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Hierarchical sensitivity analysis for simulating barrier island geomorphologic responses to future storms and sea-level rise

Abstract

This paper presents a new application of an advanced hierarchical sensitivity analysis of a new climate model of barrier island geomorphological evolution. The implemented sensitivity analysis in this study integrates a hierarchical uncertainty framework with a variance-based global sensitivity analysis to decompose the different model input uncertainties. The analysis can provide quantitative and accurate measurements for the relative importance of uncertain model input factors while considering their dependence relationships. The climate model used in this research was the barrier island profile (BIP) model, which is a new computer code developed to simulate barrier island morphological evolution over periods ranging from years to decades under the impacts of accelerated future sea-level rise and long-term changes in the storm climate. In the application of the model, the BIP model was used to evaluate the responses of a series of barrier island cross-sections derived for Santa Rosa Island, Florida, to random storm events and five potential accelerated rates of sea-level rise projected over the next century. The uncertain model input factors thus include the scenario uncertainty caused by alternative future sea-level rise scenarios and the parametric uncertainties of random storm parameters and dune characteristics. The study results reveal that the occurrence of storms is the most important factor for the evolution of sand dunes on the barrier island and the impact of sea-level rise is essential to the morphological change of the island backshore environment. The analysis can provide helpful insights for coastal management and planning. This hierarchical sensitivity analysis is mathematically general and rigorous and can be applied to a wide range of climate models.

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References

  • Bauer BO, Davidson-Arnott RGD (2002) A general framework for modeling sediment supply to coastal dunes including wind angle, beach geometry, and fetch effects. Geomorphology 49:89–108

    Article  Google Scholar 

  • CaMacho RA, Martin JL (2013) Bayesian Monte Carlo for evaluation of uncertainty in hydrodynamic models of coastal systems. J Coast Res No. 65, pp. 886–891.

  • Carruthers EA, Lane DP, Evans RL, Donnelly JP, Ashton AD (2013) Quantifying overwash flux in barrier systems: an example from Martha’s Vineyard, Massachusetts, USA. Mar Geol 343:15–28

    Article  Google Scholar 

  • Chu-Agor ML, Muñoz-Carpena R, Kiker GA, Emanuelsson A, Linkov I (2011) Exploring sea-level rise vulnerability of coastal habitats using global sensitivity and uncertainty analysis. Environ Model Softw 26:593–604

    Article  Google Scholar 

  • Claudino-Sales V, Wang P, Horwitz MH (2008) Effects of hurricane ivan on coastal dunes of Santa Rosa Barrier Island, Florida: characterized on the basis of pre- and poststorm LIDAR surveys. J Coast Res 26(3):470–484

  • Claudino-sales V, Wang P, Horwitz MH (2010) Effect of Hurricane Ivan on coastal dunes of Santa Rosa Barrier Island, Florida: characterized on the basis of pre- and poststorm LIDAR surveys. J Coast Res 26(3)470–484

  • Cox P, Stephenson D (2007) A changing climate for prediction. Science 317:207–208. https://doi.org/10.1126/science.1145956.

    Article  Google Scholar 

  • Dai H (2014) Uncertainty quantification of groundwater reactive transport and coastal morphological modeling, Florida State University.

  • Dai H, Ye M (2015) Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation. J Hydrol 528:286–300

    Article  Google Scholar 

  • Dai H, Ye M, Niedoroda AW (2015) A model for simulating Barrier Island geomorphologic responses to future storm and sea-level rise impacts. J Coast Res 528:286–300

    Google Scholar 

  • Dai H, Chen X, Ye M, Song X, Zachara JM (2017) A geostatistics informed hierarchical sensitivity analysis method for complex groundwater flow and transport modeling. Water Resour Res 53:4327–4343. https://doi.org/10.1002/2016WR019756

    Article  Google Scholar 

  • Draper D, Pereira A, Prado P, Saltelli A, Cheal R, Eguilior S, Mendes B, Tarantola S (1999) Scenario and parametric uncertainty in GESAMAC: a methodological study in nuclear waste disposal risk assessment. Comput Phys Commun 117:142–155

    Article  Google Scholar 

  • Duran O, Moore LJ (2013) Vegetation controls on the maximum size of coastal dunes. Proc Natl Acad Sci 110:17217–17222

    Article  Google Scholar 

  • FEMA Staff (2002) Flood Insurance Study, Okaloosa County, Florida and unincorporated areas. FEMA, Flood Insurance Study, number 12091CV000A

  • Grinsted A, Moore JC, Jevrejeva S (2010) Reconstructing sea level from paleo and projected temperatures 200 to 2100 AD. Clim Dyn 34:461–472

    Article  Google Scholar 

  • Habib E, Reed D (2013) Parametric uncertainty analysis of predictive models in Louisiana’s 2012 coastal master plan. J Coast Res 67:127–146

    Article  Google Scholar 

  • Hawkins E, Sutton R (2009a) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90:1095–1107

    Article  Google Scholar 

  • Hawkins E, Sutton R (2009b) The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn 37:407–418

    Article  Google Scholar 

  • Hawkins E, Sutton R (2011) Clim Dyn 37:407. https://doi.org/10.1007/s00382-010-0810-6

  • Houser C (2012) Feedback between ridge and swale bathymetry and barrier island storm response and transgression. Geomorphology 173-174:1–16

    Article  Google Scholar 

  • Houser C, Hamilton S (2009) Sensitivity of post-hurricane beach and dune recovery to event frequency. Earth Surf Process Landf 34:613–628

    Article  Google Scholar 

  • Houser C, Hapke C, Hamilton S (2008a) Controls on coastal dune morphology, shoreline erosion and barrier island response to extreme storms. Geomorphology 100:223–240

    Article  Google Scholar 

  • Houser C, Hobbs C, Saari B (2008b) Posthurricane airflow and sediment transport over a recovering dune. J Coast Res 24(4):944–953

    Article  Google Scholar 

  • IPCC (2007) Climate Change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p 996

    Google Scholar 

  • Jansen MJM (1999) Analysis of variance designs for model output. Comput Phys Commun 117:35–43

    Article  Google Scholar 

  • Jelesnianski CP, Chen J, Shaffer WA (1992) SLOSH: sea, lake and overland surges from hurricanes. NOAA, Washington, NOAA Technical Report NWS 48

  • Jevrejeva S, Moore JC, Grinsted A (2010) How will sea level respond to changes in natural and anthropogenic forcings by 2100? Geophys Res Lett 37:L07703. https://doi.org/10.1029/2010GL042947

    Article  Google Scholar 

  • Jiménez JA, Arcilla AS (2004) A long-term (decadal scale) evolution model for microtidal barrier systems. Coast Eng 51:749–764

    Article  Google Scholar 

  • Kettle NP (2012) Exposing compounding uncertainties in sea level rise assessments. J Coast Res 28:161–173

    Article  Google Scholar 

  • Kirwan ML, Guntenspergen GR, D’Alpaos A, Morris JT, Mudd SM, Temmerman ST (2010) Limits on the adaptability of coastal marshes to rising sea level. Geophys Res Lett 37:L23401. https://doi.org/10.1029/2010GL045489

    Article  Google Scholar 

  • Kish SA, Donoghue JF (2013) Coastal response to storms and sea-level rise: Santa Rosa Island, Northwest Florida, U.S.A. J Coast Res Spec Issue NO 63, 131–140.

  • Larson M, Erikson L, Hanson H (2004) An analytical model to predict dune erosion due to wave impact. Coast Eng 51:675–696

    Article  Google Scholar 

  • Larson M, Kraus NC, Connell KJ (2006) Modeling sediment storage and transfer for simulation regional coastal evolution. Proceedings 30th Coastal Engineering Conference, ASCE, pp.1–13.

  • Lu D, Ye M, Meyer PD, Curtis GP, Shi X, Niu X, Yabusaki SB (2013) Effects of error covariance structure on estimation of model averaging weights and predictive performance. Water Resour Res 49:6029–6047. https://doi.org/10.1002/wrcr.20441

    Article  Google Scholar 

  • McNamara DE, Werner BT (2008) Coupled barrier island-resort model, 1: emergent instabilities induced by strong human-landscape interactions. J Geophys Res 113:F01016. https://doi.org/10.1029/2007JF000840

    Google Scholar 

  • Meyer PD, Ye M, Rockhold ML, Neuman SP, Cantrell KJ (2007) Combined estimation of hydrogeologic conceptual model, parameter, and scenario uncertainty with application to uranium transport at the Hanford site 300 area. NUREG/CR-6940 (PNNL-16396), U.S. Nuclear Regulatory Commission, Washington, D. C.

  • Meyer PD, Ye M, Nicholson T, Neuman SP, Rockhold M (2014) Incorporating Scenario Uncertainty Within a Hydrogeologic Uncertainty Assessment Methodology, in Proceedings of the International Workshop on Model Uncertainty: Conceptual and Practical Issues in the Context of Risk-Informed Decision Making, edited by Mosleh, Ali and Jeffery Wood, International Workshop Series on Advanced Topics in Reliability and Risk Analysis, Center for Risk and Reliability, University of Maryland, College Park, MD, U.S.A., 2014, pp. 99–119. (ISSN: 1084–5658)

  • Michener WK, Blood ER, Bildstein KL, Brinson MM, Gardner LR (1997) Climate change, hurricanes and tropical storms, and rising sea level in coastal wetlands. Ecol Appl 7(3):770–801

    Article  Google Scholar 

  • Morton RA (2002) Factors controlling storm impacts on coastal barriers and beaches-a preliminary basis for near real-time forecasting. J Coast Res 18:486–501

    Google Scholar 

  • Morton RA, Paine JG, Gibeaut JC (1994) Stages and durations of post-storm beach recovery, southeastern Texas coast. J Coast Res 10(4):884–908

    Google Scholar 

  • National Research Council (1985) Glaciers, Ice Sheets, and Sea Level: Effect of a CO2-Induced Climatic Change. Washington, DC: The National Academies Press. https://doi.org/10.17226/19278

  • Niedoroda AW, Dai H, Ye M, Saha B, Kish S, Donoghue JF (2011) Barrier island responses to potential future rates of sea-level rise, Coastal Sediments 2011 Conference Proceedings, https://doi.org/10.1142/9789814355537_0016

  • Plant NG, Stockdon HF (2012) Probabilistic prediction of barrier-island response to hurricanes. J Geophys Res 117:F03015. https://doi.org/10.1029/2011JF002326.

    Google Scholar 

  • Priestas AM, Fagherazzi S (2010) Morphological barrier island changes and recovery of dunes after hurricane Dennis, St. George Island, Florida. Geomorphology 114:614–626

    Article  Google Scholar 

  • Roelvink D, Reniers AD, van Dongeren AP, van Thiel de Vries J, McCall R, Lescinski J (2009) Modelling storm impacts on beaches, dunes and barrier islands. Coast Eng 56:1133–1152

    Article  Google Scholar 

  • Sallenger AH (2000) Storm impact scale for barrier islands. J Coast Res 16(3):890–895

    Google Scholar 

  • Saltelli A (2000) What is sensitivity analysis? In: Saltelli A, Chan K, Scott M (eds) Sensitivity analysis. Wiley, Chichester, pp 3–14

    Google Scholar 

  • Saltelli A, Tarantola S, Chan K (1998) Presenting results from model based studies to decision makers: can sensitivity analysis be a defogging agent? Risk Anal 18:799–803

    Article  Google Scholar 

  • Saltelli A, Tarantola S, Chan KP-S (1999) A quantitative model independent method for global sensitivity analysis of model output. Technometrics 41:39–56

    Article  Google Scholar 

  • Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys Commun 181(2):259–270

    Article  Google Scholar 

  • Sobol’ IM (1993) Sensitivity analysis for nonlinear mathematical models. Math Models Comput Simul 1(4):407–414

  • Stone GW, Liu B, Pepper DA, Wang P (2004) The importance of extratropical and tropical cyclones on the short-term evolution of barrier islands along the northern Gulf of Mexico, USA. Mar Geol 210:63–78

    Article  Google Scholar 

  • Taylor Engineers (2007) Okaloosa Island Beach Management Feasibility Study, Okaloosa County, Florida. Unpublished consulting report, 109 p.

  • Timmons EA, Rodriguez AB, Mattheus CR, DeWitt R (2010) Transition of a regressive to a transgressive barrier island due to back-barrier erosion, increased storminess, and low sediment supply: Bogue Banks, North Carolina, USA. Mar Geol 278:100–114

    Article  Google Scholar 

  • U.S. Army Corps of Engineers (USACE) (1998) Coastal Engineering Manual. Washington, D.C.: U.S. Government Printing Office

  • Vermeer M, Rahmstorf S (2009) Global sea level linked to global temperature. Proc Natl Acad Sci 106(51):21527–21532. https://doi.org/10.1073/pnas.0907765106

    Article  Google Scholar 

  • Winter CL, Nychka D (2010) Forecasting skill of model averaging. Stoch Env Res Risk A 24:633–638

    Article  Google Scholar 

  • Wohling T, Vrugt JA (2008) Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models. Water Resour Res 44:W12432. https://doi.org/10.1029/2008WR007154.

    Article  Google Scholar 

  • Ye M, Neuman SP, Meyer PD (2004) Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff. Water Resour Res 40:W05113. https://doi.org/10.1029/2003WR002557.

    Google Scholar 

  • Ye M, Neuman SP, Meyer PD, Pohlmann KF (2005) Sensitivity analysis and assessment of prior model probabilities in MLBMA with application to unsaturated fractured tuff. Water Resour Res 41:W12429. https://doi.org/10.1029/2005WR004260.

    Google Scholar 

  • Ye M, Meyer PD, Neuman SP (2008a) On model selection criteria in multimodel analysis. Water Resour Res 44:W03428. https://doi.org/10.1029/2008WR006803.

    Google Scholar 

  • Ye M, Pohlmann KF, Chapman JB (2008b) Expert elicitation of recharge model probabilities for the Death Valley regional flow system. J Hydrol 354:102–115

    Article  Google Scholar 

  • Ye M, Pohlmann KF, Chapman JB, Pohll GM, Reeves DM (2010a) A model-averaging method for assessing groundwater conceptual model uncertainty. Ground Water 48:716–728. https://doi.org/10.1111/j.1745-6584.2009.00633.x

    Article  Google Scholar 

  • Ye M, Lu D, Neuman SP, Meyer PD (2010b) Comment on “Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window” by Frank T.-C. Tsai and Xiaobao Li. Water Resour Res 46:W02801. https://doi.org/10.1029/2009WR008501.

    Article  Google Scholar 

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Acknowledgments

This research is supported in part by the DoD Strategic Environmental Research and Development Program through contract number SERDP RC-1700 and by the DOE Early Career Award, DE-SC0008272, which was awarded to the corresponding author. We would like to thank Dr. Stewart Farrell for providing the dune data.

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Correspondence to Heng Dai.

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Dai, H., Ye, M., Hu, B.X. et al. Hierarchical sensitivity analysis for simulating barrier island geomorphologic responses to future storms and sea-level rise. Theor Appl Climatol 136, 1495–1511 (2019). https://doi.org/10.1007/s00704-018-2700-5

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