CNRD v1.0: A High-Quality Natural Runoff Dataset for Hydrological and Climate Studies in China

Reliable, spatiotemporally continuous runoff records are necessary for identifying climate change impacts and planning effective water management strategies. Existing Chinese runoff data to date have been produced from sparse, poor-quality gauge measurements at different time scales. We have developed a new, quality-controlled gridded runoff dataset, the China Natural Runoff Dataset version 1.0 (CNRD v1.0), which provides daily, monthly, and annual 0.25 degrees runoff estimates for the period 1961-2018 over China. CNRD v1.0 was generated using the Variable Infiltration Capacity (VIC) model. A comprehensive parameter uncertainty analysis framework incorporating parameter sensitivity analysis, optimization, and regionalization with 200 natural or near-natural gauge catchments was used to train the VIC model. Overall, the results show well-calibrated parameters for most gauged catchments except arid and semiarid areas, and the skill scores present high values for all catchments. For the pseudo-/test-ungauged catchments, the model parameters estimated by the multiscale parameter regionalization technique offer the best regionalization solution. CNRD v1.0 is the first free public dataset of gridded natural runoff estimated using a comprehensive model parameter uncertainty analysis framework for China. These results indicate that CNRD v1.0 has high potential for application to long-term hydrological and climate studies in China and to improve international runoff databases for global-scale studies.

Automated summary

The study introduced a new China Natural Runoff Dataset version 1.0, providing gridded runoff estimates from 1961 to 2018 using the Variable Infiltration Capacity (VIC) model. The dataset was rigorously quality-controlled and trained with multi-scale parameter regionalization technique, showing well-calibrated parameters for most catchments and high potential for application in long-term hydrological and climate studies in China.