GMS:Parameter Dialog: Difference between revisions
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==Creating/Deleting Parameters== | ==Creating/Deleting Parameters== | ||
The ''Parameters'' dialog contains the list of parameters. A new parameter can be created by selecting the '''New Parameter''' button. Each parameter that is defined should correspond to a key value that has been defined in the MODFLOW input. A parameter can be removed from the list by selecting the parameter and selecting the Delete Parameter button. The entire list of parameters can be deleted by selecting the Delete All button. | The ''Parameters'' dialog (accessed through the ''MODFLOW'' menu using the '''Parameters''' command) contains the list of parameters. A new parameter can be created by selecting the '''New Parameter''' button. Each parameter that is defined should correspond to a key value that has been defined in the MODFLOW input. A parameter can be removed from the list by selecting the parameter and selecting the '''Delete Parameter''' button. The entire list of parameters can be deleted by selecting the '''Delete All''' button. | ||
==Initialize from Model== | ==Initialize from Model== | ||
In most cases, the fastest and simplest way to create the parameter list is to use the '''Initialize from Model''' button. When this button is selected, GMS traverses the MODFLOW input data corresponding to legal parameter values and searches for key values. It is assumed that the key values are entered as negative numbers. When a unique negative number is found, a new parameter is added to the list, and a default name is given to the parameter based on the parameter type. | In most cases, the fastest and simplest way to create the parameter list is to use the '''Initialize from Model''' button. When this button is selected, GMS traverses the MODFLOW input data corresponding to legal parameter values and searches for key values. It is assumed that the key values are entered as negative numbers. When a unique negative number is found, a new parameter is added to the list, and a default name is given to the parameter based on the parameter type. | ||
NOTE: The | NOTE: The '''Initialize from Model''' command will not search for key values in the Well package. This is because negative pumping rates are perfectly common and do not necessarily correspond to key values. If wanting to define a well Q as a parameter, use the '''New Parameter''' button and manually create the parameter. | ||
==Import Optimal Values== | ==Import Optimal Values== | ||
After performing an inverse model run, the inverse code writes out a text file containing the set of optimal parameter values corresponding to the minimum calibration error. The next step is to read these values into GMS. This is accomplished by selecting the Import Optimal Values button. Once | After performing an inverse model run, the inverse code writes out a text file containing the set of optimal parameter values corresponding to the minimum calibration error. The next step is to read these values into GMS. This is accomplished by selecting the '''Import Optimal Values''' button. Once the file is opened, the optimal parameter values will be loaded into the starting value field and displayed in the parameter list. | ||
If any of | If any of the parameters use pilot points, importing the optimal values will also import and create a new dataset for the associated 2D scatter point set. | ||
==Spreadsheet== | ==Spreadsheet== | ||
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For inverse modeling, the closer the starting value is to the "optimal" value, the better the odds that the inverse model will converge and the less time it will take to converge. It is generally not a good idea to give all parameters of a given type (e.g., recharge) a constant value and let the inverse model start from that point. Ideally, field tests, soil types, ground cover, and sound modeling judgement can provide a good set of starting values. It is also a good idea to undergo some manual trial-and-error calibration prior to setting up the inverse code. | For inverse modeling, the closer the starting value is to the "optimal" value, the better the odds that the inverse model will converge and the less time it will take to converge. It is generally not a good idea to give all parameters of a given type (e.g., recharge) a constant value and let the inverse model start from that point. Ideally, field tests, soil types, ground cover, and sound modeling judgement can provide a good set of starting values. It is also a good idea to undergo some manual trial-and-error calibration prior to setting up the inverse code. | ||
[[GMS:Pilot Points|Pilot points]] can be used to define a parameter by selecting the drop-down arrow in this column and selecting the | [[GMS:Pilot Points|Pilot points]] can be used to define a parameter by selecting the drop-down arrow in this column and selecting the ''Pilot points'' option. Pilot points are an alternative to using zonation to define parameter locations. When using pilot points, using the '''Pilot Point Options''' button to choose a 2D point scatter point set and choose the appropriate interpolation options. | ||
'''Min Value''' | '''Min Value''' | ||
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This option log transforms the value during prediction process of inverse modeling and the random number generation process of stochastic modeling. The best parameters to log transform are those that can vary by orders of magnitude like hydraulic conductivity. | This option log transforms the value during prediction process of inverse modeling and the random number generation process of stochastic modeling. The best parameters to log transform are those that can vary by orders of magnitude like hydraulic conductivity. | ||
It is also recommended | It is also recommended to log transform recharge parameters if using pilot points for hydraulic conductivity and the hydraulic conductivity parameter is log transformed. | ||
===BSCAL=== | ===BSCAL=== | ||
MODFLOW documentation includes: | MODFLOW documentation includes: | ||
This value is an alternate scaling factor for the parameter, and always needs to be a positive number. If the parameter value becomes 0.0, which can occur for parameters that are not log transformed, BSCAL is used in the scaling. If the absolute value of the parameter is less than BSCAL, BSCAL is used in the scaling. The best value to use is problem dependant. Good choices are the smallest reasonable value of the parameter or a value two to | This value is an alternate scaling factor for the parameter, and always needs to be a positive number. If the parameter value becomes 0.0, which can occur for parameters that are not log transformed, BSCAL is used in the scaling. If the absolute value of the parameter is less than BSCAL, BSCAL is used in the scaling. The best value to use is problem dependant. Good choices are the smallest reasonable value of the parameter or a value two to three orders of magnitude smaller than the value specified by the starting value. If the smallest reasonable value is 0.0, a reasomable non-zero value needs to be used. BSCAL has no effect on the scaled sensitivities for log-transformed parameters. | ||
===Multiplier=== | ===Multiplier=== | ||
Select this option to include a multiplier array for RCH and HK parameters. | Select this option to include a multiplier array for RCH and HK parameters. | ||
=== | ===Dataset / Folder=== | ||
Use this button to select a multiplier array of a set of multiplier arrays (stochastic only) by selecting a folder of | Use this button to select a multiplier array of a set of multiplier arrays (stochastic only) by selecting a folder of datasets. | ||
==Stochastic Options== | ==Stochastic Options== | ||
''Standard Deviation'' | |||
:Use this field to specify the standard deviation of a parameter for a stochastic simulation. | :Use this field to specify the standard deviation of a parameter for a stochastic simulation. | ||
''Mean value'' | |||
:Use this field to specify the mean of a parameter for a stochastic simulation. | :Use this field to specify the mean of a parameter for a stochastic simulation. | ||
''Distribution'' | |||
:When the parameter is stochastic, use this option to choose between a normal or linear distribution. A random number for the parameter is generated using the distribution, the mean value (starting value) and the standard | :When the parameter is stochastic, use this option to choose between a normal or linear distribution. A random number for the parameter is generated using the distribution, the mean value (starting value) and the standard deviation. | ||
''Std Deviation'' | |||
:The standard deviation is used for a stochastic parameter to generate a random number using the chosen distribution. | :The standard deviation is used for a stochastic parameter to generate a random number using the chosen distribution. | ||
''Num Segments'' | |||
:When the parameter is stochastic and the stochastic method is Latin Hypercube, the number of segments helps determine how man total MODFLOW runs will be used. | :When the parameter is stochastic and the stochastic method is Latin Hypercube, the number of segments helps determine how man total MODFLOW runs will be used. | ||
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[[Category:MODFLOW]] | [[Category:MODFLOW]] | ||
[[Category:Parameters]] | [[Category:Parameters]] | ||
[[Category:GMS Dialogs|P]] | |||
