TITLE:
Multivariate Geostatistical Model for Groundwater Constituents in Texas
AUTHORS:
Faye Anderson
KEYWORDS:
Multivariate, Geostatistical, Groundwater, Constituents, Texas
JOURNAL NAME:
International Journal of Geosciences,
Vol.5 No.13,
December
26,
2014
ABSTRACT: Although many studies have
explored the quality of Texas groundwater, very few have investigated the
concurrent distributions of more than one pollutant, which provides insight on
the temporal and spatial behavior of constituents within and between aquifers. The
purpose of this research is to study the multivariate spatial patterns of seven
health-related Texas groundwater constituents, which are calcium (Ca), chloride
(Cl), nitrate (NO3), sodium (Na), magnesium (Mg), sulfate (SO4),
and potassium (K). Data is extracted from Texas Water Development Board’s
database including nine years: 2000 through 2008. A multivariate geostatistical
model was developed to examine the interactions between the constituents. The
model had seven dependent variables—one for each of the constituents, and five
independent variables: altitude, latitude, longitude, major aquifer and water
level. Exploratory analyses show that the data has no temporal patterns, but
hold spatial patterns as well as intrinsic correlation. The intrinsic correlation
allowed for the use of a Kronecker form for the covariance matrix. The model
was validated with a split-sample. Estimates of iteratively re-weighted
generalized least squares converged after four iterations. Matern covariance
function estimates are zero nugget, practical range is 44 miles, 0.8340
variance and kappa was fixed at 2. To show that our assumptions are reasonable
and the choice of the model is appropriate, we perform residual validation and
universal kriging. Moreover, prediction maps for the seven constituents are
estimated from new locations data. The results point to an alarmingly
increasing levels of these constituents’ concentrations, which calls for more
intensive monitoring and groundwater management.