Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

Abstract

<jats:p>A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate‐informed multi‐time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long‐term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO‐PDO time series dating back 440 yr is produced, combining seven IPO‐PDO paleo sources using an objective smoothing procedure to fit low‐pass filters to individual records. The paleo data analysis indicates that wet/dry IPO‐PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods &gt;5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO‐PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO‐PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO‐PDO dry states is found to be 15%–28% lower than the wet state at the case study sites. In comparison, an annual lag‐one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO‐PDO states.</jats:p>