Architecture

A framework Consisting of Simulation Models, Inputs, Analytical outputs, and Web application .

Modeling Application System Interactive framework (MASIF) whose components are shown in this figure is characterized by: (1) a scalable data management module for rapid and ready access to input and output data; (2) a visualization module for the exploration, description, and analysis of spatial and temporal patterns; (3) a statistical analysis module to conduct and compare model scenarios; (4) an output animation module to produce spatio-temporal time series of model output; (5) Web based interface to interact with the model (Fig. 1).

Fig. 1 Integration of Models and input output analysis.

Major components

Framework. Masif includes six software products: Visual Basic 6.0™,Oracle 8.05™, MS Access 2000™, ArcView 3.2™, MineSet 3.0™, and S-Plus 2000 2.0™. These products represent a class of existing upgradeable applications that are inherently useful for the analysis of large data sets, are widely used worldwide, and include libraries that facilitate interconnections. The major steps in the implementation of MASIF included: (a) the design of database tables for data storage and management; (b) the development of the interface to the geographic information systems (GIS) module; (c) the establishment of connections between the Visual Basic™ interface and the analytical software; and (d) the development of a user interface to integrate a specific model to the various software options (Fig. 2).

Fig. 2 MASIF software products and their connectivity.

Databases. Database tables contain the data infrastructure for MASIF: Input weather variables, soil variables tables and output variables tables, and a parameters table. The process of using a parameters table allows users to store parameters during different model runs rather than storing millions of records of model output data associated with parameter modifications. Users can recall the parameters used for a specific simulation run and re-run the model.

Mapping. Geographic information system (GIS ) module. To perform in-line mapping of simulation results, we integrated a geographic information system into MASIF by developing a set of scripts to access model output, conduct spatial interpolation of selected output variables at any place in time, and present the resulting map(s) on the computer display. The scripts were developed using ArcView™ Avenue™. In addition, modelers have access to the ArcView™ interface which allows them to optimize other GIS analytical tools provided by the software.

Connectivity. The connection between ArcView™ and Visual Basic™ is established using DDE, an MS Windows™ supported client/server mechanism that enables two applications to communicate. When the user selects the GIS option, the interface (Visual Basic™) initiates communication with ArcView™ and sets it up as the server. Visual Basic™ thus becomes the client and requests ArcView™ to implement specific tasks by issuing Avenue commands through the Dynamic Data Exchange (DDE) channel. Through Data Dynamic Exchange (DDE), Visual Basic™ launches ArcView™, closes the default ArcView™ project, opens the project containing the scripts and calls the master script with the user specified model parameters as input. Thereafter, the master script calls the necessary scripts within the project to produce the analysis specified by the user.

The connection between MineSet™ and Visual Basic™ was developed using ActiveX technology. MineSet™ provides an ActiveX control, named VizComposite, that is able to display any type of MineSet™ visualization within a Visual Basic™ application. When the user selects the Multidimensional Visualization option, a MineSet™ schema file is created based on the user input and passed to the VizComposite control. The control then interprets the schema file appropriately and displays the visualization within Visual Basic™.

The connection between S-PLUS™ and Visual Basic™ was made using an Object Linking and Embedding (OLE) Automation, which enables one application (client) to access the resources and functionalities of another application (server). In MASIF, S-PLUS™ is the application server that provides resources in the form of Type Libraries, a collection of objects, functions, properties and methods. The interface (Visual Basic™) is the application client that calls the appropriate

Type-Library entities to execute the analysis requested by the user. Upon the execution of a given S-PLUS™ command, the resulting graph-sheet or report is returned to Visual Basic™ as an OLE object and is thus embedded into MASIF.

Simulations Process

A computing process that runs the desired Model and provides the input weather and soil data to generate outputs for a selected county, all North central region, Resac region with the appropriate parameters chosen. If the model requires weather data a database weather table provides 1055 daily weather parameters (Fig. 3).

Fig. 3 Simulation Process

Analysis Process

A computational and mapping process that converts the generated model outputs into GIS mapping images through Raster generation step and subsequently shows such images to the users for analysis and interpretation through Raster Display, Model Comparison, Actual Comparison, and Animation (Fig. 4,5,6,7,8,9,10).

Fig. 5. Analysis Process Steps

Fig. 6 Analysis - Raster generation step.

Fig. 7 Analysis – Raster Display.

Fig. 8 Analysis – Model comparison.

Fig. 9 Analysis – Actual comparison.

Fig. 10 Analysis – Animation.

Web Application

A Web application has been developed using the final Models outputs. The yearly and daily analysis of Corn and Hybrid Maize have been completed and other models are under development. Corn and Hybrid Maize outputs are displayed through line charts, bar charts, and the regional maps that are generated separately by models. At the bottom of each output page weather data menus were implemented to provide a history of the weather variables data for the selected county. In addition, a regression analysis image output for the displayed region was presented. The MASIF web site also provides a 1055 county regional actual graph data analysis to show the 30 years history of corn production fluctuations. The link to the web is: http://masif.cevl.msu.edu/models.asp

Chapter

Corn Model – Sinclair - Muchow

A daily Simulation model of growth and production of corn.

uchow model simulates the corn growth and production on a daily weather input. The input variables consist of five variables for 1055 locations amounting to 11.9 million records, was structured in a relational database management system using Oracle™. The Analysis step generates Mapping images from the simulated output database.

Simulation

To Run Muchow simply click on the MASIF icon, and Model and Muchow buttons. From the Model Muchow – Linear Calculation screen select Year, Region, Parameters, and Available Water. From the year menu select one, several, or all the years. From the region menu you have the option to select one, several, All counties, or Resac counties. Click on the Run Model button and wait until the simulation is completed (Fig. 1).

Fig. 1 Model Muchow – Linear Calculation

When simulation is completed then by following the steps in the Analysis section will generate the map. In Raster Generation from the MODEL and SIM. NO. menu select Muchow and the simulation number and then Parameters, years, and days. Click on the Execute button to generate the map, Raster Display will show the map on the screen, Model Comparison compares the output maps from the two models with Chi-Square statistics and subtractions. Actual comparison compares the model with Chi-Square statistics and subtractions. Animation shows the animation production image progress for a particular year (Chapter 8).

Database Tables

· Input – NEWWEATHER

· Input – MODELTRANSACTION

· Output – MUCOUTPUT

Flowchart

Muchow Corn simulation model flowchart and input output processes. It starts by reading from model transaction and weather tables parameters and looping through years and regions and calling the Corn executable program and updating the output tables.

Fig. 2 Corn – Muchow simulation flowchart.

The input output variables and the components of the Corn Model used as the executable model in MASIF ( Fig. 3).

Fig. 4. Corn maize Model.

Program – The simulation model program is located in this folder:

C:\Masif\code\MuchowModelorig

Analysis – The detailed analysis phase is indicated in chapter 7.

Chapter

Hybrid Maize

A corn simulation Model that uses daily weather input data and generates daily outpust.

Hybrid-Maize developed by Haishun Yang. It is a program that simulates maize growth on a daily basis from emergence to physiological maturity. This version of the program simulates maize growth under optimal water and nutrient conditions. As a result, the simulated maize yield represents yield potential as determined by crop genotype and local weather conditions. The current version includes an option of water limited condition (either rainfed or irrigated), but this part of the model is still under validation. Thus, caution must be exerted when interpreting results using this option.

For simulation under optimal water and nutrient conditions, it requires daily weather data of solar radiation, and high and low temperatures; for simulation under water limited conditions, it requires extra weather data, including daily rainfall, reference evapotranspiration, and relative air humidity, as well as basic soil information, including initial soil moisture content, soil texture and bulk density. Efforts are currently being made to include nutrient limitations in maize growth simulation.

It has two options: optimal and rainfed/irrigated. For optimal (default), no other parameters need to be set. If rainfed/Irrigated is selected, a daily irrigation schedule should be filled in the grid field, but not necessarily in the sequence of time. If no irrigation events are found, the run will be treated as rainfed.

When running Current season prediction mode under Rainfed/Irrigated, the option ‘Assuming no water stress in prediction phase’ become available. When it is checked, optimal water condition will be assumed for the period beyond the up-to-date weather data of the current season (i.e., the forecasting phase), as irrigation can’t be planned without real weather data. If this option is unchecked, the model will run as usual. Under rainfed conditions (i.e., no irrigation events), however, checking this option will have no effect (the model will automatically uncheck it if it happens to be checked in this case).

It has four setting but they only need to be specified when running under rainfed/irrigated conditions. They are gravimetric water content of top-soil at start of the simulation, bulk density of the topsoil and subsoil, and maximum rooting depth. Depending on soil texture and structure, the bulk density of top soil ranges from 1.1 to 1.4 g cm^-3, while the value for subsoil is normally higher. The maximum rooting depth of maize is normally around 1 m (or 3 ft) in fields without seriously compacted subsoil layers. The value must be > 40 cm (or 16 inches) even in case of shallower rooting depth.

When simulating yield potential (i.e. under optimal water conditions) the Hybrid-Maize model requires three daily weather variables to run: total solar radiation, high temperature (T-high), and low temperature (T-low); when simulating under irrigation or rainfed conditions, extras three daily weather variables are also required: relative air humidity, rainfall, and reference evapotranspiration (ET). The data must be in a plain text file with format complied to the model specifications. The most important principle of the data format is: metric units and correct data placement. Detailed specifications for the data format are:

(1) Metric units. Solar radiation = MJ/m², temperature = ^oC, relative humidity = %, rainfall and ET = mm. If the data obtained are in other units, they have to be converted. If the data are in English units, it is most likely daily solar radiation in Langley (1 Longley=41.868 MJ/m²), and temperature in ^oF (1 ^oF=(1 – 32)/1.8 ^oC), and rainfall and ET in inch (1 inch = 25.4 mm).

(2) One day takes one row, and the variables must be in the right order in a row and separated from each other. From left to right in a row the variables must be: year, DOY (day of year), solar radiation, T-high, T-low, humidity, rainfall, and reference ET. Each value must be separated from others, either by space (one or more) or tab (one or more). For real values like temperature and rainfall, there is no limit to the number of decimals. When simulating yield potential (i.e. under optimal water conditions) and humidity, rainfall and ET are not available, the three variables must be entered as 0.

(3) The first row of the file is site information, and all the text in this row will be copied as ‘site info’ to output file of a simulation run.

(4) The second row is for the value of latitude of the site. For south hemisphere, the value must be negative. Any other text (if there is) in this row must be separated by one or more spaces or tab, and will be ignored when the program runs. If the program can’t find a value at the beginning of the second row, a warning message will pop up and the simulation will abort.

(5) The third and four row from top are for variable titles and units, but they will be ignored by the program when reading the data. For additional information refer to User’s Manual of the Hybrid-Maize Model by by Haishun Yang.

Simulation

To Run Hybrid Maize simply click on the MASIF icon, and Model and Hybrid Maize button. From the Hybrid Maize – select Year, Region, Parameters, and Water condition. From the year menu select one, several, or all the years. From the region menu you have the option to select one, several, All counties, or Resac counties. Click on the Run Model button and wait until the simulation is completed Fig. 1.

Fig. 1 – Hybrid maize Model.

The flowchart for HybridMazie simulation is indicated in Fig. 2. It starts with checking years, regions, weather, soil parameters, and running the executable Hybridmaize program and writing the output data into the HYBOUTPUT table.

Fig. 2 – Accessing and processing Hybrid Maize.

Database Tables

· Input – NEWWEATHER

· Input – SOIL

· Input – MODELTRANSACTION

Output – HYBOUTPUT.

Analysis – The detailed analysis phase is indicated in chapter 8.

Chapter

Socrates

The potential impact of climate change on Soil carbon.

ocrates model developed by Peter Grace and simulates the impact of climate changes on the Soil carbon. The Socrates model has been divided into three different models. The EXE file for all the three models is the same. The reason the model was divided into three different models was because the Socrates model as a whole run in various different modes and creates multiple outputs. The MASIF framework was designed to work for models that created single output tables. To make Socrates model compatible with MASIF we divided the Socrates model into three different models and each now produced one output table. The output was the same as before as each model had common EXE.

Simulation. The three phases of Socrates are SocratesPre (Presettlement), SocratesCur, and SocratesFut. These Socrates are run through the simulation process and creates several output tables for analysis. Fig. 1 to 3 show the three simulation phases.

Fig. 1 PreSocrates

Fig. 2 – Current Socrates (SocratesCur).

Fig. 3 – Future Socrates (SocratesFut).

Procedures and applications

Before

Now

Thus now to make changes in Socrates model we still only need to replace that one EXE by a new updated one. The codes to the three models are in following folders:

PreSoc: C:\Masif\code\PreSoc

CurrSoc: C:\Masif\code\CurrSoc

FutSoc: C:\Masif\code\FutSoc

The above folders each contain a folder called Data. In the data folder there is an EXE called Socrates.exe. That is the EXE that would be updated when any changes are made to the code of Socrates model.

Steps to follow when changes in Socrates model are made:

1. Make changes to the Socrates code.

2. Make an EXE of the new update code by clicking

File -> Make projectName.exe (projectName is the name of your project)

3. After creating the exe file, go to the folder of that project and rename the projectName.exe to Socrates.exe

4. Now copy-paste that file in the Data folder of all three models. Make sure it is copied to all three models.

5. Now open .vpb file of each model one by one and click on

File -> Make projectName.dll (projectName is the name of the model)

6. Now go to the folder of each model, copy the projectName.dll and paste it in C:\Masif\plugin

7. The new model is ready to be used from MASIF.

Now to how the Socrates model actually works. The model is designed such that the output of the first model is input to the second one and the output of second is input to third and so on. The figure on next page briefly describes how the output is being generated. The CurrSoc and FutSoc both create two output files; one of them is the actual output while the second one is the summary of the output. MASIF uses the summary files. The summary files are bolded and italicized in the figure. The FutSoc gives user and option to run that model with or without climate change. Since the outputs produced by FutSoc had the same variables in both modes; the data for both modes are stored in same table.

The outputs to all the models are saved on different oracle database tables. The output tables are linked to a main table called modeltransaction. They are linked to main table by simulation number and the name of the model. The following figure shows how the data is stored in oracle.

To access the output of the Socrates model from outside of MASIF framework follow the following steps:

1. Click Start->Programs->Oracle-OUIHome -> Application Development ->SQL Plus.

2. This will start the oracle command line utility. Use the following information to log in to oracle

User Name: masif

Password: masif

Host String: masif.world

3. This will take you to a prompt. From this prompt you can query the different output tables. To list all the tables in the data base write the following SQL on the command. Select * from tab;

After running the analysis of simulations in MASIF and creating Raster and Images, the output images and animation images can be found in the following folders:

Images:

C:\Masif\plugin\Images\SimulationNumber.

Animation Images:

C:\Masif\plugin \Animation\ModelName\SimulationNumber.

Fig.4, 5, and 6 describes how the three sections of Socrates work.

Fig. 4. Pre settlement Socrates.

Fig. 5. Current Socrates.

Fig. 6 – Future Socrates.

Analysis. See chapter 8.

Chapter

Daycent

Soil carbon model.

aycent Model is a soil carbon model developed by Colorado State University and simulates the soil carbon Fig. 1.

Fig. 1 Daycent Model.

Simulation - To run the Daycent model simply click on the ProDaycent icon, select the regions and Run Model Fig. 2.

Fig. 2 – Daycent simulation.

The Simulation model process and modules are shown in Fig. 3.

Fig. 3 – Daycent simulation process.

Analysis. See chapter 8.

Chapter

SPLUS

Graphs Statistical Analysis for model Input Outputs.

SPLUS package is linked to MASIF to make statistical analysis on the model outputs in relation to weather input variables. Fig.1 shows the steps used in processing of the data.

Fig.1 – SPLUS – MASIF Flowchart

Chapter

Web – MASIF

Web application shows the model results associated with weather.

ASIF web application presents the graphical and mapping images of the model outputs in daily and annual basis associated with the input weather data. It can be used for research and non research purposes. This Web application also shows the 30 years of actual corn yield production in the North Central Region. To access the Web application simply activate this link on the browser:

http://masif.cevl.msu.edu/masif.asp

At this point the user has the option to select the Simulation models or accessing the actual data. By selecting the Simulation Model the next page will be displayed as:

By selecting corn model the user, for example, will access a daily or yearly outputs for a

region or yearly output for all regions as:

By selecting output and a county the annual yield will be shown as:

The daily or monthly weather input for the selected county is accessible as:

Other models and the actual data will be accessible by clicking on each activity and following other pages.

Chapter

Analysis – Mapping and images

Analysis phases that generate mapping images, displaying, and comparisons .

nalysis starts by ArchMap to prepare the generated simulated data into mapping images for displaying, comparison, validations and Animations. The Analysis flowchart shows how the modules and forms function to generate and display maps (fig. 1).

Fig. 1. Analysis steps from mapping generation to Animation.

Raster Generation. The first step of Analysis is the Raster generation which translates the generated simulated output data from the MUCOUTPUT database table into GIS mapping images for 1055 counties in the North Central region. The Analysis flowchart shows how the modules and forms functions to generate and display maps. A map should be first generated by the Raster Generation process to be able to be accessible by RasterDisplay, Model comparison, Actual comparison, or Animation. (fig. 2).

Fig. 2. Steps used in frmDispOP Raster Generation form to create a Map.

Raster Display. The Raster Display and change scale sections show how the image map is processed and the scales are changed by calling several modules.(Fig. 3 and Fig. 4).

Fig. 3. Steps used in frmDisplay to show the image Map.

Fig. 4. When mapping scales are changed.

Model comparison. Model comparison is a combination of Model display, Chi square , and Subtract (Fig. 5).

Fig. 5. ChiSquare and Subtract modules used in Model comparison.

Animation. The animation process is done for a model using simulation number, parameters, and years (Fig. 6).

Fig. 6. Animation process.

Appendix

variables

Defined Input – Output table variables.

Input Variables

Input Variables
Model Name	Model Input Table	Field No	Field Name	Field Description	Field Unit
Muchow	WEATHER	1	REGION	County	Number
Muchow	WEATHER	2	RESAC	wether the county is in resac region or not	Number
Muchow	WEATHER	3	XALB	Spatial info(corrdinates ALBERS)	Number
Muchow	WEATHER	4	YALB	Spatial info(corrdinates ALBERS)	Number
Muchow	WEATHER	5	YEAR	Year	Number
Muchow	WEATHER	6	DAY	Day	Number
Muchow	WEATHER	7	SOLRAD	Solar Radiation	MegaJoules/m2
Muchow	WEATHER	8	TMAX	Maximum Temperature	Celcius
Muchow	WEATHER	9	TMIN	Minimum Temp	Celcius
Muchow	WEATHER	10	PP	Precipitation	Millimeter
Muchow	WEATHER	11	DD	Degree Days	Number (celcius)
Muchow	WEATHER	12	H20	Water	Millimeter
Muchow	WEATHER	13	DDTOMAY	Degree Days to May	Celcius
Muchow	WEATHER	14	DEPTH	Depth of Soil 1000	Millimeter
Daycent	DAYCENTINPUT	1	REGION	County	Number
Daycent	DAYCENTINPUT	3	XALB	Spatial info(corrdinates ALBERS)	Number
Daycent	DAYCENTINPUT	4	YALB	Spatial info(corrdinates ALBERS)	Number
Daycent	DAYCENTINPUT	8	YEAR	Year	Number
Daycent	DAYCENTINPUT	6	DAY	Day	Number
Daycent	DAYCENTINPUT	5	DDATE
Daycent	DAYCENTINPUT	7	MONTH	Month	Number
Daycent	DAYCENTINPUT	9	JDATE
Daycent	DAYCENTINPUT	10	TMAXC	Maximum Temperature	Celcius
Daycent	DAYCENTINPUT	11	TMINC	Minimum Temperature	Celcius
Daycent	DAYCENTINPUT	12	PPMM	Percipitation	Millimeter
Daycent	DAYCENTINPUT	13	DD10	Degree Days starting with 10	Number
Daycent	DAYCENTINPUT	14	RESAC	wether the county is in resac region or not	Number
Hybrid Maize	WEATHER	1	REGION	County	Number
Hybrid Maize	WEATHER	2	RESAC	wether the county is in resac region or not	Number
Hybrid Maize	WEATHER	3	XALB	Spatial info(corrdinates ALBERS)	Number
Hybrid Maize	WEATHER	4	YALB	Spatial info(corrdinates ALBERS)	Number
Hybrid Maize	WEATHER	5	YEAR	Year	Number
Hybrid Maize	WEATHER	6	DAY	Day	Number
Hybrid Maize	WEATHER	7	SOLRAD	Solar Radiation	MegaJoules/m2
Hybrid Maize	WEATHER	8	TMAX	Maximum Temperature	Celcius
Hybrid Maize	WEATHER	9	TMIN	Minimum Temp	Celcius
Hybrid Maize	WEATHER	10	PP	Precipitation	Millimeter
Hybrid Maize	WEATHER	11	DD	Degree Days	Number (celcius)
Hybrid Maize	WEATHER	12	H20	Water	Millimeter
Hybrid Maize	WEATHER	13	DDTOMAY	Degree Days to May	Celcius
Hybrid Maize	WEATHER	14	DEPTH	Depth of Soil 1000	Millimeter
Pre Socrates	SOCINPUT	1	REGION	County	Number
Pre Socrates	SOCINPUT	2	AREA	Area	m2
Pre Socrates	SOCINPUT	3	TMAX	Max Temperature (mean Annual)	Celcius
Pre Socrates	SOCINPUT	4	TMIN	Min Temperature (mean Annual)	Celcius
Pre Socrates	SOCINPUT	5	PP	Percipitation (mean Annual)	Millimeter
Pre Socrates	SOCINPUT	6	CLAY	Percent of clay (0-10 cm)	Percents
Pre Socrates	SOCINPUT	7	CO	Dominant Land Class	Number
Pre Socrates	SOCINPUT	8	PLANTED	Land Use Proportion	Number
Pre Socrates	SOCINPUT	9	SHRUBLAND	Land Use Proportion	Number
Pre Socrates	SOCINPUT	10	FOREST	Land Use Proportion	Number
Pre Socrates	SOCINPUT	11	HERBACEOUS	Land Use Proportion	Number
Pre Socrates	SOCINPUT	12	BD	Bulk Density	g/cm3
Pre Socrates	SOCINPUT	13	GCMTINC	change in temperature by 2100 relative to 1990	Celcius
Pre Socrates	SOCINPUT	14	GCMPINC	change in precipitation by 2100 relative to 1990	Millimeter
Pre Socrates	SOCINPUT	15	XALB	Spatial Info(corrdinates ALBERS)	Number
Pre Socrates	SOCINPUT	16	YALB	Spatial Info(corrdinates ALBERS)	Number
Curr Socrates	SOCEQUIL	1	REGION	County	Number
Curr Socrates	SOCEQUIL	2	CLASS	Class Type	Number
Curr Socrates	SOCEQUIL	3	YEARS	Years model was run	Number
Curr Socrates	SOCEQUIL	4	TOTCLASS	Total carbon for a dominant class in the region	kg/ha
Curr Socrates	SOCEQUIL	5	TOTCN	Total Carbon for the regoin	OMIT
Curr Socrates	SOCEQUIL	6	OCN	Concentration of carbon in the dominant presettlement class in the region	% soil organic carbon
Curr Socrates	SOCEQUIL	7	SOC1	Carbon in Decomposable Plant Material of dominant pre-settlement land class	kg/ha
Curr Socrates	SOCEQUIL	8	SOC2	Carbon in Resistant Plant Material of dominant pre-settlement land class	kg/ha
Curr Socrates	SOCEQUIL	9	SOC3	Carbon in protected microbial biomass pool of dominant pre-settlement land class	kg/ha
Curr Socrates	SOCEQUIL	10	SOC4	Carbon in stable (humified) organic matter pool of dominant pre-settlement land class	kg/ha
Curr Socrates	SOCEQUIL	11	SOC5	Carbon in unprotected microbial biomass pool of dominant pre-settlement land class	kg/ha
Curr Socrates	SOCEQUIL	12	AREA	Area	m2
Curr Socrates	SOCEQUIL	13	TMAX	Max Temperature	Celcius
Curr Socrates	SOCEQUIL	14	TMIN	Min Temperature	Celcius
Curr Socrates	SOCEQUIL	15	PP	Percipitation	millimeters
Curr Socrates	SOCEQUIL	16	CLAY	Clay in topsoil (0-10 cm)	%
Curr Socrates	SOCEQUIL	17	CO	Dominant Land Class	Number
Curr Socrates	SOCEQUIL	18	PLANTED	Land Use Proportion	Number
Curr Socrates	SOCEQUIL	19	SHRUBLAND	Land Use Proportion	Number
Curr Socrates	SOCEQUIL	20	FOREST	Land Use Proportion	Number
Curr Socrates	SOCEQUIL	21	HERBACEOUS	Land Use Proportion	Number
Curr Socrates	SOCEQUIL	22	BD	Bulk Density	g/cm3
Curr Socrates	SOCEQUIL	23	GCMTINC	change in temperature by 2100 relative to 1990	celsius
Curr Socrates	SOCEQUIL	24	GCMPINC	change in precipitation by 2100 relative to 1990	millimeters
Curr Socrates	SOCEQUIL	25	XALB	Spatial Info(corrdinates ALBERS)	Number
Curr Socrates	SOCEQUIL	26	YALB	Spatial Info(corrdinates ALBERS)	Number
Curr Socrates	SOCEQUIL	27	SIMULATION	Simulation No	Number
Fut Socrates	SOCCURRENT	1	REGION	County	Number
Fut Socrates	SOCCURRENT	2	CLASS	Class Type	Number
Fut Socrates	SOCCURRENT	3	YEARS	Years model was run	Number
Fut Socrates	SOCCURRENT	4	TOTCLASS	Total carbon for a dominant class in the region	kg/ha
Fut Socrates	SOCCURRENT	5	TOTCN	Total Carbon for the regoin	OMIT
Fut Socrates	SOCCURRENT	6	OCN	Concentration of carbon in the land use classes 1-4 in the region	% soil organic carbon
Fut Socrates	SOCCURRENT	7	SOC0	Carbon in Decomposable Plant Material of dominant pre-settlement land class	kg/ha
Fut Socrates	SOCCURRENT	8	SOC1	Carbon in Resistant Plant Material of dominant pre-settlement land class	kg/ha
Fut Socrates	SOCCURRENT	9	SOC2	Carbon in protected microbial biomass pool of dominant pre-settlement land class	kg/ha
Fut Socrates	SOCCURRENT	10	SOC3	Carbon in stable (humified) organic matter pool of dominant pre-settlement land class	kg/ha
Fut Socrates	SOCCURRENT	11	SOC4	Carbon in unprotected microbial biomass pool of dominant pre-settlement land class	kg/ha
Fut Socrates	SOCCURRENT	12	AREA	Area	m2
Fut Socrates	SOCCURRENT	13	TMAX	Max Temperature	Celcius
Fut Socrates	SOCCURRENT	14	TMIN	Max Temperature	Celcius
Fut Socrates	SOCCURRENT	15	PP	Percipitation	millimeters
Fut Socrates	SOCCURRENT	16	CLAY	Amount Clay Content in soil	%
Fut Socrates	SOCCURRENT	17	CO	Dominant Land Class	Number
Fut Socrates	SOCCURRENT	18	CLASSPROP0	Proportion of Agriculture Land	Number
Fut Socrates	SOCCURRENT	19	CLASSPROP1	Proportion of Shrub Land	Number
Fut Socrates	SOCCURRENT	20	CLASSPROP2	Proportion of Forest Land	Number
Fut Socrates	SOCCURRENT	21	CLASSPROP3	Proportion of Grass Land	Number
Fut Socrates	SOCCURRENT	22	BD	Bulk Density	g/cm3
Fut Socrates	SOCCURRENT	23	GCMTINC	change in temperature by 2100 relative to 1990	celsius
Fut Socrates	SOCCURRENT	24	GCMPINC	change in precipitation by 2100 relative to 1990	millimeters
Fut Socrates	SOCCURRENT	25	XALB	Spatial Info(corrdinates ALBERS)	Number
Fut Socrates	SOCCURRENT	26	YALB	Spatial Info(corrdinates ALBERS)	Number
Fut Socrates	SOCCURRENT	27	SIMULATION	Simulation No	Number

Output Variables

Output Variables
Model Name	Model Output Table	Field No	Field Name	Field Description	Field Unit
Muchow	MUCOUTPUT	1	REGION	County	Number
Muchow	MUCOUTPUT	2	X	Spatial info(corrdinates ALBERS)	Number
Muchow	MUCOUTPUT	3	Y	Spatial info(corrdinates ALBERS)	Number
Muchow	MUCOUTPUT	4	RESAC