Date of Award
January 2015
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Civil Engineering
First Advisor
Rao Govindaraju
Committee Member 1
Indrajeet Chaubey
Committee Member 2
Dev Niyogi
Committee Member 3
Venkatesh Merwade
Abstract
The current practice of drought declaration (US Drought Monitor) provides a hard classification of droughts using various hydrologic variables. However, this method does not yield model uncertainty, and is very limited for forecasting upcoming droughts. The primary goal of this thesis is to develop and implement methods that incorporate uncertainty estimation into drought characterization, thereby enabling more informed and better decision making by water users and managers. Probabilistic models using hydrologic variables are developed, yielding new insights into drought characterization enabling fundamental applications in droughts.
Recommended Citation
Ramadas, Meenu, "Probabilistic Models for Droughts: Applications in Trigger Identification, Predictor Selection and Index Development" (2015). Open Access Dissertations. 1201.
https://docs.lib.purdue.edu/open_access_dissertations/1201