Estimation of evapotranspiration rates and root water uptake profiles from soil moisture sensor array data

Evapotranspiration is arguably the least quantified component of hydrologic cycle. We propose two complementary strategies for estimation evapotranspiration rates and root water uptake profiles from soil-moisture sensor-array data. One our implementation ensemble Kalman filter (EnKF); it treats sink term in Richards equation, rather than soil moisture, as observable to update. The other a maximum likelihood estimator (MLE) applied same observable; supplemented with Fisher information matrix quantify uncertainty its predictions. use numerical experiments demonstrate accuracy computational efficiency these techniques. found EnKF be orders magnitude faster either standard or MLE, MLE procedure require an order fewer iterations converge counterpart moisture. These findings render methodologies viable practical tool evaporation rates, technique used when prior knowledge about at site elusive.