Abstract
Abstract. The absence of a compiled large-scale catchment characteristics dataset is a key obstacle limiting the development of large-sample hydrology research in China. We introduce the first large-scale catchment attribute dataset in China. We compiled diverse data sources, including soil, land cover, climate, topography, and geology, to develop the dataset. The dataset also includes catchment-scale 31-year meteorological time series from 1990 to 2020 for each basin. Potential evapotranspiration time series based on Penman's equation are derived for each basin. The 4911 catchments included in the dataset cover all of China. We introduced several new indicators that describe the catchment geography and the underlying surface differently from previously proposed datasets. The resulting dataset has a total of 125 catchment attributes and includes a separate HydroMLYR (hydrology dataset for machine learning in the Yellow River Basin) dataset containing standardized weekly averaged streamflow for 102 basins in the Yellow River Basin. The standardized streamflow data should be able to support machine learning hydrology research in the Yellow River Basin. The dataset is freely available at https://doi.org/10.5281/zenodo.5729444 (Zhen et al., 2021). In addition, the accompanying code used to generate the dataset is freely available at https://github.com/haozhen315/CCAM-China-Catchment-Attributes-and-Meteorology-dataset (last access: 26 November 2021) and supports the generation of catchment characteristics for any custom basin boundaries. Compiled data for the 4911 basins covering all of China and the open-source code should be able to support the study of any selected basins rather than being limited to only a few basins.
Highlights
Rainfall, interception, evaporation and evapotranspiration, groundwater flow, subsurface flow, and surface runoff are the main components of the terrestrial hydrological cycle
In addition to the basinwise attributes provided in CCAM, we propose HydroMLYR, a hydrology dataset for machine learning research in the Yellow River Basin (YRB) providing weekly averaged standardized streamflow data for 102 basins in the YRB
In addition to the basinwise static attributes provided in CCAM, we propose HydroMLYR, a hydrology dataset for machine learning research in the YRB (Fig. 1)
Summary
Interception, evaporation and evapotranspiration, groundwater flow, subsurface flow, and surface runoff are the main components of the terrestrial hydrological cycle. There have been efforts (Addor et al, 2017; Alvarez-Garreton et al, 2018; Chagas et al, 2020; Coxon et al, 2020) to compile different types of data sources to form large-scale hydrological datasets. These four collected datasets cover the continental United States, Chile, Brazil, and Great Britain. A lack of a compiled catchment attribute dataset is a key obstacle limiting the development of largesample hydrology research in China.
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