CLIGEN parameter regionalization for mainland China

Wenting Wang, Shuiqing Yin, Bofu Yu, Shaodong Wang
2021 Earth System Science Data  
Abstract. The 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.
more » ... rdinary kriging (OK) and universal kriging (UK) with auxiliary covariables, i.e., longitude, latitude, elevation, and the mean annual rainfall, were used to interpolate parameter values into a 10 km×10 km grid, and the interpolation accuracy was evaluated based on the leave-one-out cross-validation. Results showed that UK generally outperformed OK. The root mean square error between UK-interpolated and observed temperature-related parameters was ≤0.88 ∘C (1.58 ∘F). The Nash–Sutcliffe efficiency coefficient for precipitation- and solar-radiation-related parameters was ≥0.87, except for the skewness coefficient of daily precipitation, which was 0.78. In addition, CLIGEN-simulated daily weather sequences using UK-interpolated and observed parameters showed consistent statistics and frequency distributions. The mean absolute discrepancy between the two sequences for temperature was <0.51 ∘C, and the mean absolute relative discrepancy for solar radiation, precipitation amount, duration, and maximum 30 min intensity was <5 % in terms of the mean and standard deviation. These CLIGEN parameter values at 10 km resolution would meet the minimum data requirements for WEPP application throughout mainland China. The dataset is available at http://clicia.bnu.edu.cn/data/cligen.html (last access: 20 May 2021) and https://doi.org/10.12275/bnu.clicia.CLIGEN.CN.gridinput.001 (Wang et al., 2020).
doi:10.5194/essd-13-2945-2021 fatcat:b4vk2dovd5gfjic3ix3uae5xwm