Hydrologic landscape regionalisation using deductive classification and random forests

• Published in: PloS one 2014-11, Vol.9 (11), p.e112856-e112856
• Main Authors: Brown, Stuart C, Lester, Rebecca E, Versace, Vincent L, Fawcett, Jonathon, Laurenson, Laurie
• Format: Article
• Language: English
• Subjects: Analysis; Biology and Life Sciences; Catchment areas; Catchments; Classification; Classification - methods; Cluster Analysis; Clustering; Computer and Information Sciences; Conservation of Natural Resources - methods; Continents; Creeks & streams; Discriminant analysis; Earth Sciences; Ecohydrology; Ecology; Ecology and Environmental Sciences; Environmental science; Environmental statistics; Forests; Geography; Hydrologic cycle; Hydrologic studies; Hydrology; Image classification; Landscape; Machine learning; Methods; Multivariate analysis; Remote sensing; Resource management; Rivers; Statistical analysis; Statistics; Stream discharge; Stream flow; Streamflow; Topography; Trends; Variability; Victoria; Water resources; Water Resources - supply & distribution
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0112856

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.