Climate teleconnection-driven stochastic simulation for future water-related risk management - Lee, Taesam; Ouarda, Taha B. M. J. (Journal of Hydrology, 2025)
Water risk management has been adversely affected by climate variations, including recent climate change. Climate variations have highly impacted the hydrological cycles in the atmosphere and biosphere, and their impact can be defined with the teleconnection between climate signals and hydrological variables. Water managers should practice future risk management to mitigate risks, including the impact of teleconnection, and stochastically simulated scenarios can be employed as an effective tool to take advantage of water management preparation. A stochastic simulation model for hydrological variables teleconnected with climate signals is very useful for water managers. Therefore, the objective of the current study was to develop a novel stochastic simulation model for the simulation of synthetic series teleconnected with climate signals. By jointly decomposing the hydrological variables and a climate signal with bivariate empirical mode decomposition (BEMD), the bivariate nonstationary oscillation resampling (B-NSOR) model was applied to the significant components. The remaining components were simulated with the newly developed method of climate signal-led K-nearest neighbor-based local linear regression (CKLR). This entire approach is referred to as the climate signal-led hydrologic stochastic simulation (CSHS) model. The key statistics were estimated from the 200 simulated series and compared with the observed data, and the results showed that the CSHS model could reproduce the key statistics including extremes while the SML model showed slight underestimation in the skewness and maximum values. Additionally, the observed long-term variability of hydrological variables was reproduced well with the CSHS model by analyzing drought statistics. Moreover, the Hurst coefficient with slightly higher than 0.8 was fairly preserved by the CSHS model while the SML model is underestimated as 0.75. The overall results demonstrate that the proposed CSHS model outperformed the existing shifting mean level (SML) model, which has been used to simulate hydroclimatological variables. Future projections until 2100 were obtained with the CSHS model. The overall results indicated that the proposed CSHS model could represent a reasonable alternative to teleconnect climate signals with hydrological variables.
Keywords: Infolettre_Septembre_2025

Regional Frequency Analysis at Ungauged Sites with Multivariate Adaptive Regression Splines. - Msilini, A.; Masselot, Pierre; Ouarda, Taha B. M. J. (Journal of Hydrometeorology, 2020)
Hydrological systems are naturally complex and nonlinear. A large number of variables, many of which not yet well considered in regional frequency analysis (RFA), have a significant impact on hydrological dynamics and consequently on flood quantile estimates. Despite the increasing number of statistical tools used to estimate flood quantiles at ungauged sites, little attention has been dedicated to the development of new regional estimation (RE) models accounting for both nonlinear links and interactions between hydrological and physio-meteorological variables. The aim of this paper is to simultaneously take into account nonlinearity and interactions between variables by introducing the multivariate adaptive regression splines (MARS) approach in RFA. The predictive performances of MARS are compared with those obtained by one of the most robust RE models: the generalized additive model (GAM). Both approaches are applied to two datasets covering 151 hydrometric stations in the province of Quebec (Canada): a standard dataset (STA) containing commonly used variables and an extended dataset (EXTD) combining STA with additional variables dealing with drainage network characteristics. Results indicate that RE models using MARS with the EXTD outperform slightly RE models using GAM. Thus, MARS seems to allow for a better representation of the hydrological process and an increased predictive power in RFA.
MAG ID: 3094453233