The role of evolving short and long-term weather patterns on irrigative water demand in the U. S. Northeast and mid-Atlantic using autoregressive integrated moving average time series modeling and randomized historical data
Journal of Decision Making and Healthcare, Volume 3, Issue 1, April 2026, Pages: 21–31
ROBERT KENNEDY SMITH
Department of Mathematics and Statistics, Georgetown University, Washington, DC, USA
DER-CHEN CHANG
Department of Mathematics and Statistics, Georgetown University, Washington, DC, USA
Abstract
Historical data records show that annual and seasonal precipitation have increased in the U.S. Northeast and Mid-Atlantic over the past several decades, with higher rates evident more recently. Climate models project continuing growth in precipitation through the rest of the century. The same models also predict more frequent, intense precipitation episodes, during which soils may not be able to fully absorb water hitting the ground, and higher evapotranspiration rates, leading to increased dryness despite higher rainfall totals. This analysis uses autoregressive integrated moving average modeling to examine the impacts of short and long-term weather patterns on soil moisture levels in the Region, some of which are not explicitly accounted for in decadal climate modeling. Comparisons of calculated soil moisture utilizing historical and randomized data show that such patterns are causing increased dryness that will exacerbate soil water deficits beyond what is already projected from increased evapotranspiration and runoff.
Cite this Article as
Robert Kennedy Smith and Der-Chen Chang, The role of evolving short and long-term weather patterns on irrigative water demand in the U. S. Northeast and mid-Atlantic using autoregressive integrated moving average time series modeling and randomized historical data, Journal of Decision Making and Healthcare, 3(1), 21–31, 2026