Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model

Abstract

Study region: Yellow River Basin in China, where streamflow dynamics were significantly impacted by human activities. Study focus: We introduced a deep learning-based method, i.e., Data Integration (DI) with Long Short-Term Memory (LSTM), which leverages Global Flood Awareness System (GloFAS) streamflow data. Multiscale (Catchment, River) attributes were incorporated into the DI LSTM to represent human disturbances on land surface. We employed this method to reconstruct daily streamflow series in 60 human-regulated catchments across the Yellow River Basin, and identified the sensitivity of the DI LSTM model to the multiscale attributes. New hydrological Insights for the Region: Our findings revealed that the DI LSTM model achieved favourable performance in streamflow estimation, with the highest Kling-Gupta efficiency (KGE) reaching up to 0.9, outperforming the Regular LSTM model, which was forced by meteorological variables. Multiscale attributes can enhance the DI model performance, particularly in large catchments with significant human activities. A two-step validation demonstrated the high accuracy of the reconstructed streamflow data across the Yellow River Basin, as the KGEs for streamflow estimation in 40 catchments are over 0.6. In summary, the DI LSTM model shows great potential for reconstructing streamflow in human-regulated catchments in arid regions. The reconstructed daily streamflow data contribute valuable insights for monitoring changing hydrological conditions, especially in regions lacking extensive streamflow monitoring networks.

Keywords

• Data integration
• LSTM
• Multiscale static attributes
• Streamflow reconstruction
• Human activity
• Yellow River Basin