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[1]Gan Rong,Gu Shuqian,Gao Yong,et al.Assessment of Nonstationary Meteorological Drought in the Weihe River Basin Based on GAMLSS Model[J].Research of Soil and Water Conservation,2024,31(06):149-160.[doi:10.13869/j.cnki.rswc.2024.06.003]

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Assessment of Nonstationary Meteorological Drought in the Weihe River Basin Based on GAMLSS Model

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 31 Number of periods: 2024 06 Page number: 149-160 Column: Public date: 2024-12-10

Title:: Assessment of Nonstationary Meteorological Drought in the Weihe River Basin Based on GAMLSS Model

Author(s):: Gan Rong^1,2, Gu Shuqian^1,2, Gao Yong³, Guo Lin³, Hou Xiaoli⁴; (1.School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China; 2 Henan Key Laboratory of Groundwater Pollution Prevention and Remediation, Zhengzhou 450001, China; 3.Henan Geological Research Institute, Zhengzhou 450001, China; 4.Henan Yudong Water Conservancy Guarantee Center, Kaifeng, Henan 475000, China)

Keywords:: meteorological drought; nonstationary; GAMLSS; climate factors; Weihe River Basin

CLC:: P426.6

DOI:: 10.13869/j.cnki.rswc.2024.06.003

Abstract:: [Objective]The aims of this study are to identify the non-stationary characteristics of the descending water series affected by climate change, to evaluate the meteorological drought characteristics in the Weihe River Basin, so as to provide a more effective reference for drought management and risk assessment. [Methods]Based on GAMLSS model, a non-stationary standardized precipitation index(NSPI)with large-scale climate factors as covariables was constructed. The applicability of NSPI in the Weihe River Basin was discussed by combining with historical drought events and Copula probability analysis, and comparing with the traditional standardized precipitation index(SPI). [Results]The influence of large-scale climate factors on precipitation series was seasonal. AOI, SOI and ENSO indexes had the most extensive influence on precipitation in the study area. Moreover, the fitting effect of non-stationary model with climate factors as covariable is better than that of stationary model in each month. Combining the run course theory and historical drought events, it is found that NSPI performs better in drought event recognition, and the occurrence frequency of extreme drought events identified by NSPI is higher than that by SPI. In addition, the optimal Copula function of the two-dimensional joint distribution of drought characteristic variables of SPI and NSPI is Frank and Gumbel, respectively. By comparing the joint probability and recurrence period, it is found that SPI underestimates the risk of drought occurrence. [Conclusion]In the context of climate change, NSPI considering non-stationary characteristics shows a more accurate evaluation of drought characteristics.

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Last Update: 2024-12-10