Water cycle intensification affects solar builds in a diagonal line from New Mexico to New England

Several customers reached out to us after our previous tech talk post Federal flood data layers are obsolete — and that spells trouble for traditional solar farm planning. They wanted to know in more detail how their particular projects might be effected. While the algorithm we use is proprietary, we can share a major input to that algorithm — NASA Climatological Data — and how we use it.

The map below shows areas where the water cycle is either intensifying (blue) or weakening (red) across the continental U.S., based on measurements taken from 1945 to 2014 (excluding 1975 to 1984):

(Source: NASA Global Climate Vitals Project.)

The more saturated and deeper the blue, the faster the water cycle churns in that area. This means more precipitation in all forms, heavier precipitation volume, and shorter storm duration. Acres most affected are on an imaginary southwest-to-northeast diagonal, starting with and including most of New Mexico; up through Texas, Oklahoma, southern Nebraska, and Missouri; then Indiana, Michigan, the southeast Great Lakes and nearly all of New England.

In our reports, we use this climatological data as a broad adjustment to the Federal flood data layer: more blue, more likely the Federal data layer is undercounting. We then use local meteorological data, including actual measurements of precipitation and cloud cover, to fine tune the adjustment. This provides a good balance between speed and precision.

If you've built or are considering building a solar project in an area shaded deep blue, then the Federal flood data layer severely underestimates the true impact potential at your site. Your build site may be prone to more flooding, cloudy days, and wind disturbance than calculations from only the Federal data would suggest.

Land:Eval provides an unbiased analysis of all the factors affecting land in use for or considered for solar farms, down to the acre. Our customers benefit from up-to-date enhancements backed by field research, data science, and machine learning to better model generation outcomes.