Bayesian Belief Networks for Integrating Scientific and Stakeholders’ Knowledge to Support Nature-Based Solution Implementation

Albert Scrieciu,Umberto Fratino,Virginia Rosa Coletta,Alessandro Pagano,Raffaele Giordano

doi:10.3389/feart.2021.674618

Abstract

There is a growing interest worldwide on the potential of nature-based solutions (NBSs) as measures for dealing with water-related risks while producing multiple co-benefits that can contribute to several societal challenges and many of the sustainable development goals. However, several barriers still hamper their wider implementation, such as mainly the lack of stakeholders’ engagement and the limited integration of stakeholders’ knowledge throughout the phases of NBS design and implementation. This is a crucial aspect to guarantee that the multidimensional implications of NBSs are adequately understood and considered by decision-makers. Innovative methods and tools for improving NBS design and supporting decision-makers in overcoming the main barriers to implementation, ultimately enhancing their effectiveness, are therefore needed. The present work proposes a combined approach based on the integration of fuzzy cognitive maps, hydraulic modeling, and participatory Bayesian belief networks aiming to facilitate the stakeholders’ engagement and the knowledge integration process in NBS design and assessment. The approach was developed and implemented within the NAIAD project in the Lower Danube demo site, specifically oriented to support the process of the Potelu Wetland restoration. First, fuzzy cognitive maps are adopted as a problem structuring method for eliciting stakeholders’ risk perception and problem understanding, and for constructing a causal model describing the system as a whole, with specific attention to the expected role of the NBS in reducing flood risk and addressing the key local challenges. Second, hydraulic modeling is used to analyze the effect of extreme floods starting from the retrospective analysis of a specific event and to model the potential benefits of risk reduction measures. Last, a Bayesian belief network is used to support the model integration process and a scenario analysis with a user-friendly tool. The whole process can be replicated in other areas and is particularly suitable to support an active engagement of stakeholders (both institutional and not) in the process of NBS design and assessment.

Highlights

There is an ever-increasing number of research articles and projects dealing with the design of nature-based solutions (Albert et al, 2017; Raymond et al, 2017; Calliari et al, 2019), proving NBS suitability as valid alternatives to gray infrastructures for dealing with natural hazards (Palmer et al, 2015) and with water-related risks (Ruangpan et al, 2020)
The present work aimed to demonstrate the suitability of the Bayesian belief network (BBN) to support integration between FCMs and HM, for enhancing the effectiveness assessment of the NBS in dealing with flood risk
With respect to the capability of FCMs and BBNs to account for the diversity of stakeholders’ problem understanding, it is worth stressing the key role of FCMs in structuring individual knowledge and clearly identifying strategic objectives, vulnerabilities, and challenges for the area according to the single stakeholder’s perception

Summary

Introduction

There is an ever-increasing number of research articles and projects dealing with the design of nature-based solutions (Albert et al, 2017; Raymond et al, 2017; Calliari et al, 2019), proving NBS suitability as valid alternatives to gray infrastructures for dealing with natural hazards (Palmer et al, 2015) and with water-related risks (Ruangpan et al, 2020). As described by several authors, the success of NBSs is not exclusively related to the reduction of natural risks, but it is due to their capability to produce a wide set of socioeconomic and environmental co-benefits, whose integration in the frameworks for assessing NBS effectiveness has been advocated by several authors (Alves et al, 2018, Alves et al, 2019; Pagano et al, 2019). Scholars demonstrated that social (e.g., the perceived effectiveness), institutional (e.g., the longer time needed by NBSs to provide benefits and the limited cross-sectoral cooperation), and economic (high construction and maintenance costs for NBSs) barriers are at least as important as the physical ones (O‘Donnell et al, 2017; Sarabi et al, 2020; Vojinovic, 2020). Kabisch et al (2016), Ruangpan et al (2020) demonstrated how the lack of stakeholders’ engagement and the limited integration of stakeholders’ and scientific knowledge in NBS design represent a barrier hampering their implementation. Ineffective cooperation among different decision-makers and stakeholders could hamper NBS implementation and/or reduce its effectiveness, as discussed by Shrestha and Dhakal (2019) and Gómez Martín et al (2020)

Objectives

Methods

Results

Conclusion