资源环境科技发展态势分析平台

There is a drastic geographic imbalance in available global streamflow gauge and catchment property data, with additional large variations in data characteristics, meaning that models calibrated in one region cannot normally be migrated to another without significant modifications. Currently in these regions, non‐transferable machine learning models are habitually trained over small local datasets. Here we show that transfer learning (TL), in the sense of weight initialization and weight freezing, allows long short‐term memory (LSTM) streamflow models that were trained over the Conterminous United States (CONUS, the source dataset) to be transferred to catchments on other continents (the target regions), without the need for extensive catchment attributes available at the target location. We demonstrate this possibility for regions where data are dense (664 basins in Great Britain), moderately dense (49 basins in central Chile), and scarce with only remotely‐sensed attributes (5 basins in China). In both China and Chile, the TL models showed significantly elevated performance compared to locally‐trained models using all basins. The benefits of TL increased with the amount of available data in the source dataset, and seem to be more pronounced with more physiographic diversity. The benefits of TL were greater than pre‐training LSTM using the outputs from an uncalibrated hydrologic model. These results suggest hydrologic data around the world have commonalities which could be leveraged by deep learning, and synergies can be had with a simple modification of the current workflows, greatly expanding the reach of existing big data. Finally, this work diversified existing global streamflow benchmarks.