Improving National Water Model Evapotranspiration Forecasts Using Machine Learning Post-Processing: A California Case Study

Abstract

Evapotranspiration (ET) forecasts from the U.S. National Water Model (NWM) carry systematic biases that are most apparent in water-stressed, intensively managed regions. This study develops a machine learning (ML) post-processing framework that bias-corrects 1–7 day accumulated ET (ACCET) forecasts from the NWM medium-range configuration using NWM meteorological forcings, the raw NWM-ACCET forecast, and auxiliary spatial and temporal predictors. Three tree-based models—Random Forest (RF), XGBoost, and LightGBM—were trained on AmeriFlux eddy-covariance observations at four California model-development sites and evaluated using nested five-fold temporally blocked cross-validation with a ±7-day purge window; two additional sites were withheld for independent off-site validation. All three models substantially outperformed the raw NWM forecasts. Pooled out-of-fold performance across the four model-development sites yielded R2 values of 0.832–0.854 and mean-normalized root mean squared error (NRMSE) values of 0.427–0.460, compared with R2 = 0.086 and NRMSE = 1.206 for the raw NWM forecasts. LightGBM and XGBoost were favored over RF in this four-site out-of-fold evaluation. Independent off-site validation yielded R2 values of 0.796–0.874 and NRMSE values of 0.380–0.459, with RF showing the most robust overall performance. Converting ACCET to lead-time-specific increments revealed substantially stronger lead-time degradation than was apparent from the accumulated metrics. Feature-importance and SHapley Additive exPlanations (SHAP) analyses identified wind, lead time, seasonality, and radiation as dominant predictors. The framework offers a practical route to more reliable operational ET forecasts for water management, agriculture, and drought monitoring, with potential for extension to climatically similar regions.