Climate oscillation impacts on water supply augmentation planning

Abstract

Climate oscillations ranging from years to decades drive precipitation variability in many river basins globally. As a result, many regions will require new water infrastructure investments to maintain reliable water supply. However, current adaptation approaches focus on long-term trends, preparing for average climate conditions at mid- or end-of-century. The impact of climate oscillations, which bring prolonged and variable but temporary dry periods, on water supply augmentation needs is unknown. Current approaches for theory development in nature-society systems are limited in their ability to realistically capture the impacts of climate oscillations on water supply. Here, we develop an approach to build middle-range theory on how common climate oscillations affect low-cost, reliable water supply augmentation strategies. We extract contrasting climate oscillation patterns across sub-Saharan Africa and study their impacts on a generic water supply system. Our approach integrates climate model projections, nonstationary signal processing, stochastic weather generation, and reinforcement learning–based advances in stochastic dynamic control. We find that longer climate oscillations often require greater water supply augmentation capacity but benefit more from dynamic approaches. Therefore, in settings with the adaptive capacity to revisit planning decisions frequently, longer climate oscillations do not require greater capacity. By building theory on the relationship between climate oscillations and least-cost reliable water supply augmentation, our findings can help planners target scarce resources and guide water technology and policy innovation. This approach can be used to support climate adaptation planning across large spatial scales in sectors impacted by climate variability.

Showcasing advances and building community in modeling for sustainability

“…While many models already exist that can simulate environmental and engineered systems, some of these models have been repurposed or extended to address different contexts more relevant to sustainability and human well-being, to different audiences. One example of this comes from the paper of Fletcher et al ( 24 ), who build upon models used for water infrastructure planning, extending beyond a specific system to build middle-range theory on the relationship between climate variability and priorities in infrastructure-related climate adaptation. By applying this model together with climate model projections, nonstationary signal processing, stochastic weather generation, and reinforcement learning-based advances in stochastic dynamic control, they find that dynamic planning can help build adaptive capacity, particularly in regions that experience long-term climate oscillations.…”

FIND: A Synthetic weather generator to control drought Frequency, Intensity, and Duration

“…Other methods alter the temporal dependence structure of hydroclimatic time series, for instance by modifying the seasonality or the persistence of wet and dry conditions. Various techniques are used for this purpose, including Markov chain models (Breinl et al, 2015;Ullrich et al, 2021), spectral analysis and wavelet transforms (Steinschneider and Brown, 2013;Quinn et al, 2018;Fletcher et al, 2023), and copula methods (Borgomeo et al, 2015b;Nazemi et al, 2020). Lastly, Borgomeo et al (2015a) proposes a versatile tool that lets the user choose the objective function of the streamflow generator to optimize the streamflow properties of interest.…”