## 1 Introduction  
Global climate change is accelerating sea level rise (SLR), posing a significant and growing threat to coastal ecosystems worldwide. Projections from the Intergovernmental Panel on Climate Change [1] estimate that global mean sea levels could increase by 0.29 to 1.1 m by the year 2100, depending on the trajectory of greenhouse gas emissions. Coastal forests, located at the boundary between terrestrial and marine environments, are especially susceptible to the impacts of SLR and related hydrological shifts, such as saltwater intrusion, rising groundwater tables (GWTs), and extended periods of flooding [2].  
These hydrological stressors impose multiple physiological challenges on trees. Prolonged inundation leads to hypoxic or anoxic soil conditions, impairing root respiration and nutrient uptake, while elevated salinity disrupts osmotic balance and damages cellular membranes [3,4]. These stressors collectively contribute to the formation of \"ghost forests\"--landscapes characterized by widespread tree mortality--and replacement of once-forested areas with salt-tolerant marsh vegetation [5]. Ghost forest formation reflects not only a loss of biodiversity and forest function but also a shift in the carbon balance of coastal ecosystems. As trees die and vegetation cover diminishes, formerly productive carbon sinks can become net sources of carbon dioxide to the atmosphere [6, 7, 8]. This shift raises particular concern amid ongoing climate change, given that coastal forests serve as critical reservoirs for long-term carbon sequestration [9,10].  
Despite increased recognition of ghost forest dynamics, major knowledge gaps remain. Most existing studies have focused on describing vegetation decline or estimating overall carbon losses, but few have quantitatively identified the key drivers and nonlinear thresholds that lead to ecosystem collapse. Understanding the conditions under which mortality occurs and predicting those conditions in advance requires integrated analysis of carbon fluxes, hydrological stressors, and vegetation responses. The emerging field of studying ecological tipping points emphasizes the importance of detecting early warning signals that precede rapid ecosystem transitions [11]. However, the application of such concepts to real-world forest mortality events remains limited.  
To advance understanding of how hydrological variability influences coastal forest ecosystems, this study integrates 11 years of eddy covariance flux tower data with field-based observations of tree mortality. Our objectives are to (1) assess the effects of hydrological fluctuations on net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE); (2) determine the key drivers underlying shifts in tree mortality; and (3) develop a predictive framework for early warning by identifying critical thresholds in relevant variables through statistical modeling and Receiver Operating Characteristic (ROC) analysis. Our work offers an analysis of the interactions between groundwater table (GWT), carbon balance metrics (NEE, GPP, RE), and vegetation stress indicator (LAI). The use of Generalized Additive Modeling (GAM) and ROC analyses enable the identification of key predictors and empirically derived thresholds for mortality risk. While previous studies have examined individual drivers of ghost forest formation [5,12,13], our study provides an integrated framework that links hydrological processes with carbon dynamics and mortality outcomes at multiple temporal and spatial scales. By employing this integrative approach, we aim to enhance predictive capacity for anticipating and alleviating the effects of coastal ecosystem decline in the face of ongoing sea level rise.