Identifying Minimum Information Requirements to Improve Integrated Modeling Capabilities: Lessons Learned From Dynamic Adaptive Policy Pathways

Abstract

Integrated Assessment Models (IAMs) were initially developed to inform decision processes relating to climate change and then extended to other natural resource management decisions, including issues around integrated water resources management. Despite their intention to support long-term planning decisions, model uptake has generally been limited, partly due to their unfulfilled capability to manage deep uncertainty issues and consider multiple perspectives and trade-offs involved when solving problems of interest. In recent years, more emphasis has been put on the need for existing models to evolve to be used for exploratory modeling and analysis to capture and manage deep uncertainty. Building new models is a solution but may face challenges in terms of feasibility and the conservation of knowledge assets. Integration and augmentation of existing models is another solution, but little guidance exists on how to realize model augmentation that addresses deep uncertainty and how to use such models for exploratory modeling purposes. To provide guidance on how to augment existing models to support decisions under deep uncertainty we present an approach for identifying minimum information requirements (MIRs) that consists of three steps: (1) invoking a decision support framework [here, Dynamic Adaptive Policy Pathways (DAPP)] to synthesize information requirements, (2) characterizing misalignment with an existing integrated model, (3) designing adjustable solutions that align model output with immediate information needs. We employ the Basin Futures model to set up the approach and illustrate outcomes in terms of its effectiveness to augment models for exploratory purposes, as well as its potential for supporting the design of adaptative pathways. The results are illustrated in the context of the Brahmani River Basin (BRB) system and discussed in terms of generalization and transferability of the approach to identifying MIRs. Future work directions include the refinement and evaluation of the approach in a planning context and testing of the approach with other models.