Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China

[摘要] A long-term high-resolution national dataset of precipitation( P ), soil moisture (SM), and snow water equivalent (SWE) is necessary forpredicting floods and droughts and assessing the impacts of climate changeon streamflow in China. Current long-term daily or sub-daily datasets of P , SM, and SWE are limited by a coarse spatial resolution or the lack oflocal correction. Although SM and SWE data derived from hydrologicalsimulations at a national scale have fine spatial resolutions and takeadvantage of local forcing data, hydrological models are not directlycalibrated with SM and SWE data. In this study, we produced a daily0.1 ∘ dataset of P , SM, and SWE in 1981–2017 across China, usingglobal background data and local on-site data as forcing input andsatellite-based data as reconstruction benchmarks. Global 0.1 ∘ and local 0.25 ∘ P data in 1981–2017 are merged to reconstruct thehistorical P of the 0.1 ∘ China Merged Precipitation Analysis(CMPA) available in 2008–2017 using a stacking machine learning model. Thereconstructed P data are used to drive the HBV hydrological model to simulate SMand SWE data in 1981–2017. The SM simulation is calibrated by Soil MoistureActive Passive Level 4 (SMAP-L4) data. The SWE simulation is calibrated bythe national satellite-based snow depth dataset in China (Che and Dai, 2015)and the Moderate Resolution Imaging Spectroradiometer (MODIS) snow coverdata. Cross-validated by the spatial and temporal splitting of the CMPA data,the median Kling–Gupta efficiency (KGE) of the reconstructed P is 0.68 forall grids at a daily scale. The median KGE of SM in calibration is 0.61 forall grids at a daily scale. For grids in two snow-rich regions, the medianKGEs of SWE in calibration are 0.55 and − 2.41 in the Songhua and Liaohebasins and the northwest continental basin respectively at a daily scale.Generally, the reconstruction dataset performs better in southern andeastern China than in northern and western China for P and SM and performsbetter in northeast China than in other regions for SWE. As the first long-term0.1 ∘ daily dataset of P , SM, and SWE that combines informationfrom local observations and satellite-based data benchmarks, thisreconstruction product is valuable for future national investigations ofhydrological processes.

[学科分类] 妇产科学