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In this study, the feasibility of using inverse vadose zone modeling forestimating field-scale actual evapotranspiration (ETa) wasexplored at a long-term agricultural monitoring site in eastern Nebraska.Data from both point-scale soil water content (SWC) sensors and thearea-average technique of cosmic-ray neutron probes were evaluated againstindependent ETa estimates from a co-located eddy covariancetower. While this methodology has been successfully used for estimates ofgroundwater recharge, it was essential to assess the performance of othercomponents of the water balance such as ETa. In light of recentevaluations of land surface models (LSMs), independent estimates of hydrologicstate variables and fluxes are critically needed benchmarks. The results hereindicate reasonable estimates of daily and annual ETa from thepoint sensors, but with highly varied soil hydraulic functionparameterizations due to local soil texture variability. The results ofmultiple soil hydraulic parameterizations leading to equally goodETa estimates is consistent with the hydrological principle ofequifinality. While this study focused on one particular site, the frameworkcan be easily applied to other SWC monitoring networks across the globe. Thevalue-added products of groundwater recharge and ETa flux fromthe SWC monitoring networks will provide additional and more robustbenchmarks for the validation of LSM that continues to improve their forecastskill. In addition, the value-added products of groundwater recharge andETa often have more direct impacts on societal decision-makingthan SWC alone. Water flux impacts human decision-making from policies on thelong-term management of groundwater resources (recharge), to yield forecasts(ETa), and to optimal irrigation scheduling (ETa).Illustrating the societal benefits of SWC monitoring is critical to insurethe continued operation and expansion of these public datasets

hydrology and earth system sciences

Feasibility analysis of using inverse modeling for estimating field-scale evapotranspiration in maize and soybean fields from soil water content monitoring networks