A climate stress testing method for changes in spatially variable rainfall

With emerging evidence that anthropogenic climate change is affecting the spatial pattern of rainfall, it is important to understand how such changes affect a range of climate-sensitive systems. A method for generating multi-site rainfall sequences is presented that enables the exploration of changes in spatial rainfall patterns separately from various other potential changes (e.g. to the averages, extremes, seasonality), thereby enabling the separation of key drivers of change as part of climate stress tests. The proposed method is based on the 'inverse approach' to stochastic weather generation, and is evaluated through a case study with rainfall at 12 sites in the Barossa Valley, South Australia, using a multi-site latent variable stochastic rainfall model. The new method accurately captures (1) observed marginal attributes at each site (i.e. annual totals, seasonality, extreme rainfall, number of dry days, and intermittency), (2) spatial correlation between sites, and (3) observed attributes for areal average rainfall. We then apply the new method to stress test the response of streamflow in the region to changes in spatial rainfall patterns, quantifying how reductions in spatial correlation (while holding all other attributes constant) will lead to reduced mean annual flow and extreme flows, and increased number of low-flow days. The results show that not only can a broad range of rainfall properties (including both marginal and spatial attributes) be simulated using the inverse approach, but also that changes to the spatial properties of rainfall can have large implications on water resources systems, with the potential to amplify or dampen other changes to rainfall attributes.