Date of Award

Winter 2006

Project Type


Program or Major

Civil Engineering

Degree Name

Doctor of Philosophy

First Advisor

Jennifer Jacobs


Knowledge of soil moisture variability is essential to understand hydrologic processes at a range of scales. In this study, spatio-temporal variability of soil moisture and inter-comparison among different soil moisture products were analyzed. The variability patterns were well characterized by negative exponential fitting as function of observed sampling extent scale. The simple physical soil moisture dynamics model was identified as an alternative approach to characterize statistical soil moisture variability. The soil moisture variability was strongly related to physical properties including rainfall and topography.

Normal and log-normal distributions were recognized as the most efficient probability density functions to capture soil moisture variability patterns for all conditions. Further, these variability patterns were well maintained for root zone profile and surface soil moisture time stable characteristics can be used to upper boundary for sub-surface time stability.

Through inter-comparison analysis, average soil moisture from remotely sensed measurements, ground-based measurements, and land surface model results showed excellent agreement. However, remotely sensed soil moisture had little variation, especially during the growing season. There were complementary benefits with low random errors for the land surface model and low system errors for the remotely sensed data.

The error characteristics of remotely sensed measurements can enhance the utility of satellite observations. The remote sensing measurements can provide relative soil moisture conditions to improve runoff predictions and analyze land surface-atmosphere interactions for regional climate predictions in data limited areas. However, their extremely limited variations must be refined prior to direct application in hydrological processes.

Overall, the identified soil moisture variability patterns provide a new understanding of soil moisture dynamics and spatio-temporal variability patterns as related to physical variables. These organized characteristics are essential to predict land-atmosphere interactions, rainfall-runoff processes, and groundwater recharge processes. Practically, these findings can be used to calibrate land surface models and to estimate heterogeneity effects of land surface processes. Additionally, statistical information as a function of scale is critical to develop upscaling and down-scaling methodologies without significant loss of information. This dissertation's findings provide critical insight to hydrologic processes related to soil moisture at a range of scales.