Identifying driving hydrogeomorphic factors of coastal wetland downgrading using random forest classification models

Keqi He; Wenhong Li; Yu Zhang; Ge Sun; Steve G McNulty; Neal E Flanagan; Curtis J Richardson

doi:10.1016/j.scitotenv.2023.164995

Identifying driving hydrogeomorphic factors of coastal wetland downgrading using random forest classification models

Sci Total Environ. 2023 Oct 10:894:164995. doi: 10.1016/j.scitotenv.2023.164995. Epub 2023 Jun 19.

Authors

Keqi He¹, Wenhong Li², Yu Zhang³, Ge Sun⁴, Steve G McNulty⁴, Neal E Flanagan⁵, Curtis J Richardson⁵

Affiliations

¹ Earth and Climate Sciences, Nicholas School of the Environment, Duke University, Durham, NC 27708, USA.
² Earth and Climate Sciences, Nicholas School of the Environment, Duke University, Durham, NC 27708, USA. Electronic address: wenhong.li@duke.edu.
³ Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
⁴ Eastern Forest Environmental Threat Assessment Center, USDA Forest Service Southern Research Station, USDA Forest Service, Research Triangle Park, Raleigh, NC 27709, USA.
⁵ Duke University Wetland Center, Nicholas School of the Environment, Duke University, Durham, NC 27708, USA.

PMID: 37343878
DOI: 10.1016/j.scitotenv.2023.164995

Abstract

Coastal wetlands provide critical ecosystem services but are experiencing disruptions caused by inundation and saltwater intrusion under intensified climate change, sea-level rise, and anthropogenic activities. Recent studies have shown that these disturbances downgraded coastal wetlands mainly through affecting their hydrological processes. However, research on what is the most critical driver for wetland downgrading and how it affects coastal wetlands is still in its infancy. This study examined drivers of three types of wetland downgrading, including woody wetland loss, emergent herbaceous wetland loss, and woody wetlands converting to emergent herbaceous wetlands. By using random forest classification models for the wetland ecosystems in the Alligator River National Wildlife Refuge, North Carolina, USA, during 1995-2019, we determined the relative importance of different hydrogeomorphic processes and the dominant variables in driving the wetland downgrading. Results showed that random forest classification models were accurate (> 97 % overall accuracy) in classifying wetland downgrading. Multiple hydrogeomorphic variables collectively contributed to the coastal wetland downgrading. However, the dominant control factors varied across different types of wetland downgrading. Woody wetlands were most susceptible to saltwater intrusion and were likely to downgrade if the saltwater table was shallower than 0.2 m below the land surface. In contrast, emergent herbaceous wetlands were most vulnerable to inundation and drought. The favorable groundwater table for emergent herbaceous wetlands was between 0.34 m above the land surface and 0.32 m below the land surface, beyond which the emergent herbaceous wetland tended to disappear. For downgraded woody wetlands, their distance to canals/ditches played a crucial role in determining their fates after downgrading. The machine learning approach employed in this study provided critical knowledge about the thresholds of hydrogeomorphic variables for the downgrading of different types of coastal wetlands. Such information can help guide effective and targeted coastal wetland conservation, management, and restoration measures.

Keywords: Anthropogenic activities; Climate change; Coastal wetland downgrading; Hydrogeomorphic factors; Machine learning models; Sea-level rise.