Comments (7)
In the class, we generated our own truth field obs and then used pandas to get them in the observation data section in the obsval column. To do this, you need to workout the obs names that coorespond to the field observations. Then pandas!
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Thanks. I do remember now.
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MOD2OBS has a function to interpolate the model heads when observations are between stress periods. Can pyemu also do this, or do I need to use pandas?
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"its on the list" - currently pandas.
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it's also worth considering what an interpolated (in time) value really means. I tend to align observations to the end of a stress period whenever possible. Just a thought :)
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I almost went there! Im still waiting for some analyses about the value (or otherwise) of temporal/spatial interpolation in the larger context of decision support modeling...
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As always, it probably depends on the system and the question being asked. Which is the dominant signal - the summer/winter signal due to pumping and recharge, or the long term decline or rise due to climate, pumping and land use change? How much does it impact model outputs and prediction ranges? If your stress periods were >1yr I suspect not much at all. It would be a nice little paper. I'll add it to my never ending list!
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Related Issues (20)
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