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Hydrologic landscape regionalisation using deductive classification and random forests

Brown, Stuart C., Lester, Rebecca E., Versace, Vincent L., Fawcett, Jonathon and Laurenson, Laurie 2014, Hydrologic landscape regionalisation using deductive classification and random forests, PLoS One, vol. 9, no. 11, Article no: e112856, pp. 1-20, doi: 10.1371/journal.pone.0112856.

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Title Hydrologic landscape regionalisation using deductive classification and random forests
Author(s) Brown, Stuart C.
Lester, Rebecca E.ORCID iD for Lester, Rebecca E. orcid.org/0000-0003-2682-6495
Versace, Vincent L.ORCID iD for Versace, Vincent L. orcid.org/0000-0002-8514-1763
Fawcett, Jonathon
Laurenson, LaurieORCID iD for Laurenson, Laurie orcid.org/0000-0003-2321-7512
Journal name PLoS One
Volume number 9
Issue number 11
Season Article no: e112856
Start page 1
End page 20
Total pages 20
Publisher Public Library of Science (PLoS)
Place of publication San Francisco, Calif.
Publication date 2014-11-14
ISSN 1932-6203
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
NATURAL FLOW REGIME
LAND-USE CHANGE
UNITED-STATES
SPATIAL-RESOLUTION
STREAM-FLOW
VARIABILITY
AUSTRALIA
FRAMEWORK
ACCURACY
COVER
Summary Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of catchments and continents.
Language eng
DOI 10.1371/journal.pone.0112856
Field of Research 060208 Terrestrial Ecology
040301 Basin Analysis
040603 Hydrogeology
Socio Economic Objective 960501 Ecosystem Assessment and Management at Regional or Larger Scales
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Public Library of Science (PLoS)
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069898

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.