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

Abstract. In this study, the feasibility of using inverse vadose zone modeling for estimating field-scale actual evapotranspiration (ET_a) was explored at a long-term agricultural monitoring site in eastern Nebraska. Data from both point-scale soil water content (SWC) sensors and the area-average technique of cosmic-ray neutron probes were evaluated against independent ET_a estimates from a co-located eddy covariance tower. While this methodology has been successfully used for estimates of groundwater recharge, it was essential to assess the performance of other components of the water balance such as ET_a. In light of recent evaluations of land surface models (LSMs), independent estimates of hydrologic state variables and fluxes are critically needed benchmarks. The results here indicate reasonable estimates of daily and annual ET_a from the point sensors, but with highly varied soil hydraulic function parameterizations due to local soil texture variability. The results of multiple soil hydraulic parameterizations leading to equally good ET_a estimates is consistent with the hydrological principle of equifinality. While this study focused on one particular site, the framework can be easily applied to other SWC monitoring networks across the globe. The value-added products of groundwater recharge and ET_a flux from the SWC monitoring networks will provide additional and more robust benchmarks for the validation of LSM that continues to improve their forecast skill. In addition, the value-added products of groundwater recharge and ET_a often have more direct impacts on societal decision-making than SWC alone. Water flux impacts human decision-making from policies on the long-term management of groundwater resources (recharge), to yield forecasts (ET_a), and to optimal irrigation scheduling (ET_a). Illustrating the societal benefits of SWC monitoring is critical to insure the continued operation and expansion of these public datasets.