Comparative Evaluation of Daily Streamflow Gap-Filling Using Paired Upstream–Downstream Gauges

Complete daily streamflow time series are essential for sustainable water resources management and reliable hydrological modelling; however, even short data gaps can substantially reduce the usability of streamflow records. Recurrent missing data may lead to inefficient model calibration, decreased reliability of peak and low-flow estimates, and biased hydrological statistics. Therefore, rather than leaving missing values unfilled, it can be beneficial to infill daily streamflow using appropriate methods and to provide flags indicating imputed periods. 
In South Korea, streamflow monitoring prior to 2008 primarily focused on flood-related observations, resulting in relatively limited daily streamflow records; since then, the production of continuous daily streamflow data for water resources management has expanded. As of 2024, daily streamflow records from more than 420 gauging stations are managed and disseminated, yet a non-negligible number of stations still contain missing values due to various causes such as river works and uncertainties in stage–discharge relationships associated with the operation of hydraulic structures. 
This study comparatively evaluates gap-filling techniques using paired upstream–downstream gauging stations located in basins with diverse rainfall regimes and hydrological characteristics. We assess conventional methods widely used in practice (scaling, linear regression, and equi-percentile/quantile-based approaches) under different missing-data conditions and benchmark them against an extended long short-term memory (extended LSTM) time-series model designed for streamflow infilling. Performance is evaluated using the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS). In addition, flow duration curves (FDCs) are compared to examine each method’s ability to reproduce the post-infilling flow regime distribution. The outcomes are expected to support condition-dependent selection of gap-filling strategies and to improve the reliability of daily streamflow datasets with explicit quality flags.