A wetland's ability to vertically accrete—capturing sediment and biological matter for soil accumulation—is key for maintaining elevation to counter soil subsidence and sea level rise. Wetland soil accretion is comprised of organic and inorganic components largely governed by net primary productivity and sedimentation. Sea level, land elevation, primary productivity, and sediment accretion are all changing across Louisiana's coastline, destabilizing much of its wetland ecosystems. In coastal Louisiana, analysis from 1984 to 2020 shows an estimated 1940.858 km2 of total loss at an average rate of 53.913 km2/year. Here we hypothesize that remote sensing timeseries data can provide suitable proxies for organic and inorganic accretionary components to estimate local accretion rates. The Landsat catalog offers decades of imagery applicable to tracking land extent changes across coastal Louisiana. This dataset's expansiveness allows it to be combined with the Coastwide Reference Monitoring System's point‐based accretion data. We exported normalized difference vegetation index (NDVI) and red‐band surface reflectance data for every available Landsat 4–8 scene across the coast using Google Earth Engine. Water pixels from the red‐band were transformed into estimates of total suspended solids to represent sediment deposition—the inorganic accretionary component. NDVI values over land pixels were used to estimate bioproductivity—representing accretion's organic component. We then developed a Random Forest regression model that predicts wetland accretion rates (R2 = 0.586, MAE = 0.333 cm/year). This model can inform wetland vulnerability assessments and loss predictions, and is to our knowledge the first remote sensing‐based model that directly estimates accretion rates in coastal wetlands.

Wetlands in the Mississippi River Delta are rapidly degrading. Sea level rise and low sediment supply are widely recognized as the two main factors contributing to landto-water conversion. To determine what marsh areas are more resilient, it is fundamental to identify the drivers that regulate marsh accretion and degradation. In this study, a combination of field data and aerial images is used to determine these drivers in Terrebonne Bay, Louisiana, USA. We find that accretion and degradation patterns depend on whether the marsh is located inland in a sheltered area or facing open water. In the first case, the distance to the nearby channel is important, because during flooding of the marsh platform more sediment is deposited in the proximity of channel banks. The accretion rates of marshes facing open water are high and correlate to fetch, a proxy for the ability of waves to resuspend bottom sediment. These areas are more resilient to sea level rise, but waves are also the main mechanism of degradation, as these marshes tend to degrade by edge erosion. Consequently, we propose a bimodal evolution trajectory of the marshes in Terrebonne Bay: marshes close to the bay and facing open water accrete rapidly but are affected by lateral erosion due to waves, whereas sheltered marshes accrete slowly and degrade in large swathes due to insufficient sediment supply.

Abstract. Predicting the freezing time in lakes is pursued by means of complex mechanistic models or by simplified statistical regressions considering integral quantities. Here, we propose a minimal model (SELF) built on sound physical grounds, which focuses on the pre-freezing period that, in dimictic lakes, goes from mixed conditions (lake temperature at 4 °C) to the formation of ice (0 °C at the surface). The model is based on the energy balance involving the two main processes governing the inverse stratification dynamics: cooling of water due to heat loss and wind-driven mixing of the surface layer. They play an opposite role in determining the time required for ice formation and contribute to the large inter-annual variability observed in ice phenology. More intense cooling, indeed, accelerates the rate of decrease of lake surface water temperature (LSWT), while stronger wind deepens the surface layer, increasing the heat capacity, and thus reduces the rate of decrease of LSWT. A statistical characterization of the process is obtained with a Monte Carlo simulation considering random sequences of the energy fluxes. The results, interpreted through an approximate analytical solution of the minimal model, elucidate the general tendency of the system, suggesting a power-law dependence of the pre-freezing duration on the energy fluxes. This simple, yet physically based model is characterized by a single calibration parameter, the efficiency of the wind energy transfer to the change of potential energy in the lake. Thus, SELF can be used as a prognostic tool for the phenology of lake freezing.

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