Improving Dryland Depth to Water Table Estimate Using an Integrated Model With Three Submodels

Abstract Investigating dryland phreatic water assets requires an in‐deep understanding of depth to water table (DWT). However, current DWT methods suffer from limited accuracy and demand refinement. Therefor, this study suggests one novel integrated model cascaded by twin, assimilation, and DWT submodels. The twin submodel clones land surface model (LSM) with machine learning method (MLM) to capture LSM uncertainties, then the assimilation submodel develops an eigen‐uncertainty‐weighted four‐dimensional variational assimilation framework to optimize LSM outputs using multiple remote sensing (RS) actual evapotranspirations (AETs), thereby, the DWT submodel proposes one physical mechanism based equation with dynamical parameters constrained by optimized LSM outputs and in situ observations. Its effectiveness is evaluated through five pairs of experiments conducted in the Tarim river basin (TRB), China. Results corroborate that the RMSE, MAPE, MAE, R 2 of its estimated DWTs are improved by 21.9%–36.1%, 52.9%–58.3%, 49.5%–59.7%, 2.6%–9.3%, respectively, compared to those from pure‐MLMs using original LSM outputs against 84 validation wells across 15 basins. Additionally, the DWT trend obtained well reflects the temporal variability and spatial heterogeneity of the TRB from 2000 to 2020. Owe to its LSM independence and solid physical mechanism, the integrated model ameliorates DWT estimate through a novel insight into the dynamics between phreatic water and heat by maximizing the advantages of multi‐source quality RS AETs and LSM outputs without the adjoint models and running of LSMs.