A Bayesian time series model for reconstructing hydroclimate from multiple proxies

We propose a Bayesian model which produces probabilistic reconstructions of hydroclimatic variability in Queensland Australia. The provides standardized approach to hydroclimate reconstruction using multiple palaeoclimate proxy records derived from natural archives such as speleothems, ice cores and tree rings. method combines time-series modeling with inverse prediction quantify the relationships between given index relevant proxies over an instrumental period subsequently reconstruct back through time. present case studies for Brisbane Fitzroy catchments focusing on two indices, Rainfall Index (RFI) Standardized Precipitation-Evapotranspiration (SPEI). nature allows us estimate probability that any year was lower (higher) than minimum (maximum) value observed period. In Brisbane, RFI is unlikely (probabilities < 5%) have exhibited extremes beyond minimum/maximum values 1889 2019. However, there are several years during where likely (>50% probability) behavior what has been observed, For SPEI, observing prior beginning doesn't exceed 30% but exceeds 50% Fitzroy.