User's guide of Proceessing Modflow 5.0

Processing Modflow 155
To define a prior information, first check the Active flag in the Prior Information tab, then
enter the prior information equation in the Prior Information column. The syntax of a prior
information line is
P EQUATION stat STAT flag STAT-FLAG plot PLOT-SYMBOL (3.58)
where EUQATION is defined by eq. 3.59.
PVALUE = C1 x P1 & C2 x P2 ..... (3.59)
The components of eq. 3.58 and 3.59 need to be separater by one space. The components are
defined as follows.
- P is the code indicating that a prior information line follows.
- PVALUE: The prior information value. Specify the decimal value, even for a log-
transformed parameter.
- C1, C2: Coefficients with values as specified by the user.
- x: Indicates multiplication. Needs to be preceded and followed by one blank space.
- P1, P2: Parameter names. The parameter name of the first parameter is P1. The
parameter name of the second parameter is P2 and so on (see examples below).
- &: Indicates that the preceding and following products are to be summed; thus, it
performs like a +.
- stat is a label followed by STAT.
- STAT is a statistic value used to calculate the weight for the prior information. For log-
transformed parameters, specify the log-transformed statistic, even though PVALUE is
a decimal value.
- flag is a lable followed by STAT-FLAG.
- STAT-FLAG is a flag indicating whether STAT is a variance (STAT-FLAG=0), standard
deviation (STAT-FLAG=1) or coefficient of variation (STAT-FLAG=2).
- plot is a label followed by PLOT-SYMBOL.
- PLOT-SYMBOL is an integer printed in the UCODE output file (UCODE._ot) used for
graphical analyses. Different values for plot-symbol can be used to indicate different
types of observations so that they can be differentiated with a unique symbol on a graph.
The utility of PLOT-SYMBOL will depend on the graphical software being used.
The following lines show some examples, refer to Hill (1998) and Poeter and Hill (1998)
for more details about the use of the prior information.
3.6.6 Inverse Modeling