by: Patrick Reed
This blog reports on one of the session of the annual meeting of 2016. The sessoin was organized by Patrick Reed (Cornell University), Jan Kwakkel (TU Delft), Andrea Castelletti (Politecnico di Milano), Laura Bonzanigo (World Bank). Invited speakers were Julie Quinn (Cornell University) and Marc Jaxa-Rozen (TU Delft).
Session Focus: This session explored the interplay between short-term adaptive operations and their influence on long-term planning is particularly relevant for irreversible decisions for long-lived infrastructures that present complex ecological impacts, and must reliably meet multi-sectoral demands (e.g., reservoirs, energy production/transmission, transportation networks, etc.). A core theme throughout this whole session is that current DMDU frameworks that truly seek robustness must better exploit information feedbacks, tailor adaptivity so that triggered actions are contextually appropriate, and minimize lock in. The session was organized into three case study presentations, five posters, and an interactive serious table top game. This suite of multi-sector examples helped clarify emerging innovations and persistent challenges related to bridging the planning and management divide.
Case Study Example #1 (Speaker: Patrick Reed, Research Triangle Region, NC, USA): This case study highlighted emerging work bridging the Many-Objective Robust Decision Making (MORDM) and Dynamic Adaptive Policy Pathways (DAPP) DMDU frameworks. The example showed how the region’s water utilities’ long term infrastructure pathways are strongly shaped by their short term conservation policies and their ability to consider regional water transfers. Cooperatively developed, shared investments across four municipalities expand the capacity to use short term transfers to better manage severe droughts with fewer irreversible infrastructure options. Cooperative pathways are also important for avoiding regional robustness conflicts, where one party benefits strongly at the expense of one or more the others. A significant innovation of this work are the mix of weekly and annual dynamic risk-of-failure action triggers that allow for new information feedbacks and provide high levels of adaptivity.
Suggested Further Reading:
Zeff, H., J. Herman, P. Reed and G. Characklis (2016). “Cooperative drought adaptation: Integrating infrastructure development, conservation, and water transfers into adaptive policy pathways.” Water Resources Research, http://dx.doi.org/10.1002/2016WR018771
Case Study Example #2 (Speaker: Julie Quinn, Red River Basin, Vietnam): This case study highlighted that simple deterministic and static rule-based abstractions of reservoirs that are commonly employed in standard simulation frameworks are unable to realistically consider the complex dynamics and key sources of information that shape river basin operations. Consequently, they also fail to explore the full set of tradeoffs across alternative operating policies seeking to balance evolving multi-sector basin demands under changing hydroclimatic forcings. This case study illustrated how to better sample and quantify the adaptive capacity of a complex multi-reservoir system in the Red River Basin to manage evolving pressures related to energy security, food security, and urban flood risks.
Suggested Further Reading:
Giuliani, M., D. Anghileri, A. Castelletti, P. Nam Vu and R. Soncini-Sessa (2016). “Large storage operations under climate change: expanding uncertainties and evolving tradeoffs.” Environmental Research Letters 11(3), http://dx.doi.org/10.1088/1748-9326/11/3/035009
Case Study Example #3: (Speaker: Marc Jaxa-Rozen, Dutch ATES Planning & Management): This case study highlighted how short term centralized and cooperative control mechanisms fundamentally shape the long term value and efficiency of Aquifer Thermal Energy Storage (ATES) systems. ATES systems can significantly reduce energy demand for building heating and cooling. However, these systems are affected by uncertainties ranging from daily energy demand to multi-year geohydrological processes, leading to suboptimal outcomes under the static planning approaches which are currently used to manage this technology in the Netherlands. Price-based coordination mechanisms may yield improved performance under these uncertainties, by providing greater operational flexibility for ATES operators and supporting the design of self-organized institutional arrangements as an alternative to static permits.
Suggested Further Reading:
Rostampour, V., M. Jaxa-Rozen, M. Bloemendal and T. Keviczky (2016). “Building Climate Energy Management in Smart Thermal Grids via Aquifer Thermal Energy Storage Systems1.” Energy Procedia 97, http://dx.doi.org/10.1016/j.egypro.2016.10.019
- Michael Green, Global Sustainability Institute, “Real options and robust adaptive management in irrigated agriculture and urban drainage: supporting the next generation of UK climate change projections”.
- Steven Popper, RAND Corporation, “Future Force Planning: The Present is Prologue”.
- Lauren Cook, Carnegie Mellon University, “Using precipitation data from climate change projections in engineering resiliency applications under deep uncertainty”.
- Kim Smet, Harvard University, “Flexibility in flood management design: proactive planning under uncertainty”.
- Vivek Srikrishnan, Pennsylvania State University, “Identification of signposts for adaptive flood risk management in the Netherlands”.
Serious Table Top Interactive Game (Lead Facilitator: Julie Quinn): The session ended by engaging all of the participants to team up in a simulated river basin decision problem. The teams had to confront severe flood risks to a major city, water shortages for agriculture, and highly variable energy production. Each team had to divide its players to advocate for specific hydropower, agriculture, and urban flooding interests. The game had three stages: (1) choose between two candidate formulations, (2) specify performance requirements for your sector, and (3) exploit interactive visual analytics to explore tradeoffs and negotiate a compromise. A key take home point from the game is that “problem framing” itself is a critical deep uncertainty and false perceptions of system requirements and their tradeoffs can yield severe and unexpectedly negative unintended consequences.