Understanding Earth's Diurnal Terrestrial Water Cycle with Satellite Data, Land Surface Models, and Data Assimilation

Kim, Hyunglok, Civil Engineering - School of Engineering and Applied Science, University of Virginia
Lakshmi, Venkataraman, EN-Eng Sys and Environment, University of Virginia

Water is essential for life on Earth. The water cycle describes how water reaches plants, animals, and humans. Examining Earth’s water cycle can be a challenging task, but understating the speed at which the water cycle evolves under climate change is extremely important for life on Earth.
For over a century, humans have been modifying Earth’s climate systems by clearing forests to grow fast-growing crops and emitting heat-trapping greenhouse gases from a wide variety of sources. These changes can alter rainfall patterns by modifying outgoing energy fluxes, viz., the distribution between latent and sensible heat fluxes from land to the atmosphere. Researchers can investigate the impact of reconstructed rainfall patterns on Earth’s terrestrial surface by studying the dynamics of soil moisture (SM), as it controls the flow of water and energy, governing interaction between land surface and atmosphere.
Many methods have been proposed to estimate near-surface SM values using microwave sensors aboard satellites. However, SM estimations from microwave systems have a major limitation: they are neither spatially nor temporally continuous. In this dissertation, I discuss my research on ways to overcome these limitations by utilizing NASA’s micro-satellite constellation systems in low-earth orbit (LEO) and microwave satellite systems in sun-synchronous orbit (SSO) together with data assimilation technic. I also introduce an effective means of characterizing the errors in satellite data and assimilating satellite-based SM data into land surface models (LSMs) using the triple collocation analysis and Ensemble Kalman filter. Obtaining accurate SM data and other hydrologic variables from satellites would also improve estimations of the other water, energy, and carbon fluxes in Earth system models.
Here, I use diurnal SM data produced from satellite-based subdaily SM data assimilated LSMs I produced through my Ph.D. study. I illustrate how subdaily SM memory can vary by ecosystems and areas impacted by human intervention, thereby increasing our understanding of the diurnal water cycle speed on a global scale. This is the first study to characterize the subdaily variability of the water cycle with respect to the percentages of irrigated areas, plant isohydricity, and diversity index of land surfaces. This, in turn, would help us understand many future alterations to Earth’s processes under climate changes.

PHD (Doctor of Philosophy)
hydrology, water cycle, satellite remote sensing, land surface model, data assimilation, soil moisture, data science
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