Application of landsurface data assimilation into the simulation of soil moisture and temperature of croplands in oasis

Precisely predicting soil moisture can benefit the reasonable distribution of the limited water resources. A one dimensional landsurface data assimilation system was applied to water and heat transfer study over croplands in Jinta oasis, which was a combination of ensemble Kalman filter method and Noah Lsm (Community Noah Land Surface Model, N: National Center for Environmental Prediction, O: Oregon State University, A: Air Force, H: Hydrology Research Lab) coupled in the mesoscale models MM5 (Mesoscale Model version 5) and WRF (Weather Forecast and Research model). The results showed the improvements not only on the simulated soil moisture, but also the simulation of soil temperature and latent heat flux in the experiment of assimilation of the soil moisture of shallow layers. The simulated soil temperature can be significantly improved after the assimilation of the soil temperature. If the study is applied into the mesoscale model in the future, the result will benefit distributing water resources efficiently by means of predicting soil moisture of a region accurately.