Preprints
https://doi.org/10.5194/essd-2020-344
https://doi.org/10.5194/essd-2020-344

  01 Feb 2021

01 Feb 2021

Review status: this preprint is currently under review for the journal ESSD.

CLIGEN Parameter Regionalization for Mainland China

Wenting Wang1,2, Shuiqing Yin2, Bofu Yu3, and Shaodong Wang2 Wenting Wang et al.
  • 1Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University at Zhuhai, Zhuhai 519087, People's Republic of China
  • 2State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, People's Republic of China
  • 3Australian Rivers Institute, School of Engineering and Built Environment, Griffith University, Nathan, Qld 4111, Australia

Abstract. Stochastic weather generator CLIGEN can simulate long-term weather sequences as input to WEPP for erosion predictions. Its use, however, has been somewhat restricted by limited observations at high spatial-temporal resolutions. Long-term daily temperature, daily and hourly precipitation data from 2405 stations and daily solar radiation from 130 stations distributed across mainland China were collected to develop the most critical set of site-specific parameter values for CLIGEN. Universal Kriging (UK) with auxiliary covariables, longitude, latitude, elevation, and the mean annual rainfall was used to interpolate parameter values into a 10 km × 10 km grid and parameter accuracy was evaluated based on leave-one-out cross-validation. The results demonstrated that Nash-Sutcliffe efficiency coefficients (NSEs) between UK interpolated and observed parameters were greater than 0.85 for all parameters apart from the standard deviation of solar radiation, skewness coefficient of daily precipitation, and cumulative distribution of relative time to peak intensity, with relatively lower interpolation accuracy (NSE > 0.66). In addition, CLIGEN simulated daily weather sequences using UK-interpolated and observed inputs showed consistent statistics and frequency distributions. The mean absolute discrepancy between the two sequences in the average and standard deviation of the temperature was less than 0.51 °C. The mean absolute relative discrepancy for the same statistics for solar radiation, precipitation amount, duration and maximum intensity in 30-min were less than 5 %. CLIGEN parameters at the 10 km resolution would meet the minimum WEPP climate requirements throughout in mainland China. The dataset is availability at http://clicia.bnu.edu.cn/data/cligen.html and http://doi.org/10.12275/bnu.clicia.CLIGEN.CN.gridinput.001 (Wang et al., 2020).

Wenting Wang et al.

Status: open (until 29 Mar 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2020-344', Anonymous Referee #1, 02 Mar 2021 reply
  • RC2: 'Comment on essd-2020-344', Anonymous Referee #2, 02 Mar 2021 reply

Wenting Wang et al.

Data sets

Gridded Input Parameters for a Stochastic Weather Generator, CLIGEN, in 10-km resolution for China Wenting Wang and Shuiqing Yin https://doi.org/10.12275/bnu.clicia.CLIGEN.CN.gridinput.001

Wenting Wang et al.

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Short summary
A gridded input dataset at 10-km resolution of a weather generator, CLIGEN, was established for mainland China. Based on which, CLIGEN can generate a series of daily temperature, solar radiation and precipitation data and rainfall intensity information. In each grid, the input file contains 13 groups of parameters. All parameters were firstly calculated based on long-term observations and then interpolated by Universal Kriging. The accuracy of the gridded input dataset has been fully assessed.