The University of Arizona

Precipitation | CLIMAS

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 SW Climate Outlook

Precipitation

(data through July 17, 2013)
Data Source(s): High Plains Regional Climate Center

Despite the onset of the monsoon in parts of the Southwest, summer precipitation thus far has not compensated for dry conditions that mounted this winter. Consequently, with the exception of a few isolated regions such as southeast and central Arizona, most of Arizona and New Mexico have experienced less than 70 percent of their October 1–July 17 precipitation (Figures 2a-b). The near-average precipitation in central Arizona is due to the winter precipitation, but southeastern Arizona benefitted from some very recent monsoonal rainfall. New Mexico is fairing worse than Arizona. Most of central portions of the state have received only up to 50 percent of average rain and snow since October 1. This has contributed to extremely low water supplies on the Rio Grande (see New Mexico Reservoir Volumes).

During the past 30 days, eastern Arizona has received 150 to 400 percent of average precipitation due to an active monsoon that began around July 1 in southern regions of both states (Figures 2c–d; see Monsoon Summary). The monsoon has also delivered above-average rain to west-central New Mexico, but amounts have not been enough to compensate for the dry winter months. The onset of monsoon rain helped quell fires in parts of the region (see Fire Summary).

Notes:

The water year begins on October 1 and ends on September 30 of the following year. As of October 1, 2012, we are in the 2013 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 1981–2010. 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.