Editorial Type:
Article
Article Type:
Research Article

Simulating Hydrological Drought Properties at Different Spatial Units in the United States Based on Wavelet–Bayesian Regression Approach

Ashok K. Mishra

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Vijay P. Singh Department of Biological and Agricultural Engineering, and Department of Civil Engineering, Texas A&M University, College Station, Texas

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Print Publication:
01 Dec 2012
Collections:
Drought: Advances in Monitoring, Preparedness, and Understanding Drought Characteristics
DOI:
https://doi.org/10.1175/2012EI000453.1
Page(s):
1–23
Restricted access

Abstract

Because of their stochastic nature, droughts vary in space and time, and therefore quantifying droughts at different time units is important for water resources planning. The authors investigated the relationship between meteorological variables and hydrological drought properties using the Palmer hydrological drought index (PHDI). Twenty different spatial units were chosen from the unit of a climatic division to a regional unit across the United States. The relationship between meteorological variables and PHDI was investigated using a wavelet–Bayesian regression model, which enhances the modeling strength of a simple Bayesian regression model. Further, the wavelet–Bayesian regression model was tested for the predictability of global climate models (GCMs) to simulate PHDI, which will also help understand their role for downscaling purposes.

Current affiliation: Pacific Northwest National Laboratory, Richland, Washington.

Corresponding author address: Ashok K. Mishra, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352. E-mail address: ashok.mishra@pnnl.gov

Abstract

Because of their stochastic nature, droughts vary in space and time, and therefore quantifying droughts at different time units is important for water resources planning. The authors investigated the relationship between meteorological variables and hydrological drought properties using the Palmer hydrological drought index (PHDI). Twenty different spatial units were chosen from the unit of a climatic division to a regional unit across the United States. The relationship between meteorological variables and PHDI was investigated using a wavelet–Bayesian regression model, which enhances the modeling strength of a simple Bayesian regression model. Further, the wavelet–Bayesian regression model was tested for the predictability of global climate models (GCMs) to simulate PHDI, which will also help understand their role for downscaling purposes.

Current affiliation: Pacific Northwest National Laboratory, Richland, Washington.

Corresponding author address: Ashok K. Mishra, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA 99352. E-mail address: ashok.mishra@pnnl.gov
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