GIS Spatial-Temporal Modeling of Water Systems in Greater Toronto Area, Canada
摘要
Modeling landscape with high-resolution digital elevation model (DEM) in a geographic information system can provide essential morphological and structural information for modeling surface processes such as geomorphologic process and water systems. This paper introduces several DEM-based analysis processes applied to characterize distribution and their interactions of ground and surface water systems in the Greater Toronto Area (GTA), Canada. The stream networks and drainage basin systems were derived from the DEM with 30 m resolution and the regularities of the surface stream and drainage patterns were modeled from a statistical/multifractal point of view. Together with the elevation and slope of topography, other attributes defined from modeling the stream system, and drainage networks were used to associate geological, hydrological and topographical features to water flow in river systems and the locations of artesian aquifers in the study area. Stream flow data derived from daily flow measurements recorded at river gauging stations for multi-year period were decomposed into drainage-area dependent and drainage-area flow components by two-step frequency and spatial analysis processes. The latter component was further demonstrated to relate most likely to the ground water discharge. An independent analysis was conducted to model the distribution of aquifers with information derived from the records of water wells. The focus was given on quantification of the likelihood of ground water discharge to river and ponds through flowing wells, springs and seepages. It has been shown that the Oak Ridges Moraine (ORM) is a unique glacial deposit that serves as a recharge layer and that the aquifers in the ORM underlain by Hilton Tills and later deposits exposed near the steep slope zone of the ridges of ORM provide significant discharge to the surface water systems (river flow and ponds) through flowing wells, springs and seepages. Various statistics (cross- and auto-correl