Multiple causes of nonstationarity in the Weihe annual low-flow series
Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-02-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/22/1525/2018/hess-22-1525-2018.pdf |
| _version_ | 1818961920464519168 |
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| author | B. Xiong L. Xiong J. Chen C.-Y. Xu C.-Y. Xu L. Li |
| author_facet | B. Xiong L. Xiong J. Chen C.-Y. Xu C.-Y. Xu L. Li |
| author_sort | B. Xiong |
| collection | DOAJ |
| description | Under the background of global climate change and local
anthropogenic activities, multiple driving forces have introduced various
nonstationary components into low-flow series. This has led to a high demand
on low-flow frequency analysis that considers nonstationary conditions for
modeling. In this study, through a nonstationary frequency analysis framework
with the generalized linear model (GLM) to consider time-varying distribution
parameters, the multiple explanatory variables were incorporated to explain
the variation in low-flow distribution parameters. These variables are
comprised of the three indices of human activities (HAs; i.e., population,
POP; irrigation area, IAR; and gross domestic product, GDP)
and the eight measuring indices of the climate and catchment conditions
(i.e., total precipitation <i>P</i>, mean frequency of precipitation events
<i>λ</i>, temperature <i>T</i>, potential evapotranspiration (EP), climate
aridity index AI<sub>EP</sub>, base-flow index (BFI), recession constant <i>K</i>
and the recession-related aridity index AI<sub><i>K</i></sub>). This framework was
applied to model the annual minimum flow series of both Huaxian and Xianyang
gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise
regression for the optimal explanatory variables show that the variables
related to irrigation, recession, temperature and precipitation play an
important role in modeling. Specifically, analysis of annual minimum
30-day flow in Huaxian shows that the nonstationary distribution
model with any one of all explanatory variables is better than the one
without explanatory variables, the nonstationary gamma distribution model
with four optimal variables is the best model and AI<sub><i>K</i></sub> is of the
highest relative importance among these four variables, followed by IAR,
BFI and AI<sub>EP</sub>. We conclude that the incorporation of multiple indices
related to low-flow generation permits tracing various driving forces. The
established link in nonstationary analysis will be beneficial to analyze
future occurrences of low-flow extremes in similar areas. |
| first_indexed | 2024-12-20T12:21:06Z |
| format | Article |
| id | doaj.art-b47811ff04294003ad69563069162c93 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-20T12:21:06Z |
| publishDate | 2018-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-b47811ff04294003ad69563069162c932022-12-21T19:40:58ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382018-02-01221525154210.5194/hess-22-1525-2018Multiple causes of nonstationarity in the Weihe annual low-flow seriesB. Xiong0L. Xiong1J. Chen2C.-Y. Xu3C.-Y. Xu4L. Li5State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, P.R. ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, P.R. ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, P.R. ChinaState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, P.R. ChinaDepartment of Geosciences, University of Oslo, P.O. Box 1022 Blindern, 0315 Oslo, NorwayState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, P.R. ChinaUnder the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation <i>P</i>, mean frequency of precipitation events <i>λ</i>, temperature <i>T</i>, potential evapotranspiration (EP), climate aridity index AI<sub>EP</sub>, base-flow index (BFI), recession constant <i>K</i> and the recession-related aridity index AI<sub><i>K</i></sub>). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AI<sub><i>K</i></sub> is of the highest relative importance among these four variables, followed by IAR, BFI and AI<sub>EP</sub>. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.https://www.hydrol-earth-syst-sci.net/22/1525/2018/hess-22-1525-2018.pdf |
| spellingShingle | B. Xiong L. Xiong J. Chen C.-Y. Xu C.-Y. Xu L. Li Multiple causes of nonstationarity in the Weihe annual low-flow series Hydrology and Earth System Sciences |
| title | Multiple causes of nonstationarity in the Weihe annual low-flow series |
| title_full | Multiple causes of nonstationarity in the Weihe annual low-flow series |
| title_fullStr | Multiple causes of nonstationarity in the Weihe annual low-flow series |
| title_full_unstemmed | Multiple causes of nonstationarity in the Weihe annual low-flow series |
| title_short | Multiple causes of nonstationarity in the Weihe annual low-flow series |
| title_sort | multiple causes of nonstationarity in the weihe annual low flow series |
| url | https://www.hydrol-earth-syst-sci.net/22/1525/2018/hess-22-1525-2018.pdf |
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