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Parameterization Framework and Quantification Approach for Integrated Risk and Resilience Assessments.

Authors
  • Cains, Mariana Goodall1
  • Henshel, Diane1
  • 1 School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana, USA. , (India)
Type
Published Article
Journal
Integrated Environmental Assessment and Management
Publisher
Wiley (John Wiley & Sons)
Publication Date
Aug 25, 2020
Identifiers
DOI: 10.1002/ieam.4331
PMID: 32841472
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

A growing challenge for risk, vulnerability, and resilience assessment is the ability to understand, characterize, and model the complexities of our joint socio-ecological systems, often delineated with differing natural (e.g. watershed) and imposed (e.g. political) boundaries at the landscape scale. To effectively manage such systems in the increasingly dynamic, adaptive context of environmental change, we need to understand not just food web interactions of contaminants or the flooding impacts of sea level rise and storm surges, but rather the interplay between social and ecological components within the inherent and induced feedforward and feedback system mechanisms. Risk assessment, in its traditional implementation, is a simplification of a complex problem in order to understand the basic cause and effect relationships within a system. This approach allows risk assessors to distill a complex issue into a manageable model that quantifies, or semi-quantifies, the effect(s) of an adverse stressor. Alternatively, an integrated risk and resilience assessment moves toward a solution-based assessment with the incorporation of adaptive management practices as one of four parts of system resilience (i.e. prepare, absorb, recover, and adapt), and directly considers the complexities of the system(s) being modeled. We present the Multi-level Risk and Resilience Assessment Parameterization framework for the systematic parameterization and deconstruction of management objectives and goals into assessment metrics and quantifiable risk measurement metrics and complementary resilience measurement metrics. As a proof-of-concept, the presented framework is paired with the Bayesian Network-Relative Risk Model for a human-focused subset of a larger risk and resilience assessment of climate change impacts within the Charleston Harbor Watershed of South Carolina. This new parameterization framework goes beyond traditional simplification and embraces the complexity of the system as a whole, which is necessary for a more representative analysis of an open, dynamic complex system. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

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