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Abstract In this study, the authors incorporate an operational-like irrigation scheme into the Noah land surface model as part of the Weather Research and Forecasting Model (WRF). A series of simulations, with and without irrigation, is conducted over the Southern Great Plains (SGP) for an extremely dry (2006) and wet (2007) year. The results show that including irrigation reduces model bias in soil moisture and surface latent heat (LH) and sensible heat (SH) fluxes, especially during a dry year. Irrigation adds additional water to the surface, leading to changes in the planetary boundary layer. The increase in soil moisture leads to increases in the surface evapotranspiration and near-surface specific humidity but decreases in the SH and surface temperature. Those changes are local and occur during daytime. There is an irrigation-induced decrease in both the lifting condensation level ( Z LCL ) and mixed-layer depth. The decrease in Z LCL is larger than the decrease in mixed-layer depth, suggesting an increasing probability of shallow clouds. The simulated changes in precipitation induced by irrigation are highly variable in space, and the average precipitation over the SGP region only slightly increases. A high correlation is found among soil moisture, SH, and Z LCL . Larger values of soil moisture in the irrigated simulation due to irrigation in late spring and summer persist into the early fall, suggesting that irrigation-induced soil memory could last a few weeks to months. The results demonstrate the importance of irrigation parameterization for climate studies and improve the process-level understanding on the role of human activity in modulating land–air–cloud interactions.
Qian et al. (Fri,) studied this question.