Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system
Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jia, Yuxue [verfasserIn] Zhang, Qi [verfasserIn] Xue, Chenyang [verfasserIn] Tang, Hongwu [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
Nonstationary frequency analysis |
|---|
| Übergeordnetes Werk: |
Enthalten in: The science of the total environment - Amsterdam [u.a.] : Elsevier Science, 1972, 903 |
|---|---|
| Übergeordnetes Werk: |
volume:903 |
| DOI / URN: |
10.1016/j.scitotenv.2023.166329 |
|---|
| Katalog-ID: |
ELV065159446 |
|---|
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| 245 | 1 | 0 | |a Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system |
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| 520 | |a Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01–0.03 m/year) and increased (0.17–1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river–lake systems. | ||
| 650 | 4 | |a Nonstationary frequency analysis | |
| 650 | 4 | |a Uncertainty | |
| 650 | 4 | |a Extreme low lake levels | |
| 650 | 4 | |a Generalized Pareto distribution | |
| 650 | 4 | |a Poyang Lake | |
| 700 | 1 | |a Zhang, Qi |e verfasserin |4 aut | |
| 700 | 1 | |a Xue, Chenyang |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Hongwu |e verfasserin |4 aut | |
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10.1016/j.scitotenv.2023.166329 doi (DE-627)ELV065159446 (ELSEVIER)S0048-9697(23)04954-9 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Jia, Yuxue verfasserin aut Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01–0.03 m/year) and increased (0.17–1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river–lake systems. Nonstationary frequency analysis Uncertainty Extreme low lake levels Generalized Pareto distribution Poyang Lake Zhang, Qi verfasserin aut Xue, Chenyang verfasserin aut Tang, Hongwu verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 903 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:903 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 903 |
| spelling |
10.1016/j.scitotenv.2023.166329 doi (DE-627)ELV065159446 (ELSEVIER)S0048-9697(23)04954-9 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Jia, Yuxue verfasserin aut Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01–0.03 m/year) and increased (0.17–1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river–lake systems. Nonstationary frequency analysis Uncertainty Extreme low lake levels Generalized Pareto distribution Poyang Lake Zhang, Qi verfasserin aut Xue, Chenyang verfasserin aut Tang, Hongwu verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 903 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:903 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 903 |
| allfields_unstemmed |
10.1016/j.scitotenv.2023.166329 doi (DE-627)ELV065159446 (ELSEVIER)S0048-9697(23)04954-9 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Jia, Yuxue verfasserin aut Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01–0.03 m/year) and increased (0.17–1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river–lake systems. Nonstationary frequency analysis Uncertainty Extreme low lake levels Generalized Pareto distribution Poyang Lake Zhang, Qi verfasserin aut Xue, Chenyang verfasserin aut Tang, Hongwu verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 903 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:903 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 903 |
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10.1016/j.scitotenv.2023.166329 doi (DE-627)ELV065159446 (ELSEVIER)S0048-9697(23)04954-9 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Jia, Yuxue verfasserin aut Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01–0.03 m/year) and increased (0.17–1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river–lake systems. Nonstationary frequency analysis Uncertainty Extreme low lake levels Generalized Pareto distribution Poyang Lake Zhang, Qi verfasserin aut Xue, Chenyang verfasserin aut Tang, Hongwu verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 903 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:903 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 903 |
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10.1016/j.scitotenv.2023.166329 doi (DE-627)ELV065159446 (ELSEVIER)S0048-9697(23)04954-9 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Jia, Yuxue verfasserin aut Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01–0.03 m/year) and increased (0.17–1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river–lake systems. Nonstationary frequency analysis Uncertainty Extreme low lake levels Generalized Pareto distribution Poyang Lake Zhang, Qi verfasserin aut Xue, Chenyang verfasserin aut Tang, Hongwu verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 903 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:903 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 903 |
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333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system Nonstationary frequency analysis Uncertainty Extreme low lake levels Generalized Pareto distribution Poyang Lake |
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nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system |
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Nonstationary frequency analysis and uncertainty quantification for extreme low lake levels in a large river-lake-catchment system |
| abstract |
Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01–0.03 m/year) and increased (0.17–1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river–lake systems. |
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Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01–0.03 m/year) and increased (0.17–1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river–lake systems. |
| abstract_unstemmed |
Extreme hydrological events have become increasingly frequent on a global scale. The middle Yangtze River also faces a substantial challenge in dealing with extreme flooding and drought. However, the long-term characteristics of the extreme hydrological regime have not yet been adequately recognized. Moreover, there is uncertainty in the extreme value estimation, and this uncertainty needs to be distinguished and quantified. In this study, we investigated the nonstationary frequency characteristics of extreme low lake levels (ELLLs), taking the Poyang Lake as an example. Daily lake levels from 1960 to 2022 were utilized to estimate the return level using the generalized Pareto distribution (GPD). The uncertainty from three sources, i.e., the parameter estimator, threshold selection, and covariate, was quantified via variance decomposition. The results indicate that (1) the parameter estimator is the predominant source of uncertainty, with a contribution rate of approximately 87 %. The total uncertainty of the covariate, threshold, and interaction term is only 13 %. (2) Two indexes, namely the annual minimum water level (WLmin) and the days with peak over the 90 % threshold per year (DPOT90), decreased (0.01–0.03 m/year) and increased (0.17–1.39 days/year), respectively, indicating a progressively severe drought trend for Poyang Lake. (3) The return level with return period of 5 to 100 years significantly decreased after the early 21st century. A large spatial heterogeneity was identified for the variation in the return level, and the change rate of the return level with a 100-year return period ranged from 5 % to 40 % for the whole lake. (4) The ELLLs had a stronger correlation with the catchment discharge than with the Yangtze River discharge and the large-scale atmospheric circulation indices. This study provides a methodology with reduced uncertainty for nonstationary frequency analysis (NFA) of ELLLs exemplified in large river–lake systems. |
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| score |
7.3978424 |