A Bayesian time series model for reconstructing hydroclimate from multiple proxies

Abstract

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