Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/1/2008
Publication Date: 6/1/2008
Publication URL: http://hdl.handle.net/10113/16890
Citation: Famiglietti, J.S., Ryu, D., Berg, A.A., Rodell, M., Jackson, T.J. 2008. Field observations of soil moisture variability across scales. Water Resources Research. 44: W01423. http://dx.doi.org/10.1029/2006WR005804. Interpretive Summary: Numerous studies have suggested that the realistic representation of spatial variability of surface soil moisture content can improve the predictive skill of hydrologic, weather prediction, and general circulation models, including processes such as evapotranspiration and runoff, precipitation, and atmospheric variability. In this study, soil moisture variability with respect to both spatial scale and field-mean moisture content was characterized. More than 36,000 ground-based measurements of soil moisture collected during the Southern Great Plains (SGP) Hydrology Experiments of 1997 and 1999, and the Soil Moisture Experiments (SMEX) of 2002 and 2003 were combined and analyzed to infer the statistical behavior of soil moisture variations at six distinct spatial scales and across a range of wetness conditions. Empirical relationships between soil moisture variations and mean moisture content were derived across scales. Such relationships can be used to estimate the uncertainty in field observations of mean moisture content. Moreover, the work described here can provide insight into improving the parameterization of surface soil moisture variations in land surface models. The results of this study may be applicable to regions with similar climatic, topographic, and land surface features to our study area in the central US. This is the first study to document the behavior of soil moisture variability over this range of extent scales using ground-based measurements. The results will contribute not only to efficient and reliable satellite validation, but also to better utilization of remotely sensed soil moisture products for enhanced modeling and prediction.
Technical Abstract: In this study, over 36,000 ground-based soil moisture measurements collected during the SGP97, SGP99, SMEX02, and SMEX03 field campaigns were analyzed to characterize the behavior of soil moisture variability across scales. The field campaigns were conducted in Oklahoma and Iowa in the central USA. The Oklahoma study region is sub-humid with moderately rolling topography, while the Iowa study region is humid with low-relief topography. The relationship of soil moisture standard deviation, skewness and the coefficient of variation versus mean moisture content was explored at six distinct extent scales, ranging from 2.5 m to 50 km. Results showed that variability generally increases with extent scale. The standard deviation increased from 0.036 cm3/cm3 at the 2.5-m scale to 0.071 cm3/cm3 at the 50-km scale. The log standard deviation of soil moisture increased linearly with the log extent scale, from 16 m to 1.6 km, indicative of fractal scaling. The soil moisture standard deviation versus mean moisture content exhibited a convex upward relationship at the 800-m and 50-km scales, with maximum values at mean moisture contents of roughly 0.17 cm3/cm3 and 0.19 cm3/cm3, respectively. An empirical model derived from the observed behavior of soil moisture variability was used to estimate uncertainty in the mean moisture content for a fixed number of samples at the 800-m and 50-km scales, as well as the number of ground-truth samples needed to achieve 0.05 cm3/cm3 and 0.03 cm3/cm3 accuracies. The empirical relationships can also be used to parameterize surface soil moisture variations in land surface and hydrological models across a range of scales. To our knowledge, this is the first study to document the behavior of soil moisture variability over this range of extent scales using ground-based measurements. Our results will contribute not only to efficient and reliable satellite validation, but also to better utilization of remotely sensed soil moisture products for enhanced modeling and prediction.