Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management
Abstract Hydrological time series often present nonstationarities such as trends, shifts, or oscillations due to anthropogenic effects and hydroclimatological variations, including global climate change. For water managers, it is crucial to recognize and define the nonstationarities in hydrological...
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| Format: | Article |
| Language: | English |
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Wiley
2023-07-01
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| Series: | Earth's Future |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2022EF003049 |
| _version_ | 1797771015674134528 |
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| author | Taesam Lee Taha B. M. J. Ouarda |
| author_facet | Taesam Lee Taha B. M. J. Ouarda |
| author_sort | Taesam Lee |
| collection | DOAJ |
| description | Abstract Hydrological time series often present nonstationarities such as trends, shifts, or oscillations due to anthropogenic effects and hydroclimatological variations, including global climate change. For water managers, it is crucial to recognize and define the nonstationarities in hydrological records. The nonstationarities must be appropriately modeled and stochastically simulated according to the characteristics of observed records to evaluate the adequacy of flood risk mitigation measures and future water resources management strategies. Therefore, in the current study, three approaches were suggested to address stochastically nonstationary behaviors, especially in the long‐term variability of hydrological variables: as an overall trend, shifting mean, or as a long‐term oscillation. To represent these options for hydrological variables, the autoregressive model with an overall trend, shifting mean level (SML), and empirical mode decomposition with nonstationary oscillation resampling (EMD‐NSOR) were employed in the hydrological series of the net basin supply in the Lake Champlain‐River Richelieu basin, where the International Joint Committee recently managed and significant flood damage from long consistent high flows occurred. The detailed results indicate that the EMD‐NSOR model can be an appropriate option by reproducing long‐term dependence statistics and generating manageable scenarios, while the SML model does not properly reproduce the observed long‐term dependence, that are critical to simulate sustainable flood events. The trend model produces too many risks for floods in the future but no risk for droughts. The overall results conclude that the nonstationarities in hydrological series should be carefully handled in stochastic simulation models to appropriately manage future water‐related risks. |
| first_indexed | 2024-03-12T21:30:26Z |
| format | Article |
| id | doaj.art-482f852025644f7cb67f2aa6ccc6c4da |
| institution | Directory Open Access Journal |
| issn | 2328-4277 |
| language | English |
| last_indexed | 2024-03-12T21:30:26Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Earth's Future |
| spelling | doaj.art-482f852025644f7cb67f2aa6ccc6c4da2023-07-27T19:18:31ZengWileyEarth's Future2328-42772023-07-01117n/an/a10.1029/2022EF003049Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk ManagementTaesam Lee0Taha B. M. J. Ouarda1Department of Civil Engineering Gyeongsang National University 52828 Jinju501 Jinju‐daero South KoreaCanada Research Chair in Statistical Hydro‐Climatology Institut national de la recherche scientifique, Centre Eau Terre G1K 9A9 QC Quebec490 de la Couronne CanadaAbstract Hydrological time series often present nonstationarities such as trends, shifts, or oscillations due to anthropogenic effects and hydroclimatological variations, including global climate change. For water managers, it is crucial to recognize and define the nonstationarities in hydrological records. The nonstationarities must be appropriately modeled and stochastically simulated according to the characteristics of observed records to evaluate the adequacy of flood risk mitigation measures and future water resources management strategies. Therefore, in the current study, three approaches were suggested to address stochastically nonstationary behaviors, especially in the long‐term variability of hydrological variables: as an overall trend, shifting mean, or as a long‐term oscillation. To represent these options for hydrological variables, the autoregressive model with an overall trend, shifting mean level (SML), and empirical mode decomposition with nonstationary oscillation resampling (EMD‐NSOR) were employed in the hydrological series of the net basin supply in the Lake Champlain‐River Richelieu basin, where the International Joint Committee recently managed and significant flood damage from long consistent high flows occurred. The detailed results indicate that the EMD‐NSOR model can be an appropriate option by reproducing long‐term dependence statistics and generating manageable scenarios, while the SML model does not properly reproduce the observed long‐term dependence, that are critical to simulate sustainable flood events. The trend model produces too many risks for floods in the future but no risk for droughts. The overall results conclude that the nonstationarities in hydrological series should be carefully handled in stochastic simulation models to appropriately manage future water‐related risks.https://doi.org/10.1029/2022EF003049stochastic simulationnonstationarytrendshifting meanoscillationwater resources |
| spellingShingle | Taesam Lee Taha B. M. J. Ouarda Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management Earth's Future stochastic simulation nonstationary trend shifting mean oscillation water resources |
| title | Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management |
| title_full | Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management |
| title_fullStr | Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management |
| title_full_unstemmed | Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management |
| title_short | Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management |
| title_sort | trends shifting or oscillations stochastic modeling of nonstationary time series for future water related risk management |
| topic | stochastic simulation nonstationary trend shifting mean oscillation water resources |
| url | https://doi.org/10.1029/2022EF003049 |
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