(R.J. Yao).
Abstract Modeling and prediction of soil hydrologic processes
require the identification of soil moisture spatial-temporal patterns
and effective methods allowing the data observations to be used across
different spatial and temporal scales.
This work presents a methodology
for the combination of spatially- and temporally-extensive soil moisture
data obtained in the Shale Hills Critical Zone Observatory (CZO) from
2004 to 2010. The soil moisture data sets were decomposed into spatial
Empirical Orthogonal Function (EOF) patterns, and their relationship
with various geophysical parameters was examined to determine the
dominant factors contributing to the profiled soil moisture variability.
The EOF analyses indicated that one or two EOFs of soil moisture could
explain 76-89% of data variation. The primary EOF pattern had high
values clustered in the valley region, and conversely low values located
in the sloped hills. We suggest a
novel approach to integrate the spatially-extensive manually measured
datasets with the temporally-extensive automated monitored datasets
based on the EOF analysis. Given the data accessibility, the current
data merging framework has provided the methodology for the coupling of
the mapped and monitored soil moisture datasets, as well as the
conceptual coupling of slow and fast pedologic and hydrologic functions.
This successful coupling implies that a combination of different
extensive moisture data has provided interesting insights into
understanding of hydrological processes at multiple scales.
Keywords: soil moisture, data merging, Empirical Orthogonal
Function