Application of Weights of Evidence Method for Assessment of Flowing Wells in the Greater Toronto Area, Canada

Natural Resources Research, Vol. 13, No. 2, June 2004 ( C 2004) Application of Weights of Evidence Method for Assessment of Flowing Wells in the Greater Toronto Area, Canada Qiuming Cheng 1,2 Received 27 October 2003; accepted 6 March 2004 Flowing wells extracted from the Ministry of Ontario Environment and Energy (MOEE) water well data set from the Oak Ridges Moraine (ORM) area, Ontario, Canada, were treated as training point set to evaluate the potential distribution of artesian aquifers and their spatial associations with other geological and topological features in the study area. Evidential layers of geological and topographical features were constructed on the basis of the digital elevation model (DEM) and a geological map using GIS buffering functions in conjunction with weights of evidence method. It has been demonstrated that the locations of the flowing wells in the Oak Ridges Moraine area are associated spatially with the distances, (a) 500 5000 m from the oak ridges moraine deposits, (b) 500 4000 m from thick drift layer delineated on the drift thickness map created from water well data, and (c) 1500 2500 m from steep slope zones with slope above 8 degree calculated from a DEM. Applying a combination of these conditions can reduce the predicting target areas of having flowing wells by two thirds. Outcomes of this research are important both because the impact of the results on understanding of characteristics of aquifers and their relationships with other geological and topographical features and because it generates a probability map showing the potential location of artesian aquifers in the ORM area. In addition, the methodologies used in the paper will be applicable for modeling the distributions of other types of objects such as surface water bodies and low flow of streams in a watershed context in the study area. KEY WORDS: Hydrological modeling, data integration, spring, spatial analysis, Urban environment system. INTRODUCTION The Oak Ridges Moraine in the Greater Toronto Area is recognized as a major aquifer complex within Ontario. It is one of the most heavily used groundwater sources in Canada. It also is a geologically and hydrogeologically unique and environmentally sensitive area in Canada. There is a population of about 6 million living in the Greater Toronto Area and rapid urbanization in the ORM has been observed. It has been interesting to the local residents and researchers 1 Department of Earth and Atmospheric Science, Department of Geography, York University, 4700 Keele Street, Toronto, Canada, M3J 1P3; e-mail: qiuming@yorku.ca. 2 Changjiang Scholar, China University of Geosciences, Wuhan China; e-mail: qiuming@cugb.edu.cn. whether rapid urbanization in the ORM area could cause impact on the nature water systems that in turn affect environmental and ecological systems. To understand the groundwater system and its interaction to the surface-water system in the area may provide scientific evidences for decision makers in assessment of water resources and development planning in the area. The ongoing research project that the author and his research group has been conducting in the area is to develop the proper spatial modeling techniques for processing the diverse data to enhance the understanding of the spatial characteristics of surface and groundwater distributions, their controlling factors (geological, hydrological, and topographical) and interaction of groundwater discharge and surfacewater distributions. A number of spatial models have been constructed to test the hypotheses. These include 77 1520-7439/04/0600-0077/1 C 2004 International Association for Mathematical Geology

78 Cheng a DEM-based watershed fractal modeling developed for quantifying the spatial patterns of surface stream networks and drainage systems derived from DEM and for characterizing the geological constraints on evolution of stream networks and drainage basin systems (Cheng and others, 2001); a watershed-based regression model for characterizing the hydraulic properties of lithological units (Han and Cheng, 2000); a spatial statistical analysis was applied to study the distributions of surface-water bodies (lakes and ponds) derived from remote-sensing images and the spatial relationships between the locations of water bodies and the contacts of Oak Ridges Moraine (Lu and Cheng, 1999); and an integrated spatial and frequency analysis model was proposed to separate base flow from total river flow recorded in river gauging stations in the area. The results have shown that ORM discharges significant amount of water to rivers through seeps, springs, and flowing wells (Lu, 2001). Further works have been devoted to investigate the influence of topographical and geomorphologic attributes of drainage basins on spatial temporal distribution of river flows and their responses to the precipitation events (Ko and Cheng, 2004). A proposal for development of a WebGIS system for predicting river flow on the basis of precipitation events and physical properties of drainage basin systems have been proposed (Cheng, Ko, and Yuan, 2004). One purpose of the research being conducted in the ORM is to establish a web-based geographical information system for managing and predicting river flows in the main gauged and ungauged streams. This paper focuses on analyzing spatial locations of flowing wells and how they are associated with geological and topographical factors. It may provide insight to the distribution of springs and seeps which discharge significant amount water to river systems. FLOWING WELL DISTRIBUTIONS IN THE ORM AREA Generally, it is recognized that groundwater discharge is one of the main contributors to the low flow of streams in the Oak Ridges Moraine Area. Groundwater discharge to streams may be through spring and seeps where the water level can reach the surface. A number of big spring sites have been well documented, the economically less significant springs and most areas with seeps, however, remain unknown. Flowing-wells are wells with water levels above the surface because of hydrostatic pressure. The Ministry of Ontario Environment and Energy (MOEE) data set, newly revised by the Geological Survey of Canada (GSC; Russell and others, 1996, 1998), contains about 57000 water wells located cross the area. Most of the wells were drilled as water wells and a small number of wells were for engineering purposes. Most of these water wells were drilled since the 1960 s for water supply with depths ranging up to few hundred feet. The spatial distribution of 353 of these wells selected with water levels above the surface is shown superimposed on DEM Figure 1. Comparing these flowing wells with other wells in the data set there is no noticeable differences in terms of age, depths of the wells, and types of wells. However, the locations of the flowing wells are not completely random in the area. Clustering distributions also are shown in Figure. 1. These flowing wells indicate the areas with watertable above the surface. Two main tasks of this study were (1) to test statistically the potential spatial association of the locations of these flowing wells and other geological and topographical features; and (2) to generate a potential map on the basis of these geological and topographical features showing the zone with high probability of having flowing wells and springs. To achieve the goals, several geological and topographical features were extracted first from a geological map, a DEM and water well database using proper GIS techniques and their spatial associations with the locations of flowing wells were further quantified by the weights of evidence method. Finally the extracted features strongly associated with the location of flowing wells were integrated to form an index map that shows the potential zones in the ORM for favorable locations of flowing wells and springs. THE ASSOCIATION OF FLOWING WELLS AND OTHER GEOLOGICAL AND TOPOGRAPHICAL FEATURES Sharpe and others (1997) established a geological and hydrogeological model consisting of six principal stratigraphic elements for the are, that was selected as the geological hypothesis for the selections of geological and topographical features to test the spatial associations with the distribution of the flowing wells. A simplified surfacial geological map is shown in Figure 2 (Sharpe and others, 1997). Among these six lithology elements, the ORM is an extensive stratified glaciofluvial-glaciolacustrine deposits 150 km long in east-west orientation, 5 to 15 km wide, and thickness up to 150 m. It forms a prominent ridge of sand

Application of Weights of Evidence Method for Assessment 79 Figure 1. Flowing wells (black dots) in Oak Ridges Moraine area extracted from MOEE data set (Russell and others, 1998). Background map is shaded relief DEM with 30-m spatial resolution (Kenny, 1997). and gravel (Fig. 2). The lower contact of the ORM is an irregular channeled surface of Newmarket Till. The channels may be confined within, or have eroded through, the Newmarket Till into the lower drift be- low (Sharpe and others 1997). It is recognized generally that the ORM is the main source of recharge in the region (Sharpe and others, 1997). Studies also indicated that recharge may take place through the till Figure 2. Surficial geology of ORM (Sharpe and others, 1997).

80 Cheng units adjacent the moraine (Sharpe and others, 1997). The channels developed in the Newmarket Tills contain mainly sandy sediments related to the ORM complex and some channels contain thick gravels (Sharpe and others, 1997). These channels may be hydrologically significant as high yield aquifers (Sharpe and others, 1997). To test the influence of the ORM on the locations of the flowing wells, the spatial correlation between the flowing wells and the distances from the contacts of the ORM was calculated using weights of evidence method (Bonham-Carter, 1994). MOEE water well data set has been used to define the bedrock surface and to estimate the thickness of drift in the area (Russell and others, 1996) (binary patterns of a thicker drift layer is shown in Fig. 3). The thickness of drift ranges from 150 to 550 ft. Most parts of the ORM are corresponding to the thicker drift. Thicker drift and high elevation may cause hydrostatic pressure difference from neighboring areas. Therefore, a thick drift zone was extracted to test the spatial association of the locations of flowing wells and the distances from the thick drift layer. It can be seen intuitively from the locations of wells in Figure 1 superimposing on the DEM that most of the wells are located in those areas with negative relief and nearby steep slope zones, which may cause hydrostatic pressure because of significant gravity dif- ference. Therefore, steep slope zone was extracted from DEM to associate the locations of flowing wells. In order to test the spatial correlations between the locations of flowing wells and the distances from ORM, a thick drift layer and steep slope zones, Arc- WofE: an ArcView extension for weights of evidence mapping (Kemp, Bonham-Carter, and Raines, 1999), has been used to combine the GIS buffering function and spatial statistics. (a) Flowing Wells and Distance from ORM. Ten buffer zones of unequal intervals (interval widths are 0.5 km, 1 km, and 2 km, respectively) were created around the outline of ORM using ArcView. The patterns of ORM and buffer zone (500 5000 m) around ORM are shown in Figure 4 with the flowing wells superimposed for comparison purpose. It shows that most wells are located close to the ORM. The statistics obtained from Arc-WofE are shown in Table 1. The first column of the table is the number of buffer zones, followed by the class values (distance cutoffs), the area of the buffer zone, the number of wells fallen in the buffer zone, and the rest of the columns for Figure 3. Binary patterns showing thicker drift map defined with cutoff thickness 300 ft. Thickness map was derived from DEM and water wells (Russell and others, 1996). Black dots represent flowing wells.

Application of Weights of Evidence Method for Assessment 81 Figure 4. Buffer zones created around ORM formation on Figure 2 using ArcView. Polygons with dark patterns represent outline of ORM. Gray patterns represent buffer zone of 500 to 5000 m from ORM. Zone was determined by weights of evidence method such that it reaches optimum spatial correlation with locations of flowing wells. Black dots represent flowing wells. correlation coefficients and their significance levels. The last two columns are contrast (spatial correlation coefficient) and its t-value (measure of significance level), respectively. The statistics of Table 1 show that the buffer zones (500 5000 m) (Fig. 4) occupy about 40% of the total study area (9600 km 2 ) but contain 72% of the total number of wells (253 out of 353 in total), which implies the significant positive correlation (C > 0 and C/S(C) > 1.2) between the locations of the wells and the buffer zones from 500 to 5000 m. (b) Flowing Wells and Distance from Thick Drift layer. Based on optimum spatial correlations between the locations of wells and the drift thickness, the drift thickness was separated into two groups: thicker and thinner layers with 300 ft cutoff (seen in Fig. 3). To test the spatial correlation between the flowing wells and the distance from the outline of the extracted thicker drift layer, buffer zones of unequal intervals were created around the outline of thicker drift layer. The spatial statistics calculated using Arc-WofE are shown in Figure 5 and Table 1, respectively. The results in Table 1 show that the locations of wells are significantly correlated (C > 0 and C/S(C) > 1.3) with the buffer zones from 500 to 4000 m. The zone defined with distance 500 4000 m from the thicker drift layer is shown in Figure 5 with flowing wells superimposed. (c) Flowing Wells and Distance from Steep Slope Zones. In order to test the spatial association between locations of flowing wells and slope of topography, the slope in unit of degree was calculated from the DEM of 30-m resolution for each square neighborhood of size 150 m 150 m(5 5 grids) using ArcView Spatial Analyst (ESRI Inc., 1999). High slope zones then were defined with a cutoff value 8 degree, determined considering the spatial correlation between the locations of wells and the slope zones. Similarly, buffer zones of unequal intervals were created around the outline of the slope zone defined. Then spatial statistics were calculated using Arc-WofE and the results are shown in Figure 6 and Table 1, respectively. The results in Table 1

82 Cheng Table 1. Spatial Correlation Between Flowing Wells and Distances from ORM, Slope and Drift Thickness Calculated by Weights of Evidence CLASS CLASS VALUE AREA #POINTS W+ S(W+) W S(W ) C S(C) C/S(C) Distance To High Slope Zone (Slope > = 8 degree) 1 0 500 (m) 2580.42 42 0.83 0.15 0.19 0.05 1.02 0.16 6.18 3 1000 1500 3256.98 100 0.18 0.10 0.08 0.06 0.26 0.12 2.21 4 1500 2000 1098.03 64 0.48 0.12 0.08 0.05 0.56 0.14 4.00 5 2000 2500 1353.81 89 0.61 0.10 0.14 0.06 0.75 0.12 6.01 6 2500 3000 426.28 13 0.19 0.28 0.01 0.05 0.19 0.28 0.69 7 3000 3500 391.91 18 0.23 0.24 0.01 0.05 0.24 0.24 0.99 8 3500 4000 190.45 10 0.37 0.32 0.01 0.05 0.38 0.32 1.16 9 4000 305.96 16 0.37 0.25 0.01 0.05 0.38 0.26 1.47 Distance To ORM 1 0 500(m) 1292.73 10 1.57 0.31 0.12 0.05 1.69 0.32 5.26 2 500 1000 629.31 47 0.75 0.15 0.07 0.05 0.83 0.16 5.15 3 1000 2000 892.44 82 0.98 0.11 0.17 0.06 1.15 0.13 8.82 4 2000 3000 980.74 52 0.39 0.14 0.05 0.05 0.44 0.15 2.90 5 3000 4000 882.85 41 0.25 0.15 0.03 0.05 0.28 0.17 1.66 6 4000 5000 683.32 31 0.22 0.18 0.01 0.05 0.24 0.19 1.29 7 5000 7000 1317.25 46 0.04 0.15 0.01 0.05 0.04 0.16 0.30 8 7000 9000 930.28 18 0.64 0.23 0.05 0.05 0.70 0.24 2.86 9 9000 11000 671.06 16 0.43 0.25 0.02 0.05 0.46 0.25 1.78 10 11000 1389.74 9 1.75 0.33 0.13 0.05 1.89 0.33 5.58 Distance From Thick Drift Layer (>300 ft) 1 0 500 2484.85 74 0.20 0.11 0.06 0.06 0.27 0.13 2.05 2 500 1000 1175.73 51 0.18 0.14 0.02 0.05 0.20 0.15 1.34 3 1000 2000 1423.25 93 0.61 0.10 0.15 0.06 0.76 0.12 6.15 4 2000 4000 1372.37 68 0.32 0.12 0.06 0.06 0.38 0.13 2.77 5 4000 6000 935.08 21 0.49 0.22 0.04 0.05 0.54 0.22 2.37 6 6000 8000 472.35 10 0.55 0.31 0.02 0.05 0.58 0.32 1.79 7 8000 10000 328.65 4 1.12 0.50 0.02 0.05 1.14 0.50 2.26 11000 1200 8 0 222.08 5 0.49 0.45 0.01 0.05 0.50 0.45 1.11 12000 1400 9 0 145.76 4 0.29 0.50 0.00 0.05 0.29 0.50 0.58 10 14000 1091.85 22 0.61 0.21 0.05 0.05 0.66 0.22 3.00 Posterior Probability calculated from a combination of two binary zones: Distance from ORM and distance from steep slope zone 1 0.013 0.017 4153.57 53 1.07 0.13 0.41 0.05 1.49 0.15 9.91 2 0.017 0.036 1387.50 41 0.21 0.15 0.03 0.05 0.25 0.16 1.48 3 0.036 0.038 8.25 0 Prior Probability 0.0363 4 0.038 0.048 2996.40 146 0.30 0.08 0.16 0.07 0.47 0.11 4.27 5 0.104 0.105 1062.97 112 1.13 0.10 0.27 0.06 1.41 0.12 11.80 show that the correlation between the locations of wells and the buffer zones from 1500 2500 m are significant (C > 0 and C/S(C) > 4). Figure 6 shows the binary zone from 1500 2500 m from the steep slope zone above 8 degree. (d) Flowing Wells and the Combinations of Distances from ORM, Thick Drift Layers and High Slope Zones. We have defined three binary buffer zones (500 5000 m from the ORM, 500 4000 m from thick drift layer, and 1500 2500 m from steep slope zones) highly correlated with the locations of the flowing wells. The correlations between the well locations and the combinations of these three patterns can be evaluated by posterior probability mapping and testing of conditional independency. The buffer zones around ORM (500 5000 m) and the zone around thicker drift layer (500 4000 m) are conditionally dependent of each other; therefore, the combination of these two patterns does not cause significant change of the correlation. The buffer zone around

Application of Weights of Evidence Method for Assessment 83 Figure 5. Binary buffer zones of 500 4000 m (dark gray patterns) around thick drift layer defined on Figure 3. Binary patterns were determined by weights of evidence method. Black dots represent flowing wells. Figure 6. Patterns showing steep slope zone (>8 degree) calculated by ArcView. Dots present flowing wells.

84 Cheng Table 2. Summery of Correlation Between Flowing Well Locations and Combination of Distance from ORM and Distance from High Slope Zones (Value is the Brackets is Prior Probability) Posterior Prob. Correlation (C) Significance C/S(C) Distance to ORM Distance to ORM Distance to ORM Distance to ORM Distance to ORM Distance to ORM <500 or >5000 m 500 5000 m <500 or >5000 m 500 5000 m <500 or >5000 m 500 5000 m ( A ) (A) ( A) (A) ( A) (A) Distance to High Slope 0.015 (0.036) 0.048 (0.036) 1.49 0.47 9.9 4.2 Zone <1500 or >2500 m ( B) Distance to High Slope 0.029 (0.036) 0.105 (0.036) 0.25 1.41 1.48 11.8 Zone 1500 25000 m (B) ORM and the buffer zone around the steep slope zone (1500 2500 m) are conditionally independent and the combination of these two layers can improve the correlation between the locations of flowing wells and the combination, which can be seen in Table 2. Combining two binary maps: (A) the buffer zone around ORM; and (B) the buffer zone around the steep slope zone gives four combinations (AB, AB, AB, A B) each of which can be treated as binary patterns to calculate the probability of containing flowing wells. These types of probabilities are termed posterior probability or conditional probability. In contrast, the probability of a randomly select area containing flowing wells is termed priori probability. Both priori and posterior probabilities were calculated with 1 km 2 unit cell. The prior probability is estimated as the ratio of cells containing flowing wells for the total cells covering the entire study area. It is estimated to be about 0.036 (353 cells containing wells/total 9600 cells). The calculated posterior probabilities of a cell with a given combination of the two buffer zones (AB, AB, AB, A B) are shown both Figure 7. Posterior probability map calculated by Arc-WofE on basis of combination of buffer zone (500 5000 m) around ORM and buffer zone (1500 2500 m) around steep slope zones determined in Figures 4 and 6, respectively.

Application of Weights of Evidence Method for Assessment 85 in Figure 7 as map format and statistics in Table 2. The cells within both buffer zones ( AB) have the largest posterior probability 0.105 ( P[W AB]) which is three times as high as the prior probability ( P[W]). The worse case if the cells fall out of both buffer zones ( A B) will have a posterior probability 0.013 0.017 ( P[W A B]) lower than the priori probability. The results indicate that the intersection of the buffer zone (500 5000 m) around ORM and the buffer zone (1500 2500 m) around the steep slope zone (AB) occupies 11% of the total area but contains 32% flowing wells. On contrast, the intersection of the buffer zone (beyond 500 5000 m) around ORM and the buffer zone (beyond 1500 2500 m) around the steep slope zone ( A B) occupies 43% of the total area but contains only 15% flowing wells. Similar conclusion can be drawn from the values of correlation coefficient C and the significance level C/S(C) in Table 2. CONCLUSIONS Spatial statistical analysis conducted here has demonstrated that the location of flowing wells in the ORM area is correlated with the distances from ORM, thick drift layer, and steep slope zones within the arranges of 500 5000 m to ORM, 500 4000 m to thick drift layer, and 1500 2500 m to the steep slope zones, respectively. The combination of distances from ORM and from steep slope zones yields higher correlation and the areas with this combination occupy only about one tenth of the entire study area but contain almost one third of the total flowing wells which translates into the posterior probabilities three times higher than the prior probability of the randomly selected cells from the study area. The posterior probability map has illustrated the zones with high potential for flowing well locations and these zones should be favorable for spring and seeps as well as a watertable above the surface. The results have not only demonstrated that the distribution of the artesian aquifers of the area with respect to the ORM and topographical features but also delineated the zones potentially involving characteristic interaction of groundwater systems and surface water system through flowing wells, springs and seeps. The posterior probability map can be integrated further to other layers of data such as river flow to study the interactions of ground discharge to river networks. ACKNOWLEDGMENTS Thanks are due to the Geological Survey of Canada, Ottawa, and Ontario Geological Survey for providing the data sets as cited in the text for the study. The author thanks the anonymous reviewer for critical review of the manuscript and constructive comments. This research was supported jointly by NSERC Individual Discovery Grant (OGP0183993), a Chinese 973 Project (G1999045708) and a Chinese 863 Spatial Information Extraction Project (2002AA135090). REFERENCES Bonham-Carter, G. F., 1994, Geographic Information Systems for geoscientists, modeling with GIS: Pergamon Press, Oxford, 398 p. Cheng, Q., Ko, C., and Yuan, Y., 2004, Fundamental modeling preparation and preliminary design for developing a webbased GIS for predicting runoff volume and flooding events in the Greater Toronto Area: paper in CD Proc. Ann. GIS Conf. GeoTec Event, Toronto, Canada, 6 p. Cheng, Q., Russell, H., Sharpe, D., Kenny, F., and Pin, Q., 2001, GIS-based statistical and fractal/multifractal analysis of surface stream patterns in the Oak Ridges Moraine: Computers & Geosciences, v. 27, no. 2, p. 1 14. ESRI Inc., 1999, ArcView GIS: ESRI Inc. Technical document, Redland, California, Han, S., and Cheng, Q., 2000, GIS-based hydrogeological parameter modeling: Earth Geosciences, an English Jour. China Univ. Geosciences, v. 11, no. 2, p. 131 133. Kemp, L. D., Bonham-Carter, G. F., and Raines, G. L., 1999, Arc- WofE: Arcview extension for weights of evidence mapping: http://gis.nrcan.gc.ca/software/arcview/wofe. Kenny, F., 1997, A chromo-stereo enhanced digital elevation model of the Oak Ridges Moraine area, southern Ontario: Geol. Survey Canada, Open File 3374, map 1:200,000. Ko, C., and Cheng, Q., 2004, Spatial analysis of river flow and participation data in the Greater Toronto Area, Canada: Computers & Geosciences, in press. Lu, X., 2001, GIS-based spatial and statistical analysis of the stream low flow in the Oak Ridges Moraine: unpubl. masters thesis, York Univ., 116 p. Lu, X., and Cheng, Q., 1999, Preliminary study of the detection of the channels in the Oak Ridges Moraine area using TM and Radarsat data: oral presentation with abstract at the GAC/MAG 99 meeting, Sudbury, Canada. Russell, H. A. J., Logan, C., Brennand, T. A., Hinton, M. J., and Sharpe, D. R. 1996, Regional geoscience database for the Oak Ridges Moraine project (southern Ontario): Geol. Survey Canada, Current Research 1996-E, p. 191 200.

86 Cheng Russell, H. A. J., Brennand, T. A., Logan, C., and Sharpe, D. R. 1998, Stardardization and assessment of geological descriptions from water well records, Greater Toronto and Oak Ridges Moraine areas, southern Ontario: Geol. Survey Canada, Current Research 1998-E, p. 89 102. Sharpe, D. R., Barnett, P. J., Brennand, T. A., Finley, D., Gorrel, G., and Russell, H. A. J., 1997, Surficial geology of the Greater Toronto and Oak Ridges Moraine areas, compilation map sheet: Geol. Survey Canada, Open File 3062, map 1:200 000.