Drought over the past century in Texas and New Mexico: reducing inhomogeneities in long-term climate records via statistical methods to study drought

• Main Author: McRoberts, Douglas Brent
• Other Authors: Nielsen-Gammon, John W.
• Format: Others
• Language: en_US
• Published: Texas A&M University 2008
• Subjects: Drought
• Online Access: http://hdl.handle.net/1969.1/85898

This research looks at the past century of Texas and New Mexico climate in order to create datasets sufficient for documenting climatic variations. Inhomogeneities in climate records are defined as variations in climatic records caused by factors other than weather and climate. While there are indirect methodologies for inferring climate records such as tree rings and ice cores, it is the instrumental network that constitutes the most spatially and temporally complete record of land surface climate since the onset of the Industrial Revolution. A statistical method by Sun and Peterson (2005a) called Inverse Weighting of Square Distance (IWSD) will be used to reduce the inhomogeneities in climate records. The National Weather Service Cooperative Observer Program (COOP) network of stations will be used for this analysis. A subset of the extensive COOP network, called the United States Historical Climate Network (USHCN), will be used as a foundation for this study. The analysis and resulting datasets from this climatic study show precipitation trends and periods of drought and will be useful for decisions regarding future policies on drought. The result of the interpolation process was the creation of several COOP and USHCN datasets. Several of the datasets were investigated to determine the spatial characteristics of precipitation over the 20th century in Texas and New Mexico. The datasets are in good agreement that the most severe drought period of the 20th century in Texas and New Mexico was in the 1950s. The frequency of pluvial periods was higher toward the end of the 20th century, with most USHCN stations showing an increasing trend when a linear regression analysis was done on each station's precipitation data.