Posted Thu, 16 May 2013 15:02:33 GMT by mariaS

I tried to simulate an unsaturated groundwater stream into a lake. I defined 1st and 2nd BCs as transient, also material properties for conductivity, unsatureted flow porosity and defined the van-Genuchten- parameter.

Nevertheless, my simulated hydraulic heads are always about 1-2m higher then the levels on the observation points, and therefore exaggerating the confindence intervalls.

what can be the source of this exaggeration? what do I have to change in the material properties?
Posted Fri, 17 May 2013 14:09:37 GMT by Michael
I observed that kind of situation so many times.....

I don't have a scientific answer to your question, but I saw that situation occurred when the hydraulic conductivity on the layer 1 is very low. If it's the case, just try to reduce your K values and see what's happen.

Posted Mon, 20 May 2013 17:33:13 GMT by mariaS
thanks a lot, that helped to pass it in the right direction, so the simulated hydraulic heads are now in the right convergence interval. but i actually wonder, what is the physical explanation, because maybe the right solution could also solved, when changing another parameter (like alpha or n [van-Genuchten-Parameter].

Anyone could help?
Posted Wed, 12 Jun 2013 03:32:12 GMT by
its not clear exactly what you are modeling, but it seems to me that the simulated hydraulic conductivities are smaller than you expect or that you have errors in your model that cause the higher than expected simulated heads (or both).  Other explanations are possible.

Posted Thu, 04 Jul 2013 07:37:56 GMT by Denim Umeshkumar Anajwala
Whenever doing calibration, you will be confronted with some non-uniqueness of the solution. In the simplest case, you might not know whether you need to decrease recharge or increase conductivity. If confindence levels on both sets of input data are equally low and head observations are the only calibration targets, there is no way to do the 'right' thing. So either you manage to come up with additional calibration target or parameter data, or you need to at least qualitatively think about what it would mean for your results (in terms of decision support done with the model, not in terms of resulting hydraulic heads!) if your decision was wrong. Going further, fully-blown uncertainty analysis (e.g., applying PEST) could help to identify parameter correlations and result uncertainty.

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