2021
DOI: 10.58799/ofr-616
|View full text |Cite
|
Sign up to set email alerts
|

Evaluation of water-level trends using spatiotemporal kriging in the Mimbres Basin, southwest New Mexico

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3

Citation Types

0
3
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(3 citation statements)
references
References 34 publications
(64 reference statements)
0
3
0
Order By: Relevance
“…This simple model captures the general trough shape of both surfaces, declining to the southeast. Eleven wells whose redbed elevations were identified as outliers from the trend model using the criteria defined by Rawling (2022) were not used in further analysis. Three outliers from the water-level trend model were identified using the same criteria, but these were retained because of the much fewer number of water-level elevation data points (75 vs. 358).…”
Section: Geostatistical Interpolationsmentioning
confidence: 99%
See 2 more Smart Citations
“…This simple model captures the general trough shape of both surfaces, declining to the southeast. Eleven wells whose redbed elevations were identified as outliers from the trend model using the criteria defined by Rawling (2022) were not used in further analysis. Three outliers from the water-level trend model were identified using the same criteria, but these were retained because of the much fewer number of water-level elevation data points (75 vs. 358).…”
Section: Geostatistical Interpolationsmentioning
confidence: 99%
“…In addition to the structure of the sample variograms, use of anisotropic models was suggested by the elongate study area, the strong northwest-southeast trend in the paleochannel surface topography and water-level elevations, and the geometry of the network of wells used for water-level measurements. Final variogram models were chosen to minimize the mean-squared error (MSE) of both the model fit to the sample variogram and the residuals of leave-oneout cross-validation (Rawling, 2022).…”
Section: Geostatistical Interpolationsmentioning
confidence: 99%
See 1 more Smart Citation