A Tool for Rapid Assessment of Hydrological Connectivity Patterns in Texas Coastal Wetlands: Linkages between Tidal Creeks and Coastal Ponds
Vol. 10 No. 1 (2019). Cover Photo: Painted bunting at Madla Park, Grey Forest, Texas. ©2018 Grace Hardy.


Aransas National Wildlife Refuge
freshwater inflows
saltwater ponds
water level
whooping crane (Grus americana)

How to Cite

Swannack, T., Wozniak, J., Grant, W. E., & Davis, S. E. (2019). A Tool for Rapid Assessment of Hydrological Connectivity Patterns in Texas Coastal Wetlands: Linkages between Tidal Creeks and Coastal Ponds. Texas Water Journal, 10(1), 46–59. https://doi.org/10.21423/twj.v10i1.7073


Coastal salt marshes are heterogeneous, spatially complex ecosystems. The degree of hydrological connectivity in these systems can be a significant driver in the flux of energy, organisms, and nutrients across the marsh landscape. In tidally driven systems, the frequency and magnitude of hydrological connection events results in the creation of a matrix of intermittently connected coastal wetland habitats, some of which may be hydrologically isolated or partially drained at any given time. Previous approaches to understanding landscape-level hydrologic connectivity patterns have required either intensive long-term monitoring or spatially explicit modeling. In this paper, we first describe a 13-month field study in the Guadalupe Estuary of the Texas Gulf Coast that linked hydrological connectivity patterns between a saltwater pond to water levels in an adjacent tidal creek and nearby San Antonio Bay. We next describe the integration of these field data with high-resolution digital elevation models and environmental parameters to develop a spatially explicit model that is a Simulation of Landscape-level Oscillations in Salt Marsh Hydroperiod (SLOSH). We evaluated the ability of SLOSH to simulate trends in landscape-level patterns of hydrological connectivity between a tidal creek and an inland marsh pond. Magnitude and periodicity of simulated and observed water-level fluctuations in the pond were similar. Highest creek water levels, resulting in high frequency and duration of hydrological connectivity with the pond, corresponded with the highest bay water levels, which occurred during September and October. Lowest creek water levels, resulting in low frequency and duration of hydrological connectivity, corresponded with the lowest bay water levels, which occurred during December through February. By simulating the pulsing structure of salt marsh hydrology, SLOSH creates the foundation on which to assess how additional drivers (precipitation, wind, freshwater inflows, etc.) can influence coastal marsh hydrology and overall ecology.

Citation: Swannack TM, Wozniak JR, Grant WE, Davis SE III. 2019. A tool for rapid assessment of hydrological connectivity patterns in Texas coastal wetlands: linkages between tidal creeks and coastal ponds. Texas Water Journal. 10(1):46-59. Available from: https://doi.org/10.21423/twj.v10i1.7073.



Ahearn DS, Viers JH, Mount JF, Dahlgren RA. 2006. Priming the productivity pump: Flood pulse driven trends in sus¬pended algal biomass distribution across a restored flood¬plain. Freshwater Biology. 51:1417-1433.

Alizad K, Hagen SC, Morris JT, Medeiros SC, Bilskie MV, Weishampel JF. 2016. Coastal wetland response to sea lev¬el rise in a fluvial estuarine system. Earth’s Future. 4(11): 483-497. doi:10.1002/2016EF000385.

Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapo¬transpiration: Guidelines for computing crop water requirements. Rome (Italy): Food and Agricultural Orga¬nization of the United Nations. FAO irrigation and drainage paper 56. Available from: http://www.fao.org/3/ x0490e/x0490e00.htm#Contents.

Allen RG, Walter IA, Elliot R, Howell T, Itenfisu D, Jensen M, editors. 2005. The ASCE standardized reference evapo¬transpiration equation. Reston (Virginia): American Soci¬ety of Civil Engineers.

Bales JD, Wagner CR, Tighe KC, Terziotti S. 2007. Lidar-de¬rived flood-inundation maps for real-time flood-mapping applications, Tar River Basin, North Carolina. Reston (Virginia): U.S. Geological Survey. U.S. Geological Survey Scientific Investigations Report 2007-5032, 42 p.

Bornette G, Amoros C, Lamouroux N. 1998. Aquatic plant diversity in riverine wetlands: the role of connectivity. Freshwater Biology. 39:267-283.

Butzler RE, Davis III SE. 2006. Growth patterns of Carolina wolfberry (Lycium carolinianum L.) in the salt marshes of Aransas National Wildlife Refuge, Texas, USA. Wetlands. 26:845-853.

Byrd KB, Windham-Myers L, Leeuw T, Downing B, Morris JT, Ferner MC. 2016. Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model. Ecosphere. 7(11):1-27 (e01582).

Chavez-Ramirez F. 1996. Food availability, foraging ecology, and energetics of whooping cranes wintering in Texas [dis¬sertation]. College Station (Texas): Texas A&M University.

Davis, SE, Allison B, Driffill M, Zhang S. 2009. Influence of vessel-induced drawdown currents on tidal creek hydrody¬namics in Aransas National Wildlife Refuge, Texas, USA. Implications on sediment dynamics. Journal of Coastal Research. 25(2):359-365.

Day JW, Crump BC, W. Kemp M, Yáñez-Arancibia A. 2012. Estuarine Ecology. 2nd edition. Hoboken (New Jersey): Wiley-Blackwell. 568 p.

Fagherazzi S, Furbish DJ. 2001. On the shape and widening of salt marsh creeks. Journal of Geophysical Research: Oceans. 106(C1):991-1003.

Fagherazzi S, Kirwan ML, Mudd SM, Guntenspergen GR, Temmerman S, D’Alpaos A, van de Koppel J, Rybczyk JM, Reyes E, Craft C, Clough JC. 2012. Numerical mod¬els of salt marsh evolution: Ecological, geomorphic, and climatic factors. Reviews of Geophysics. 50(1).

Hargreaves GL, Hargreaves GH, Riley JP. 1985. Agricultural benefits for Senegal River Basin. Journal of Irrigation and Drainage Engineering. 111:113-124.

Holmes KW, Chadwick OA, Kyriakidis PC. 2000. Error in a USGS 30-meter digital elevation model and its impact on terrain modeling. Journal of Hydrology. 233:154-173.

Hunt HE, Slack RD. 1989. Winter diets of whooping and sandhill cranes in South Texas. The Journal of Wildlife Management. 53(4):1150-1154.

Kenward T, Lettenmaier DP, Wood EF, Fielding E. 2000. Effects of digital elevation model accuracy on hydrologic predictions. remote sensing of environment. 74(3):432- 444.

Kienzle S. 2004. The effect of DEM raster resolution on first order, second order and compound terrain derivatives. Transactions in GIS. 8(1):83-111.

Leibowitz SG, Vining KC. 2003. Temporal connectivity in a prairie pothole complex. Wetlands. 23(1):13-25.

Lindsay JB. 2006. Sensitivity of channel mapping techniques to uncertainty in digital elevation data. International Jour¬nal of Geographical Information Science. 20(6):669-692.

Mariotti G, Fagherazzi S. 2010. A numerical model for the coupled long‐term evolution of salt marshes and tidal flats. Journal of Geophysical Research: Earth Surface. 115(F1).

Miller CJ, Davis SE, Roelke DL, Li HP, Driffill MJ. 2009. Fac¬tors influencing algal biomass in intermittently connected, subtropical coastal ponds. Wetlands. 29(2):759-771.

Mitsch WJ, Gosselink JG. 2007. Wetlands. 5th edition. New York (New York): John Wiley & Sons. 456 p.

Moorhead KK, Brinson MM. 1995. Response of wetlands to rising sea level in the lower coastal plain of North Carolina. Ecological Applications. 5(1):261-271.

[NCEI] National Centers for Environmental Information (formerly National Climatic Data Center). Date. Ashe¬ville (North Carolina): National Centers for Environment Information. National Oceanic and Atmospheric Admin¬istration. Available from: https://www.ncdc.noaa.gov/.

Nicholls RJ. 2004. Coastal flooding and wetland loss in the 21st century: Changes under the SRES climate and socio-economic scenarios. Global Environmental Change. 14(1):69-86.

Odum WE, Odum EP, Odum HT. 1995. Nature’s pulsing par¬adigm. Estuaries and Coasts. 18:547-555.

Omer CR, Nelson EJ, Zundel AK. 2003. Impact of varied data resolution on hydraulic modeling and floodplain delinea¬tion. Journal of the American Water Resources Associa¬tion. 39:467-475.

Park RA, Trehan MS, Mausel PW, Howe RC. 1989. Effects of sea-level rise on coastal wetlands. In: Smith JB, Tirpak DA, editors. The potential effects of global climate change on the United States: Appendix B end. Washington (D.C.): U.S. Environmental Protection Agency. EPA- 230-05-89- 052. 1-1 to 1-55.

Poulter B, Halpin PN. 2008. Raster modelling of coastal flood¬ing from sea-level rise. International Journal of Geographic Information Science. 22(2):167-182.

Prado P, Alcaraz C, Jornet L, Caiola N, Ibáñez C. 2017. Effects of enhanced hydrological connectivity on Mediterranean salt marsh fish assemblages with emphasis on the endan¬gered Spanish toothcarp (Aphanius iberus). PeerJ, 5:e3009. Available from: https://doi.org/10.7717/peerj.3009.

Pringle CM. 2001. Hydrologic connectivity and the manage¬ment of biological reserves: A global perspective. Ecologi¬cal Applications. 11(4):981-998.

Ragan AN, Wozniak JR. 2019. Linking hydrologic connectiv¬ity in salt marsh ponds to fish assemblages across a het¬erogeneous coastal habitat. Journal of Coastal Research. 35(3):545-558.

Rawls WJ, Ahuji LR, Brakensliek DL. 1992. Estimating soil hydraulic properties from soils data. In: Van Genuchten MTh, Leij F.J, Lund LJ, editors. Proceedings of the Inter¬national Workshop on Indirect Methods for Estimating the Hydraulic Properties of Unsaturated Soils. Riverside, California, 1989 October 11-13. Riverside (California): U.S. Salinity Laboratory, Agricultural Research Service, U.S. Department of Agriculture. P. 329-340.

Stehn T. 2003. Whooping cranes during the 2002-2003 win¬ter. Unpublished file report. Austwell (Texas): US Fish and Wildlife Service. Aransas National Wildlife Refuge.

Stehn T. 2004. Whooping cranes during the 2003-2004 win¬ter. Unpublished file report. Austwell (Texas): US Fish and Wildlife Service, Aransas National Wildlife Refuge.

Stehn T. 2005. Whooping cranes during the 2004-2005 win¬ter. Unpublished file report. Austwell (Texas): US Fish and Wildlife Service, Aransas National Wildlife Refuge.

[TCOON] Texas Coastal Ocean Observation Network. 1997- 2009. Corpus Christi (Texas): Conrad Blucher Institute, Texas A&M University-Corpus Christi [date accessed]. Available from: http://lighthouse.tamucc.edu/over¬view/031, Data compiled from the database.

[TNRIS] Texas Natural Resources Information System. Dates. Austin (Texas): Texas Water Development Board. [date updated; date accessed].

Thornthwaite CW. 1948. An approach toward a rational clas¬sification of climate. Geographical Review. 38(1):55-94.

[USGS] U.S. Geological Survey. Calhoun, Nueces, Willacy, and Hidalgo counties lidar. Beginning date–ending date or just one date. U.S. Geological Survey [date updated; date accessed].

Valiela I, Teal JM, Volkmann S, Shafer D, Carpenter EJ. 1978. Nutrient and particulate fluxes in a salt marsh ecosystem: Tidal exchanges and inputs by precipitation and ground¬water. Limnology and Oceanography. 23(4):798-812.

Ward JV, Tockner K, Schiemer F. 1999. Biodiversity of flood¬plain river ecosystems: ecotones and connectivity. Regulat¬ed Rivers: Research & Management. 15:125-139.

Wilcox BP, Dean DD, Jacob JS, Sipocz A. 2011. Evidence of surface connectivity for Texas coast depressional wetlands. Wetlands. 31(3):451-458.

Wozniak JR, Swannack TM, Butzler R, Llewellyn C, Davis III SE. 2012. River inflow, estuarine salinity, and Caro¬lina wolfberry fruit abundance: Linking abiotic drivers to whooping crane food. Journal of Coastal Conservation. 16(3):345-354.

Zedler JB, Winfield T, Williams P. 1980. Salt marsh produc¬tivity with natural and altered tidal circulation. Oecologia. 44(2):236-240.

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Copyright (c) 2019 Todd Swannack, Jeffery Wozniak, William E Grant, Stephen E Davis