DAPP-MD: A multi-dimensional framework for adaptive planning under deep uncertainty and interacting risks

Climate adaptation planning increasingly requires decisions about interventions in systems where multiple hazards, uncertain drivers, and risk interactions occur simultaneously. Adaptive planning approaches such as Dynamic Adaptive Policy Pathways (DAPP) support decision-making under deep uncertainty by sequencing actions over time, but typically represent changing conditions along a single uncertainty axis. This simplification can obscure how independent drivers and interacting risks influence adaptation decisions. This paper introduces Dynamic Adaptive Policy Pathways for Multi-Dimensional Decision Problems (DAPP-MD), an extension of DAPP that represents adaptation tipping points and intervention portfolios across multiple uncertain conditions. The framework enables decision-makers to explore how combinations of uncertainties influence the timing and effectiveness of adaptation actions, and to evaluate interactions among interventions addressing different risks. We demonstrate the approach using a stylised case study that shows how representing multiple uncertainty dimensions reveals cross-risk synergies, trade-offs, and alternative adaptation pathways that are not visible when uncertainties are collapsed onto a single axis. These findings highlight both the potential and the practical challenges of applying adaptive planning in multi-risk contexts. DAPP-MD provides a structured approach for designing adaptive strategies when multiple uncertain conditions and interacting risks must be considered simultaneously. • Proposes a DAPP-MD an adaptive planning tool for complex decision problems. • Expands the concept of Adaptation Tipping Points into a multi-dimensional space. • Identifies key limitations and implementation needs to be addressed implementation.