Keywords
exploratory modelling, adaptation pathways, uncertainty, water management
Start Date
1-7-2012 12:00 AM
Abstract
Sustainable water management in a changing environment full of uncertainty is a profound challenge. To deal with uncertainties, dynamic adaptive policies can be used. Such policies can change over time in response to how the future unfolds, to what we learn about the system, to changes in environment, and to changes in societal preferences. This paper presents a model driven approach that supports the development of dynamic adaptive policies, and illustrates the approach using a hypothetical case. The key idea of the approach is that the dynamic behavior of a fast and simple Integrated Assessment Meta Model (IAMM) is explored across a wide variety of uncertainties. Next, the performance of a set of candidate policy actions is assessed across these uncertainties. This provides insight into the sell-by date of the various actions, and thus when to modify or replace the action. From this, we deduce a logical sequencing of actions, constituting potential pathways. The performance of these pathways is in turn assessed, and iteratively improved. The hypothetical case is inspired by a river reach in the Rhine delta of the Netherlands. Like in the real world, this case is characterized by uncertainties about the future (e.g. climate change, socioeconomic developments, and natural variability), uncertainties about the system (e.g. chance on dike failure in relation to high water levels), and uncertainties about societal preferences (e.g. weight society gives to nature). Using a rule induction algorithm, we identify the vulnerabilities and opportunities presented by each pathway. We modify the pathways to address these opportunities and vulnerabilities through capitalizing and defensive actions. With the results it is possible to make an informed decision on a dynamic adaptive policy in a changing environment that is able to achieve the intended objectives despite the multitude of uncertainties present.
Computer assisted dynamic adaptive policy design for sustainable water management in river deltas in a changing environment
Sustainable water management in a changing environment full of uncertainty is a profound challenge. To deal with uncertainties, dynamic adaptive policies can be used. Such policies can change over time in response to how the future unfolds, to what we learn about the system, to changes in environment, and to changes in societal preferences. This paper presents a model driven approach that supports the development of dynamic adaptive policies, and illustrates the approach using a hypothetical case. The key idea of the approach is that the dynamic behavior of a fast and simple Integrated Assessment Meta Model (IAMM) is explored across a wide variety of uncertainties. Next, the performance of a set of candidate policy actions is assessed across these uncertainties. This provides insight into the sell-by date of the various actions, and thus when to modify or replace the action. From this, we deduce a logical sequencing of actions, constituting potential pathways. The performance of these pathways is in turn assessed, and iteratively improved. The hypothetical case is inspired by a river reach in the Rhine delta of the Netherlands. Like in the real world, this case is characterized by uncertainties about the future (e.g. climate change, socioeconomic developments, and natural variability), uncertainties about the system (e.g. chance on dike failure in relation to high water levels), and uncertainties about societal preferences (e.g. weight society gives to nature). Using a rule induction algorithm, we identify the vulnerabilities and opportunities presented by each pathway. We modify the pathways to address these opportunities and vulnerabilities through capitalizing and defensive actions. With the results it is possible to make an informed decision on a dynamic adaptive policy in a changing environment that is able to achieve the intended objectives despite the multitude of uncertainties present.
