Hi Eric,
thank you very much for your quick and helpful answer.
I could now figure out the problem: Unfortunately, I performed kriging as an exact interpolator and therefore didn’t use a nugget at all. So there was no kriging standard error at the measurement locations despite the “Measurement Error” being 100%.
As the tool “Densify Sampling Network” needs a measurement error I altered my isotropic semivariogram model (Type: Stable; Parameter: 1.4; Range: 1,200 m; Partial Sill: 5.86 m) by adding
- (I) a small nugget of 0.01 (that deteriorates the cross-validation results),
- (II) an extremely small nugget of 0.00001 (with negligible effects on the cross-validation results).
Then the tool ran successfully both variants creating two point features classes, each containing the 137 measurement stations in the order of decreasing StdErr values (which I interpreted as decreasing importance). As expected, variant (II) results in smaller StdErr values compared to variant (I). However, the ranking of the 137 stations is similar but not identical: There are minor differences between both rankings with a maximum difference of 5 ranks.
So regarding my target of identifying stations that could decommissioned the tool “Densify Sampling Tool” gives me good hints but I’m not fully satisfied. Originally, I thought of performing the monitoring network reduction in another way:
I intended to use a cross-validation procedure by sequentially removing each of the 137 station to register the increase of the mean kriging standard error of all prediction point locations (nearly 21,000 points on a regular grid). The station leading to the smallest increase of the mean kriging standard error would be decommissioned and I would restart the procedure with the remaining 136 stations based on the new Geostatistical Layer. However, the manual procedure is too time consuming.
But there would be another way leading to the same results more straightforward using the mean kriging weights of the measurement stations: Firstly, the stations with their associated kriging weights for all single prediction point locations need to be determined (which can be done manually for any single prediction point location in the Search Neighborhood page of the Geostatistical Wizard). Secondly, the mean kriging weight of every station would be averaged over all prediction point locations. The station with the least mean kriging weight would be decommissioned and then the procedure is repeated based on the new Geostatistical Layer. Unfortunately, again I see no way other than the absolutely unrealistically manual procedure…
Is there any chance to get one of my two intended procedures automatized? Do you have any advice for my decommission target?
Cheers!