Adaptive policies have emerged as a valuable strategy for dealing with uncertainties by recognising the capacity of systems to adapt over time to new circumstances and surprises. The efficacy of adaptive policies hinges on detecting ongoing change and ensuring that actions are indeed taken if and when necessary. This is operationalised by including a monitoring system composed of signposts and triggers in the design of the plan. A well-designed monitoring system is indispensable for the effective implementation of adaptive policies. Despite the importance of monitoring for adaptive policies, the present literature has not considered criteria enabling the a-priori evaluation of the efficacy of signposts. In this paper, we introduce criteria for the evaluation of individual signposts and the monitoring system as a whole. These criteria are relevance, observability, completeness, and parsimony. These criteria are intended to enhance the capacity to detect the need for adaptation in the presence of noisy and ambiguous observations of the real system. The criteria are identified from an analysis of the information chain, from system observations to policy success, focusing on how data becomes information. We illustrate how models, in particular, the combined use of stochastic and exploratory modelling can be used to assess individual signposts, and the whole monitoring system according to these criteria. This analysis provides significant insight into critical factors that may hinder learning from data. The proposed criteria are demonstrated using a hypothetical case, in which a monitoring system for a flood protection policy in the Niger River is designed and tested.
Large, complex coastal regions often require a combination of interventions to lower the risk of flooding to an acceptable level. In practice, a limited number of strategies are considered and interdependencies between interventions are often simplified. This paper presents the Multiple Lines of Defence Optimization System (MODOS)‐model. This quick, probabilistic model simulates and evaluates the impact of many flood risk reduction strategies while accounting for interdependencies amongst measures. The simulation includes hydraulic calculations, damage calculations, and the effects of measures for various return periods. The application and potential of this model is shown with a conceptual and simplified case study, based on the Houston‐Galveston Bay area. The analyses demonstrate how the MODOS‐model identifies trade‐offs within the system and shows how flood risk, cost, and impact respond to flood management decisions. This improved understanding of the impact of design and planning choices can benefit the discussions in finding the optimal flood risk reduction strategy for coastal regions.
Global mean sea-level rise (SLR) has accelerated since 1900 from less than 2 mm/year during most of the century to more than 3 mm/year since 1993. Decision-makers in coastal countries, however, require information on SLR at the regional scale, where detection of an acceleration in SLR is difficult, because the long-term sea-level signal is obscured by large inter-annual variations with multi-year trends that are easily one order of magnitude larger than global mean values. Here, we developed a time series approach to determine whether regional SLR is accelerating based on tide gauge data. We applied the approach to eight 100-year records in the southern North Sea and detected, for the first time, a common breakpoint in the early 1990s. The mean SLR rate at the eight stations increases from 1.7±0.3 mm/year before the breakpoint to 2.7±0.4 mm/year after the breakpoint (95% confidence interval), which is unprecedented in the regional instrumental record. These findings are robust provided that the record starts before 1970 and ends after 2015. Our method may be applied to any coastal region with tidal records spanning at least 40 years, which means that vulnerable coastal communities still have time to accumulate the required time series as a basis for adaptation decisions in the second half of this century.
Abstract. Coastal cities combine intensive socioeconomic
activities and investments with high exposure to flood hazards. Developing
effective strategies to manage flood risk in coastal cities is often a
costly and complicated process. In designing strategies, engineers rely on
computationally demanding flood simulation models, but they can only compare
a limited number of strategies due to computational constraints. This limits
the efficacy of standard flood simulation models in the crucial conceptual
phase of flood risk management. This paper presents the Flood Risk Reduction
Evaluation and Screening (FLORES) model, which provides useful risk
information in this early conceptual phase. FLORES rapidly performs numerous
simulations and compares the impact of many storms, strategies, and future
scenarios. This article presents FLORES and demonstrates its merits in a
case study for Beira, Mozambique. Our results demonstrate that expansion of
the drainage capacity and strengthening of its coastal protection in the
southwest are crucial components of any effective flood risk management
strategy for Beira.
Climate change raises serious concerns for policymakers that want to ensure the success of long-term policies. To guarantee satisfactory decisions in the face of deep uncertainties, adaptive policy pathways might be used. Adaptive policy pathways are designed to take actions according to how the future will actually unfold. In adaptive pathways, a monitoring system collects the evidence required for activating the next adaptive action. This monitoring system is made of signposts and triggers. Signposts are indicators that track the performance of the pathway. When signposts reach pre-specified trigger values, the next action on the pathway is implemented. The effectiveness of the monitoring system is pivotal to the success of adaptive policy pathways, therefore the decision-makers would like to have sufficient confidence about the future capacity to adapt on time. “On time” means activating the next action on a pathway neither so early that it incurs unnecessary costs, nor so late that it incurs avoidable damages. In this paper, we show how mapping the relations between triggers and the probability of misclassification errors inform the level of confidence that a monitoring system for adaptive policy pathways can provide. Specifically, we present the “trigger-probability” mapping and the “trigger-consequences” mappings. The former mapping displays the interplay between trigger values for a given signpost and the level of confidence regarding whether change occurs and adaptation is needed. The latter mapping displays the interplay between trigger values for a given signpost and the consequences of misclassification errors for both adapting the policy or not. In a case study, we illustrate how these mappings can be used to test the effectiveness of a monitoring system, and how they can be integrated into the process of designing an adaptive policy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.