Preprints
https://doi.org/10.5194/essd-2022-79
https://doi.org/10.5194/essd-2022-79
 
04 Apr 2022
04 Apr 2022
Status: a revised version of this preprint is currently under review for the journal ESSD.

HRLT: A high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China

Rongzhu Qin, Zeyu Zhao, Jia Xu, Jian-Sheng Ye, Feng-Min Li, and Feng Zhang Rongzhu Qin et al.
  • State Key Laboratory of Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, 730000, China

Abstract. Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km and over a long-term period (1961 to 2019). It has been named the HRLT and the dataset is publicly available at https://doi.org/10.1594/PANGAEA.941329 (Qin and Zhang, 2022). In this study, the daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning, the generalized additive model, and thin plate splines. It is based on the 0.5° × 0.5° grid dataset from the China Meteorological Administration, together with covariates for elevation, aspect, slope, topographic wetness index, latitude, and longitude. The accuracy of the HRLT daily dataset was assessed using observation data from meteorological stations across China. The maximum and minimum temperature estimates were more accurate than the precipitation estimates. For maximum temperature, the mean absolute error (MAE), root mean square error (RMSE), Pearson’s correlation coefficient (Cor), coefficient of determination after adjustment (R2), and Nash-Sutcliffe modeling efficiency (NSE) were 1.07 °C, 1.62 °C, 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, R2, and NSE were 1.08 °C, 1.53 °C, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, R2, and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of the other three existing datasets and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution or over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy, is suitable for future environmental analyses, especially the effects of extreme weather.

Rongzhu Qin et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-79', Qiang Zhang, 06 May 2022
  • CC1: 'Comment on essd-2022-79', Fei Mo, 23 Jun 2022
    • AC3: 'Reply on CC1', Feng Zhang, 16 Sep 2022
  • RC2: 'Comment on essd-2022-79', Anonymous Referee #2, 13 Sep 2022
    • AC4: 'Reply on RC2', Feng Zhang, 16 Sep 2022

Rongzhu Qin et al.

Data sets

HRLT: A high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China Rongzhu Qin, Feng Zhang https://doi.org/10.1594/PANGAEA.941329

Rongzhu Qin et al.

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Short summary
A new high-resolution daily gridded maximum temperature, minimum temperature, and precipitation dataset for China (HRLT) with a spatial resolution of 1 × 1 km for the period 1961 to 2019. These datasets were valuable for the crop modeler and climate change studies We created the HRLT dataset using comprehensive statistical analyses, which included machine learning, the generalized additive model, and thin-plate splines.