Skip to main content
SearchLogin or Signup

Understanding How Uncertainty and Demand for Stability Constrain Transformative Planning Options

Published onDec 24, 2019
Understanding How Uncertainty and Demand for Stability Constrain Transformative Planning Options

Over the last two decades, a nexus for climate variability and anthropogenic disturbance have undermined basic assumptions of stationarity in hydrological regimes that have guided the design of water management policy and infrastructure [1]. Historically, these systems were designed to operate within empirically determined boundaries, and thereby guarantee reliable water supply, capacity to meet growing demand, and contain risks to safety and property. With the recognition that stationarity is “dead,” system architects understand that urban and rural populations in the Mississippi River Watershed (MRW) remain vulnerable to environmental challenges capable of overwhelming overburdened engineered systems. Despite the critical need for new management paradigms unobligated to historical pattern, few have emerged, and society is left in a unique period of uncertainty.

Strategic planning for this unique “new normal” in socio-ecological systems presents an efficient and potentially life-saving approach to threats posed to MRW populations in developed landscapes, but planning and investing during periods of uncertainty has been shown to be particularly difficult. Often, the narrative moves to high-level, somewhat abstract concepts such as “sustainability” and “resilience” as potential solutions. In doing so, planning responses to emerging threats range widely, from abandonment and ad hoc mitigation to adaptive and transformative approaches. Redman [2] distinguishes between actions that lead to adaptation, and those that lead to transformation. Adaptations are targeted and often local approaches to adjust to shocks. In contrast, transformations often involve radical restructuring of systems once they become untenable or undesirable.

However, the ability for communities to adopt and fund adaptation or transformational solutions is not well understood (as compared to mitigation or abandonment) despite the possibility for sustainable outcomes. The ability to choose adaptive or transformational solutions is likely contingent on a community’s capacity to tolerate uncertainty, and the degree of local demand for stability in infrastructural systems [2]. Further, the scope of planning is frequently contingent on the nature of projected disturbance, whether it is slow or fast, and how much stands to be lost, as well as how much time it might take to revisit. Of interest is that all contingencies listed (disturbance characteristics, toleration of uncertainty and demand for stability) all require detailed contextualization for deep understanding, and that understanding may be informed through systematic study, improving decision-making.

I represent a group of North Carolina-based researchers interested in the specifics of socio-ecological resilience, particularly as they relate to sustainable rural-urban development and the development of testable theory. To advance this agenda, we have developed a generalizable platform to facilitate learning and generate collaborative solutions when applied to “wicked” socio-ecological dilemmas across a diverse range of stakeholders.

Founded on a complex adaptive systems worldview, the platform helps to identify and populate parameters using participatory geospatial modeling [3]. This platform, Tangible Landscape (TL), has enabled untrained participants to overcome knowledge barriers, render complex stratagem and engage in collaborative decision-making [4][5]. These tools include advanced simulation models (e.g. urban growth and biological invasion, as well as biophysical models), and new ways for laypeople to interact with those simulation models in order to conduct scenario analyses. Said another way, TL uses rich, interactive visualizations (backed by data and rigorous models) to remove barriers and “gamify” scenario analyses.

To date, complex adaptive system framing and TL have been used to explore the problem of collective action in managing a forest epidemic in West Coast forests. More recently, we have been working to fund similar explorations of North Carolina’s dynamic Atlantic coastline in the face of sea level rise [6][7], which in turn has led us to see similarities in the MRW. We hope to evolve our scope but not exclude the very important questions regarding evidence-driven action in specific locales at specific times. We ask,

“How does uncertainty and the desire for stability influence the way people and government plan for crises and chronic environmental-scale disturbances in densely populated riverine regions?”

H1: High social capital, high information/knowledge is associated with risk-taking, and makes available adaptive, transformational responses

H2: Low social capital, low information/knowledge is associated with need for stability, which limits choices to mitigation, abandonment

To answer this question, we seek collaborative research using dynamics within the MRW to expose and characterize dynamic feedback loops between changing hydrological regimes and human intervention, and reveal effects associated with uncertainty in both environmental and social systems. Specifically, we propose to use Geospatial Participatory Frameworks [3] (Figure 1) to ensure “humans in the loop” in modeling systems. In this way, we can also examine the relationship between sustainability as a norm, and the value-neutral, solutions-based approaches embodied by resilience theory.

<p class="">Figure 1. Process flows needed in Participatory Geospatial Research. Image courtesy of Francesco Tonini.</p>

Figure 1. Process flows needed in Participatory Geospatial Research. Image courtesy of Francesco Tonini.


References cited are provided below (citations).


Douglas Shoemaker

University of North Carolina-Charlotte, Director of Research and Outreach, Center for Applied Geographic Information Science

dshoema1@uncc.edu

This event is supported by the National Science Foundation, Award #1929601. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Comments
0
comment

No comments here