MULTISCALE SPATIO-TEMPORAL BIG DATA FUSION OF HYDROLOGICAL VARIABLES FROM POINT TO SATELLITE FOOTPRINT SCALES

Abstract

Soil moisture (SM) and evapotranspiration (ET) are key climate variables governing environmental processes from local to global scales. The global burgeoning of SM and ET datasets holds a significant potential in improving our understanding of multiscale hydrological dynamics. The primary issues that hinder the fusion of SM and ET data are (1) different resolution of the data instruments, (2) inherent spatial variability in SM and ET caused due to atmospheric and land surface controls, (3) measurement errors caused due to imperfect retrievals of instruments, and (4) massive size of the datasets. This dissertation aims to develop data fusion algorithms to combine multiscale data and improve understanding of multiscale SM and ET dynamics while accounting for the above-mentioned challenges. The research questions answered in this dissertation include 1) determining the effects of surface and atmospheric controls on the spatio-temporal mean and covariance of SM using a non-stationary geostatistical algorithm; 2) predicting SM across multiple scales and quantifying the effects of surface physical controls (soil texture, vegetation, topography) and rainfall on SM distribution as well as their effect on retrieval errors of soil moisture platforms; 3) providing a novel framework to fuse SM data for continental scale analysis and 4) improving existing ET data fusion algorithms by accounting for uncertainty in retrievals and incorporating ancillary data/domain knowledge. It was found that the variance and correlation structure of SM varies significantly with spatial heterogeneity in land surface controls for a watershed in Winnipeg, Canada. For the same watershed, the proposed data fusion framework was applied to combine point, airborne and satellite SM data and it was adept at assimilating and predicting SM distribution across all three scales. The data fusion framework was then extended to combine point and satellite SM data across Contiguous US and the effects of physical controls on SM distribution were quantified. For ET data fusion, a state-space modeling framework was developed to combine daily ET satellite data for three agricultural sites in Texas and it was found that when compared with daily Eddy-Covariance ET data, the proposed approach outperformed the traditional fusion algorithm.