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TitleGIS-based spatial and statistical analysis of the stream low flow in the Oak Ridges Moraine area, southern Ontario
AuthorLu, X C
ThesisMSc, York University
Source 2001, 116 pages
Year2001
Documentthesis
Lang.English
Mediapaper
ProvinceOntario
NTS30M/13; 30M/14; 30M/15; 30M/16NW; 30M/16NE; 31C/04SW; 31C/04NW; 31D/01; 31D/02; 31D/03; 31D/04
AreaToronto; Newmarket; Whitby; Trenton; Port Hope; Aurora
Lat/Long WENS-80.0000 -77.7500 44.2500 43.7500
Subjectsmathematical and computational geology; hydrogeology; groundwater; groundwater flow; groundwater resources; groundwater regimes; stream flow; streams; drainage systems; models; glacial deposits; tills; aquifers; statistical analysis; Oak Ridges Moraine; geographic information system; geographic information system applications; digital elevation models; drainage basins; Cenozoic; Quaternary
Illustrationssketch maps; cross-sections; graphs; tables
AbstractStream networks and drainage basins were delineated from a DEM based on 98 gauging stations in the Oak Ridges Moraine (ORM) area, Stream low flow was calculated two ways: using a conventional minimum of n-day average method and using the average of n-day minimum method, for each of the stations, together with other topographic parameters calculated for all drainage basins, Area-perimeter and length-area fractal models have been applied to calculate the fractal dimensions of the stream networks in the drainage basin context. The correlations between the stream low flow calculated for each station and the topographic parameters (area, perimeter of drainage basins, total stream length), slope and lithology units calculated for the drainage basins with the stations as seed points were studied statistically and spatially. Multivariate regression between low flow and other drainage area-related variables such as area and perimeter of drainage basins, and total length of stream segments in the basins provides a useful means for removal of the overall influence of basin size-related parameters on the stream low flow.
GEOSCAN ID216796