Revised global estimates of resilience to sea level rise for tidal marshes

Abstract

Earth system models are widely used to estimate future changes in wetland extent but do not incorporate surface elevation change (SEC) into predicting wetland's real responses to sea level rise (SLR). A machine learning model (MLM) was used to investigate the impact of multiple drivers on SEC and sediment accretion rate (SAR) in tidal marshes, and an earth system model (i.e. integrated climate and wetland migration model) was developed to predict the response of tidal marshes to SLR. The earth system model incorporates factors influencing SEC found by the MLM. Firstly, global data on SAR and SEC for tidal marshes was synthesised and the MLM was used to examine the drivers for SEC and SAR, including tidal range and frequencies, sediment loadings, precipitation, elevation, latitude, sea ice and/or relative SLR (RSLR). Human disturbance resulted in less sediment accretion and existing conservation activities were inefficient in promoting sediment accretion. Secondly, an integrated climate and wetland migration model was developed to assess the resilience of global tidal marshes responding to future SLR in Matlab by incorporating SEC, RSLR, climatic zones, tidal inundation, elevation and latitude into the model. The model was implemented under representative concentration pathways (RCPs) 2.6, 4.5 and 8.5, as well as nature-based human adaptation scenarios. Under the RCPs and nature-based human adaptation scenarios, tidal marshes will gain 53%-58% of the current global area by 2100 if sufficient sediment loadings and accommodation space allow landward migration. If current accommodation space is maintained, net global areal losses of 23%-30% are possible. Hotspots of future marsh loss are largely in North America, Australia and China. Projections for most SLR scenarios see marsh area peaking in the mid rather than late 21st century. Ecogeomorphic feedbacks affect rates of sediment accumulation but cannot be incorporated into the earth system model. The importance of nature-based adaptation was highlighted in enhancing the resilience of tidal marshes to future SLR.