HR: 13:55h
AN: H42H-02    [PDF]
TI: Functional Relationship to Describe Temporal Statistics of Soil Moisture Averaged Over Different Depths
AU: * Puma, M J
EM: mpuma@princeton.edu
AF: Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544 United States
AU: Celia, M A
EM: celia@princeton.edu
AF: Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544 United States
AU: Rodriguez-Iturbe, I
EM: irodrigu@princeton.edu
AF: Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544 United States
AU: Guswa, A J
EM: aguswa@smith.edu
AF: Picker Engineering Program, Smith College, Northampton, MA 01063 United States
AB: Remote sensing techniques typically provide estimates of soil moisture for the top 5 cm of the soil column. Most plants have roots that extend deeper than 5 cm, where the roots interact with soil water to influence processes such as transpiration, CO$_{2}$ assimilation, and biomass production. Consequently, soil moisture deeper than 5 cm is an essential component of the soil, plant, and climate system. Detailed simulation studies, highly resolved in space and time, show that a physically controlled relationship exists among instantaneous soil moisture integrated through different soil depths. This dynamic relationship evolves in time as a function of the hydrologic inputs and soil and vegetation characteristics. When soil moisture is calculated at coarser time intervals, such as daily, the structure of the relationship breaks down and becomes undetectable. Since practical measurements tend to be performed at daily intervals or longer, the structure seen at highly resolved temporal scales cannot be captured. If statistical measures are used instead of instantaneous values, the limitation of measurement frequency can be overcome and predictions of mean and variance for variables like soil moisture can be defined over any soil averaging depth {\it d}. Then the measured ({\it d=5 cm}) mean and variance can be related to the mean and variance of soil moisture over any averaging depth {\it d}. For a water-limited ecosystem, we use a detailed simulation model to compute mean and variance for soil moisture over a number of growing seasons, driven by stochastic rainfall forcing over a range of climate conditions, with the mean and variance calculated for different soil averaging depths {\it d}. Based on these results, we generate curves that relate the temporal mean and variance of daily-averaged saturation for different averaging depths. These curves are smooth and well behaved, indicating that relationships between soil moisture statistics for different averaging depths exist for the systems under consideration. We seek a predictive framework that relates these curves to dimensionless parameters characterizing the soil, plant, and climate system.
DE: 1640 Remote sensing
DE: 1818 Evapotranspiration
DE: 1866 Soil moisture
DE: 1875 Unsaturated zone
DE: 3360 Remote sensing
SC: Hydrology [H]
MN: 2003 Fall Meeting