Incorporating the logistic regression into a decision-centric assessment of climate change impacts on a complex river system

Abstract

Climate change is a global stressor that can undermine water management policies developed with the assumption of stationary climate. While the response-surfacebased assessments provided a new paradigm for formulating actionable adaptive solutions, the uncertainty associated with the stress tests poses challenges. To address the risks of unsatisfactory performances in a climate domain, this study proposed the incorporation of the logistic regression into a decision-centric framework. The proposed approach replaces the "response surfaces" of the performance metrics typically used for the decision-scaling framework with the "logistic surfaces" that describes the risk of system failures against predefined performance thresholds. As a case study, water supply and environmental reliabilities were assessed within the eco-engineering decision-scaling framework for a complex river basin in South Korea. Results showed that humandemand-only operations in the river basin could result in the water deficiency at a location requiring environmental flows. To reduce the environmental risks, the stakeholders could accept increasing risks of unsatisfactory water supply performance at the sub-basins with small water demands. This study suggests that the logistic surfaces could provide a computational efficiency to measure system robustness to climatic changes from multiple perspectives together with the risk information for decision-making processes.

Author response to referee comment #1

“…In the revised manuscript we will better explain that the main objective is to explore how we can integrate fuzzy thresholds in vulnerability assessment approaches, and how we can combine this ambiguity with the uncertainty inherent to a bivariate response. The first method indeed uses the logistic regression previously employed (Kim et al 2019) as one of the ways to convey this uncertainty, proposing a division of the exposure space by probability of success. This probability of success at each coordinate aims at capturing part of the hydro-climatic uncertainty that the 2 variables of the exposure space do not capture.…”

“…In the revised version, we will shorten the material and method section by focusing on fuzzy thresholds combined with one generating method: the logistic regression. This choice is motivated by the fact that this approach has received a lot of attention recently in the literature (in addition to Kim et al, 2019; the paper will cite Quinn et al, 2018, Lamontagne et al, 2019, Hadjimichael et al, 2020Marcos-Garcia et al, 2020).…”

Possibilistic response surfaces combining fuzzy targets and hydro-climatic uncertainty in flood vulnerability assessment

“…If possible though, it can be convenient to directly integrate information about remaining uncertainty within the response surface itself. It can be represented through a transition zone between success and failure domains, as performed by Kim et al (2019) with a logistic regression. Besides, most studies use gridded sampling of the exposure space, which is a horizontal aggregation that also results in information loss like in the case of digital elevation models (Huang, 2000), and which in this case can also under-estimate risks.…”

“…Response surfaces have been illustrated by many case studies (e.g. Nazemi et al, 2013, Turner et al, 2014, Whateley et al, 2014, Herman et al, 2015, Steinschneider et al, 2015, Spence et al, 2016, Pirttioja et al, 2019, Ray et al, 2020, expanded to many-objectives or stakeholder systems (Poff et al, 2016;Culley et al, 2016, Kim et al, 2019 and sometimes officially adopted in management processes (Moody and Brown, 2013, Weaver et al, 2013.…”

“…Testing a limited number of stressors as explaining variables therefore leads to a response function that returns uncertain performance. Kim et al (2019) propose to associate probabilities to uncertain response functions through logistic regression, while Tanner et al (2019) do so with a Bayesian belief network model. It is here noted that most response surfaces follow gridded sampling, which can also be a loss of information (e.g.…”