Spatial and temporal structures of soil moisture fields.

摘要

Soil moisture is often referred to as the water content in the upper several meters of soil, and understanding its spatial and temporal structure is important for applications in atmospheric dynamics and water resources, among others, because of its control on the water and energy balance of the land surface. The spatial and temporal structures of soil moisture fields are studied by applying three linked methodologies: theoretical study, data analysis, and numerical simulation. A particular solution to a linear stochastic partial differential equation is developed for estimating soil moisture based on rainfall observations. A time-weighted average of cumulative rainfall is shown to be better than a simple moving average for linking rainfall fields to soil moisture fields. Without considering the correlation between rainfall and topography, topographic effects on soil moisture could be misinterpreted. Analysis of temporal variations in the scaling characteristics of remotely-sensed soil moisture fields reveals three distinct scaling regimes during dry-down periods: (1) atmospheric-dominated; (2) transitional; and (3) land surface characteristic-dominated.; The TOPLATS model is used to predict and further study soil moisture. Some spatial and temporal structures shown on remotely-sensed soil moisture images are captured by the model. Through investigating the effects of the uncertainties in the land surface characteristics and rainfall data on soil moisture simulations, it was found that (1) the accuracy of the simulated soil moisture was improved by using finer resolution rainfall data; (2) vegetation fraction and root depth are two critical land cover parameters for modeling soil moisture; (3) the choice of the resolution of the soil texture maps used in soil moisture modeling depends on the objectives of simulations; and (4) among soil hydraulic parameters, the pore size distribution index is the most sensitive parameter controlling the simulated soil moisture. Finally, the numerical simulations show that the loss coefficients required by the analytic solution to the linear stochastic partial differential equation relating soil moisture fields to rainfall can be estimated from Leaf Area Index (LAI) and saturated hydraulic conductivity.