Abstract
As an essential part of the hydrological cycle, precipitation directly contributes to surface runoff and river runoff formation. Simulation on precipitation variables can effectively solve the adverse effects on hydrological assessment in some areas with insufficient or even no runoff observation. With the widespread use of various weather generators, the traditional stochastic hydrological simulation methods tend to be gradually replaced. To compare these two approaches mentioned above, this paper utilizes the precipitation records from 1958 to 2011 at nine meteorological stations within Huaihe River System in Henan Province to evaluate a stochastic hydrological simulation method, SARIMA model, and two types of weather generators, WeaGETS and LARS-WG, through the comparison of statistical characteristics regarding precipitation variables, such as mean, mean square error, extreme value and coefficient of variation. The results show that (1) on the annual scale, SARIMA has a better performance to reproduce the mean and mean square error as well as the extreme precipitation events than weather generators; (2) regarding the monthly-scale precipitation simulation, SARIMA is good at reproducing the statistical properties of monthly precipitation at the average level, while WeaGETS and LARS-WG work better in simulating monthly precipitation extremes; (3) compared with weather generators, SARIMA is highly constrained by the observed records, and among these two weather generators, WeaGETS scores higher on monthly precipitation simulation under the same sample length conditions. In conclusion, the traditional hydrological simulation method, SARIMA, and weather generators, WeaGETS and LARS-WG, have both benefits and drawbacks. The appropriate choice depends on different research backgrounds and purposes.
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Codes used during the study are available from the first author by request.
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Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 52079037, 52009029), the Fundamental Research Funds for the Central Universities (Grant No. B200202032), the China Postdoctoral Science Foundation (Grant No. 2020T130169).
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Methodology, YLH.; conceptualization, ZPA; software, YLH and ZFL; validation, MYF; formal analysis, YLH. and WH; writing—original draft, YLH and LJY; writing – review and editing, YLH and XCJ; supervision, ZPA; funding acquisition, ZPA
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Yang, L., Zhong, Pa., Zhu, F. et al. A comparison of the reproducibility of regional precipitation properties simulated respectively by weather generators and stochastic simulation methods. Stoch Environ Res Risk Assess 36, 495–509 (2022). https://doi.org/10.1007/s00477-021-02053-6
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DOI: https://doi.org/10.1007/s00477-021-02053-6