Quantifying the Value of Technology and Policy Innovation in Water Resource Portfolios

Recent water infrastructure planning models demonstrate that explicitly accounting for hydrological changes in long‐term water planning reduces costs and increases system robustness. However, current models do not consider the effects of other changes occurring in the system over long time horizons, namely technology and policy innovation. By leveraging state‐of‐the‐art dynamic control techniques, we propose a suite of tools that explicitly relate hydrologic change, technology innovation, and policy interventions to system‐level water costs. We design robust and cost‐optimal strategies for water supply expansion under hydrological variability and hundreds of plausible innovation states, for example, treatment cost and diversification improvements, deployment times, and demand reduction campaigns. Our approach circumvents the need to pre‐assign probabilities to innovation scenarios and, instead, directly calculates the effects of technology, policy, and hydrology variations on system cost and planning strategies to identify avenues of impactful innovation. These tools can support technology development hubs, urban, state, and federal water planners in anticipating cost implications of technology and policy innovation at the local level, and prioritizing high‐impact innovation goals based on quantitative targets. Results for the case study of Santa Barbara, California identify high‐value innovation attributes for the city as well as combinations of innovation attributes across technologies and policies, and quantify their potential utility cost reductions. Plain Language Summary Planning for water infrastructure is difficult due to the long‐term planning horizons and changes in climate, policy, and technology that can occur in these systems. Many studies have focused on incorporating climate uncertainty but have ignored the potential impact of innovation in policy and technology. The lack of tools to assess the impact of innovation on water infrastructure planning is problematic on two fronts. First, water planners lack decision support tools to guide their strategies to yield potential benefits of innovation. Second, technology and policy innovation researchers lack tools to prioritize R&D advances and policy changes based on their impact on water system‐level costs. This work addresses this gap by generating robust and cost‐optimal strategies for water supply expansion under hydrological variability and hundreds of plausible innovation states. This is cruc