Research Finds Irrigation Gaps May Skew Air Quality Forecasts

Study suggests computer models used by agencies may not accurately represent heat and moisture exchange between farmland and atmosphere

Published on Mar. 5, 2026

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A new study led by a Penn State doctoral student found that computer models used to predict air pollution can seriously misrepresent how heat and moisture move between farmland and the atmosphere, potentially skewing air quality forecasts used for policy decisions. The researchers evaluated the Weather Research and Forecasting (WRF) model, a widely used regulatory weather model, and found it performs very differently in California's San Joaquin Valley versus the Mid-Atlantic region.

Why it matters

Accurate air quality forecasts are critical for protecting public health by alerting communities to dangerous levels of pollutants linked to asthma attacks, heart disease and premature death. This study suggests that gaps in how models represent irrigation and land use details could lead to underestimates of pollution near the surface where people breathe.

The details

The team found that in California's San Joaquin Valley, the WRF model makes irrigated farm fields appear far too hot and dry, overestimating heat flow from the surface to the air by about 274% and underestimating the cooling effect from evaporation by about 68%. This is because the model excludes irrigation, failing to capture how added water cools and moistens the surface. In the Mid-Atlantic, model errors were smaller and more balanced. The researchers said including a representation of irrigation could strengthen air quality forecasts in heavily farmed regions.

  • The study was published in the journal Agricultural and Forest Meteorology in 2026.

The players

Fan Wu

A doctoral student in Penn State's Department of Meteorology and Atmospheric Science and the lead author of the study.

Ken Davis

A professor of meteorology and atmospheric science at Penn State and a member of the research team.

Weather Research and Forecasting (WRF) model

A widely used regulatory weather model that the researchers evaluated in the study.

Pleim-Xiu Land Surface Model (PX LSM)

A land module within the WRF model that is used by air quality agencies in California and the Mid-Atlantic region.

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What they’re saying

“These significant heat flux errors over irrigated fields can distort air quality forecasts. If the model puts too much heat into the atmosphere, it makes the atmospheric boundary layer too deep, giving pollutants in the model more room to dilute. That can lead to underestimates of pollution near the surface, where people breathe.”

— Fan Wu, Doctoral student (Mirage News)

“If WRF better represented irrigation and land use details, we would expect more accurate simulations of daytime PM2.5 and ozone concentrations in state modeling systems, which could help agencies create more effective plans to reduce pollution.”

— Fan Wu, Doctoral student (Mirage News)

“Sometimes these complex systems contain compensating errors. If better surface modeling improves both the weather and air quality simulations - and early signs in the San Joaquin Valley suggest it does - then we're headed in the right direction.”

— Ken Davis, Professor of meteorology and atmospheric science (Mirage News)

What’s next

The researchers are testing whether tools like NASA's Land Information System or a simpler irrigation module can reduce the surface heat flux errors they identified. They need to first show that these approaches improve the weather model, and then determine whether states can realistically implement them.

The takeaway

This study highlights the importance of accurately representing irrigation and land use details in computer models used for air quality forecasting. Improving these models could lead to more effective plans to reduce pollution and better protect public health in heavily farmed regions.