Can transfer learning improve hydrological predictions in the alpine regions?

Abstract

The Tibetan Plateau (TP) is an important Asian water tower for livelihood, irrigation, hydropower, and downstream ecosystems. Hydrological predictions in the TP have long been limited by sparse and discontinuous observations. Transfer learning (TL) technology may improve hydrological predictions by pre-training deep learning (DL) models on data-rich areas and then applying them to data-limited areas. However, the extent to which the DL and TL models work in alpine regions and the physical knowledge they provide remain unclear. Models were pretrained using data from 671 catchments across the U.S. and fine-tuned using data from four basins around the TP. Our results show that streamflow data with different temporal resolutions (monthly or daily) have little effect on discharge predictions when using DL. The number of discharge observations needed to enable acceptable performance of the DL models (Nash-Sutcliffe Efficiency coefficient greater than 0.6) depends on the hydrological characteristics of the catchments, in particular, how closely they conform to a general rainfall-runoff system. Climate forcing data determine the performance of streamflow prediction, whereas other attributes (i.e., soil and geology) have less significant impacts on prediction. The effectiveness of the DL and TL models is limited because groundwater contributes significantly to river discharge. This study provides an updated understanding of the application of DL and TL to quantify hydrological changes in the global cryosphere environment.