Date of Award
1-17-2013
Document Type
Thesis
Degree Name
Environmental Sciences, MS
First Advisor
Yoensang Hwang
Committee Members
Rick Clifft; Thomas Risch; Yoensang Hwang
Call Number
LD 251 .A566t 2012 L33
Abstract
Drought forecast is substantial in water management and agriculture planning. The historical climate records, including drought indices, temperature, and precipitation, provide useful information for drought forecasts. Arkansas has suffered severe droughts in recent years and study has been rarely done for this area. In this study, a local nonparametric autoregressive model and stochastic approaches are applied to produce ensemble drought forecasts with associated confidence. Various resampling techniques are tested for monthly forecasts of Palmer drought severity index (PDSI) and standardized precipitation index of both three- and twelve-month (SPI03 & SPI12) in Arkansas climate divisions. Normalized Root Mean Square Error (NRMSE), Kuiper Skill Score (KSS), rank histograms, and probability density distributions are employed to verify and compare residual-resampling techniques regarding accuracy and variability. Overall improvements are remarkable, especially from models incorporated predictions of long-term precipitation. The degree of suitability of proposed forecast models varies by different drought indices and climate divisions.
Rights Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Liu, Yan, "Mult Ensemble Drought Forecasts for Arkansas Applying Available Resampling Techniques" (2013). Student Theses and Dissertations. 835.
https://arch.astate.edu/all-etd/835
