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Pennsylviana State University (2015)

IMPROVING ESTIMATES OF ROOT ZONE SOIL MOISTURE TO UNDERSTAND ECOSYSTEM DROUGHT SENSITIVITY

Baldwin, Douglas C

Titre : IMPROVING ESTIMATES OF ROOT ZONE SOIL MOISTURE TO UNDERSTAND ECOSYSTEM DROUGHT SENSITIVITY

Auteur : Baldwin, Douglas C

Université de soutenance  : Pennsylviana State University

Grade : Doctor of Philosophy (PhD) 2015

Résumé partiel
Extreme droughts can drastically inhibit the growth of terrestrial forest ecosystems, limiting their ability to assimilate and sequester atmospheric carbon dioxide, an important ecosystem service. Temperate forests in particular are an important component of Earth’s terrestrial carbon ‘sink,’ but ecosystem carbon (C) flux monitoring since the 1990s shows that C uptake in temperate forests is more vulnerable to drought than previously estimated. Climate models typically project that drought events will become more frequent and intense across temperate regions in coming decades. As a result, soil moisture is an increasingly crucial variable to monitor for diagnosing and forecasting drought occurrence and severity. Advancements in satellite technology now facilitate more precise and accurate daily retrievals of global near-surface soil moisture content but satellite data must be downscaled to finer resolutions before it can be used in hydrologic models to estimate subsurface root zone soil moisture content over broad spatial extents. This dissertation explores how a combination of ground based measurements, satellite data, and hydrologic modeling can be used to generate high-resolution predictions of root zone soil moisture content. Root zone soil moisture observations are also used to evaluate the sensitivity of temperate forest growth, estimated by eddy flux towers, to drought in multiple locations in northern temperate forests in the United States.The opening chapter reviews research that connects root zone soil moisture to plant water stress and the development of hydrologic models that estimate root zone soil moisture across large areas using satellite soil moisture data. Chapter two demonstrates how an ensemble Kalman filter (EnKF) data assimilation algorithm can be integrated with a physically based hydrologic model that predicts root zone soil moisture with satellite data. This model system can produce root zone soil moisture predictions close to observed in situ measurements. The model is also rigorously tested in a ‘perfect model test’ experiment before its application with in situ data.

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Page publiée le 4 juin 2021