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

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.

Automated summary

Hydrological time series often show nonstationarities due to human activities and global climate change. It is important for water managers to identify and define these nonstationarities in hydrological records and appropriately model and simulate them. In this study, three approaches were suggested to address stochastically nonstationary behaviors, and different models were employed to represent these options for hydrological variables. The results indicate that the EMD-NSOR model can reproduce long-term dependence and generate manageable scenarios, while the SML model does not properly reproduce long-term dependence critical for simulating sustainable flood events. It is concluded that nonstationarities in hydrological series should be carefully handled in stochastic simulation models to manage future water-related risks.