Local Forecast Accuracy and the Implications to Smart Irrigation Technology

Ryan Bridges, Sonali Chokshi, Christina Lilligre, Sam Myer, and Dr. Steven Fassnacht, Warner College of Natural Resources, Colorado State University, 1401 Campus Delivery, Fort Collins CO 80523.

A significant amount of water is needed to take care of the average Fort Collins lawn. The average residential water use more than doubles during the summer months and up to 58% of the total water demanded goes to outdoor purposes during the same time. In 2018 and 2019, the city of Fort Collins noticed this excessive water usage so they offered rebates to residents for smart irrigation controllers. The smart controllers were intended to reduce outdoor water use however, studies have shown for low-water users, water consumption actually increased after residents implemented a smart irrigation controller. As the city of Fort Collins continues to grow, the demand for water will increase, thus by making irrigation systems more efficient, residents can greatly reduce their overall water consumption. The goal of this research is to determine the accuracy of weather forecasts to determine when to water the lawn, in order to reduce the amount of water that residents waste. To do this, we gathered five-day forecasts from the National Weather Service for Fort Collins from November 2019 to the present to compare to actual meteorological data, focusing on temperature, precipitation, wind, and cloud cover. Our analyses show that the forecasted and actual temperatures vary greatly, with forecasted temperatures tending to underestimate both minimum and maximum daily temperatures. These forecasts will be further implemented in modeling evapotranspiration, using a modified Penman-Monteith equation. 

Additional Abstract Information

Presenters: Ryan Bridges, Sonali Chokshi, Sam Myer, Christina Lilligren

Institution: Colorado State University

Type: Poster

Subject: Environmental Science & Sustainability

Status: Approved

Time and Location

Session: Poster 6
Date/Time: Tue 2:00pm-3:00pm
Session Number: 4659