An approach of spatially- and temporally-extensive soil moisture data
combination based on EOF analysis
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 analyses. 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
our understanding of hydrological processes at multiple scales.