An improved irrigation scheme utilizing realistic irrigation signals detected from SMAP soil moisture data

Abstract:

Existing irrigation schemes in land surface model cannot accurately capture irrigation timing and water amount. Here we developed an improved irrigation scheme which triggers irrigation by using irrigation signals detected from SMAP satellite soil moisture data. Assessed by site irrigation observations, this improved scheme could accurately capture the daily irrigation application over the heavily irrigated areas in northern China. Compared with previous irrigation schemes, the accuracy of irrigation signals capture increased from 25% to 71%. Meanwhile, it also helped mitigate overestimated or underestimated regional annual irrigation water amount. Moreover, this improved scheme incorporated into Noah-mp land surface model notably improves the simulation of the daily fluctuation of soil moisture. These results provide a new framework for improved modeling of irrigation effects on water cycle and land-atmosphere interactions.