Performance mapping and weighting for the evapotranspiration models of the OpenET ensemble
Evapotranspiration (ET) accounts for the majority of water available from precipitation in the terrestrial water cycle, and improvements to the accuracy, resolution, and coverage of ET data can enhance hydrologic models and assessments. The OpenET collaboration of six remotely sensed ET modeling teams has demonstrated that an ensemble approach to ET estimation generally provides improved accuracy relative to individual ensemble members. The performance of individual models has been shown to vary by land cover type and climate zone, but a thorough study of the variables that influence model performance differences has not yet been conducted. In this paper, we model the performance of OpenET models relative to flux tower data as a function of variables such as land cover type and precipitation. These performance models are used to map estimated OpenET model performance across the conterminous United States. We develop relative weights based on these modeled performance metrics and show that a performance-weighted ensemble improves accuracy relative to the current OpenET ensemble method to varying degrees. The monthly mean absolute error of the weighted ensemble is reduced relative to the current method by 8% in agricultural settings, by 23% in shrublands and mixed forests, and by 5% in grasslands and evergreen forests. We produce weight maps that can be used to generate performance-weighted ensemble values for OpenET data. The results can be used to inform model selection and provide insight about the controls on model performance that could lead to model refinement.