, Volume 34, Issue 6, pp 1123–1132 | Cite as

Generating Nested Wetland Catchments with Readily-Available Digital Elevation Data May Improve Evaluations of Land-Use Change on Wetlands

Original Research


The important ecosystem functions wetlands perform are influenced by land-use changes in their surrounding uplands and thus, identifying the upland area that flows into a wetland is important. We provide a method to define wetland catchments as the portion of the landscape that flows into a wetland; we allowed catchments to be nested and include other wetlands and their catchments, forming a hydrologic wetland complex. We generated catchments using multiple sources and resolutions of digital elevation data to evaluate whether catchment sizes generated from those data were similar. While non-contributing areas, or sinks, differed between elevation data sets, catchment areas were similar among high-resolution LiDAR- and IfSAR-derived data and readily available lower resolution data from the National Elevation Dataset. Accordingly, the higher-resolution DEM data, which may be expensive or not available, will not likely yield more accurate wetland catchment boundaries in flat or glaciated landscapes. We contend that this method to generate wetland catchments can be used to improve wetland studies where the location of a wetland within a catchment is important. Furthermore, the size of the catchment is important for understanding how wetlands respond to climate, land-use practices, and contamination.


Watershed delineation Hydrological model DEM ArcHydro LiDAR IfSAR National elevation dataset 

Supplementary material

13157_2014_571_MOESM1_ESM.pdf (899 kb)
Online Resource 1Flow chart from Model Builder for the non-contributing areas analysis (PDF 899 kb)
13157_2014_571_MOESM2_ESM.pdf (96 kb)
Online Resource 2Python script for the non-contributing areas analysis (PDF 96 kb)
13157_2014_571_MOESM3_ESM.pdf (147 kb)
Online Resource 3Flow chart from Model Builder for the catchment delineation analysis (PDF 146 kb)
13157_2014_571_MOESM4_ESM.pdf (15 kb)
Online Resource 4Python script for the catchment delineation procedure (PDF 14 kb)


  1. Anteau MJ (2012) Do interactions of land use and climate affect productivity of waterbirds and prairie-pothole wetlands? Wetlands 32:1–9. doi:10.1007/s13157-011-0206-3 CrossRefGoogle Scholar
  2. Anteau MJ, Afton AD, Anteau ACE, Moser EB (2011) Fish and land use influence gammarus lacustris and hyalella azteca (Amphipoda) densities in large wetlands across the upper midwest. Hydrobiologia 664:69–80. doi:10.1007/s10750-010-0583-2 CrossRefGoogle Scholar
  3. Arheimer B, Wittgren HB (2002) Modelling nitrogen removal in potential wetlands at the catchment scale. Ecol Eng 19:63–80. doi:10.1016/S0925-8574(02)00034-4 CrossRefGoogle Scholar
  4. Arnold JG, Allen PM, Bernhardt G (1993) A comprehensive surface-groundwater flow model. J Hydrol 142:47–69. doi:10.1016/0022-1694(93)90004-S CrossRefGoogle Scholar
  5. Bates DM, Maechler M, Bolker BM (2011) lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-42. http://CRAN.R-project.org/package=lme4
  6. Brown MT, Vivas MB (2005) Landscape development intensity index. Environ Monit Assess 101:289–309PubMedCrossRefGoogle Scholar
  7. Carle MV (2011) Estimating wetland losses and gains in coastal North Carolina: 1994–2001. Wetlands 31:1275–1285. doi:10.1007/s13157-011-0242-z CrossRefGoogle Scholar
  8. R Development Core Team (2011) R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.r-project.org/
  9. Dewberry (2010) IFSAR Quality Assurance (QA) report: Intermap’s pilot cell of Alaska USGS. http://ifsar.gina.alaska.edu/data/2010/PROJECT/Delivery201204/Reports/Alaska_IntermapPilot_Cell_11_IfSAR_QCReport_01052011_Final.pdf.
  10. Duke G, Kienzle S (2003) Improving overland flow routing by incorporating ancillary road data into digital elevation models. J Spat Hydrol 3:1–27Google Scholar
  11. ESRI (2010a) ArcGIS v. 10.0. ESRI, Environmental Systems Research Institute, Redlands, CAGoogle Scholar
  12. Euliss NH, Mushet DM, Wrubleski DA (1999) Wetlands of the prairie pothole region: invertebrate species composition, ecology, and management. In: Batzer DP, Rader RB, Wissinger SA (eds) Invertebrates in freshwater wetlands of North America: ecology and management. Wiley, New York, pp 471–514Google Scholar
  13. Freeman TG (1991) Calculating catchment area with divergent flow based on a regular grid. Comput Geosci 17:413–422CrossRefGoogle Scholar
  14. Galatowitsch SM, Whited D, Lehtinen R et al (2000) The vegetation of wet meadows in relation to their land-use. Environ Monit Assess 60:121–144CrossRefGoogle Scholar
  15. Gesch D (2007) The National Elevation Dataset. In: Maune D (ed) Digital elevation model technologies and applications: the DEM Users manual, 2nd edn. American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, pp 99–118Google Scholar
  16. Gleason RA, Euliss NH (1998) Sedimentation of prairie wetlands. Great Plains Res 8:97–112Google Scholar
  17. Gritzner J, Millett BV, Neill MO (2009) Modeling surface-flow characteristics in glaciated landscapes. proceedings of the 2009 ESRI international user conference. ESRI, Environmental Systems Research Institute, p a1221Google Scholar
  18. Harding JS, Benfield EF, Bolstad PV et al (1998) Stream biodiversity: the ghost of land use past. Proc Natl Acad Sci 95:14843–14847PubMedCentralPubMedCrossRefGoogle Scholar
  19. Hayashi M, van der Kamp G, Schmidt R (2003) Focused infiltration of snowmelt water in partially frozen soil under small depressions. J Hydrol 270:214–229. doi:10.1016/S0022-1694(02)00287-1 CrossRefGoogle Scholar
  20. Hodgson ME, Jensen JR, Schmidt L et al (2003) An evaluation of LIDAR- and IFSAR-derived digital elevation models in leaf-on conditions with USGS Level 1 and Level 2 DEMs. Remote Sens Environ 84:295–308CrossRefGoogle Scholar
  21. Houlahan JE, Findlay CS (2003) The effects of adjacent land use on wetland amphibian species richness and community composition. Can J Fish Aquat Sci 60:1078–1094. doi:10.1139/F03-095 CrossRefGoogle Scholar
  22. Houlahan JE, Findlay CS (2004) Estimating the “critical” distance at which adjacent land-use degrades wetland water and sediment quality. Landsc Ecol 19:677–690CrossRefGoogle Scholar
  23. Houlahan JE, Keddy P, Makkay K, Findlay CS (2006) The effects of adjacent land use on wetland species richness and community composition. Wetlands 26:79–96CrossRefGoogle Scholar
  24. Intermap (2012a) NEXTMap Americas data sheet. https://www.intermap.com/Portals/0/doc/Brochures/Architecture/NEXTMap Americas Data Sheet.pdf.
  25. Intermap (2012b) NEXTMap Digital Terrain Model. http://www.intermap.com/en-us/databases/nextmap.aspx.
  26. International Water Institute (2012) Red River basin mapping initiative. http://www.iwinst.org/lidar/.
  27. James LA, Watson DG, Hansen WF (2007) Using LiDAR data to map gullies and headwater streams under forest canopy: South Carolina, USA. Catena 71:132–144. doi:10.1016/j.catena.2006.10.010 CrossRefGoogle Scholar
  28. Jenkins DG, McCauley LA (2006) GIS, SINKS, FILL, and disappearing wetlands. proceedings of the 2006 ACM symposium on applied computing - SAC’06. ACM Press, New York, p 277CrossRefGoogle Scholar
  29. Li J, Wong DWS (2010) Effects of DEM sources on hydrologic applications. Comput Environ Urban Syst 34:251–261. doi:10.1016/j.compenvurbsys.2009.11.002 CrossRefGoogle Scholar
  30. Martz LW, Garbrecht J (1998) The treatment of flat areas and depressions in automated drainage analysis of raster digital elevation models. Hydrogeol J 12:843–855Google Scholar
  31. McCartney M, Cai X, Smakhtin V (2013) Evaluating the Flow Regulating Functions of Natural Ecosystems in the Zambezi River Basin. 59 p.Google Scholar
  32. McCauley LA, Jenkins DG (2005) GIS-based estimates of former and current depressional wetlands in an agricultural landscape. Ecol Appl 15:1199–1208. doi:10.1890/04-0647 CrossRefGoogle Scholar
  33. McCauley LA, Jenkins DG, Quintana-Ascencio PF (2013a) Isolated wetland loss and degradation over two decades in an increasingly urbanized landscape. Wetlands 33:117–127. doi:10.1007/s13157-012-0357-x CrossRefGoogle Scholar
  34. McCauley LA, Jenkins DG, Quintana-Ascencio PF (2013b) Reproductive failure of a long-lived wetland tree in urban lands and managed forests. J Appl Ecol 50:25–33. doi:10.1111/1365-2664.12006 CrossRefGoogle Scholar
  35. Mitsch WJ, Gosselink JG (2000) Wetlands, 3rd edn. Wiley, New YorkGoogle Scholar
  36. Moreno-Mateos D, Comín F, Pedrocchi C, Causapé J (2009) Effect of wetlands on water quality of an agricultural catchment in a semi-arid area under land use transformation. Wetlands 29:1104–1113CrossRefGoogle Scholar
  37. Murphy PN, Ogilvie J, Meng F-R, Arp P (2008) Stream network modelling using lidar and photogrammetric digital elevation models : a comparison and field verification. Hydrol Process 22:1747–1754. doi:10.1002/hyp CrossRefGoogle Scholar
  38. Norman JM, Houghtalen RJ, Johnston WJ (2001) Hydraulic design of highway culverts, Second Edition. Publication No. FHWA-NHI-01-020. U.S. Department of Transportation, Federal Highway AdministrationGoogle Scholar
  39. Poppenga SK, Worstell BB, Stoker JM, Greenlee SK (2010) Using selective drainage methods to extract continuous surface flow from 1-Meter lidar-derived digital elevation data. U.S. Geological Survey Scientific Investigations Report 2010–5059. 12 pGoogle Scholar
  40. Quinn P (2004) Scale appropriate modelling: representing cause-and-effect relationships in nitrate pollution at the catchment scale for the purpose of catchment scale planning. J Hydrol 291:197–217. doi:10.1016/j.jhydrol.2003.12.040 CrossRefGoogle Scholar
  41. Remmel TK, Todd KW, Buttle J (2008) A comparison of existing surficial hydrological data layers in a low-relief forested Ontario landscape with those derived from a LiDAR DEM. For Chron 84:850–865CrossRefGoogle Scholar
  42. Rieger W (1998) A phenomenon-based approach to upslope contributing area and depressions in DEMs. Hydrol Process 12:857–872CrossRefGoogle Scholar
  43. Rubbo MJ, Kiesecker JM (2005) Amphibian breeding distribution in an urbanized landscape. Conserv Biol 19:504–511CrossRefGoogle Scholar
  44. Shaw DA, van der Kamp G, Conly FM et al (2012) The fill-spill hydrology of prairie wetland complexes during drought and deluge. Hydrol Process 26:3147–3156. doi:10.1002/hyp.8390 CrossRefGoogle Scholar
  45. Shaw DA, Pietroniro A, Martz LW (2013) Topographic analysis for the prairie pothole region of Western Canada. Hydrol Process 27:3105–3114. doi:10.1002/hyp.9409 Google Scholar
  46. Sloan C (1972) Ground-water hydrology of prairie potholes in North Dakota. Geological Survey Professional Paper 585-C. Washington D.C.Google Scholar
  47. Tiner RW (2003) Geographically isolated wetlands of the United States. Wetlands 23:494–516. doi:10.1672/0277-5212(2003)023[0494:GIWOTU]2.0.CO;2 CrossRefGoogle Scholar
  48. Tompkins T, Whipps W, Manor L et al (1997) Wetland effects on hydrological and water quality characteristics of a mid- Michigan river system. In: Trettin C, Jurgensen M, Grigal D, Gale M (eds) Northern Forested Wetlands: ecology and management. CRC Press, Boca Raton, pp 273–385Google Scholar
  49. U.S. Department of Agriculture-Natural Resource Conservation Service (2005) Earth Dams and Reservoirs Technical Release 60. 40 pGoogle Scholar
  50. U.S. Fish and Wildlife Service (2003) National Wetlands Inventory Data. http://www.fws.gov/wetlands
  51. U.S. Geological Survey (2006) National Elevation Dataset. http://ned.usgs.gov/
  52. U.S. Geological Survey (2010) National Hydrography Dataset - 24 k. http://nhd.usgs.gov/
  53. U.S. Geological Survey, U.S. Department of Agriculture-Natural Resource Conservation Service (2009) Federal guidelines, requirements, and procedures for the national Watershed Boundary Dataset: U.S. Geologcal Survey Techniques and Methods 11-A3. 55 pGoogle Scholar
  54. Verhoeven JTA, Arheimer B, Yin C, Hefting MM (2006) Regional and global concerns over wetlands and water quality. Trends Ecol Evol 21:96–103. doi:10.1016/j.tree.2005.11.015 PubMedCrossRefGoogle Scholar
  55. Wang L, Liu H (2006) An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. Int J Geogr Inf Sci 20:193–213. doi:10.1080/13658810500433453 CrossRefGoogle Scholar
  56. Wang Y, Zheng T (2005) Comparison of light detection and ranging and national elevation dataset digital elevation model on floodplains of North Carolina. Nat Hazards Rev 6:34–40. doi:10.1061/(ASCE)1527-6988(2005) 6 CrossRefGoogle Scholar
  57. Wang L, Lyons J, Kanehl P, Gatti R (1997) Influences of watershed land use on habitat quality and biotic integrity in Wisconsin streams. Fisheries 22:6–12. doi:10.1577/1548-8446(1997)022<0006 CrossRefGoogle Scholar
  58. Wechsler SP (2007) Uncertainties associated with digital elevation models for hydrologic applications: a review. Hydrol Earth Syst Sci 11:1481–1500CrossRefGoogle Scholar
  59. Winter TC (2003) Hydrological, chemical, and biological characteristics of a prairie pothole wetland complex under highly variable climate conditions: the Cottonwood Lake area, east-central North Dakota. U.S. Geological Survey professional paper 1675. U.S. Geological Survey, Denver, COGoogle Scholar
  60. Wright C, Wimberly M (2013) Recent land use change in the Western Corn Belt threatens grasslands and wetlands. Proc Natl Acad Sci 110:4134–4139. doi:10.1073/pnas.1215404110 PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© US Government 2014

Authors and Affiliations

  1. 1.South Dakota State University, U.S. Geological Survey, Northern Prairie Wildlife Research CenterJamestownUSA
  2. 2.Department of Forest and Wildlife EcologyUniversity of Wisconsin – MadisonMadisonUSA
  3. 3.U.S Geological SurveyNorthern Prairie Wildlife Research CenterJamestownUSA

Personalised recommendations