Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water-Related Risk Management - Lee, Taesam; Ouarda, Taha B. M. J. (Earth's Future, 2023)
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.
_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022EF003049
Keywords: Infolettre; Infolettre_Octobre_2023; Infolettre_Septembre_2023

Copula-based joint distribution modelling of precipitation, temperature and humidity events in the assessments of agricultural risks, with a case study in Morocco - Latif, Shahid; El Ouadi, Ihssan; Ouarda, Taha B. M. J. (Stochastic Environmental Research and Risk Assessment, 2025)
The agriculture sector is profoundly impacted by the abiotic stresses in arid or semi-arid regions that experience extreme weather patterns related to temperature (T), precipitation (P), humidity (H), and other factors. This study adopts a flexible approach that incorporates the D-vine copula density to analyze trivariate (and bivariate) joint and conditional hazard risk. The methodology was applied to a case study in the Ait Ben Yacoub region of Morocco. Monthly series for T, H, and P were modeled using the Weibull-2P and Weibull-3P models, selected based on fitness statistics. The survival BB8 copula was best described as joint dependence for pair T–P, rotated BB8 270 degrees copula for T–H, while rotated Joe 270 degrees copula for P–H. The analysis of joint probability stress focused on both primary joint scenarios (for OR and AND-hazard conditions) and conditional return periods (RPs) for trivariate and bivariate case. Lower univariate RPs resulted in higher marginal quantiles for T and lower for H and P events. Lower trivariate (and bivariate) AND-joint RPs (or higher concurrence probabilities) were associated with higher T with lower P and H quantiles. The occurrence of trivariate (and bivariate) events was less frequent in the AND-joint case compared to the OR-joint case. The conditional joint RP of T (or T with P, or T with H) was significantly affected by different P (at 10th and 25th percentile) and H (at 5th and 25th percentile) (or P, or H) conditions. Lower conditional RPs of T (or T with H, or T with P) had resulted at given low P and H (or low P, or low H levels). In conclusion, the estimated risk statistics are vital for the study region, highlighting the need for effective adaptation and resilience planning in agriculture crop management.
Keywords: Infolettre_Septembre_2025