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It is inherently difficult to plan water systems for a future that is non-predictive. This paper introduces a novel perspective for the design and operation of potable water systems under increasing water quality volatility ( e.g. , a relatively rapid and unpredicted deviation from baseline water quality). Increased water quality volatility and deep uncertainty stress water systems, confound design decisions, and increase the risk of decreased water system performance. Recent emphasis on resilience in drinking water treatment has partly addressed this issue, but still establishes an adversarial relationship with change. An antifragile system benefits from volatile change. By incorporating antifragility, water systems may move beyond resilience and improve performance with extreme events and other changes, rather than survive, or fail and quickly recover. Using examples of algal blooms, wildfires, and the COVID-19 pandemic, this work illustrates fragility, resilience, and antifragility within physicochemical process design including clarification, adsorption and disinfection. Methods for increasing antifragility, both individual process options and new system design tools, are discussed. Novel physicochemical processes with antifragile characteristics include ferrate preoxidation and magnetic iron (nano)particles. New design tools that allow for systematic evaluation of antifragile opportunities include artificial neural networks and virtual jar or pilot “stress testing”. Incorporating antifragile characteristics represents a trade-off with capital and/or operating cost. We present a real options analysis approach to considering costs in the context of antifragile design decisions. Adopting this antifragile perspective will help ensure water system improved performance during extreme events and a general increase in volatility. 

Abstract Vulnerability‐based frameworks are increasingly used to better understand water system performance under climate change. This work advances the use of stochastic weather generators for climate vulnerability assessments that simulate weather based on patterns of regional atmospheric flow (i.e., weather regimes) conditioned on global‐scale climate features. The model is semiparametric by design and includes (1) a nonhomogeneous Markov chain for weather regime simulation; (2) block bootstrapping and a Gaussian copula for multivariate, multisite weather simulation; and (3) modules to impose thermodynamic and dynamical climate change, including Clausius‐Clapeyron precipitation scaling, elevation‐dependent warming, and shifting dynamics of the El Niño–Southern Oscillation (ENSO). In this way, the model can be used to evaluate climate impacts on water systems based on hypotheses of dynamic and thermodynamic climate change. The model is developed and tested for cold‐season climate in the Tuolumne River Basin in California but is broadly applicable across the western United States. Results show that eight weather regimes exert strong influences over local climate in the Tuolumne Basin. Model simulations adequately preserve many of the historical statistics for precipitation and temperature across sites, including the mean, variance, skew, and extreme values. Annual precipitation and temperature are somewhat underdispersed, and precipitation spell statistics are negatively biased by 1‐2 days. For simulations of future climate, the model can generate a range of Clausius‐Clapeyron scaling relationships and modes of elevation‐dependent warming. Model simulations also suggest a muted response of Tuolumne climate to changes in ENSO variability.