Probabilistic Models for Droughts: Applications in Trigger Identification, Predictor Selection and Index Development

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.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil Engineering

Year

2015

Author dc:creator

• Ramadas, Meenu

Contributors dc:contributor

• Rao Govindaraju
• Indrajeet Chaubey
• Dev Niyogi
• Venkatesh Merwade

• Copula
• Crop Water stress
• Hidden Markov Model
• Hydrologic Drought Triggers
• Multivariate Drought Indices
• Probabilistic Drought Classification

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/1201

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-2417