User's guide of Proceessing Modflow 5.0

156 Processing Modflow
P 10 = 0.5 x P1 & 0.5 x P2 stat 0.2 flag 2 plot 4
P 1.03E-3 = 1.0 x P1 & 1.45 x P2 & -3.9 x P3 & 2.123 x P4 stat 2.0 flag 2 plot 3
P 1.344 = 2.12 x P3 & 3.2 x P6 stat 2.2 flag 2 plot 1
< Control Data
The control data are used to control the sensitivity and regression calculations and define the
inversion algorithm. The items of the control data (Fig. 3.56) are described below.
- Convergence criterion (TOL): When parameter values change less than this fractional
amount between regression iterations, parameter estimation converges (0.01 is
recommended).
- Convergence criterion (SOSR): When the sum-of-squared, weighted residuals change
less than this fractional amount over three regression iterations, parameter estimation
converges. Ideally, for the final results convergence is achieved by satisfying the TOL
criterion so that SOSR can be equal to 0.0 (in which case SOSR is not used as a
convergence criteria). Values of SOSR of 0.01 and even 0.1 can be useful, however, in
the early stages of model calibration because it stops the regression when it is not
progressing.
- Maximum number of regression iterations (MAX-ITER) is self-explanatary. Starting
with twice the number of parameters is recommended.
- Maximum fractional parameter change (MAX-CHANGE) is the maximum fractional
change of a parameter value allowed in one regression iteration. For example, if MAX-
CHANGE = 2.0, a parameter value of 1.0 will not be allowed to change by more than 2.0
(MAX-CHANGE times the parameter value). Consequently, the new value will be
between -1.0 and 3.0. A parameter value of 2.0 will not be allowed to change more than
a value of 4.0 (again, MAX-CHANGE times the parameter value), and the new value will
be between -2.0 and 6.0). This maximum change is applied to the physical parameter
value, not its log transformed value. Exceptions are discussed in Hill (1998, Appendix
B). MAX-CHANGE = 2.0 is common, but smaller values may help an oscillating
regression to converge.
- Differencing method controls the method used to calculate sensitivities during the
parameter-estimation iterations. Starting with the forward differencing method is
recommended.
- Apply quasi-Newton update when sum-of-squared, weighted residuals changes less
than 0.01 over three regression iteration. According to Hill (1998), applying the quasi-
Newton update may facilitate convergence of highly nonlinear problems.
3.6.6 UCODE (Inverse Modeling)