A Numerical Study of the Agricultural Irrigation Effects on Summer Soil Moisture and Near-Surface Meteorology in California’s Central Valley

Abstract California’s Central Valley (CV) is one of the most productive agricultural regions in the world, relying significantly on irrigation. This study explores the impacts of agricultural irrigation on soil moisture and near-surface meteorology in the CV. Employing the Weather Research and Forecasting (WRF) Model with a modified irrigation module, the WRF-irrigation simulation (WIR3D) reproduces observed humid surface soil moisture in the CV, as identified by satellite data. Without irrigation effects (WoIR), the model presents a dry surface soil moisture bias, with an average value below 0.05 m 3 m −3 over the CV. A comparison with surface-station observations reveals that the mean bias of the simulated 2-m dewpoint temperature is −1.62°C in WoIR but only 0.05°C in WIR3D. The 2-m temperature change by irrigation could be cooling or warming. The complexity of the temperature change arises from the combination of evapotranspirative cooling, soil heat flux, and radiative feedback, with differences between daytime evapotranspirative cooling and nighttime greenhouse gas effects. Compared with derived planetary boundary layer height (PBLH) from ceilometer observations, irrigation reduces simulated maximum positive PBLH bias by approximately 580 m (53%), resulting in a daily wind speed decrease of 0.5 m s −1 . In addition, this study examines the irrigation spatial heterogeneity effect, and the results show the variations of approximately 30% and 40% differences in PBLH and other near-surface variables between incorporating a county-level versus uniform irrigation rate over the CV. These findings underscore the importance of better integrating irrigation practices to improve weather forecasting in the CV.