Coastal wetlands are critical in global carbon sequestration, but their biogeochemical cycling is highly sensitive to sediment mixing. Here, we quantified the impacts of storm-induced mixing on organic carbon (OC) storage across China's coastal wetlands using multiple radionuclides and machine learning (SHAP model). Field observations and SHAP results identified the storm surge as a key driver of sediment mixing, whose impact weakens with increasing offshore distance and vegetation cover. The vegetated, supratidal site in the Yellow River estuary wetland was less affected by the storm surge with a sediment mixing depth of only 18 cm. Sediment mixing was observed at 80% of stations from the literature in China's coastal wetlands, mainly driven by river discharge and waves. Furthermore, a negative correlation between sediment mixing depth and soil organic carbon (SOC) density (r = −0.42) in the top meter was found. For regions with SOC density > 5 kg m⁻², the depths of sediment mixing are lower than 40 cm. Under the medium-forcing scenario SSP2-4.5 and the high-forcing scenario SSP5-8.5, the average sediment mixing depth in China's coastal wetlands is expected to increase by 19% and 28% by 2100, leading to corresponding declines in SOC density of 2.5% and 3.5%. The findings show that intensified sediment mixing caused by climate warming will weaken the carbon storage capacity of coastal wetlands, providing a crucial scientific basis for the sustainable management of coastal wetlands and the formulation of carbon sink enhancement strategies.

The risk of infection by enteric pathogens in bathing waters is generally monitored by using fecal indicator bacteria (FIB). Mechanistic models are efficient tools to predict FIB concentrations in bathing waters, both in near-future forecasting and in long-term climate change projections. However, most existing mechanistic FIB models are limited by the availability of observations for validation and incorporation of all relevant physical, biological, and chemical (physico-biochemical) processes. Therefore, the quantitative influence of different physio-biochemical processes and impact factors is missing. To enhance the understanding of FIB fate in different aquatic systems, we developed a comprehensive yet generically applicable physico-biochemical model, focused on Escherichia coli (E. coli). It includes a die-off module and a sediment interaction module. Separate validation of the two sub-modules demonstrated the reliability of our modeling approach. The die-off module shows a higher R² value (0.88) and lower RMSE value (1.1 day^-1) than the existing models (0.48–0.79, and 1.8–7.2 day ^-1). This demonstrated an improvement by adding Ultraviolet-A and Ultraviolet-B (UVB) inactivation and UV spectrum extinction due to colored dissolved organic matter (CDOM) absorption. According to our sediment module validation, considering the impact of sediment composition on E. coli attachment can improve the allocation of E. coli between waters and sediments. Sensitivity analysis showed that 1) photo-inactivation is important in low CDOM waters, but not in high CDOM waters, where the UV penetration is limited; 2) the impact of sediment interaction can extend the duration of a peak event in high turbid waters. This work demonstrated the dominant impact factors in different aquatic systems for E. coli prediction. The new generic model enables better simulation of bathing water quality across different types of aquatic environments, which can be a useful tool to inform management at bathing sites. Future applications can choose processes selectively from the new FIB physico-biochemical model and couple it with appropriate hydrological/hydrodynamic models to address specific environmental conditions and research purposes.