Variability of Atmospheric Evaporative Demand

Abstract

Terrestrial evapotranspiration (ET) is a key component of the hydrological cycle, surface energy balance, and the carbon cycle. Atmospheric evaporative demand (AED) is the primary climate control of ET and plays a critical role in predicting the variability of water demand, and in quantifying aridity, irrigation, and hydropower production in a changing climate. In recent decades, our understanding of climate change has grown dramatically. However, our ability to predict trends in hydrological extremes, such as droughts under climate change, is hampered by the uncertainty in AED estimates. The attempt to quantify the variability of AED has been complicated by the use of numerous definitions of AED and by uncertainties in the choice of model structure, parameterization, and input data. The objective of this dissertation is to disentangle the complex drivers of AED, in order to improve the quantification of AED and the modeling of ET and associated drought by reducing these uncertainties. Chapter 1 introduces the concept of AED and provides an overview of the entire dissertation. Chapter 2 identifies the key climate drivers of short-term variability in AED and evaluates the ability of current potential evapotranspiration (PET) methods to represent AED. Chapter 3 focuses on improving estimates of AED and actual ET by reducing the uncertainty in radiation forcing data. Chapter 4 constructs a global AED dataset and further assesses the uncertainty of AED that arises from different model structures and parameters. Chapter 5 provides a simple approach to improve estimates of maximum ET and provides a path toward better monitoring of droughts. Finally, Chapter 6 links AED to actual ET by exploring the factors which drive the seasonality of the ratio of actual to potential ET. It summarizes the ability of current large-scale land surface models to represent the identified relationship between AED and ET. Overall, this dissertation advances our understanding of how climate and vegetation factors drive the variability of AED and improves our ability to model AED and ET. This work also provides novel insights for drought monitoring by highlighting the importance of the proper selection of quantifying approach for AED and specifying the surface characteristics.