Date of Award

2018

Document Type

Open Access Dissertation

Department

Geography

Sub-Department

College of Arts and Sciences

First Advisor

Gregory Carbone

Abstract

Drought is a devastating, recurring, and widespread natural hazard that affects natural habitats, ecosystems, and economic and social sectors. Within the agricultural sector, droughts can reduce soil-water availability, affect water and soil quality, contribute to crop failures and pasture losses, and severely reduce crop yield. Effective drought quantification and early warning are critical for drought risk adaptation. Moreover, future drought risks could be exacerbated due to climate change. Modeling how climate change might influence future drought risks is of great importance in natural resources and water resources planning management. This dissertation has three parts. 1) The first part compares and evaluates six trend simulation models to simulate the nonlinear trend and two decomposition models to remove the nonlinear trend from the yield time series. Study resultsfindthat a locally weighted regression model, coupled with a multiplicative decomposition model, is the most appropriate data self-adaptive detrending method, which allows spatial visualization of drought impact on corn yield inUSby highlighting six historical major drought events. 2) The second part develops a new agriculturally-based drought index, called the Integrated Scaled Drought Index (ISDI). This index incorporates important components controlling agriculturaldrought, such as vegetation, temperature, precipitation, and soil moisture. The robustness and usefulness of this indexisvalidated by multiple data sources. This index integrates the benefits of numerical model simulation and remote sensing technology to account for interannual variability of drought for the longest possible time-frame in the satellite era. 3) The third part focuses on identifying hotspots

and uncertainty in agricultural drought projections by analyzing surface soil moisture outputs from CMIP5 multi-model ensembles (MMEs) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios. This part investigates the MME annual and seasonal percentage change of surface soil moisture and examines the change in duration, frequency, severity, and spatial extent of severe agricultural drought. This part also quantifies and partitions three sources of uncertainty associated with these drought projections: internal variability, model uncertainty, and scenario uncertainty, and examines the spatiotemporal variability of annual and seasonal signal to noise (S/N) change in soil moisture anomalies across the globe and for different lead times.

Rights

© 2018, Junyu Lu

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