Model configuration, parameterization, and evaluation
ELM simulations were conducted for a temperate low marsh at the Plum Island Ecosystems Long Term Ecological Research site (PIE-LTER). The PIE-LTER low marsh is located in the Plum Island Sound in northern Massachusetts (42.7345, -70.8382) and has a humid continental climate. The site has a tidal range of 2.5-3 m and the low marsh elevation is approximately 1 m. Spartina alterniflora is the dominant vegetation. The site is instrumented during the growing season with an eddy covariance tower, water level sensors, and a conductivity sensor.
Hydrological parameters for ELM (marsh level relative to tidal channel, mean water level height) were parameterized for the low marsh using observed water level data from PIE-LTER (Giblin 2019, Giblin 2022). Salinity optimum and tolerance (µ and ʎ ) were parameterized by starting with literature values for optimal salinity for S. alterniflora (Vasquez et al. 2006, Maricle and Lee, 2006) and tolerance values used to model other brackish and salt marsh species with a Gaussian function (Li et al. 2021). Parameters were then adjusted until modeled gross primary production daily values (GPP) showed good agreement with measured GPP at the low marsh eddy covariance tower (Giblin and Forbrich 2022a, b, c), evaluated using root mean square error (RMSE) values. The plant functional type was set as a C4 grass and we used PFT parameters (including leaf traits, photosynthetic capacity, and growth allocation) optimized in a previous modeling study forS. patens in Chesapeake Bay (O’Meara et al., 2021).
We examined how observed GPP rates correlated with observed water level and salinity concentrations. To control for phenological patterns in GPP rates, we first obtained a growing season curve by averaging the noon GPP measured in 2018, 2019, and 2020 on each day followed by calculating a 60-day rolling average to smooth the three-year average. This growing season curve was then removed from the noon measurement of each year’s dataset (i.e. 2018 noon GPP – seasonal average noon GPP). Pearson’s correlation was used to determine the correlation of detrended GPP with water level and salinity.
We drove the model with meteorological data collected at PIE-LTER (Giblin 2019, Giblin 2020, Giblin 2021), salinity concentrations measured at xx (Giblin and Forbrich 2022a, b, c) and estimated water level using tide constituents available from NOAA Tides and Currents (https://tidesandcurrents.noaa.gov/). Data was gapfilled using linear interpolation. We assumed one salinity value for the entire soil column because ELM does not currently represent salt transport through soil layers. Model simulations were spun up using 100 years of accelerated decomposition followed by 100 years of regular spinup (Koven et al. 2013, Thornton et al. 2005). In default simulations, plant responses to salinity and inundation were inactive, representing the previous model vegetation function under site-specific meteorological, hydrological, and salinity conditions. The second set of simulations used the same meteorological, salinity, and water level forcing while including vegetation response functions to salinity and flooding. We evaluated model performance by comparing GPP from the default model simulations, improved model simulations, and measured GPP from the field site. A third set of scenarios were conducted for systematic changes in salinity by adding or subtracting a constant value (-5, +5, +10 ppt) from the 2018 salinity measurements used to force the improved model. Finally, the improved model was used to conduct scenarios for systematic changes in water level by changing the mean tide water level parameter (-10, +10, +20, +50 cm).