Climate-informed stochastic hydrological modelling

Several large scale ocean-atmosphere climate mechanisms are known to inWuence the Australasian climate, including the El Niño Southern Oscillation (ENSO), the Interdecadal PaciVc Oscillation (IPO) and the closely related PaciVc Decadal Oscillation (PDO), the Indian Ocean Dipole (IOD) and the Southern Annular Mode (SAM). The variability explained by these mechanisms presents a valuable means for improving the ability of stochastic models to characterise temporal and spatial hydrological behaviour. Whilst there is signiVcant interannual (and higher frequency) hydrological variability due to ENSO, the IOD and SAM, water supply reservoirs typically have suXcient carry-over storage to maintain supply through shorter (e.g. El Niņo) drought spells with run-lengths up to 12-18 months. Reservoir systems are usually not designed however to cope with decadal-scale dry periods. The evidence of low frequency hydrological variability due to the IPO-PDO and the vulnerability of water supply systems to prolonged drought sequences prompt the need for a better understanding of this variability. If poorly understood, the IPO-PDO phenomenon could present a signiVcant threat to the security of water resources. This study develops a new combined palaeoclimate IPO (CPIPO) index using multiple sources of palaeoclimate data from around the PaciVc basin, in order to better characterise low frequency PaciVc Ocean variability (IPO-PDO) going back around 440 years. The resulting distribution of IPO-PDO run-lengths has a mean of 16.7 years, a mode of 10-15 years, a standard deviation of 10.6 years and 90% probability limits of 5 and 36 years. The CPIPO was found to be an improvement in representing IPO-PDO variability during the instrumental period in comparison to previous IPO-PDO palaeoclimate reconstructions. The index therefore provides the best available estimate of PaciVc decadal climate variability for the last four centuries. Despite this, signiVcant uncertainty still exists in relation to the underlying physical mechanisms and impacts of the IPO-PDO. Further work remains to understand the proportion of the variability in the IPO-PDO run-length distribution that can be attributed to multi-decadal persistence or uncertainty in the multi-decadal persistence. This study does not discount the possibility that an improved understanding of the physical mechanisms of the IPO-PDO, in particular its relation to the El Niño Southern Oscillation, could result in diUerent estimates of the IPO-PDO persistence structure. This study assesses a range of candidate stochastic models for the IPO-PDO. Formal model selection identiVes the gamma distribution as a suitable stochastic model for the simulation of IPO-PDO run-lengths. In doing so, this study shows that the widely-used Hidden State Markov (HSM) model and related Markov family models with their monotonically decreasing run-length probability density and mode at one are structurally inappropriate for the simulation of quasi-periodic data such as the run-lengths of the IPO-PDO. This study then develops a general framework for incorporating modes of climate variability into stochastic hydrological models. The framework, termed the climate-informed multi-