Comment on hess-2022-135

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, the lack of local correction, or the lack of direct calibration. 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 onsite data as forcing input and satellite-based data as reconstruction benchmarks. Long-term global 0.1° and local 0.25° P data are merged to reconstruct the P from the short-term 0.1° China Merged Precipitation Analysis (CMPA) using a stacking machine learning model. Long-term SM data are reconstructed by the HBV hydrological model with SM calibrated by Soil Moisture Active Passive Level 4 (SMAP-L4) data. Long-term SWE data are also reconstructed by the HBV hydrological model with SWE 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. For all grids at the national and daily scale, the median Kling-Gupta Efficiency (KGE) of the reconstructed P and SM are 0.68 and 0.61 respectively. For grids in two snow-rich regions at the daily scale, the median KGEs of the reconstructed SWE are 0.55 and -2.41 in the Songhua and Liaohe Basin and the Continental Basin respectively. 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 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.