Publications

Yang, WC; Yang, HB; Li, CM; Wang, TH; Liu, ZW; Hu, QF; Yang, DW (2022). Long-term reconstruction of satellite-based precipitation, soil moisture,and snow water equivalent in China. HYDROLOGY AND EARTH SYSTEM SCIENCES, 26(24), 6427-6441.

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 degrees 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 degrees and local 0.25 degrees P data in 1981-2017 are merged to reconstruct the historical P of the 0.1 degrees 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.1 Introduction A long-term national terrestrial hydrological dataset with high spatiotemporal resolutions can be used in many hydrological applications such as exploring the controls of rainfall-runoff events (Tarasova et al., 2020; Yang et al.,2020b; Stein et al., 2021), predicting floods and droughts(Van Steenbergen and Willems, 2013; Reager et al., 2014;Abelen et al., 2015), and assessing the impacts of climate change on streamflow and floods (Sharma et al., 2018;Bloschl et al., 2019; Li et al, 2019). As key variables in the hydrological cycle, precipitation (P), soil moisture (SM),and snow water equivalent (SWE) generate riverine run off and determine the wetness states of the basins. Although long-term (at least 30 years) daily P, SM, and SWE can be obtained from many data products in China, these products suffer from a coarse spatial resolution and a lack of local in-formation. Published by Copernicus Publications on behalf of the European Geosciences Union.

DOI:
10.5194/hess-26-6427-2022

ISSN:
1607-7938