Discussion
We modeled the effect of salinity and flooding on carbon uptake mechanistically by incorporating the relationship between salinity, flooding, and root water uptake. These modeling updates improved representation of GPP and were validated by GPP measurements taken at Plum Island Estuary. At hourly to annual time scales, modeled GPP aligned well with field observations. Incorporating salinity-induced inhibition of root water uptake brought simulations from 3x too high to near the values observed in the field. Incorporating flood-induced inhibition of carbon uptake decreased the estimated GPP by a smaller proportion compared to the salinity function, but it more accurately simulated variations over tidal cycles.
Salt marshes are well known for their strict zonation by elevation; small deviations from salinity concentration and hydroperiod result in community changes or loss of marsh habitat in lower elevations. S. alterniflora is a dominant species in salt marshes along North America’s temperate east coast, including at the Plum Island Estuary site included in this study. Although S. alterniflora grows better at lower salinities, other plants compete with it in fresh and brackish conditions; S. alterniflora dominates salt marshes because of its tolerance to salinity concentrations and hydroperiods that kill most other species (Crain et al., 2004; Hwang and Morris, 1994). We set the optimal salinity parameter at zero to represent maximum growth in freshwater and tried several tolerance parameters to determine the value that closely simulated GPP observed in the field. Although our tolerance parameterization resulted in reasonable simulations of GPP, our model is too sensitive to salinity. Annual average salinity fluctuates by 2-3 ppt due to variation in evaporation and precipitation. Site measurements of salinity from 2020 were an average of 3 ppt higher than salinity from 2018 and measured GPP decreased by 6%, whereas the model scenarios showed an average of 5 ppt higher salinity resulted in a 40% decrease in GPP. We think the application of the Gaussian function is appropriate for mechanistic representation of salinity’s limitation of carbon uptake, but the model currently lacks representation of multiple plant adaptations to salt exposure. This salinity function targets osmotic inhibition of root water uptake, but does not yet account for ways vegetation can mitigate salt exposure after uptake (Munns and Tester, 2008). plants can also take up salt and excrete it or store it in vacuoles until chronic exposure exceeds a toxicity threshold (Munns and Tester, 2008; Naidoo et al., 1992; Vasquez et al., 2006). For example, S. alterniflorauses solutes to adjust osmotic pressure in aboveground tissues and uses salt glands for excretion (Bradley and Morris, 1991; Naidoo et al., 1992; Vasquez et al., 2006). However, we have improved model simulations considerably from the default, and this approach has been effective in modeling biomass for other marsh species.
This model does not yet account for several important coastal wetland processes, including sediment trapping, stimulated growth, and increasing vertical accretion with moderate sea level rise. These processes are critical for understanding carbon cycling and marsh stability on decadal scales (Kirwan et al., 2010; Morris et al., 2002). Because this is the first attempt to incorporate salt marsh-specific controls on GPP in ELM, we kept our representation of flooding simple: the proportion of aboveground tissue underwater did not contribute to photosynthesis in each time step. However, this approach may be too simplistic to provide meaningful model simulations, since inundation stimulation of S. alterniflora productivity is a major control on carbon dynamics in salt marshes (Kirwan et al., 2016; Morris et al., 2013). Currently, ELM estimates plant height of grasses proportionally to LAI, which may be appropriate for approximating the impact of salinity stress on plants, but not flooding. We used Li et al.’s Gaussian approach to model vegetation response to salinity (2021), but they also used this Gaussian approach for vegetation response to flooding. In this approach, plants have an optimal flooding depth and a flooding tolerance parameter. If we adopt this approach, we will need to parameterize the salt marsh grass plant functional types for these additional flood parameters, but this information is available for S. alterniflora(Morris et al., 2013). In either approach, flooding above the marsh surface is addressed, but the impacts of soil waterlogging are not. Addressing soil waterlogging will likely require modeling available oxygen and other more detailed soil biogeochemistry; this work is progressing (Sulman et al. in prep?, O’Meara et al. 2021?).
Our model developments focused on improving representation of GPP in salt marshes, but to fully represent coastal wetland carbon cycling, ELM should be able to model vegetation spanning the gradient from saline to freshwater wetlands. Our approach is flexible to model response curves of vegetation adapted to fresh or brackish conditions; even upland vegetation could be given a low salinity tolerance to model the carbon dynamics of saltwater intrusion into upland forests. However, the default model parameterization predicted GPP values 3x the measured values without the salinity response. Although individual plants may be much more productive when grown in fresh or brackish water, typically freshwater tidal wetlands are not 3x more productive than salt marshes (need to cite here). The default model therefore predicts unreasonably high values for freshwater wetland productivity, likely because the model was originally designed for upland systems and assumes that vegetation productivity is primarily limited by dry soil conditions. Thus, existing parameterization of graminoid plants likely overestimates productivity under non-water-limited conditions, so that when ELM is used to model a wetland, high soil moisture allows plants unrealistically high photosynthesis rates. As mentioned in the previous paragraph, another problem is that ELM doesn’t address anoxic soil conditions, which decrease root metabolism or incur metabolic costs for flood tolerance traits (Colmer and Voesenek, 2009; Naidoo et al., 1992). Improvement of vegetation in freshwater saturated environments will be required before the model can be used to investigate carbon dynamics along a salinity gradient.
A strength of implementing coastal wetlands into ELM and our flexible approach to salinity is the potential to scale this model from point simulations to global scales. In this study, we used salinity measurements to force model simulations, combined with water levels modeled using NOAA tide pattern models. Expanding this model framework to broader scales will require estuary-scale measurements or models of coupled hydrology and salinity. These could be derived from estuary-scale measurement networks or estuary models. Simulating coastal wetland vegetation at continental to global scales within a fully coupled earth system model will require tidal patterns and salinity to be supplied by ocean or hydrological models. Current ocean models operate at resolutions too coarse to directly simulate coastal wetland tide heights and estuary salinities, but ongoing developments in high-resolution coastal models are making progress toward coupled coastal process capabilities.