Exploring How Our Models and Modelling Shape Scenario Discovery

By Patrick Reed

Institutionally complex water resources systems have evolved globally to deal with omnipresent challenge of hydrological variability and extremes. The evolving vulnerability and resilience of these systems is a challenging scientific question for model-based decision support methods. This session[1] highlights that DMDU methods must carefully represent and explore how the interplay between co-evolving natural river basin processes and the human systems that depend on them. Our understanding of issues such as water scarcity is highly dependent on our models’ abilities to represent the governance and legal institutions, physical infrastructures, and natural elements of river basins. The DMDU community has produced a significant number of innovations for water resources planning and management. To date, these contributions demonstrate the value of transitioning to exploratory modeling, scenario discovery, and providing the impetus to act by clarifying the most consequential vulnerabilities. That being said, many challenges remain for representing these complex systems and aiding stakeholders to identify the robust actions that will sustain their environmental and human system services.

The work by Rosello et al. in this session contributes an extension of Basin Futures, a newly developed web based integrated water resources management model developed by CSIRO in Australia, to facilitate Dynamic and Adaptive Policy Pathways (DAPP) in river basin contexts. Often in DMDU applications, the “model” foundation of our exploratory analyses is often tacitly assumed to be credible and relevant to the problem’s at hand.  These are large assumptions in complex water resources systems where representation of processes, infrastructure, and institutions can be severely challenging, especially if they are co-evolving. Moreover, the model as a boundary object in deliberative decision support that facilitates shared insights across stakeholders is an often-overlooked need. As our exploratory modeling progresses through the “state-action-consequence” feedbacks that occur in the many sampled states-of-the-world (SOWs), modeling tools that aid deliberative discussion and understanding of how system conceptions, performance perceptions, and preferences across key tradeoffs vary across stakeholders is critical to the very basic goal of scenario discovery (i.e., finding those actions and worlds that are most consequential).

So, a core assumption in DMDU methods seeking to find consequential actions and scenarios, is the so-called “scenario neutral” assumption where the SOWs explored are not assigned specific likelihoods. They are treated neutrally by assuming components SOWs are equally likely initially and subsequently judged for their relevance/likelihood later with stakeholder interactions that draw on scenario discovery methods. This cornerstone of our engagement with deep uncertainties is motivated by a lack of consensus on distributional forms, specific likelihoods, and the contextual framing of the suite of factors that shape vulnerabilities in complex planning problems.  Quinn et al. show that our exploratory scenario analyses may not be so neutral after all. The work asks whether we get the same assessments of robustness and vulnerability using three different rival framings for water scarcity (history-focused, climate change-focused, and paleo data focused) within the Upper Colorado River Basin of Colorado (UCRB). The UCRB is an institutionally complex water resources system with hundreds of multi-sector users posing potentially increasing demands simultaneously to a persistent trend of drought. Interestingly, this highlights that commonly employed assumptions in exploratory modeling such as that factors are uniform independent and identically distributed can lead to very different vulnerability and robustness inferences. A broader take-home point of the work is that there is no strictly neutral way to explore complex DMDU problems and that the logic of exploratory modeling itself should encourage us to be exploratory in our experimental designs themselves. Hadjimichael et al. continue in the UCRB taking to heart the advice of Quinn et al. by expanding the suite of deeply uncertain factors to include more multi-sector human demands versus solely climate as a stressor. Beyond expanding stressors, this work highlights the challenge and importance posed by fine-grained massively multi-stakeholder robustness analysis. It is common in many modeling frameworks to treat users or demands as aggregate sectors (ag, urban, residential, industrial, etc.). This work highlights that these aggregations may miss critical insights and inferences in the complex networks of users that are typical in major river basins. This is especially true for systems like the UCRB where legal institutions (“prior appropriation doctrine”) fundamentally shape allocative water balances in a myriad of ways depending on users’ sector, geographic location, rights, and unique operational needs. This work highlights that simple rules such as “senior rights users” are uniformly less vulnerable to water scarcity may not always be true.  Likewise, neighboring farmers with similar rights and close proximity to one another can sometimes have dramatically different stressors that impact their vulnerabilities as well as overall degrees of vulnerability. Again, these issues tie to the other studies in the session in that we should be very careful in how we represent, aggregate, and explore institutionally complex river basins in DMDU applications.

[1] 2020 DMDU Meeting Session Title: Innovations for Scenario Discovery and New Methods. Chair: Patrick Reed. Session Presenters: Caroline Rosello, Julianne Quinn, and Antonia Hadjimichael

About this Blog Post:

This blog post is part of a series of posts contributed by the chairs of the 2020 DMDU Annual Meeting. For more information about the Annual Meeting, including registration, visit our website at 2020.deepuncertainty.org. We hope to (virtually) see you soon!

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