Incorporating the logistic regression into a decision-centric assessment of climate change impacts on a complex river system
<p>Climate change is a global stressor that can undermine water management policies developed with the assumption of stationary climate. While the response-surface-based assessments provided a new paradigm for formulating actionable adaptive solutions, the uncertainty associated with the stres...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-02-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/23/1145/2019/hess-23-1145-2019.pdf |
| Summary: | <p>Climate change is a global stressor that can undermine water management
policies developed with the assumption of stationary climate. While the
response-surface-based 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 human-demand-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.</p> |
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| ISSN: | 1027-5606 1607-7938 |