DOI: 10.57760/sciencedb.00364
title: A daily 0.25°×0.25° hydrologically based land surface flux dataset for conterminous China, 1961-2017
title: 1961-2017年中国0.25°×0.25° 逐日地表水文数据集(VIC-CN05.1)

description: The lack of long-term consistent observational hydrological datasets over large scales brings great uncertainties to hydroclimatic research in China. This paper describes a daily 0.25°×0.25° terrestrial hydrological dataset (VIC-CN05.1) for China during 1961-2017. The dataset is simulated by the latest variable infiltration capacity (VIC) model and driven by pure station-based atmospheric forcings and high-resolution soil parameters based on field surveys. Multiple observational (in situ and remote sensing) products and five statistical metrics, including biases, relative error (RE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R), are used to validate the model outputs. The results indicate that the seasonal cycles of simulated total runoff and composite runoff from the University of New Hampshire and Global Runoff Data Centre (UNH/GRDC) show good agreement in the Yangtze, southeast, and Pearl River basins, with positive REs≤7% and NSEs≥0.87. At the Changbaishan, Haibei Shrubland, and Qianyanzhou flux towers, VIC-CN05.1 reproduces the monthly latent heat fluxes well during 2003-2005. The monthly modelled 0-10 cm soil moisture anomalies are highly correlated to in situ measurements (438 stations) during 2003-2016, with mean R=0.80. The spatial patterns of simulated and observed snow depth, snow water equivalent (SWE), and snow cover in winter are generally consistent in the northeastern and northwestern China. Except for the northwest basin, the simulated monthly terrestrial water storage changes in most river basins (particularly the Yangtze, Huai, and Yellow River basins) match well with observations based on the Gravity Recovery and Climate Experiment (GRACE) during 2003-2016, with positive NSEs and mean R=0.41. This terrestrial hydrology dataset is valuable for hydroclimatic research in China, such as model evaluations and long-term trend analyses.
description: The lack of long-term consistent observational hydrological datasets over large scales brings great uncertainties to hydroclimatic research in China. This paper describes a daily 0.25°×0.25° terrestrial hydrological dataset (VIC-CN05.1) for China during 1961-2017. The dataset is simulated by the latest variable infiltration capacity (VIC) model and driven by pure station-based atmospheric forcings and high-resolution soil parameters based on field surveys. Multiple observational (in situ and remote sensing) products and five statistical metrics, including biases, relative error (RE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R), are used to validate the model outputs. The results indicate that the seasonal cycles of simulated total runoff and composite runoff from the University of New Hampshire and Global Runoff Data Centre (UNH/GRDC) show good agreement in the Yangtze, southeast, and Pearl River basins, with positive REs≤7% and NSEs≥0.87. At the Changbaishan, Haibei Shrubland, and Qianyanzhou flux towers, VIC-CN05.1 reproduces the monthly latent heat fluxes well during 2003-2005. The monthly modelled 0-10 cm soil moisture anomalies are highly correlated to in situ measurements (438 stations) during 2003-2016, with mean R=0.80. The spatial patterns of simulated and observed snow depth, snow water equivalent (SWE), and snow cover in winter are generally consistent in the northeastern and northwestern China. Except for the northwest basin, the simulated monthly terrestrial water storage changes in most river basins (particularly the Yangtze, Huai, and Yellow River basins) match well with observations based on the Gravity Recovery and Climate Experiment (GRACE) during 2003-2016, with positive NSEs and mean R=0.41. This terrestrial hydrology dataset is valuable for hydroclimatic research in China, such as model evaluations and long-term trend analyses.