Probabilistic Models for Droughts: Applications in Trigger Identification, Predictor Selection and Index Development
Date of Award
Doctor of Philosophy (PhD)
Committee Member 1
Committee Member 2
Committee Member 3
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.
Ramadas, Meenu, "Probabilistic Models for Droughts: Applications in Trigger Identification, Predictor Selection and Index Development" (2015). Open Access Dissertations. 1201.