Recent Conditions
Published May 26, 2011

Precipitation

 (through 5/18/11)
Data Source(s): High Plains Regional Climate Center

Since the water year began on October 1 most storms have passed north of Arizona and New Mexico with a few traversing across central Arizona and New Mexico. However, those few storms have brought little precipitation to the southern half of the two states as the La Niña winter circulation pattern continued into May (Figures 2a-b). The southeastern third of Arizona and the southern half of New Mexico have received less than 50 percent of average precipitation through mid-May. The northwestern third of Arizona and the eastern end of the Navajo Nation in New Mexico have received 100–200 percent of average.

The last 30 days have brought less than 2 percent of average precipitation to the southern half of both states. While late April and early May tend to be dry, the southern counties in both states have seen no storms this year. The wettest area is the Sangre de Cristo Mountains in north-central New Mexico, which has received 150–800 percent of average precipitation (Figures 2c–d). Arizona’s wet spots are in western Mohave County with 150–400 percent of average precipitation. The northern third of Arizona and the northeastern corner of New Mexico have received 25–100 percent of average. The dry conditions are exacerbating the drought and the elevated wildfire risk in the southern counties of both states.
 

Notes:

The water year begins on October 1 and ends on September 30 of the following year. As of October 1, 2010, we are in the 2011 water year. The water year is a more hydrologically sound measure of climate and hydrological activity than is the standard calendar year.

Average refers to the arithmetic mean of annual data from 1971–2000. Percent of average precipitation is calculated by taking the ratio of current to average precipitation and multiplying by 100.

The continuous color maps (Figures 2a, 2c) are derived by taking measurements at individual meteorological stations and mathematically interpolating (estimating) values between known data points. Interpolation procedures can cause aberrant values in data-sparse regions.

The dots in Figures 2b and 2d show data values for individual meteorological stations.
 

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