Analysis of large scale spatial variability of soil moisture using a geostatistical method.
Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to validate modeled soil moisture by the Agriculture Meteorological (AGRMET) model using in situ measurements of soil moisture from a state-wide environmental monitoring network (Oklahoma Mesonet). The results show that AGRMET data produces larger spatial decorrelation compared to in situ based soil moisture data. The precipitation storms drive the soil moisture spatial structures at large scale, found smaller decorrelation length after precipitation. This study also evaluates the geostatistical approach for mitigation for quality control issues within in situ soil moisture network to estimates at soil moisture at unsampled stations.
NOAA-Cooperative Remote Sensing Science & Technology Center, (NOAA-CREST), City University of New York, NY 10031, USA. tlakhankar@ccny.cuny.edu
Tarendra
T
Lakhankar
Andrew S
AS
Jones
Cynthia L
CL
Combs
Manajit
M
Sengupta
Thomas H
TH
Vonder Haar
Reza
R
Khanbilvardi
D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods*; D015233:Models, Statistical; D012987:Soil / Q000032:analysis* / Q000737:chemistry*; D014867:Water / Q000032:analysis*
D012987:Soil; D014867:Water