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A Statistical Estimator of the Spatial Distribution of the Water‐Table Altitude
Author(s) -
Sepúlveda Nicasio
Publication year - 2003
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.2003.tb02569.x
Subject(s) - linear regression , water table , altitude (triangle) , mathematics , table (database) , simple linear regression , statistics , residual , streams , hydrology (agriculture) , sampling (signal processing) , mean squared error , environmental science , geology , geometry , computer network , geotechnical engineering , filter (signal processing) , algorithm , computer science , groundwater , computer vision , data mining
An algorithm was designed to statistically estimate the areal distribution of water‐table altitude. The altitude of the water table was bounded below by the minimum water‐table surface and above by the land surface. Using lake elevations and stream stages, and interpolating between lakes and streams, the minimum water‐table surface was generated. A multiple linear regression among the minimum water‐table altitude, the difference between land‐surface and minimum water‐table altitudes, and the water‐level measurements from surficial aquifer system wells resulted in a consistently high correlation for all groups of physiographic regions in Florida. A simple linear regression between land‐surface and water‐level measurements resulted in a root‐mean‐square residual of 4.23 m, with residuals ranging from — 8.78 to 41.54 m. A simple linear regression between the minimum water table and the water‐level measurements resulted in a root‐mean‐square residual of 1.45 m, with residuals ranging from –7.39 to 4.10 m. The application of the multiple linear regression presented herein resulted in a root‐mean‐square residual of 1.05 m, with residuals ranging from — 5.24 to 5.63 m. Results from complete and partial F tests rejected the hypothesis of eliminating any of the regressors in the multiple linear regression presented in this study.