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Deep Sea Res
Dai, H., Ye, M., & Niedoroda, A. W. (2015). A Model for Simulating Barrier Island Geomorphologic Responses to Future Storm and Sea-Level Rise Impacts.
Journal of Coastal Research
This paper presents the Barrier Island Profile (BIP) model, a new computer code developed to simulate barrier island morphological evolution over periods ranging between years and decades under the impacts of accelerated sea-level rise and long-term changes in the storm climate. The BIP model is a multiline model that represents the time-averaged dynamics of major barrier island features from front beach to backshore. Unique contributions of BIP to coastal modeling include a dynamic linking of interacting barrier island features and consideration of both future sea-level rise and storm climate impacts. The BIP model has the built-in capability of conducting Monte Carlo (MC) simulations to quantify predictive uncertainty caused by uncertainty in sea-level rise scenarios and storm parameters. For a series of barrier island cross-sections derived from the characteristics of Santa Rosa Island, Florida, BIP was used to evaluate their responses to random storm events and five potential accelerated rates of sea-level rise projected over a century. The MC simulations using BIP provide multiple realizations of possible barrier island morphologic responses and their statistics, such as mean and variance. The modeling results demonstrate that BIP is capable of simulating realistic patterns of barrier island profile evolution over the span of a century using relatively simple representations of time-and space-averaged processes with consideration of uncertainty of future climate impacts.
Beach sand dunes
Santa Rosa Island
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Lewis, D. B. (2016). Response of wetland soil carbon to groundwater conservation: Probabilistic outcomes from error propagation.
Water loss compromises functions performed by wetland ecosystems. Efforts to rehabilitate wetland function typically begin with attempts to reestablish hydrology. These activities are often not monitored, so tools to extract information from them could partly offset the lost opportunity to learn from whole-ecosystem hydrological manipulation. In 2002, groundwater abstraction was lessened by 35% throughout 1700 km(2) of west-central Florida (USA). I assembled a pathway of correlations to project how this hydrological manipulation affected water levels and soil carbon (C) storage in overlying wetlands. Parameter values and residual error in these statistical models were resampled from known variances, thereby propagating uncertainty through the pathway of relationships, and expressing the response of soil C probabilistically. Projected soil C probability distributions were most distinguishable between full and moderate (30% less) abstraction. With more severe abstraction cutbacks, gains in projected soil C became more marginal and uncertain, suggesting that wetland soil C pools are not notably impacted by low-volume groundwater abstraction. Reducing uncertainty in projected soil C will require better understanding the dynamic response of soil C to increases in the amount of time that wetland soil is inundated. The step-by-step error propagation routine presented here is a platform for assimilating information from diverse sources in order to project probabilistic responses of ecosystem function to wetland restoration attempts, and it helps identify where further certainty is most wanted in a pathway of cause-effect relationships.
Soil organic matter
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