Giovanni Formentin

Hello Thomas,
I agree with your way of setting the problem. Have you previous experiencies of instability by using very thin layers? Otherwise, I'd start giving the layers representing the liner a thickness comparable to the real one, enlarging it (should be easy if the layer is flat) as it shows instability. By this way, you may avoid to introduce an equivalent K.

Bye,

Giovanni

In situations like this, I use to add reasonably invented (I know it doesn't sound very scientific!) data, consistent with the geological information I have, to constrain the interpolation. There's also the possibility to make different datasets and check the sensitivity of the model to the unavailable data.

Giovanni

I would convert the lines to points and then interpolate. Any interpolation method will assign values all over the domain. The problem is simply that the farther the nodes from your data, the more unreliable will be the result.
Bye,

Giovanni

You shall define a transfer condition with a transfer rate. Thus you specify the head and, by the transfer rate, approximately the ratio between conductivity and distance from the head condition. Refer to the manual for a more accurate description.

Giovanni

Are you trying with Assign / Database? On my Feflow 5.3, I'm not allowed to regionalize starting from a polygon SHP. I need a point SHP.
Where have you linked the SHP with the DAT? On Arcview? If so, the link is just internal to the GIS.
Have you checked to be working with Global coordinates if your SHP is in site coordinates?

Giovanni

1. Drill a hole.
2. If you know the cross-flow between river and aquifer or their heads, go by inverse calibration. If it results in a 200 meters colmation layer, there's something else wrong...
Good luck,

Giovanni

I can't help you on the first matter, but as far as I remember I had no problems with unit transfers.

On the second matter, I agree with you in that PEST is just a tool, it can't build the model by itself.
As a tool, it is indeed helpful in highlighting the errors in the model: if it minimizes the RMS, and you're sure that it's the global minimum (not a local one), and you get unreasonable parameter values, than:

1. your head, flux or concentration measures are wrong
AND/OR
2. you're focusing only on some parameters, keeping fixed (i.e. considering well known) parameters that should be changed together with the first ones
AND/OR
3. your conceptual model is wrong, you're not simulating what really happens.

Unfortunately, PEST can't tell where's the problem, understanding the system's structure is all in our hands.

Good luck!

Giovanni Formentin

Weights should reflect:
- first of all, measure errors
- then, the importance of the observation for your predictions (i.e. observations you'd like to simulate as best as possible).

As described in "Effective Groundwater Model Calibration, Hill & Tiederman, Wiley, 2007", a good way is to set the weight = 1/S^2
where S = standard error for every measure.

For head measures, different errors sum up (errors in the measure, in the datum, well not fully penetrating...).

When it's impossible to estimate all the errors (and then sum the variances), a procedure (see Hill and Tiederman, p. 295) is:

-  I assume that errors are normally distributed;
-  I'm 95% sure than the measure is 25 +/-1 m a.s.l.;
-> from the tabulated standard normal distribution, at a  (1 - 95% =) 5% significance level corresponds a critical value of 1.96
-> 1.96*S = 1 m
--> S = 1/1.96
--> Weight = 1/S^2.

Maybe other ways are viable.
Good work,
Giovanni Formentin