fig. 3 shows the comparison between calculated monthly potential evapotranspiration and 10 860 effectively monthly-observed pan evaporation data of 905 stations. The correlation is very well given by (R 2 = 0.9288). Fig. 4 and Fig. 5 show the distributed potential evapotranspiration and the aridity index of China. Fig. 6 shows the aridity index under three prescribed climate scenarios, which include raising mean temperature by 1.5, 3.0 and 4.5°C and raising precipitation by 10%. The area of potential evapotranspiration and aridity index of each range were accumulated to get the percentage distribution of China (see Fig. 7 and Fig. 8). To show the difference clearly, we calculated the average potential evapotranspiration and aridity index of China under present situation and the three prescribed climate scenarios (see Table 2). With the raised temperature, the areas of potential evapotranspiration that less than 2000 mm reduce, and the areas that larger than 2000 mm increase sharply. So we can conclude that the potential evapotranspiration will increase with the raising of temperature. Concerning with the change of aridity index under three prescribed climate scenarios, it is very clear that China becomes a slightly wetter under the first prescribed climate scenario, and a little drier under the last prescribed climate scenario than the current
Fig. 3. Comparison between simulated potential evapotranspiration and observed monthly pan evaporation.
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Fig. 4. Spatial distribution of potential evapotranspiration (mm) in China.
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climate condition. However, the second climate scenario brought a little change. 4. Discussion Ecological modeling varies diversely in the
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world, and it is impossible to construct a special model to solve all the problems for all scales. But we can draw some common ground by investigating most of the ecological models. Ecologists should design some experiments with explicit purposes, and collect correlative data as much as possible. Then, they need process the data, do
Fig. 5. Spatial distribution of aridity index in China.
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Fig. 6. Spatial distribution of aridity index in China under three prescribed climate scenarios; (a) raising mean air temperature by 1.5°C and precipitation by 10%; (b) raising mean air temperature by 3.0°C and precipitation by 10%; and (c) raising mean air temperature by 4.5°C and precipitation by 10%.
some statistics, conceive their models, and define the models with mathematical description, letters
and figures. The subsequent task is programming, which will take most of their time. Most of the
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software used in RS and GIS can not provide high capability for modeling analysis. So, if ecologists want to build their models with the assistance of remotely sensed data and GIS, they
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should also probe into some basic computer technology used in these fields. Therefore, it is very significant for modelers to create validated ecological modeling platforms to help ecologists process-
Fig. 6. (Continued)
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Fig. 6. (Continued)
ing the data, statistically analyzing and revealing the ecological principles, and employing the remotely sensed data and GIS into ecological model directly. The quick developments of high-tech, e.g. RS,
GIS, GPS, and the Digital Earth provide some favorable tools for ecological study. At the same time, the developments of high-tech need the collaboration of ecological studies to solve the environmental problems. Ecological study, including
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Fig. 7. Percentage distribution of potential evapotranspiration in China under present situation and three prescribed climate scenarios; (a) present situation; (b) raising mean air temperature by 1.5°C and precipitation by 10%; (c) raising mean air temperature by 3.0°C and precipitation by 10%; and (d) raising mean air temperature by 4.5°C and precipitation by 10%.
ecological modeling, can express the profound processes of some important environmental issues, such as global change, protection of bio-diversity, sustainable developments and agricultural production. We tried to build a platform for building of landscape models, and provided the tools for statistical analysis and application of remotely sensed data and GIS directly in landscape modeling. The computer technology, statistical analysis, RS, GIS and Digital Earth were integrated into this platform, to reveal the ecological regulations with landscape modeling. It will be helpful to understand the ecological processes and principles of spatial pattern.
5. Conclusions Digital Ecological Model can be regarded as a piece of programming platform, which integrates the GIS, remotely sensed data analysis and statistic analysis into spatial simulation. It will enable ecologists to build process-based landscape model on this platform and display their fruits on the
Internet. This software was programmed with Java, as application programs to be run at any operating systems. Some programs, including Table 2 The average potential evapotranspiration and aridity index of China under different climatic scenarios Climate scenario
Average potential evapotranspiration
Average aridity index
Present climatic situation Raising mean temperature by 1.5°C and precipitation by 10% Raising mean temperature by 3.0°C and precipitation by 10% Raising mean temperature by 4.5°C and precipitation by 10%
1932.98 (mm)
0.7402
2035.97 (mm)
0.7205
2140.83 (mm)
0.7424
2247.48 (mm)
0.7636
250 Z. Shang et al. / Ecological Modelling 139 (2001) 235–252 Fig. 8. Percentage distribution of aridity index in China under present situation and three prescribed climate scenarios; (a) present situation; (b) raising mean air temperature by 1.5°C and precipitation by 10%; (c) raising mean air temperature by 3.0°C and precipitation by 10%; and (d) raising mean air temperature by 4.5°C and precipitation by 10%.
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statistic analysis and graphic edition can also be conducted on the Internet as Java applets. As a case study, a spatial model was built with DEM, to calculate the potential evapotranspiration and the 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°C, and raising precipitation by 10%, to assess the sensitivity of China water condition to global climate change. The simulated potential evapotranspiration increase greatly with the raising of air temperature. However, the changes of aridity index are complicated. Comparing with present situation, China will become a little wetter under the first climate scenario and a little drier under the third climate scenario. The aridity index of China under the second climate scenario does not change as great as that under the other two scenarios. These may be because the raising precipitation will counteract the increase of potential evapotranspiration. Acknowledgements This research was jointly supported by Chinese National Natural Science Foundations (Numbers 39899370, 39770133, and 39725006). We would like to express our sincere appreciation to Laboratory of Quantitative Vegetation Ecology, which provided the climatic data, relief map and raster elevation data. References Baveco, J.M., Lingeman, R., 1992. An object-oriented tool for individual-oriented simulation: host-parasitoid system application. Ecol. Modell. 61, 267 – 286. Bergen, K.M., Dobson, M.C., 1999. Integration of remotely sensed radar imagery in modeling and mapping of forest biomass and net primary production. Ecol. Modell. 122, 257 – 274. Bobba, A.G., Singh, V.P., Bengtsson, L., 1999. Application of environmental models to different hydrological systems. Ecol. Modell. 125, 15 –50. Chorley, R.J., Haggett, P., 1968. Trend-surface mapping in geographical research. In: Berry, B.J.L., Marble, D.F. (Eds.), Spatial Analysis: A Reader in Statistical Geography. Prentice-Hall Inc, Englewood Cliffa, NJ, pp. 195 – 217.
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Clemen, T., 1998. The use of scale information for integrating simulation models into environmental information systems. Ecol. Modell. 108, 107 – 113. Cliff, A.D., Haggett, P., Ord, J.K., Bassett, K.A., Davies, R.B., 1975. Elements of Spatial Structure: A Quantitative Approach. Cambridge University Press, Cambridge. Congleton, W.R., Pearce, B.R. Jr, Parker, M.R., Beal, B.F., 1996. Mariculture siting: a GIS description of inter-tidal areas. Ecol. Modell. 116, 63 – 76. Congleton, W.R., Pearce, B.R.Jr, Beal, B.F., 1997. A C + + implementation of an individual/landscape model. Ecol. Modell. 103, 1 – 18. Cyr, L., Bonn, F., Pesant, A., 1995. Vegetation indices derived from remote sensing for an estimation of soil protection against water erosion. Ecol. Modell. 79, 277 – 293. Downing, K., Reed, M., 1996. Object-oriented migration modelling for biological impact assessment. Ecol. Modell. 93, 203 – 220. Dozier, J., Outcalt, S.I., 1979. An approach to energy balance simulation over rugged terrain. Geograph. Anal. 11, 65 – 85. Dozier, J., Frew, J., 1990. Rapid calculation of terrain parameters for radiation modeling from digital elevation data. IEEE T. Geosci. Remote 28, 963 – 969. Elston, D.A., Buckland, S.T., 1993. Statistical modelling of regional GIS data: an overview. Ecol. Modell. 67, 81 – 102. Ferreira, J.G., 1995. ECOWIN- an object-oriented ecological model for aquatic ecosystems. Ecol. Modell. 79, 21 – 34. Frere, M., Pruitt, W.O., 1979. Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper 24, Rome, pp. 41-45. Fu, B., 1983. Mountainous Climate. Chinese Science Press, Beijing, p. 270. Fu, B., Weng, D., 1994. Microclimatology. Chinese Meteorological Press, Beijing. Gao, G., Lu, Y., 1988. Climatology. Chinese Meteorological Press, Beijing, p. 468. Gao, Q., 1994. Dynamic modeling of ecosystems with spatial heterogeneity. A structured approach implemented in Windows environment. Ecol. Modell. 85, 241 – 252. Gauthier, L., Gary, C., Zekki, H., 1999. GPSF: a generic and object-oriented framework for crop simulation 116, 253 – 268. Gittins, R., 1968. Trend-surface analysis of ecological data. J. Ecol. 56, 845 – 869. Goetz, S.J., Prince, S.D., Goward, S.N., Thawley, M.M., Small, J., 1999. Satellite remote sensing of primary production: an improved production efficiency modeling approach. Ecol. Modell. 122, 239 – 256. Graetz, R.D., 1990. Remote sensing of terrestrial ecosystem structure: an ecologists pragmatic view. In: Hbbs, R.J., Mooney, H.A. (Eds.), Remote Sensing of Biosphere Functioning. Springer, New York, pp. 5 – 30. He, H.S., Mladenoff, D.J., Boeder, J., 1999. An object-oriented forest landscape model and its representation of tree species. Ecol. Modell. 119, 1 – 9.
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Hill, M.J., 1998. Defining the white clover zone in eastern mainland Australia using a model and a geographic information system. Ecol. Modell. 86, 245 – 252. Hong, S.H., David, J.M., Joel, B., 1999. An object-oriented forest landscape model and its representation on tree species. Ecol. Modell. 119, 1 –19. Houghton, J.T., Jenkins, G.J., Ephraums, J.J., 1990. Climate Change: The IPCC Scientific Assessment. Cambridge University Press, Cambridge. Ke, Z., He, J., Chi, T., 1992. Digital Elevation Models. Chinese Scientific and Technical Press, Beijing. Lorek, H., Sonnenschein, M., 1998. Object-oriented support for modelling and simulation of individual-oriented ecological models. Ecol. Modell. 108, 77 – 96. Monteith, J.L., 1965. Evaporation and Environment, Symposium Society Experiment Biology XIX. Cambridge University Press, London, pp. 205 –234. Mooij, W.M., Boersma, M., 1996. An object-oriented simulation framework for individual-based simulations (OSIRIS): Daphnia population dynamics as an example. Ecol. Modell. 93, 139 – 154. Pan, E., Zhou, Y., 1984. Multivariate Statistic Programs (for DJS-21 Computer). Chinese Atomic Energy Press, Beijing, pp. 79– 91.
Plummer, S.E., 2000. Perspectives on combining ecological process models and remotely sensed data. Ecol. Modell. 129, 169 – 186. Power, M., 1996. The predictive validation of ecological and environmental model. Ecol. Modell. 90, 257 – 270. Sellers, P.J., Mintz, Y., Sud, Y.C., Dalcher, A., 1986. A simple biosphere model (SiB) for use with general circulation models. J. Atmos. Sci. 43, 505 – 531. Silvert, W., 1993. Object-oriented ecosystem modelling. Ecol. Modell. 68, 91 – 118. Skop, E., Sørensen, 1998. GIS-based modelling of solute fluxes at the catchment scale: a case study on the agricultural contribution to the riverine nitrogen loading in the Vejle Fjord catchment. Dnk. Ecol. Modell. 106, 291 – 310. Tang, G.P., Li, X.B., Fischer, G., Prieler, S., 2000. Climate change and its impacts on China’s agriculture. Acta Geograph. Sin. 55, 129 – 138. Turner, M.G., Gardner, R.H., 1990. Quantitative methods in landscape ecology: an introduction. In: Turner, M.G., Gardner, R.H. (Eds.), Quantitative Methods in Landscape Ecology. Springer, New York, pp. 3 – 16. Xie, X., Zuo, D., Tang, D., 1991. Evapotranspiration of the Field — Measurement and calculation. Chinese Meteorological Press, Beijing, p. 237.
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Digital ecological model and case study on China water condition Zongbo Shang a,*, Qiong Gao a,b, Dianan Yang a a
Laboratory of Quantitati6e Vegetation Ecology, Institute of Botany, The Chinese Academy of Sciences, Nanzincun 20, Xiangshan, Beijing 100093, People’s Republic of China b MDE Key Lab of En6ironmental Change and Natural Disaster, Institute of Resources Science, Beijing Normal Uni6ersity, Beijing 100875, People’s Republic of China Received 12 May 2000; received in revised form 22 December 2000; accepted 9 January 2001
Abstract 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°C, and raising precipitation by 10%, to assess the potential impacts of global climate change on China water condition. © 2001 Elsevier Science B.V. All rights reserved. Keywords: Digital ecological model; Digital Earth; Water condition; Global climate change
* Corresponding author. Tel.: + 86-10-62591431 ext. 6511; fax: +86-10-82595962. E-mail address: [email protected] (Z. Shang).
1. Introduction During the last two decades, many ecologists devoted their attention to landscape models (Turner and Gardner, 1990), searching for the principles of spatial pattern on ecological pro-
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cesses, and demonstrating the processes and constrained changes across scales. It is just for the last decade that spatial simulation became feasible, profiting from the theoretic progresses in landscape ecology, the fast development of computer technology, and the frequent application of remote sensing (RS) and geographic information system (GIS) in landscape ecology modeling. There has been increasing evidences (Sellers et al., 1986; Graetz, 1990; Cyr et al., 1995; Bergen and Dobson, 1999; Goetz et al., 1999) of interaction between the remote sensing and ecological process communities. Plummer (2000) classified the approaches for using remotely sensed data in ecological process models into some alternative strategies: (1) to provide estimates of variables which were required for driving ecological process models; (2) to test, validate or verify predictions of ecological process models; and (3) to update or adjust ecological process model predictions. With the wide use of GIS in ecological studies (Elston and Buckland, 1993; Congleton et al., 1996; Hill, 1998; Skop and Sørensen, 1998), it become more and more significant for spatial simulation. At the same time, ecological modeling carries through a thorough reform. For instance, applications of object-oriented design (OOD) in the modeling of biological systems are becoming more and more popular (Baveco and Lingeman, 1992; Silvert, 1993; Ferreira, 1995; Downing and Reed, 1996; He et al., 1999; Hong et al., 1999). Ecologists (Gao, 1994; Mooij and Boersma, 1996; Power, 1996; Congleton et al., 1997; Clemen, 1998; Bobba et al., 1999; Gauthier et al., 1999) began building some environmental information systems or frameworks, to provide software tools for ecological simulation study. However, the landscape modeling falls behind greatly with the developments of some high-tech, e.g. information science, computer science, GIS, RS, Global Position System (GPS), and Digital Earth (http://digitalearth.gsf.nasa.gov/ VP19980131.html). Lorek and Sonnenschein (1998) gave the long list of problems in the individual-oriented models e.g. 1. Ecologists must be programmers. 2. Models equal computer programs.
3. There is no separation between models and experiments. 4. Analysis depends on models. 5. There are no methodical supports. 6. There is no interaction and visualization between users and the simulation. 7. It is difficult to communicate between theoretical ecology and field ecology. There are some other problems that always hinder the progress of the landscape model. We can give some instances of them. (1) No model can address issues at all scales, and thus ecologists must build numerous models for different researching scale, at the time that they have not found the excellent techniques for scaling up (or scaling down) their models. (2) Explicit and inexplicit assumptions must be made according to the researching subjects and region, which restricts the model to be used on other subjects or regions. (3) All model programmers have to deal with the large amount of data, which hinder them to focus on the ecological processes. (4) Little software is available for ecologists to make direct use of the remotely sensed data to build their model, or to do some statistical analysis. (5) The great gap between ecological model and GIS prevents ecologists from making efficient use of the powerful tool — GIS to spatially analyze the ecosystems. As there is no model can fit for all scales, it becomes significant to build a programming platform for spatial simulation. We can use it to manage the geo-referenced data, to provide some common statistic methods, to build a platform for programming, to make full use of the remotely sensed data, to integrate the landscape model with GIS perfectly, and to build some programs or display their fruits on the Internet. Digital Ecological Model is a piece of software that was designed and developed for this purpose.
2. Methods To build a process-based landscape model, one always goes through some procedures after prescribing the researching purposes and objects: (1) data processing; (2) model definition; (3) model building, programming and parameterization; (4)
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model validation; and (5) results analysis. Digital Ecological Model is a platform to assistant ecologists to build their landscape model favorably. It is composed of six parts — DEMGIS, DEMTSA, DEMSTA, DEMMOD, DEMVIEW, and DEMAPPLET. Fig. 1 illustrates the basis structure of Digital Ecological Model. DEMGIS is directed to feature main functions of GIS, such as building of digital elevation model, management of geo-referenced database, translation of vector data into raster data, and graphics analysis with different projections. DEMTSA is used to interpolate the climatic data (collected from meteorological observation station) into raster data, with trend surface analysis method (TSA) and interpolation method. As a plug-in for GIS, DEMSTA is a tool for statistic analysis. It has been found to be useful for discovering the principles of ecological processes in a large landscape, and helpful for model definition. DEMMOD is the most impor-
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tant platform for building a process-based landscape model. It can be used to manage the experimental data, climatic data, remotely sensed data and topography data in the model conveniently. Therefore, ecologists could concentrate on the ecological processes, instead of swarms of data, and the model programming. What they should do is to ‘write’ the model with the visual interface — Visual Programming Interface of Digital Ecological Model (DEMVPI) and then the model will be written into Ecological Description Language of Digital Ecological Model (DEMEDL) automatically. DEMMOD is a simple, visual, object-oriented and interpreted platform. It uses Ecological Model Interpreter of Digital Ecological Model (DEMEMI) to interpret and run the ecological model, create the geographic graphs of predefined variables with requested projections, and statistically analyze the simulated results with experimental data. As geo-
Fig. 1. The structure of Digital Ecological Model.
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graphic graphs are always strict for publication, we can use DEMVIEW to edit the graphs. DEMAPPLET can link a Java applet with georeferenced database (such as simulated results or remotely sensed data) to be shown on the Internet. Digital Ecological Model was programmed with the object-oriented language — Java, with the free software — JDK1.2, downloaded from the homepage of Sun Company (http:// java.sun.com/). Application programs and Java applet are the two formats.
2.1. DEMGIS DEMGIS is a software to accomplish major functions of traditional GIS. It can translate vector data (e.g. DXF format drawn with AutoCAD) into raster data. It can build a digital elevation model and graphically analyze the geo-spatial data (raster or vector data) with sorts of projections (e.g. orthographic projection, conical projection, cylindrical projection, Mercator projection, azimuthal projection, and spherical projection). But there are some remarkable differences between DEMGIS and traditional GIS software, as DEMGIS is based on the Representational Model of Geo-referenced Object (RMGO), E(S, T, A)
(1)
where E() is the representation of digital geo-referenced objects; S are spatial coordinates, such as longitude, latitude and elevation; T are temporal coordinates; A are attributes of geo-referenced objects. Both the topographic and the temporal relationships are assigned importance levels to express the attributions of geo-referenced objects. Each object will be impacted by other objects, especially by the adjacent objects, and thus change diversely and show different states.
2.2. DEMTSA Raster climatic data are significant for spatial simulation study. Interpolation is a common method for interpolating the climatic data that come from distantly scattered meteorological observation stations into raster data, which has its benefits for simplicity and convenience, and vice
versa for disability of analyzing spatial regulations of scattered data. Trend surface analysis (Chorley and Haggett, 1968; Gittins, 1968) is a statistical model with superiority on describing the spatial (or spatial-temporal) distribution regulations of certain factors. This technique (Cliff et al., 1975) uses multiple-regression method to separate trend from residual variation. Longitude, latitude and sometimes elevation are always selected as factors for trend surface model (Pan and Zhou, 1984). In a large-scale, the spatial distribution of climatic variables are impacted by large-scale environmental factors, such as latitude, distance from sea, influence of great mountains, plateaus or basins, and atmospheric circulation backgrounds, and local environmental factors, e.g. slope gradient, slope aspect, shading degree, hypsography, vegetative and microclimatic factors. In some ways (Gao and Lu, 1988), longitude, latitude and elevation can be regarded as the dominant factors that effect the climatic characters of an extensive region. But climatic distribution of variable has its non-determinacy, for the spatial-temporal regulations will be fluctuated by random environment. It is found that in a certain extension, with the short of temporal scale, the functions of random events will ‘cover up’ the spatial-temporal regulations greatly. To decrease the function of random events, we integrate the spatial function by time and get the average spatial distribution of climate variables in a certain period. Hypothesizing that for some certain climatic variables, e.g. temperature, precipitation, relative humidity and sunshine fraction, the functions of large-scale environmental factors and local environmental factors can be separated from each other, and we can get, Yp = fp{, u, H}+gp{h, i, V...} +|p
(2)
where Yp is average spatial distribution of climate variables; is latitude; u is longitude; H is elevation; h is slope gradient; i is slope aspect; V is vegetative factor; fp is the function of large-scale environmental factors; gp is the function of local environmental factors; and |p is function of random events. TSA is a special multivariate statistical analysis method, which has superiority for spatial (or spatial–temporal) analysis and separating functions
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of large topography factors from local environmental factors and random events. Pan and Zhou (1984) gave the mathematical theory of TSA. Supposing a dot in a three-dimensional space, with coordinate of (xt, yt, zt) and observation data of wt, we use three-dimensional trend surface models (TSM) to simulate spatial distribution of such dots aggregate, W = GA + m
(3)
where W is matrix of observation data, and m is residual matrix, both are vector-matrix (N ×1); N is the number of observation data; G is vectormatrix (N×q); A is matrix of coefficients (q ×1); and q is determined by s, rank of TSA. (s +1)(s + 2)(s +3) q= 6
(4)
Here we give D, one row vector of G, which has been omitted the subscripts for code of observation dot, D ={1, z, z 2 …, z s, y, yz, yz 2 … yz s − 1 …, y s − 1z, y s, x, xz, xz 2 …, xz s − 1, xy, xyz, …, xyz s − 2 …, xy s − 1, x 2x 2z …, x 2z s − 2, x 2y, x 2yz …, x 2yz s − 3 …, x s − 1y, x s} (5) TSA can be calculated with least squares method or generalized inverse matrix method. As the second method has its superiority for accuracy, and convenience for higher order TSM, it is used here. With Gram–Schmidt’s method of orthogonalization (Pan and Zhou, 1984), we can calculate the Moore –Penrose’s generalized inverse matrix G + of real matrix G, and get the shortest least squares solution A0 of A, A0 = G+Z
(6)
With TSA, we can get the function of largescale topography easily, as we use latitude, longitude and the elevation for the three independent variables, and use climatic variables (e.g. temperature, precipitation, relative humidity, and sunshine fraction) for dependent variables. After getting the TSM, and the residual matrix, we can calculate grid data of certain climatic variables,
Y. i, j = Di, jA0 T
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(7)
where Y. I, j is the simulated result of climatic variables, and A0 T is coefficient vector. The residuals can be regarded as the synthetical functions of local environmental factors and random events, which cannot be explained by largescale topography analysis. Residuals are interpolated into each grid, to get fully information about spatial distribution of climatic variables. The distance from neighbor meteorological stations is used as the impact coefficient, and the interpolation method is expressed as, t
% Vi /di VP =
i=1 t
(8)
% 1/di
i=1
where VP is interpolated result of climatic variable; di is distance from neighbor station; Vi is residual of neighbor station; and t is the number of neighbor stations. By searching at four directions (north, south, west and east), the nearest six stations are selected with at least one station at each direction, and the residuals of these six stations are used for interpolation. By adding the TSM results and the interpolation results, we can get the raster data of each climatic variable.
2.3. DEMSTA DEMSTA provides some widely used statistic methods; e.g. correlation analysis, variance analysis, multiple linear regression, stepwise regression, principal component analysis, discriminatory analysis, fuzzy cluster analysis, and TSA. Both experimental data saved in ‘dbf’ or ‘txt’ format can be used directly with DEMSTA. Furthermore, DEMSTA was designed at the very start as a plug-in for GIS on a personal computer and the Internet. It has been proved to be useful to reveal the principles by statistical analysis on landscapes.
2.4. DEMMOD Landscape model differentiates greatly from the patch model. Both the heterogeneous environmental factors and other objects (especially the
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adjacent objects) will affect the development of the focused object. Gao (1994) gave the differential equation models to describe the process of landscape model,
(ui (uj ( 2uj (uj ( 2uj , Pk, x, y, t =fi uj, , , , (t (x (x (y (y 2 i, j =1, 2, … , n k =1, 2, … , m
(9)
where ui, uj are the state variable vector of dimension n; Pk is the parameter vector of dimension m; x, y denote spatial coordinates and t denotes time. To define the ecological models, we use an Ecological Description Language of Digital Ecological Model (DEMEDL) to describe the mechanisms of most of the ecological processes described by Eq. (9). Furthermore, we built the software named as Visual Programming Interface of Digital Ecological Model (DEMVPI) to record an ecological model in DEMEDL. With this tool, it becomes simple for an ecologist to ‘write’ a landscape model on computer, with his own data and model definition, to describe the ecological processes in his interesting region and scale, to solve the ecological problems and to draw his conclusion. We classify the possible variables in landscape models into some sorts: environmental variables, system variables, state variables, parameters, constants, spatial variables, and temporal variables. In some ways, we can regard the climatic, edaphic and geographical variables as the environmental variables, because they always carry the environmental background. The climatic raster data can be generated from the spot records of meteorological observation stations with DEMTSA. The edaphic and geographical raster data can be transformed from vector data with DEMGIS. Then we can get the raster data of these environmental variables. The remotely sensed data can also be considered as a sort of ‘environmental variable data’, because they describe the integrated information of environment. System variables are some important variables used to create geography map for analysis, or generate the register files for model validation. They should be marked clearly and cautiously, as the DEMMOD will log them at regular intervals. The state vari-
ables represent the variables necessary in the middle progress. Parameters vary in different spatial-temporal ‘cells’, while constants are fixed. The spatial variables describe the spatial position of ‘cells’, e.g. longitude and latitude, or x and y coordinate. The temporal variables describe the period that can be represented with absolute time or the step sequence. DEMMOD provides a special sorts of variables, named ‘artifical variables’. These ‘artifical variables’ can only be generated from the original data, especially the environmental data. But they are necessary in spatial simulation, such as the slope gradient and slope aspect, the exoatmospheric solar radiation and the duration of sunshine and sunset. Furthermore, these ‘artifical variables’ can be calculated with some sound methods. Here, we use the digital elevation model (Ke et al., 1992) to calculate the slope gradient and slope aspect, and used the digital elevation model (Dozier and Outcalt, 1979; Dozier and Frew, 1990) and analytical equations (Fu, 1983; Fu and Weng, 1994) to calculate the exoatmospheric solar radiation and duration of sunshine and sunset. Some expression symbols, conditional expression, cycle expression are provided in DEMMOD also. With the assistants of DEMVPI, ecologists can build a landscape model with simple and convenient operations, just like clicking the mouse or inputting a character or number. The model will be written into DEMEDL and thus a file with extension name as ‘mod’ will be created automatically to record this model. Ecological Model Interpreter of Digital Ecological Model (DEMEMI) is responsible for interpreting the program written in the DEMEDL, compiling, running the model and displaying the result. It can ‘understand’ the model and check mistakes. Then, it will run the model, create the log files for recording system variables, and display the results. As the whole system was written with object-oriented language — Java, and the program has been compiled into ‘classes’, DEMEMI can use the functions of DEMGIS directly to generate a geography map with different projections, or use the functions of DEMSTA to statistically analyze the simulated results with experimental data.
Z. Shang et al. / Ecological Modelling 139 (2001) 235–252 Table 1 Comparison between simulated results with trend surface model and observed data Climate variables
N
R2
Mean temperature Precipitation Mean relative humidity Mean sunshine fraction
25 524 25 236 25 236 24 852
0.9898 0.8765 0.8826 0.9008
2.5. DEMVIEW and DEMAPPLET DEMVIEW is the software to view and edit the created geography graphics with DEMMOD or DEMGIS. DEMAPPLET is used to link the database with a Java applet for displaying the geo-referenced data on the Internet.
3. Application of digital ecological model, a case study A major application (Clemen, 1998) of environmental information systems is the simulation of landscape management and global change scenarios. Predictions for the global greenhouse effect for the mid-21st century gave increases in mean air temperature of between 1.5 and 4.5°C and altered precipitation (Houghton et al., 1990). Tang et al. (2000) estimated that the climate under doubled atmospheric CO2 concentration with three sound GCMs: HadCM2, CGCM1 (The first version of the Canadian Global Coupled Model) and ECHAM4. The results show that air temperature will increase for about 2.5, 3.5 and 2.6°C, and the total precipitation will increase for 10.4, 4.7 and 10.4%, respectively, in China. As a case study, we give three prescribed climate scenarios, which include raising mean temperature by 1.5, 3.0 and 4.5°C, and raising precipitation by 10%. A spatial model was built with DEM to calculate the spatial distribution of precipitation, potential evapotranspiration, and aridity index under the present situation and these three prescribed climate scenarios, to assess the potential impact of global climate change on China water condition.
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3.1. Pretreatment Large-scale relief maps and topography raster data were used to build digital elevation models of China with DEMGIS. As TSA is fit for large scale, China area was divided into eight subareas — (1) Northeastern China; (2) Northern China; (3) Middle China; (4) Southern China; (5) Southeastern China; (6) Inner Mongolia; (7) Northwestern China; and (8) Tibet plateau. To avoid the difference of climatic variables’ spatial distribution among these subareas, each subarea was overlapped and included some area of neighboring subareas. With DEMTSA and monthly meteorological data of 954 meteorological stations of China, from 1951 to 1980, three rank’s trend surface models (TSM) were calculated to analyze the spatial distribution regulations of four climatic variables (i.e. mean air temperature, precipitation, mean relative humidity, and mean sunshine fraction) of 12 months for these eight subareas. Table 1 shows the comparison between simulated results and observed data of these four climatic variables. Fig. 2 shows the spatial distribution of annual precipitation. With TSM and interpolation method, we built the raster database of these four climate variables of 12 month. In the simulation study, we found that TSA is not suitable for spatial analysis on wind velocity, so the raster data of mean wind velocity were calculated with interpolation methods only.
3.2. Mathematics description Amended Penman’s equations (Monteith, 1965; Frere and Pruitt, 1979) were widely used to calculate the potential evapotranspiration, for its sound physics theory. Xie et al. (1991) developed an amended Penman’s method for China, ETP =
!
"
P0 D n × x× QA a+ b P k N
− |T 4(0.56− 0.079 ea) 0.1+0.9 + 0.26(ea − ed)(1+ c× U2) /
n N
n
P0 D × +1 P k (10)
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Fig. 2. Spatial distribution of annual precipitation (mm) in China.
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where ETP is potential evapotranspiration; P0 and P are air pressure on the sea level and at the research spot; D is curve slope gradient of saturated vapor pressure’s change with temperature; k is psychrometric constant; Qa is exoatmospheric solar radiation; ea is saturated vapor pressure, and ed is water vapor pressure; U2 is wind velocity at height of 2m; | is Stefen-Boltzman constant; n is amending coefficient; a, b and c are coefficients. Aridity index (Gao and Lu, 1988) has been used as an index for arid-humid climate analysis. Here the aridity index is expressed as, AI = log10
ETP R
(11)
where AI is aridity index; ETP is potential evapotranspiration; and R is annual precipitation. With the assistants of DEMVPI, we built a spatial model to estimate potential evapotranspiration and aridity index. The model was written in DEMEDL with DEMMOD automatically. Then, we used DEMEMI to run the model, log the results and create geographic graphs.
3.3. Simulation results The monthly climatic data of 905 meteorological observation stations during the period of 1951– 1980 were used to calculate the potential evapo-
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transpiration. Fig. 3 shows the comparison between calculated monthly potential evapotranspiration and 10 860 effectively monthly-observed pan evaporation data of 905 stations. The correlation is very well given by (R 2 = 0.9288). Fig. 4 and Fig. 5 show the distributed potential evapotranspiration and the aridity index of China. Fig. 6 shows the aridity index under three prescribed climate scenarios, which include raising mean temperature by 1.5, 3.0 and 4.5°C and raising precipitation by 10%. The area of potential evapotranspiration and aridity index of each range were accumulated to get the percentage distribution of China (see Fig. 7 and Fig. 8). To show the difference clearly, we calculated the average potential evapotranspiration and aridity index of China under present situation and the three prescribed climate scenarios (see Table 2). With the raised temperature, the areas of potential evapotranspiration that less than 2000 mm reduce, and the areas that larger than 2000 mm increase sharply. So we can conclude that the potential evapotranspiration will increase with the raising of temperature. Concerning with the change of aridity index under three prescribed climate scenarios, it is very clear that China becomes a slightly wetter under the first prescribed climate scenario, and a little drier under the last prescribed climate scenario than the current
Fig. 3. Comparison between simulated potential evapotranspiration and observed monthly pan evaporation.
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Fig. 4. Spatial distribution of potential evapotranspiration (mm) in China.
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climate condition. However, the second climate scenario brought a little change. 4. Discussion Ecological modeling varies diversely in the
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world, and it is impossible to construct a special model to solve all the problems for all scales. But we can draw some common ground by investigating most of the ecological models. Ecologists should design some experiments with explicit purposes, and collect correlative data as much as possible. Then, they need process the data, do
Fig. 5. Spatial distribution of aridity index in China.
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Fig. 6. Spatial distribution of aridity index in China under three prescribed climate scenarios; (a) raising mean air temperature by 1.5°C and precipitation by 10%; (b) raising mean air temperature by 3.0°C and precipitation by 10%; and (c) raising mean air temperature by 4.5°C and precipitation by 10%.
some statistics, conceive their models, and define the models with mathematical description, letters
and figures. The subsequent task is programming, which will take most of their time. Most of the
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software used in RS and GIS can not provide high capability for modeling analysis. So, if ecologists want to build their models with the assistance of remotely sensed data and GIS, they
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should also probe into some basic computer technology used in these fields. Therefore, it is very significant for modelers to create validated ecological modeling platforms to help ecologists process-
Fig. 6. (Continued)
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Fig. 6. (Continued)
ing the data, statistically analyzing and revealing the ecological principles, and employing the remotely sensed data and GIS into ecological model directly. The quick developments of high-tech, e.g. RS,
GIS, GPS, and the Digital Earth provide some favorable tools for ecological study. At the same time, the developments of high-tech need the collaboration of ecological studies to solve the environmental problems. Ecological study, including
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Fig. 7. Percentage distribution of potential evapotranspiration in China under present situation and three prescribed climate scenarios; (a) present situation; (b) raising mean air temperature by 1.5°C and precipitation by 10%; (c) raising mean air temperature by 3.0°C and precipitation by 10%; and (d) raising mean air temperature by 4.5°C and precipitation by 10%.
ecological modeling, can express the profound processes of some important environmental issues, such as global change, protection of bio-diversity, sustainable developments and agricultural production. We tried to build a platform for building of landscape models, and provided the tools for statistical analysis and application of remotely sensed data and GIS directly in landscape modeling. The computer technology, statistical analysis, RS, GIS and Digital Earth were integrated into this platform, to reveal the ecological regulations with landscape modeling. It will be helpful to understand the ecological processes and principles of spatial pattern.
5. Conclusions Digital Ecological Model can be regarded as a piece of programming platform, which integrates the GIS, remotely sensed data analysis and statistic analysis into spatial simulation. It will enable ecologists to build process-based landscape model on this platform and display their fruits on the
Internet. This software was programmed with Java, as application programs to be run at any operating systems. Some programs, including Table 2 The average potential evapotranspiration and aridity index of China under different climatic scenarios Climate scenario
Average potential evapotranspiration
Average aridity index
Present climatic situation Raising mean temperature by 1.5°C and precipitation by 10% Raising mean temperature by 3.0°C and precipitation by 10% Raising mean temperature by 4.5°C and precipitation by 10%
1932.98 (mm)
0.7402
2035.97 (mm)
0.7205
2140.83 (mm)
0.7424
2247.48 (mm)
0.7636
250 Z. Shang et al. / Ecological Modelling 139 (2001) 235–252 Fig. 8. Percentage distribution of aridity index in China under present situation and three prescribed climate scenarios; (a) present situation; (b) raising mean air temperature by 1.5°C and precipitation by 10%; (c) raising mean air temperature by 3.0°C and precipitation by 10%; and (d) raising mean air temperature by 4.5°C and precipitation by 10%.
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statistic analysis and graphic edition can also be conducted on the Internet as Java applets. As a case study, a spatial model was built with DEM, to calculate the potential evapotranspiration and the 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°C, and raising precipitation by 10%, to assess the sensitivity of China water condition to global climate change. The simulated potential evapotranspiration increase greatly with the raising of air temperature. However, the changes of aridity index are complicated. Comparing with present situation, China will become a little wetter under the first climate scenario and a little drier under the third climate scenario. The aridity index of China under the second climate scenario does not change as great as that under the other two scenarios. These may be because the raising precipitation will counteract the increase of potential evapotranspiration. Acknowledgements This research was jointly supported by Chinese National Natural Science Foundations (Numbers 39899370, 39770133, and 39725006). We would like to express our sincere appreciation to Laboratory of Quantitative Vegetation Ecology, which provided the climatic data, relief map and raster elevation data. References Baveco, J.M., Lingeman, R., 1992. An object-oriented tool for individual-oriented simulation: host-parasitoid system application. Ecol. Modell. 61, 267 – 286. Bergen, K.M., Dobson, M.C., 1999. Integration of remotely sensed radar imagery in modeling and mapping of forest biomass and net primary production. Ecol. Modell. 122, 257 – 274. Bobba, A.G., Singh, V.P., Bengtsson, L., 1999. Application of environmental models to different hydrological systems. Ecol. Modell. 125, 15 –50. Chorley, R.J., Haggett, P., 1968. Trend-surface mapping in geographical research. In: Berry, B.J.L., Marble, D.F. (Eds.), Spatial Analysis: A Reader in Statistical Geography. Prentice-Hall Inc, Englewood Cliffa, NJ, pp. 195 – 217.
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