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

Abstract. A long-term high-resolution national dataset of precipitation
(P), soil moisture (SM), and snow water equivalent (SWE) is necessary for
predicting floods and droughts and assessing the impacts of climate change
on 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 of
local correction. Although SM and SWE data derived from hydrological
simulations at a national scale have fine spatial resolutions and take
advantage of local forcing data, hydrological models are not directly
calibrated with SM and SWE data. In this study, we produced a daily
0.1∘ dataset of P, SM, and SWE in 1981–2017 across China, using
global background data and local on-site data as forcing input and
satellite-based data as reconstruction benchmarks. Global 0.1∘
and local 0.25∘P data in 1981–2017 are merged to reconstruct the
historical P of the 0.1∘ China Merged Precipitation Analysis
(CMPA) available in 2008–2017 using a stacking machine learning model. The
reconstructed P data are used to drive the HBV hydrological model to simulate SM
and SWE data in 1981–2017. The SM simulation is calibrated by Soil Moisture
Active Passive Level 4 (SMAP-L4) data. The SWE simulation is calibrated by
the national satellite-based snow depth dataset in China (Che and Dai, 2015)
and the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover
data. 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 for
all grids at a daily scale. The median KGE of SM in calibration is 0.61 for
all grids at a daily scale. For grids in two snow-rich regions, the median
KGEs of SWE in calibration are 0.55 and −2.41 in the Songhua and Liaohe
basins and the northwest continental basin respectively at a daily scale.
Generally, the reconstruction dataset performs better in southern and
eastern China than in northern and western China for P and SM and performs
better in northeast China than in other regions for SWE. As the first long-term
0.1∘ daily dataset of P, SM, and SWE that combines information
from local observations and satellite-based data benchmarks, this
reconstruction product is valuable for future national investigations of
hydrological processes.
