Reconstruction of natural streamflow without water management, e.g., irrigation and reservoir regulation is fundamental to the sustainable management of water resources. In China, previous reconstructions from sparse and poor-quality gauge measurements have led to large biases in simulation of the interannual and seasonal variability of natural flows.
In the current study, the researchers adopted a well-trained and tested land surface model coupled to a routing model with flow direction correction to reconstruct this very first high-quality gauge-based natural streamflow dataset for China, covering all its 330 catchments during the period from 1961 to 2018.
Using their quality control approach, the researchers obtained a stronger positive linear relationship between upstream routing cells and drainage areas, after flow direction correction. The proposed parameter-uncertainty analysis framework incorporating sensitivity analysis, optimization and regionalization, further minimized the biases between modeled and inferred natural streamflow acquired from natural or near-natural gauges. Using such quality control approaches, the behavior of the natural hydrological system is well represented by the model which achieves high skill metric values of the monthly streamflow, with about 83% and 56% of the 330 hydrological stations possessing NSE (Nash-Sutcliffe efficiency coefficient) and KGE (Kling-Gupta efficiency coefficient)> 0.7, respectively.
The proposed construction scheme has important implications for similar simulation studies in other regions. The developed low bias long-term natural streamflow datasets should be also useful for understanding the mechanism of hydrological processes, quantifying the interaction of each component of the terrestrial water cycle system, and supporting the future river management activities in China.
See the article:
High-quality reconstruction of China’s natural streamflow
https://doi.org/10.1016/j.scib.2021.09.022.
Science Bulletin