A Bayesian approach for temporally scaling climate for modeling ecological systems

Area of Science:

• Ecology
• Climate Science
• Hydrology

Background:

• Climate change necessitates incorporating climate variables into ecological models.
• The Standardized Precipitation Evapotranspiration Index (SPEI) is a valuable drought index for ecological studies due to its temporal flexibility.
• Limited research exists on selecting appropriate timescales for SPEI calculations.

Purpose of the Study:

• To develop a Bayesian modeling approach for estimating SPEI timescales.
• To apply this method to model historical and contemporary wetland hydrologic dynamics.
• To investigate if climate differences between eras explain changes in wetland water levels.

Main Methods:

• Developed a Bayesian approach to estimate SPEI timescale parameters.
• Applied the model to wetland data from historical (pre-1970) and contemporary (post-2003) eras.
• Analyzed changes in wetland water surface area in relation to climate fluctuations.

Main Results:

• Wetland water surface area correlated positively with wetter conditions.
• Wetlands exhibited less change in response to climate fluctuations in the contemporary era compared to the historical era.
• The average SPEI timescale parameter was larger in the historical period than in the contemporary period.

Conclusions:

• The Bayesian approach effectively estimates relevant SPEI timescales for ecological modeling.
• Observed shifts in SPEI timescale may indicate complex interactions between climate and wetland hydrologic response.
• Further research is needed to disentangle the influence of climate timing versus hydrologic response on wetland dynamics.