Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world

Abstract

Sustainable water management in a changing environment full of uncertainty is profoundly challenging. To deal with these uncertainties, dynamic adaptive policies that can be changed over time are suggested. This paper presents a model-driven approach supporting the development of promising adaptation pathways, and illustrates the approach using a hypothetical case. We use robust optimization over uncertainties related to climate change, land use, cause-effect relations, and policy efficacy, to identify the most promising pathways. For this purpose, we generate an ensemble of possible futures and evaluate candidate pathways over this ensemble using an Integrated Assessment Meta Model. We understand 'most promising' in terms of the robustness of the performance of the candidate pathways on multiple objectives, and use a multi-objective evolutionary algorithm to find the set of most promising pathways. This results in an adaptation map showing the set of most promising adaptation pathways and options for transferring from one pathway to another. Given the pathways and signposts, decision-makers can make an informed decision on a dynamic adaptive plan in a changing

Cambio climático y planificación hidrológica: ¿es adecuado asumir un porcentaje único de reducción de aportaciones para toda la demarcación?

“…Este programa de adaptación se puede diseñar para que sea posible aplicar medidas según evolucione el sistema, como sugiere la metodología de vías de adaptación dinámicas; (Haasnoot et al, 2013;Kwakkel et al, 2015), que especifica dos tipos de acciones: las que se deben adoptar de forma inmediata para prepararse a corto plazo y aquéllas que se deben considerar ahora para posibilitar otras opciones de adaptación en un horizonte temporal mayor. Respecto al cuarto aspecto, es importante señalar que, aunque la incertidumbre asociada al modelo hidrológico es inferior a la que introducen los modelos climáticos, no es en ningún caso despreciable dadas las diferencias existentes entre las aportaciones generadas con los tres modelos utilizados (Témez, GR2M y HBV-light).…”

Structuring Climate Adaptation through Multiple Perspectives: Framework and Case Study on Flood Risk Management

“…Model driven pathway approaches (e.g., Haasnoot et al [96], Kwakkel et al [97]) use physically based models such as hydraulic models and scenario generation techniques to find out the most promising pathways in terms of performance robustness towards multiple objectives. Selection of an adaptation pathway for implementation from among these pathways can be made based on: (i) set thresholds such as estimated annual damages (e.g., CRIDA [98]); (ii) the net present value of pathways (e.g., Gersonius et al [37]) or benefit cost analysis (e.g., Aerts et al [99]); (iii) likelihood of occurrence among all plausible scenarios (e.g., Buurman & Babovic [100]); (iv) assessing all the aforementioned 'objectives' using multi-objective evolutionary algorithms which could avoid the narrowing down to a pathway based on aggregated objectives (e.g., Kasprzyk et al [101]).…”

Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty

“…Examples of the application of RO in the development of long-term water management strategies can be found in Kwakkel et al (2014), Giuliani et al (2014), Herman et al (2014), Kang and Lansey (2013) and Beh et al (2015b) and for adaptive policymaking in Hamarat et al (2012). Within this research it was found that RO could handle complex, deeply uncertain problems with large numbers of possible solutions.…”

“…Furthermore an evaluation of robustness measures from different DMMs discovered each DMM ranked solutions to differing performance levels (Herman et al, 2015). Numerous more individual and comparative DMMs studies have been conducted within the context of WRM adaptive planning with specific attention to a measure of robustness (Ghile et al 2014;Haasnoot et al 2013;Jeuland and Whittington 2014;Kwakkel et al 2014;Lempert and Groves 2010;Li, et al 2009;Moody and Brown 2013;Paton et al 2014a;Tingstad et al 2013;Turner et al 2014a;Whateley et al 2014), including investigations into risk-based metrics for analysing adaptation strategy performance (Borgomeo et al 2014;Brown and Baroang 2011;Hall et al 2012a;Kasprzyk et al 2012;Turner et al 2014b) and various new scenario-based methods for ordering and mapping the deep uncertainties within modern WRM problems (Beh et al 2015a;Kang and Lansey 2013;2014;Nazemi et al 2013;Singh et al 2014;Weng et al 2010). However further testing and comparison of DMMs on real world case studies could be highly beneficial especially in regard to evaluating alternative definitions and calculations of system robustness to uncertainty, the methods of scenario generation and the process of adaptation strategy selection and evaluation.…”