Dépôt numérique

Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management.


Téléchargements par mois depuis la dernière année

Plus de statistiques...

Lee, Taesam ORCID logoORCID: https://orcid.org/0000-0001-5110-5388 et Ouarda, Taha B. M. J. ORCID logoORCID: https://orcid.org/0000-0002-0969-063X (2023). Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management. Earth's Future , vol. 11 , nº 7. e2022EF003049. DOI: 10.1029/2022EF003049.

[thumbnail of P4341.pdf]
Télécharger (4MB) | Prévisualisation


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.

Type de document: Article
Mots-clés libres: stochastic simulation; nonstationary; trend; shifting mean; oscillation; water resources
Centre: Centre Eau Terre Environnement
Date de dépôt: 02 nov. 2023 14:45
Dernière modification: 02 nov. 2023 14:45
URI: https://espace.inrs.ca/id/eprint/13724

Gestion Actions (Identification requise)

Modifier la notice Modifier la notice