Deep learning integrating scale conversion and pedo-transfer function to avoid potential errors in cross-scale transfer

Pedo-transfer functions (PTFs) relate soil/landscape static properties to a wide range of model inputs (e.g., soil hydraulic parameters) that are essential to soil hydrological modeling. Combining PTFs and hydrological models is a powerful strategy allowing the use of soil/landscape static properties for the generalization of large-scale modeling. However, since the spatial scales of soil hydraulic parameters required for model inputs and soil/landscape static properties are often not identical, cross-scale transfer is required, which can be a significant source of errors. Here, we investigate uncertainties in cross-scale transfer and develop an approach that avoids them. The proposed method uses the convolutional neural network (CNN) as a cross-scale transfer approach to directly map soil/landscape static properties to soil hydraulic parameters across different spatial scales. The proposed CNN approach is applied under two different estimation strategies to invert the hydraulic parameters of a soil-water balance model and subsequently the quality of the parameters is assessed. Both synthetical and real-world results around the conterminous United States indicate that in general the employed end-to-end strategy is superior to the two-step strategy. The CNN-based integrated model successfully reduces potential errors in cross-scale transfer and can be applied to other areas lacking information on hydraulic parameters or observations. The proposed method can be extended to improve parameter estimation in earth system models and enhance our understanding of key hydrological processes.