Learning about climate change uncertainty enables flexible water infrastructure planning

Abstract

Water resources planning requires decision-making about infrastructure development under uncertainty in future regional climate conditions. However, uncertainty in climate change projections will evolve over the 100-year lifetime of a dam as new climate observations become available. Flexible strategies in which infrastructure is proactively designed to be changed in the future have the potential to meet water supply needs without expensive over-building. Evaluating tradeoffs between flexible and traditional static planning approaches requires extension of current paradigms for planning under climate change uncertainty which do not assess opportunities to reduce uncertainty in the future. We develop a new planning framework that assesses the potential to learn about regional climate change over time and therefore evaluates the appropriateness of flexible approaches today. We demonstrate it on a reservoir planning problem in Mombasa, Kenya. This approach identifies opportunities to reliably use incremental approaches, enabling adaptation investments to reach more vulnerable communities with fewer resources.

Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty

“…For example, a 30‐year moving average of annual reservoir inflow may be a useful indicator for water supply adaptation; for flood risk, a 50‐year estimate of the 99th percentile daily streamflow might be more appropriate. Many of the studies in Table use long‐term hydroclimatic indicators to trigger infrastructure actions (Fletcher et al, ; Hui et al, ; Kwakkel et al, ; Trindade et al, ; Zeff et al, ). Others rely on short‐term indicators such as reservoir storage to trigger operational actions, which may be adapted over time as a response to climate change (Mortazavi‐Naeini et al, ; Paton et al, ).…”

“…Control methods aim to optimize the policy mapping indicators to actions, thus determining the optimal magnitude, timing, and sequence of actions in response to the evolution of the system. Studies in Table using control approaches therefore account for all of these implementation decisions (Fletcher et al, ;Hui et al, ). Other studies select a subset of these aspects to optimize.…”

“…This goal fundamentally aligns with that of an optimal control problem, though not all dynamic planning studies have been framed this way. Policy design involves optimizing the sequence, timing, and/or threshold values of observed variables to initiate adaptations, which can be supported by optimal control methods such as stochastic dynamic programming (SDP; Fletcher et al, ;Hui et al, ) or policy search (Kwakkel et al, ; Zeff et al, ). Additionally, several hybrid frameworks that combine optimization with adaptive management have been used to support the policymaking process, including Dynamic Adaptive Policy Pathways (Haasnoot et al, ; Walker et al, ) and Engineering Options Analysis, which determines whether infrastructure investments should be made now or deferred (de Neufville & Smet, ).…”

Machine learning for geographically differentiated climate change mitigation in urban areas

“…(Rolnick et al, 2019) provide a broad review on ML applications for tackling climate change, and find relevant applications spanning many domains. Most successful applications in climate change include Earth system analysis (Reichstein et al, 2019), such as modelling multi-scale atmospheric processes (Rasp, Pritchard, & Gentine, 2018), and modelling climate impacts at high resolution by making use of big data from satellites, weather stations, radars, and other sources to specify the consequences of hurricanes and deforestation on ecosystems, or of drought on crop yields (Atlas AI, 2020;Fletcher, Lickley, & Strzepek, 2019;McDowell et al, 2015). However, ML is not yet a common tool in climate change mitigation communities.…”

A simple drought risk analysis procedure to supplement water resources management planning in England and Wales

“…Indeed, this has been a fast moving field of academic endeavour over recent years with significant advances in adaptive planning tools which enable flexibility in modifying engineering projects in the context of least‐cost water supply investment scheduling. For example, Real Options Analysis has been deployed using multistage stochastic mathematical programming (Erfani et al ., 2018), an approach which has influenced wider assessments of flexible investment strategies for the water sector under climate change uncertainty (Fletcher et al ., 2019). Other work has explored ways of improving the robustness of engineered water resources systems under different levels of risk, thereby allowing an explicit trade‐off between incremental increases in robustness and investment costs for a given level of risk (Borgomeo et al ., 2018).…”