Skip to main content

Advertisement

SpringerLink
Log in

Delineation and Quantification of Wetland Depressions in the Prairie Pothole Region of North Dakota

  • Original Research
  • Published:
Wetlands Aims and scope Submit manuscript

Abstract

The Prairie Pothole Region of North America is characterized by numerous, small, wetland depressions that perform important ecological and hydrological functions. Recent studies have shown that total wetland area in the region is decreasing due to cumulative impacts related to natural and anthropogenic changes. The impact of wetland losses on landscape hydrology is an active area of research and management. Various spatially distributed hydrologic models have been developed to simulate effects of wetland depression storage on peak river flows, frequently using dated geospatial wetland inventories. We describe an innovative method for identifying wetland depressions and quantifying their nested hierarchical bathymetric/topographic structure using high-resolution light detection and ranging (LiDAR) data. This contour tree method allows identified wetland depressions to be quantified based on their dynamic filling-spilling-merging hydrological processes. In addition, wetland depression properties, such as surface area, maximum depth, mean depth, storage volume, etc., can be computed for each component of a depression as well as the compound depression. We successfully applied the proposed method to map wetland depressions in the Little Pipestem Creek watershed in North Dakota. The methods described in this study will provide more realistic and higher resolution data layers for hydrologic modeling and other studies requiring characterization of simple and complex wetland depressions, and help prioritize conservation planning efforts for wetland resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Barnes R, Lehman C, Mulla D (2014) Priority-flood: an optimal depression-filling and watershed-labeling algorithm for digital elevation models. Computers & Geosciences 62:117–127. doi:10.1016/j.cageo.2013.04.024

    Article  Google Scholar 

  • Blaschke T (2010) Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65:2–16. doi:10.1016/j.isprsjprs.2009.06.004

    Article  Google Scholar 

  • Cowardin LM, Carter V, Golet FC, LaRoe ET (1979) Classification of wetlands and deepwater habitats of the United States. U.S. Department of the Interior, Fish and Wildlife Service, Washington, 131 pp

    Google Scholar 

  • Dahl TE (1990) Wetlands losses in the United States, 1780’s to 1980’s. Report to the congress. U.S. Department of the Interior, Fish and Wildlife Service, Washington, 13 pp

    Google Scholar 

  • Dahl TE (2014) Status and trends of prairie wetlands in the United States 1997 to 2009. U.S. Department of the Interior, Fish and Wildlife Service, Ecological Services, Washington, 67 pp

    Google Scholar 

  • Ehsanzadeh E, Spence C, van der Kamp G, McConkey B (2012) On the behaviour of dynamic contributing areas and flood frequency curves in North American Prairie watersheds. Journal of Hydrology 414–415:364–373. doi:10.1016/j.jhydrol.2011.11.007

    Article  Google Scholar 

  • Euliss NH et al (2004) The wetland continuum: a conceptual framework for interpreting biological studies. Wetlands 24:448–458

    Article  Google Scholar 

  • Gleason RA, Tangen BA, Laubhan MK, Kermes KE, Euliss NH Jr (2007) Estimating water storage capacity of existing and potentially restorable wetland depressions in a subbasin of the Red River of the North. U.S. Geological Survey Open-File Report 2007–1159. 36pp

  • Gonga-Saholiariliva N, Gunnell Y, Petit C, Mering C (2011) Techniques for quantifying the accuracy of gridded elevation models and for mapping uncertainty in digital terrain analysis. Progress in Physical Geography 35:739–764

    Article  Google Scholar 

  • Haag KH, Lee TM, Herndon DC, County P, Water TB (2005) Bathymetry and vegetation in isolated marsh and cypress wetlands in the northern Tampa Bay area, 2000–2004. U.S. Geological Survey Scientific Investigations Report 2005–5109. 49 pp

  • Hayashi M, Van der Kamp G (2000) Simple equations to represent the volume-area-depth relations of shallow wetlands in small topographic depressions. Journal of Hydrology 237:74–85

    Article  Google Scholar 

  • Huang S, Dahal D, Young C, Chander G, Liu S (2011a) Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota. Remote Sensing of Environment 115:3377–3389

    Article  Google Scholar 

  • Huang S, Young C, Feng M, Heidemann K, Cushing M, Mushet DM, Liu S (2011b) Demonstration of a conceptual model for using LiDAR to improve the estimation of floodwater mitigation potential of Prairie Pothole Region wetlands. Journal of Hydrology 405:417–426

    Article  Google Scholar 

  • Huang C, Peng Y, Lang M, Yeo IY, McCarty G (2014) Wetland inundation mapping and change monitoring using landsat and airborne LiDAR data. Remote Sensing of Environment 141:231–242

    Article  Google Scholar 

  • Jin S, Yang L, Danielson P, Homer C, Fry J, Xian G (2013) A comprehensive change detection method for updating the national land cover database to circa 2011. Remote Sensing of Environment 132:159–175

    Article  Google Scholar 

  • Johnson RR, Oslund FT, Hertel DR (2008) The past, present, and future of prairie potholes in the United States. Journal of Soil and Water Conservation 63:84A–87A

    Article  Google Scholar 

  • Johnston CA (2013) Wetland losses due to row crop expansion in the dakota prairie pothole region. Wetlands 33:175–182. doi:10.1007/s13157-012-0365-x

    Article  Google Scholar 

  • Kweon IS, Kanade T (1994) Extracting topographic terrain features from elevation maps. CVGIP: Image Understanding 59:171–182. doi:10.1006/ciun.1994.1011

    Article  Google Scholar 

  • Lane C, D’Amico E (2010) Calculating the ecosystem service of water storage in isolated wetlands using LiDAR in north central Florida, USA. Wetlands 30:967–977. doi:10.1007/s13157-010-0085-z

    Article  Google Scholar 

  • Lang M, McCarty G (2009) LiDAR intensity for improved detection of inundation below the forest canopy. Wetlands 29:1166–1178. doi:10.1672/08-197.1

    Article  Google Scholar 

  • Le PV, Kumar P (2014) Power law scaling of topographic depressions and their hydrologic connectivity. Geophysical Research Letters 41:1553–1559

    Article  Google Scholar 

  • Li S, MacMillan RA, Lobb DA, McConkey BG, Moulin A, Fraser WR (2011) LiDAR DEM error analyses and topographic depression identification in a hummocky landscape in the prairie region of Canada. Geomorphology 129:263–275. doi:10.1016/j.geomorph.2011.02.020

    Article  Google Scholar 

  • Lindsay JB (2004) Coping with topographic depression in digital terrain analysis. PhD thesis, University of Western Ontario

  • Lindsay JB, Creed IF (2006) Distinguishing actual and artefact depressions in digital elevation data. Computers and Geosciences 32:1192–1204. doi:10.1016/j.cageo.2005.11.002

    Article  Google Scholar 

  • Liu H, Wang L, Sherman D, Gao Y, Wu Q (2010) An object-based conceptual framework and computational method for representing and analyzing coastal morphological changes. International Journal of Geographical Information Science 24:1015–1041. doi:10.1080/13658810903270569

    Article  Google Scholar 

  • McCauley L, Anteau M (2014) Generating nested wetland catchments with readily-available digital elevation data may improve evaluations of land-use change on wetlands. Wetlands 1–10. doi:10.1007/s13157-014-0571-9

  • Miller MW, Nudds TD (1996) Prairie landscape change and flooding in the Mississippi River Valley. Conservation Biology 10:847–853

    Article  Google Scholar 

  • Minke A, Westbrook C, van der Kamp G (2010) Simplified volume-area-depth method for estimating water storage of prairie potholes. Wetlands 30:541–551. doi:10.1007/s13157-010-0044-8

    Article  Google Scholar 

  • Ouyang ZT, Becker R, Shaver W, Chen JQ (2014) Evaluating the sensitivity of wetlands to climate change with remote sensing techniques. Hydrological Processes 28:1703–1712. doi:10.1002/hyp.9685

    Article  Google Scholar 

  • Shaw DA, Pietroniro A, Martz L (2013) Topographic analysis for the prairie pothole region of Western Canada. Hydrological Processes 27:3105–3114

    Google Scholar 

  • Shook K, Pomeroy J (2012) Changes in the hydrological character of rainfall on the Canadian prairies. Hydrological Processes 26:1752–1766

    Article  Google Scholar 

  • Sloan CE (1972) Ground-water hydrology of prairie potholes in North Dakota. Professional paper 585-c. U.S. Government Printing Office, Washington

    Google Scholar 

  • Tiner RW (1997) NWI maps: what they tell us. National Wetlands Newsletter 19:7–12

    Google Scholar 

  • Todhunter PE, Rundquist BC (2004) Terminal lake flooding and wetland expansion in Nelson County, North Dakota. Physical Geography 25:68–85

    Article  Google Scholar 

  • Townsend-Small A, Pataki DE, Liu H, Li Z, Wu Q, Thomas B (2013) Increasing summer river discharge in southern California, USA, linked to urbanization. Geophysical Research Letters 40:4643–4647. doi:10.1002/grl.50921

    Article  Google Scholar 

  • Ullah W, Dickinson WT (1979a) Quantitative description of depression storage using a digital surface model: I. Determination of depression storage. Journal of Hydrology 42:63–75. doi:10.1016/0022-1694(79)90006-4

    Article  Google Scholar 

  • Ullah W, Dickinson WT (1979b) Quantitative description of depression storage using a digital surface model: II. Characteristics of surface depressions. Journal of Hydrology 42:77–90. doi:10.1016/0022-1694(79)90007-6

    Article  Google Scholar 

  • Wang L, Liu H (2006) An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. International Journal of Geographical Information Science 20:193–213

    Article  CAS  Google Scholar 

  • Winter TC (1989) Hydrologic studies of wetlands in the northern prairie. In: Van der valk AG (ed) Northern prairie wetlands. Iowa State University Press, Ames, pp 16–54

    Google Scholar 

  • Winter TC, Rosenberry DO (1995) The interaction of ground water with prairie pothole wetlands in the Cottonwood Lake area, east-central North Dakota, 1979–1990. Wetlands 15:193–211

    Article  Google Scholar 

  • Wu Q (2011) Object-oriented representation and analysis of coastal changes for hurricane-induced damage assessment. Electronic Thesis or Dissertation, University of Cincinnati

  • Wu Q, Lane C, Liu H (2014) An effective method for detecting potential woodland vernal pools using high-resolution LiDAR data and aerial imagery. Remote Sensing 6:11444–11467. doi:10.3390/rs61111444

    Article  Google Scholar 

  • Wu B, Yu B, Huang C, Wu Q, Wu J (2016) Automated extraction of ground surface along urban roads from mobile laser scanning point clouds. Remote Sensing Letters 7:170–179. doi:10.1080/2150704X.2015.1117156

    Article  Google Scholar 

  • Yang J, Chu X (2013) Quantification of the spatio-temporal variations in hydrologic connectivity of small-scale topographic surfaces under various rainfall conditions. Journal of Hydrology 505:65–77. doi:10.1016/j.jhydrol.2013.09.013

    Article  Google Scholar 

Download references

Acknowledgments

We are grateful to the State of North Dakota’s GIS Hub, which provided LiDAR data and aerial photographs to support this research. We would also like to thank two anonymous reviewers. Their comments and suggestions have been very helpful for improving the quality of this paper. This paper has been reviewed in accordance with the U.S. Environmental Protection Agency’s peer and administrative review policies and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. Statements in this publication reflect the authors’ professional views and opinions and should not be construed to represent any determination or policy of the U.S. Environmental Protection Agency.

Author information

Authors and Affiliations

  1. Department of Geography, State University of New York, Binghamton University, Binghamton, NY, 13902, USA

    Qiusheng Wu

  2. Office of Research and Development, U.S. Environmental Protection Agency, 26 W. Martin Luther King Dr., Cincinnati, OH, 45268, USA

    Charles R. Lane

  3. CSS-Dynamac c/o U.S. Environmental Protection Agency, 26 W. Martin Luther King Dr., Cincinnati, OH, 45268, USA

    Qiusheng Wu

Authors
  1. Qiusheng Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charles R. Lane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiusheng Wu.

Appendix

Appendix

The proposed contour tree method for depression identification has been implemented as an ArcGIS toolbox – Depression Identification Analyst, which will be freely available upon request. The Depression Identification Tool asks the user to provide a single input, the LiDAR DEM, and then executes the aforementioned procedures with user-specified parameters (base contour, contour interval, minimum depression area, minimum depression depth, etc.) automatically to create depression polygons at different hierarchical levels. All depression results can be saved as ESRI Shapefile or Geodatabase format.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Q., Lane, C.R. Delineation and Quantification of Wetland Depressions in the Prairie Pothole Region of North Dakota. Wetlands 36, 215–227 (2016). https://doi.org/10.1007/s13157-015-0731-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13157-015-0731-6

Keywords

Search

Navigation