Intercomparison of Runoff and River Discharge Reanalysis Datasets at the Upper Jinsha River, an Alpine River on the Eastern Edge of the Tibetan Plateau

This study assesses the effectiveness and limitations of publicly accessible runoff and river discharge reanalysis datasets through an intercomparison in the Upper Jinsha River, an alpine region with substantial hydropower potential on the eastern edge of the Tibetan Plateau. The examined datasets are the European Centre for Medium-Range Weather Forecast Reanalysis version 5 (ERA5-Land), the Global Flood Awareness System (GloFAS), the Global Reach-Level Flood Reanalysis (GRFR), and the China Natural Runoff Dataset (CNRD). These datasets are created using various meteorological forcing, runoff generation models, river routing models, and calibration methods. To determine the causes of discrepancies, additional simulations were carried out. One simulation, driven by meteorological forcing similar to that of ERA5-Land and GloFAS but utilizing the uncalibrated NoahMP land surface model at a higher spatial resolution, was included to evaluate the effects of meteorological inputs, spatial resolution, and calibration on runoff estimation. Runoff from all datasets was rerouted on a high-resolution river network derived from the 3-arcsecond Multi-Error-Removed Improved-Terrain Hydrography (MERIT-Hydro) dataset, allowing for a comparison between vector- and grid-based river routing models for discharge estimates. The intercomparison is grounded in observations from three gauging stations—Zhimenda, Gangtuo, and Benzilan—at monthly, daily, and hourly scales. The results suggest that model calibration has a more significant influence on runoff and discharge estimates than meteorological data. Calibrated datasets, such as GloFAS and GRFR, perform better than others, despite variations in the forcing data. The runoff characteristics-based calibration method used in GRFR exhibits superior performance at Zhimenda and Benzilan. However, at Gangtuo, GRFR’s performance is unsatisfactory, highlighting the limitation of the machine learning-based method in regions with rugged terrain and limited observations. Vector-based river routing models demonstrate advantages over grid-based models. GloFAS, which uses a grid-based routing model, encounters difficulties in simultaneously producing accurate runoff and discharge estimates. The intercomparison shows that GRFR’s river routing is sub-optimally configured. However, when GRFR’s runoff rerouted, the performance of discharge improves substantially, attaining a Kling–Gupta efficiency of approximately 0.9. These findings offer valuable insights for the further development of reanalysis datasets in this region.