Challenges and prospects of climate change impact assessment on mangrove environments through mathematical models. (April 2023)

• Record Type: Journal Article
• Authors: Fanous, Majdi; Eden, Jonathan M.; Remesan, Renji; Daneshkhah, Alireza
• Is Part Of: Environmental modelling & software. Volume 162(2023)
• Publication Date: 2023-04
• Subjects: Mangrove environments -- Climate change -- Hydro-morphodynamic modelling -- Adaptation policies -- Machine learning -- Data-driven modelling; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2023.105658
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The impacts of climate change, especially sea-level rise, are an increasing threat to the world's coastal regions. Following recommendations made by the United Nations about the preservation of mangrove environments, particularly given their potential for effective natural defence against wave-driven hazards, a series of experiments have been conducted to quantify the ability of mangroves to counter climate change impacts. To date, these experiments have been limited by computational cost and inability to model multiple scenarios. With improved data quality and availability, machine learning has enormous potential to supplement, or even replace, existing numerical methods. This article presents both an outline of the importance of protecting mangrove environments and a review of methods currently used to quantify the capacity of mangroves to adapt to climate change impacts. In view of the limitations of existing numerical methods, the article also discusses the potential of machine learning as an efficient and effective alternative. Highlights: Sea-level rise imposes on important threat to mangrove environments. Ecosystem-based adaptation strategies protects and restores mangrove environments. Numerical modelling makes implementation of solutions challenging. Machine learning models could act as a surrogate for the numerical models.