中国高质量天然径流量估算

High-quality reconstruction of China’s natural streamflow

国家社会经济的可持续发展与可用淡水资源量关系密切.我国人口众多,水资源供需矛盾较突出.现有的天然河川径流资料缺失率较高,参考站点密度不足,在年际和季节变化尺度上存在较大偏差,难以客观揭示大尺度河川径流变化的自然规律.本研究基于VIC(The Variable Infiltration Capacity)分布式水文模型,结合参数不确定分析、流向校正和统计后处理方法,建立了一套长时序、多站点、高质量的天然河川径流资料.全国330个水文站分别约有83%和56%水文站的NSE值、KGE值大于0.70,该天然径流数据集可以为变化环境下水文过程模拟与水资源综合管理提供重要基础数据与科学服务.

Reconstruction of natural streamflow 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.Here we use a well-trained and tested land surface model coupled to a routing model with flow direction correction to reconstruct the first high-quality gauge-based natural streamflow dataset for China,covering all its330 catchments during the period from 1961 to 2018.A stronger positive linear relationship holds between upstream routing cells and drainage areas,after flow direction correction to 330 catchments.We also introduce a parameter-uncertainty analysis framework including sensitivity analysis,optimization,and regionalization,which further minimizes biases between modeled and inferred natural streamflow from natural or near-natural gauges.The resulting behavior of the natural hydrological system is represented properly by the model which achieves high skill metric values of the monthly streamflow,with about 83%of the 330 catchments having Nash-Sutcliffe efficiency coefficient(NSE)>0.7,and about56%of the 330 catchments having Kling-Gupta efficiency coefficient(KGE)>0.7.The proposed construction scheme has important implications for similar simulation studies in other regions,and the developed low bias long-term national datasets by statistical postprocessing should be useful in supporting river management activities in China.