Salt marshes are among the most important coastal wetlands and provide critical ecological services,including climate regulation,biodiversity maintenance,and blue carbon sequestration.However,most salt marshes worldwi...Salt marshes are among the most important coastal wetlands and provide critical ecological services,including climate regulation,biodiversity maintenance,and blue carbon sequestration.However,most salt marshes worldwide are shrinking,owing to the effects of natural and human factors,such as climate change and artificial reclamation.Therefore,it is essential to understand the decline in the morphological processes of salt marshes,and accordingly,the likely evolution of these marshes,in order to enable measures to be taken to mitigate this decline.To this end,this study presented an extensive systematic review of the current state of morphological models and their application to salt marshes.The emergence of process-based(PB)and data-driven(DD)models has contributed to the development of morphological models.In morphodynamic simulations in PB models,multiple physical and biological factors(e.g.,the hydrodynamics of water bodies,sediment erosion,sediment deposition,and vegetation type)have been considered.The systematic review revealed that PB models have been extended to a broader interdisciplinary field.Further,most DD models are based on remote sensing database for the prediction of morphological characteristics with latent uncertainty.Compared to DD models,PB models are more transparent but can be complex and require a lot of computational power.Therefore,to make up for the shortcomings of each model,future studies could couple PB with DD models that consider vegetation,microorganisms,and benthic animals together to simulate or predict the biogeomorphology of salt marsh systems.Nevertheless,this review found that there is a lack of unified metrics to evaluate model performance,so it is important to define clear objectives,use multiple metrics,compare multiple models,incorporate uncertainty,and involve experts in the field to provide guidance in the further study.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.U2040204)the Jiangsu Provincial Natural Science Foundation of China(Grants No.BK2020020,BK20220979,and BK20220993)the Fundamental Research Funds for the Central University(Grant No.B220202057).
文摘Salt marshes are among the most important coastal wetlands and provide critical ecological services,including climate regulation,biodiversity maintenance,and blue carbon sequestration.However,most salt marshes worldwide are shrinking,owing to the effects of natural and human factors,such as climate change and artificial reclamation.Therefore,it is essential to understand the decline in the morphological processes of salt marshes,and accordingly,the likely evolution of these marshes,in order to enable measures to be taken to mitigate this decline.To this end,this study presented an extensive systematic review of the current state of morphological models and their application to salt marshes.The emergence of process-based(PB)and data-driven(DD)models has contributed to the development of morphological models.In morphodynamic simulations in PB models,multiple physical and biological factors(e.g.,the hydrodynamics of water bodies,sediment erosion,sediment deposition,and vegetation type)have been considered.The systematic review revealed that PB models have been extended to a broader interdisciplinary field.Further,most DD models are based on remote sensing database for the prediction of morphological characteristics with latent uncertainty.Compared to DD models,PB models are more transparent but can be complex and require a lot of computational power.Therefore,to make up for the shortcomings of each model,future studies could couple PB with DD models that consider vegetation,microorganisms,and benthic animals together to simulate or predict the biogeomorphology of salt marsh systems.Nevertheless,this review found that there is a lack of unified metrics to evaluate model performance,so it is important to define clear objectives,use multiple metrics,compare multiple models,incorporate uncertainty,and involve experts in the field to provide guidance in the further study.
