Mapping outlet points used for watershed delineation onto DEM-derived stream networks

Transcription

1 WATER RESOURCES RESEARCH, VOL. 44,, doi: /2007wr006507, 2008 Mapping outlet points used for watershed delineation onto DEM-derived stream networks John B. Lindsay, 1,2 James J. Rothwell, 3 and Helen Davies 4 Received 10 September 2007; revised 6 June 2008; accepted 23 June 2008; published 28 August [1] Outlet point positions taken from hydrometric stations commonly do not coincide with stream locations extracted from digital elevation models (DEMs). This is a serious problem for accurate watershed delineation of data sets containing numerous outlets, which is critical in regional-scale studies that relate catchment characteristics to basin responses. The advanced outlet repositioning approach (AORA), presented here, replicates the processes involved in manual outlet placement while reducing inefficiency and potential for blunders. The technique uses water body names to identify locations for outlet repositioning that are consistent with nearby outlets. The AORA performance was compared against two existing automated techniques using 993 stations in seven basins in northwest England. The AORA had the fewest repositioning errors in each basin and nearly halved the overall number of errors in the data set, compared with the second-best method. This work highlights the potential errors that may be present in studies that have employed existing automated watershed mapping methods. Citation: Lindsay, J. B., J. J. Rothwell, and H. Davies (2008), Mapping outlet points used for watershed delineation onto DEMderived stream networks, Water Resour. Res., 44,, doi: /2007wr Introduction [2] Watersheds are often mapped to study and manage hydrological, geomorphological, and landscape ecological phenomena [Band, 1989]. Their importance to the study of hydrogeomorphic and ecological processes lies in the fact that the size and shape of a watershed influences the geometry of the enclosed river network. This, in turn, affects the amount, distribution, and timing of runoff [Rodriguez- Iturbe and Valdes, 1979]. Adjacent watersheds are separated by drainage divides that usually coincide with ridges. Drainage divides are meaningful boundaries with respect to the flow of energy and matter across landscapes [Mark, 1988]. In the past, drainage divides were often manually delineated by interpreting contour maps. Since the 1980s, however, automated techniques for mapping divides [e.g., Band, 1986, 1989; Jenson and Domingue, 1988; Jenson, 1991; Mackay and Band, 1998; Liang and Mackay, 2000] have become ubiquitous, and watershed delineation algorithms are now routinely available in geographical information systems (GIS) for use in hydrological studies. [3] There are two distinct types of applications involving watershed delineation. In the first application type, watershed mapping is used to aggregate a landscape into a collection of nested areas, usually based on the topology of a stream network, which then serve as the spatial units for 1 Department of Geography, University of Guelph, Guelph, Ontario, Canada. 2 Now at Department of Geography, College of Social and Applied Human Sciences, University of Guelph, Guelph, Ontario, Canada. 3 Department of Environmental and Geographical Sciences, Manchester Metropolitan University, Manchester, UK. 4 Centre for Ecology and Hydrology, Wallingford, UK. Copyright 2008 by the American Geophysical Union /08/2007WR mapping or modeling a phenomenon [Band, 1986; Jenson and Domingue, 1988; Garbrecht and Martz, 1993]. This is common, for example, in the application of lumped and semilumped parameter simulation models such as the Soil and Water Assessment Tool (SWAT) model [Arnold et al., 1994]. In the second application type, an outcome variable (e.g., stream discharge or water chemistry) is measured at various locations along a river network, and watershed mapping is then used to measure basin characteristics (e.g., drainage density, lithology, or the proportion of a particular vegetation type) that serve as predictors. In this type of watershed mapping application, basin outlets often correspond to stream gauging stations or water quality monitoring stations. This watershed application type is what Jenson [1991] refers to as watershed delineation for sample surveys, and examples can be commonly found in many fields of study [e.g., Bonham-Carter et al., 1987; Smart et al., 2001; Jarvie et al., 2002; Davies and Neal, 2004; Evans et al., 2006; Donohue et al., 2006; Meynendonckx et al., 2006; Helliwell et al., 2007]. The distinguishing characteristic that separates the two types of watershed delineation applications therefore is whether or not phenomena are measured at basin outlets. [4] While the impact of uncertainty in the representation of topography on drainage divide mapping has been studied previously [e.g., Oksanen and Sarjakoski, 2005], very little research has focused on the impacts of uncertainty in the specification of basin outlet locations produced by automated watershed mapping techniques. For clarity of terms, an outlet point is defined here as a nonzero value grid cell in an outlet image that is used to specify a point of interest to a watershed delineation algorithm. Outlets are usually assigned unique identifiers, and their corresponding watersheds are assigned these same values. Accurate outlet placement is one of the most critical factors in watershed 1of9

2 LINDSAY ET AL.: MAPPING POINTS ONTO DEM-DERIVED STREAM NETWORKS delineation. Locations that are near to one another can have markedly different watersheds, owing to the nature of surface flow path networks [Jenson, 1991]. Perhaps surprisingly, the handling of outlet data in automated watershed mapping applications is not straightforward, particularly in studies involving numerous nested catchments with dataassociated outlets. This paper examines the challenges involved in handling large data sets of outlets. A novel automated approach for repositioning outlet points for watershed delineation is presented, and the performance of this technique is evaluated using two existing approaches as a basis for comparison. 2. Automated Watershed Mapping [5] Automated watershed mapping algorithms generally operate by identifying all locations that are connected to an outlet point by an overland flow path [Band, 1986; Jenson and Domingue, 1988; Band, 1989; Jenson, 1991; Mackay and Band, 1998; Liang and Mackay, 2000]. Overland flow paths are typically modeled by interrogating digital topographic data, usually in the form of a regular-grid digital elevation model (DEM) [O Callaghan and Mark, 1984; Freeman, 1991; Quinn et al., 1991; Tarboton, 1997]. Several steps are generally involved in extracting watersheds from DEMs. Artifact topographic depressions and flat areas are frequently removed from DEMs prior to watershed delineation [Jenson and Domingue, 1988; Rieger, 1998; Planchon and Darboux, 2001; Lindsay and Creed, 2005]. This preprocessing step ensures that each grid cell is connected to the edge of the elevation model by a continuous flow path. A flow-direction network, which describes inflowing and outflowing connectivity among neighboring grid cells, is then calculated from the hydrologically corrected DEM. Flow-direction data can then used to traverse flow paths in order to identify upslope grid cells connected to each outlet in a data set. [6] In the first watershed delineation application type, i.e., those involving the partitioning of a landscape into a collection of subbasins, outlet points always coincide with digital streams because it is the topology of the drainage network that determines the number and position of outlets. Stream confluences serve as outlets in these applications. In contrast, outlets frequently do not coincide with significant flow paths in applications of watershed mapping that involve data-associated outlets. The reasons why this occurs are discussed further in section 3. It is important, however, to emphasize that for watershed mapping, the accuracy of outlet positioning is less important than the need for outlets to correspond to the appropriate stream link in the drainage network. That is, the accuracy of the mapped watershed is more important than the positioning of the outlet with respect to its true location. Therefore it is common practice to modify an outlet image to account for this problem. This additional step is critically important for the accuracy of mapped watersheds, and therefore it can have significant implications for studies that relate basin characteristics to hydrometric data. 3. Mapping Outlet Points Onto the Digital Stream [7] There are two main factors that contribute to the case where an outlet is not coincident with the digital representation of its stream. First, station coordinates may have been imprecisely recorded, particularly if they have been acquired from a printed map rather than using a global positioning system (GPS). The precision with which outlets are positioned is further reduced because they must be resampled onto the raster grid, using a vector-raster conversion process [Piwowar et al., 1990]. Thus an outlet s location will be affected by the grid resolution of the outlet image. The second reason why outlets and digital streams may not coincide is related to the imperfect ability of DEMs to represent topography and the limited ability of a flow algorithm to accurately simulate overland flow paths. These issues are particularly relevant in flatter parts of landscapes (e.g., floodplains) where the information contained in the DEM may not be sufficient to replicate the actual stream course [Martz and Garbrecht, 1998]. [8] Imprecision in the position of outlets relative to the digital stream network can be manifested in several types of errors. Outlets can be positioned on the correct stream link but upstream or downstream of their appropriate location, on the wrong stream link, or off the drainage network altogether. (The term stream link refers here to a length of stream between two confluences or between a channel head and a confluence.) The impact on mapped watersheds of having an outlet positioned slightly upstream or downstream of its appropriate location is usually quite small. However, the impact of an outlet positioned on the wrong stream link or off of the drainage network entirely can be substantial. Since stream grid cells tend to be sparse compared with nonstream cells, the occurrence of offstream outlets is quite common. Fortunately, off-stream outlets are relatively easy to spot because their watersheds are significantly reduced in size, often consisting of only a few grid cells. Figure 1 demonstrates the extent to which mapped watersheds can be affected by this problem. Watersheds were mapped for the 86 hydrometric station locations listed for the state of Vermont in the U.S. National Water Information System (NWIS) [Maddy et al., 1990] using a 90-m-resolution DEM derived from 3 arc sec shuttle radar topography mission (SRTM) elevation data [Farr and Kobrick, 2000]. More than 60% of the mapped watersheds were incorrectly delineated as a result of off-stream outlets, evident by their very small extents, apparent in Figure 1 as outlets that have no obvious catchment area. [9] Outlet points can be manually adjusted to coincide with the digital stream using tools for grid-cell-based image manipulation. Although manual repositioning of outlets is ideal because it allows the user to incorporate knowledge of the site, this is a time-consuming and tedious task that is prone to human error in the form of blunders. Manual placement of outlets can be entirely impractical for large collections of outlets, such as those that are obtained from national hydrometric data archives (e.g., the U.S. NWIS and the U.K. National River Flow Archive). An automated technique for repositioning outlets on DEM-derived river networks is needed in these data-rich applications. [10] The most widely used automated technique for repositioning outlets is the Snap Pour Point tool available in ArcGIS, a popular commercial GIS package. This tool moves outlets to the grid cell of highest flow accumulation within a specified search distance, centered on the outlet cell. Although Snap Pour Point is effective at reducing the 2of9

3 LINDSAY ET AL.: MAPPING POINTS ONTO DEM-DERIVED STREAM NETWORKS maps without careful inspection and the aid of additional information. Stations are commonly located near confluences in many data sets, implying that repositioning outlets using the Snap Pour Point tool will frequently misrepresent the size and shape of watersheds. [11] Jenson [1991] describes another automated method for positioning outlet points onto the digital stream network. Her method, developed for use at the U.S. Geological Survey, moves outlets to the nearest cell possessing a flow-accumulation value equal to or greater than a specified threshold. Like Snap Pour Point, Jenson s method requires a user-specified search distance, determining the size of the window centered on each outlet in which to look for stream cells. An important difference, however, lies in the fact that an outlet can be relocated to any cell within the search window in Jenson s method. In fact, an outlet will not be relocated using this scheme if it is initially positioned on a stream cell. Although this is a conceptual improvement over the Snap Pour Point approach of outlet repositioning, it is still based solely on localized information. As such, there is potential for errors of the type described in Figure 3. Figure 1. Catchments extracted from the locations of the 86 stations listed in the U.S. National Water Information System for the state of Vermont without any outlet repositioning. The gray tones of individual catchments are random to emphasize divides. Notice the abundance of outlets that have watersheds with very small extents (not apparent at this scale), resulting from off-stream outlets. Two examples of these off-stream outlets have been highlighted with arrows. occurrence of spurious off-stream outlets, it is an imperfect solution. Watershed maps are highly sensitive to the search distance (Figure 2), and it can be difficult to determine a suitable value for this parameter a priori. Clearly it is possible that off-stream outlets can still be present even after processing (Figure 3a). As such, the search distance should be large enough to fix all off-stream outlets in a data set. In practice, users often select the smallest search distance that produces a watershed map that does not contain conspicuously small watersheds. For the Vermont NWIS outlet data set, the watershed map produced by running Snap Pour Point with a search distance of seven grid cells contained no obvious off-stream outlets (Figure 2). This approach to determining the search distance can however introduce additional errors to the data set. Snap Pour Point moves all outlets, even those that are already located on significant flow paths, downstream by the search distance. This can result in inappropriate watersheds where outlets are located near confluences due to tributary-to-mainstream errors (Figure 3b) and captured-tributary errors (Figure 3c). These errors occur because outlets are moved to inappropriate links in the stream network. Mapped watershed size and shape can be highly affected by the inappropriate capture of tributaries, i.e., the addition of adjacent drainage areas. In some severe cases, it is possible that entirely wrong watersheds can be identified, without the inclusion of any part of the appropriate catchment (Figure 3d). Unfortunately, these errors are difficult to spot in watershed 3of9 Figure 2. The impact of increasing the search distance parameter of the Snap Pour Point tool on watersheds delineated from the Vermont data set described in Figure 1.

4 LINDSAY ET AL.: MAPPING POINTS ONTO DEM-DERIVED STREAM NETWORKS Figure 3. Common errors that can occur as a result of outlet repositioning. Furthermore, Jenson s method has not been widely implemented in available software. [12] Clearly the limitations of existing automated methods for outlet repositioning have serious implications for any study that relates the hydrometric data from a group of stations to mapped basin characteristics. This situation is unacceptable because of the fundamental role that watershed delineation plays in hydrogeomorphic analyses. It is worsened by the increasing need to rely on automated outlet placement for regional-scale studies involving numerous outlets, driven by the widespread availability of long-term national river flow and water quality data archives. 4. Advanced Outlet Repositioning Approach [13] Human operators are able to produce a satisfactory outlet mapping by interpreting the position of an outlet within the wider context of the river network and other surrounding outlets and by incorporating ancillary information, such as the names of the rivers and lakes upon which outlets are situated. Water body names are usually available for outlets in station site descriptions or other metadata. Snap Pour Point and Jenson s method both only consider the position of individual outlets within a small area, and do not take advantage of any ancillary station information. These limitations imply that these automated methods will produce sub-optimal results in most applications involving large outlet data sets, particularly when the data contain numerous nested outlets in close proximity to stream confluences. The AORA has been developed to address these limitations by mimicking the approach to outlet repositioning taken by human operators Description [14] The advanced outlet repositioning approach (AORA) uses the following four criteria to move outlets: [15] 1. More than one outlet cannot occupy a single grid cell. [16] 2. Where the flow paths starting from two outlets with the same water body name converge, the downstream outlet should be positioned directly on the flow path of the upstream outlet (Figure 3a). [17] 3. An outlet on water body B cannot be repositioned onto the flow path connecting upstream and downstream outlets on water body A (Figure 3b). [18] 4. Outlets should be moved to the nearest stream grid cell that satisfies each of the previous criteria. [19] Like Jenson s method, criterion 4 of the AORA ensures that all outlets are repositioned onto the digital drainage network. Thus the AORA has an implicit assumption that the points of interest are in fact located on water bodies. [20] Table 1 lists the inputs to the AORA and describes their functions. The search distance and the flow-accumulation threshold are the input parameters that require the greatest consideration by the user. The search distance should be set to a value that ensures that the search window surrounding each outlet point will contain stream cells. As stream cells are defined by the flow-accumulation threshold (i.e., streams are cells with flow accumulations greater than a threshold value), the two parameters are actually linked. In practice, there are few consequences to using a large search distance, because similar to Jenson s method, the AORA is quite insensitive to this parameter. This is because the nearest stream cell that satisfies each of the repositioning 4of9

5 LINDSAY ET AL.: MAPPING POINTS ONTO DEM-DERIVED STREAM NETWORKS Table 1. Inputs to the Advanced Outlet Repositioning Approach Input Name Original outlet image Flow-direction image Flow-accumulation image Outlet/water body file Search distance Flow-accumulation threshold Output image name Description and Function raster image containing the locations of outlets before repositioning; outlets are designated by unique positive numeric identifiers, and background grid cells are designated as null values raster image that specifies the D8-derived flow direction for each grid cell; this image is used to traverse flow paths raster image that specifies the flow-accumulation of each grid cell text file containing information about the water body name associated with each outlet; file contains two tab-separated columns corresponding to the unique numeric identifiers in the outlet image and the water body names, respectively distance (in grid cells) around each outlet over which a target grid cell for repositioning will be sought a value used to specify target (stream) cells in the flow-accumulation image; this value should be in the same units as the flow-accumulation image the name to be assigned to the output image at The development of the AORA has been guided by the fact that automated outlet repositioning is a necessary processing step in watershed mapping when outlet data sets are too large to be practically adjusted by manual means. Large outlet collections often occupy vast spatial extents (e.g., the Vermont outlet data set described above). This implies a need to process very large DEMs, something that poses a significant challenge for many GIS. The implementation of the AORA that has been provided in TAS GIS has therefore been designed to handle massive grids (>50 million cells) and very large outlet collections (>1000 sites). In addition to the output map of repositioned outlets, the software has an option to automatically map the subcatchments draining directly to each outlet. Again, this watershed delineation algorithm has been designed to handle massive DEMs. 5. Performance Evaluation 5.1. Methods [24] The performance of the AORA was evaluated using an extensive data set of manually repositioned outlets. The Snap Pour Point method and a modified version of Jenson s Table 2. Steps in the Automated Outlet Repositioning Approach criteria will be identified. Thus the AORA target cell can lie anywhere within the search window, whereas Snap Pour Point will always reposition outlets on the edge of the specified search window in a properly conditioned DEM. If an outlet is coincident with a stream cell that satisfies the first three repositioning criteria, the AORA will not move the outlet from its original location. [21] The flow-accumulation parameter should be set to a value that ensures that all outlets are positioned near streams. An appropriate value for the threshold parameter can be determined a priori by extracting a stream network from the flow-accumulation image and overlaying the outlet points. There should be few outlets that are located upstream of channel heads, i.e., in zero-order basins. A lower flow-accumulation threshold results in a more extensive digital stream network, or higher drainage density, and vice versa. [22] Table 2 outlines the steps involved in the AORA. There are two basic components to the process. In the first component (steps 3 and 4 in Table 2), outlets that are in violation of criterion 2 are identified and fixed. Outlets that have not already been repositioned in the previous component are then processed (steps 6 and 7 in Table 2), ensuring that each of the repositioning criteria are respected. Notice that outlets are not moved in the outlet image (Table 1). Instead, the row and column entries of repositioned outlets are updated in the outlet array (step 2 in Table 2). Working with the outlet array rather than of the relatively large outlet image improves the efficiency of the algorithm Implementation [23] The AORA has been implemented in Terrain Analysis System (TAS) GIS, a freely available software package for performing spatial analysis operations in the environmental sciences [Lindsay, 2005]. This software is available online Step Description 1 Assign an integer to each water body name in the outlet/water body file to serve as a numeric identifier. 2 Create an array to store outlet identifiers, row and column numbers, and the associated water body identifiers for each outlet. 3 Scan the outlet array for groups of outlets that have the same water body identifier. 4 For each outlet group (a group must contain more than one outlet): (a) Traverse the flow path starting from each outlet in the group using the flow-direction data. (b) For each grid cell encountered along a flow path, store the flow length and flow-accumulation value of the previous cell in a temporary image. Where two or more flow paths converge (i.e., flow path confluences), store the maximum flow length and flow-accumulation value of the inflowing cells. (c) Identify any minor tributaries in the flow path network contained in the temporary image as links with shorter flow lengths and/or flow-accumulation values less than that stored in the confluence grid cell. (d) Find the nearest main stem cell in the temporary image for each minor tributary outlet and update the row and column numbers in the outlet array. (e) Keep track of which outlets have been repositioned by the above procedure. 5 Reinitialize the temporary image with zeros. 6 Traverse the flow paths starting from outlets in each of the outlet groups and store the associated water body identifiers in the corresponding cells of the temporary image. 7 For outlets that have not been repositioned, find the nearest cell with a flow-accumulation value greater than the threshold (Table 1) and that is either zero in the temporary image or has a value equal to the outlet s water body identifier. Update the row and column numbers of all repositioned outlets in the outlet array. 8 Create the output image and set grid cells corresponding to the row and column entries in the outlet array to the associated outlet identifiers. 5of9

6 LINDSAY ET AL.: MAPPING POINTS ONTO DEM-DERIVED STREAM NETWORKS Table 3. Study Basin Characteristics and Hydrometric Station Numbers Basin Area (km 2 ) Maximum Relief (m) Number of Stations Derwent Eden Kent Lune Mersey Ribble Wyre [1991] method were also used to reposition outlets in order to evaluate whether the AORA can provide a significant improvement over existing methods. Jenson s [1991] method was modified to disallow repositioning of multiple outlets to a single grid cell. Both the Snap Pour Point and Jenson s method algorithms were implemented in TAS GIS. [25] The Environment Agency (EA) of England and Wales routinely monitors water quality at a series of fixed locations. In recent years, the link between water quality at these monitoring sites and catchment characteristics has been explored using various GIS approaches [e.g., Jarvie et al., 2002; Davies and Neal, 2004, 2007]. These studies have utilized ArcGIS, or its predecessors, to delineate catchments. The station locations of the EA water quality monitoring program provide a suitable data set for evaluating the performance of the automated watershed outlet repositioning methods. Since the EA sampling network is very extensive, a subsample of 993 stations within seven study basins was selected for testing (Table 3). The study basins, located within northwest England, demonstrated a range of basin sizes and physiographic characteristics. [26] DEMs were acquired for each of the seven basins from the integrated hydrological digital terrain model (IHDTM) data set provided by the Centre for Ecology and Hydrology (CEH). These data possessed a 50-m grid resolution and a 0.1-m vertical resolution. The DEMs were originally interpolated by the CEH from Ordnance Survey (OS) 1:50,000 digitized contours, spot heights, and river networks [cf. Morris and Heerdegen, 1988; Morris and Flavin, 1994]. [27] The EA monitoring station coordinates associated with each of the seven study basins were rasterized onto grids of the same dimensions as the basin DEMs. These grids served as the initial ( no processing ) outlet images, i.e., the basis for the manual and automated outlet repositioning. Outlets were manually repositioned by a human operator possessing a familiarity with the river networks gained from many years experience living in northwest England. The operator consulted maps, satellite imagery, and the station site descriptions associated with each outlet. In addition to information about the water body name upon which outlets were situated, station site descriptions also contained useful information about the relative position of stations, e.g., Lords Brook above confluence with River Medlock and River Irwell at Manchester Boating Club. A second human operator checked the data thoroughly for errors in data entry. Outlets were excluded from the analysis if there was doubt about their appropriate positioning on the digital stream network. As such, there was a high degree of confidence in the manual positioning of the 993 outlets that were included in the evaluation. [28] Before running the AORA, water body names needed to be extracted from station site descriptions for entry into the outlet/water body file, described in Table 1 (i.e., the file containing the outlet point identifiers and associated water body names). Station descriptions typically contain nominal and locational components. For example, the station description River Medlock prior to confluence with Mersey contains the water body name River Medlock, and the remaining portion of the description gives the location of the station relative to other geographic features. A program, named WaterbodyNameCleaning, was written to aid with the creation of the outlet/water body file and is provided within TAS GIS. The program is able to separate the water body name from locational information in station descriptions. Errors in the spelling of individual station names can be identified and easily removed. The program calculates the Levenshtein distance between each pair of station water body names to identify possible input errors in the textual data. Although some degree of manual effort is inevitable with this step, WaterbodyNameCleaning largely automates the process of creating the outlet/water body file. The authors were able to process the station descriptions associated with each of the seven study basins in less than one half an hour. [29] Outlets were repositioned using the AORA with a search distance of 101 grid cells and a flow-accumulation threshold of 270 grid cells in each study basin. This threshold parameter was determined by comparing the extent of stream networks with the coverage of outlet points to ensure that outlets were not located considerably upstream of channel heads. These same parameter values were used for the outlet mapping based on Jenson s method. Outlets were also repositioned automatically using the Snap Pour Point approach based on 1, 2, and 3 grid cell search distances (centered around the outlet cell), referred to henceforth as SPP-1, SPP-2, and SPP-3, respectively. An analysis of the flow-accumulation values of basin outlets showed that zero to two off-stream outlets resulted from using SPP-3 on the seven study basin data sets. Therefore, in practice, a user would likely select the outlet mapping produced by SPP-3 rather than SPP-1 or SPP-2 as the basis for further analysis. [30] In some instances there are several candidates for outlet positioning that are equally valid, usually situated within one or two grid cells of one another. Therefore the automated methods for repositioning outlets were not expected to identify the same grid cell as the manual method. In fact, because of the nature of Snap Pour Point, outlets repositioned with this algorithm rarely coincide exactly with manual placement. However, in terms of the accuracy of mapped watersheds, it is important that automatically repositioned outlets are located on the same stream link as manually repositioned outlets. Watersheds that are mapped from outlet points that are one or two grid cells apart on the same stream link will be nearly identical in most cases, with very little impact on the overall size and shape of catchments. As such, the performance of the automated outlet repositioning methods was evaluated using a stream-link-based comparison with the manually mapped data. Unique numeric identifiers were assigned to each link 6of9

7 LINDSAY ET AL.: MAPPING POINTS ONTO DEM-DERIVED STREAM NETWORKS Table 4. Errors in Outlet Locations for Seven Study Basins and Six Methods of Outlet Repositioning Basin Number of Outlets on Incorrect Stream Links AORA Jenson SPP-1 a SPP-2 SPP-3 No Processing b Derwent Eden Kent Lune Mersey Ribble Wyre Average SD Total incorrect Percent incorrect a SPP refers to the Snap Pour Points outlet repositioning technique. The number designation refers to the size of the search distance, in grid cells. b Outlets were not repositioned before watershed delineation. in the stream networks that were extracted from each of the study basin DEMs. The link numbers associated with automatically repositioned outlet grid cells were compared with the link numbers of the manually placed outlet cells. Outlets for which these values were not the same were deemed to be incorrectly positioned in the automatically derived outlet image Results [31] Table 4 shows the number of incorrectly mapped outlets for each of the automated procedures. Overall, the AORA incorrectly repositioned 5.8% of the 993 hydrometric stations in the data set, which represented nearly half of the number of erroneously positioned outlets produced by the second best approach, Jenson s method (Table 4). SPP-1 mapped 18.4% of the outlets to the incorrect stream link. Increasing the search distance to three grid cells (SPP-3) nearly doubled the number of incorrectly placed outlets (36.7%). Evidently, there is a compromise made in the application of the Snap Pour Point approach between the number of off-stream outlets and the number of outlets suffering from the other serious errors in repositioning described in Figure 3. [32] Two-proportion z tests were used to evaluate whether difference in the overall proportion of incorrectly positioned outlets caused by the AORA (5.8% of the 993 stations in the data set) and the other automated methods was statistically significant. For each test, the assumptions that n 1 p 1 > 10, n 1 (1 p 1 ) > 10, n 2 p 2 > 10, and n 2 (1 p 2 ) > 10, where p 1 and p 2 are the proportions and n 1 and n 2 are the sample sizes (993), were found to be valid. The findings supported the hypotheses that the overall proportion of incorrectly repositioned outlets resulting from application of the AORA differed from that of Jenson s method (z = 4.28, p < 0.001) andspp-1(z = 8.75, p < 0.001). Comparisons were not made with SPP-2, SPP-3, and the no-processing scenario because the results of these methods provided obviously poorer accuracies. Also, note that Bonferroni corrections were applied to control for the possibility of Type I errors because multiple comparisons were conducted. With respect to quantifying the improvement in overall performance, the proportion of incorrectly repositioned outlets caused by the AORA was found to be ± (95% confidence limits) lower than the proportion of erroneously positioned outlet by Jenson s method and ± lower than SPP-1. [33] The AORA provided the most accurate positioning of outlets in each of the seven study basins (Table 4). In fact, the pattern of outlet repositioning accuracy of AORA > Jenson > SPP-1 > SPP-2 > SPP-3 > no processing was found in most of the basins; the Wyre basin results demonstrated that Jenson s method provided equivalent accuracy to SPP-1, and SPP-2 and SPP-3 also provided similar results. A Wilcoxon matched-pairs signed-ranks test revealed that the AORA incorrectly repositioned significantly fewer outlets per basin than the second best performing algorithm, Jenson s method (N = 7, z = 2.37, p = 0.018). This nonparametric test was used instead of the matched-pairs t test because the small sample size was likely to violate the normality assumption of the t test. There did not appear to be a relation between the improvement in outlet repositioning provided by the AORA and basin size or relief. 6. Discussion [34] Although the AORA provided a substantially improved mapping of the EA hydrometric data set compared with other automated techniques, some errors did still occur. These errors were evident where there was not enough information contained in the data to properly locate an outlet. That is, most of the errors associated with the AORA outlet mapping procedure occurred where there was a single outlet located on a stream. Most instances where single outlets occur on isolated streams are not problematic because repositioning criterion 4 (move outlets to the nearest stream cell) generally does an adequate job of identifying appropriate locations. This is because outlets are usually closer to their actual stream links than they are to others. In rare instances, however, outlets were actually closer to, or even coincident with, inappropriate stream links. The AORA is robust enough to cope with outlets that are erroneously positioned near inappropriate stream links when the outlet is nested. However, when this occurs and the outlet is in isolation, the algorithm can incorrectly reposition the outlet. In these cases, the high-level information about the relative positioning of outlets contained in the station site description (e.g., River A prior to confluence with River B) was critical for correctly locating outlets during the manual placement. [35] It is likely that an algorithm that takes advantage of the locational component of station descriptions, and other related ancillary data (e.g., reported basin size), could further improve the accuracy of outlet repositioning. The development of such an algorithm may be challenging, however, because there is no standard format for these data and they may be of uncertain quality. Furthermore, higher levels of metadata are not always available. Thus, while outlet repositioning accuracy could be improved by incorporating these metadata, doing so may limit the application of the method to hydrometric data sets with appropriate attributes. The AORA, using widely available data on station water body names, is less restrictive and thus should be applicable to most data sets. [36] Clearly any error in the water body names assigned to individual stations in a data set could result in inaccuracies in outlet repositioning using the AORA. This issue is 7of9

8 LINDSAY ET AL.: MAPPING POINTS ONTO DEM-DERIVED STREAM NETWORKS largely addressed by the WaterbodyNameCleaning utility, which is used to create the outlet/water body file. This program is effective at locating typographical and other types of errors in the station description data. Unfortunately, if an entirely inappropriate water body name has been assigned to a station (e.g., Trout Creek instead of Beaver Brook), the error is likely to remain in the data set unless it is spotted by the analyst. Thus the AORA is vulnerable to this type of error. It should also be noted that while there was no appreciable difference in the speed of any of the automated outlet algorithms (i.e., they had comparable computational efficiencies; the time required for outlet repositioning is much less than watershed delineation) for all study basins, the AORA requires the added preprocessing effort associated with cleaning the station description data. As previously stated, this step can be very efficient using the WaterbodyNameCleaning utility. Thus given the potential for substantially improved accuracy in basin delineation, the effort in collecting and processing station description data is likely to be worthwhile in many applications. [37] The AORA algorithm is vulnerable to the occurrence of multiple, distinct water bodies with the same name in a data set. Two of the study basin data sets contained water body names that referred to two separate streams. No attempt was made to differentiate between same-named water bodies in the analysis. In fact, it is not uncommon in performing a regional-scale analysis to find river names that refer to more than one water body. This is particularly true of common names such as Trout Creek, Bear Creek, Big Brook, etc. When a data set contains multiple streams with the same water body names that drain to different basin outlets (i.e., to different locations on the DEM edge), the AORA can differentiate the streams because their flow paths will not converge. It is perhaps less common to find two streams within a single river network with the same name. If this situation arises within a data set, however, errors in repositioning will not occur so long as flow path confluences are beyond the search windows (i.e., 101 grid cells in the current application) around each of the nonnested outlets. The potential for error can be eliminated if the user is able to differentiate between the streams in the outlet/water body file (e.g., Red Brook 1 and Red Brook 2). [38] None of the automated outlet repositioning methods is well suited to application in basins containing large lakes, specifically to the repositioning of outlets located along lake inlets or outlets. Most flow-routing algorithms force flow paths through lakes, sometimes resulting in artifact stream networks with unusual topology. Thus outlets that are located along a lakeshore can be repositioned great distances to unexpected positions nearer the center of lakes. Analysts should be cautious of results in basins with extensive or numerous lakes. These data should be verified by manual means. 7. Summary [39] Hydrogeomorphic and landscape ecology studies often relate various watershed characteristics to hydrometric data collected at stations along rivers. The first step in this type of analysis involves delineation of the station catchments from a DEM. However, the coordinates of a station will very often not coincide precisely with the digital representation of the river on which the station is located. Although outlets can be positioned by hand relatively accurately, it is impractical to manually adjust large numbers of station locations. The increasing availability of national hydrometric data sets is likely to foster further interest in the types of regional-scale studies that require watershed delineation for large numbers of stations spread over extensive areas. Thus automated techniques for repositioning outlets onto the digital stream network are expected to play an increasingly important role in research studies in the future. [40] This paper presented a new technique for automatically repositioning outlets, the AORA. The AORA takes advantage of the additional information contained in metadata associated with outlets, specifically the water body names on which outlets are situated. The method ensures that outlets on the same water body occupy the same flow path and that an outlet is not repositioned onto a stream link occupied by outlets of a dissimilar water body name. This improved technique was found to perform significantly better than two existing automated approaches to outlet repositioning when applied to a large data set of 993 outlets within seven study basins in northwest England. Clearly, the method is particularly suited to dense collections of nested outlets. The substantial information about the relative positioning of outlets contained in the water body name data of large collections of outlets will often lead to considerable improvements in performance. However, even when applied to much smaller data sets (50 outlets), the AORA was found to perform better than the second best automated technique (Jenson s method) and considerably better than the much used commercial outlet repositioning tool, Snap Pour Point. [41] Outlet repositioning will likely always be a process requiring some degree of human intervention. However, the goal of any automated outlet repositioning technique is to take some of the burden away from the human operator and to reduce the subjectivity, the need for user knowledge of the area, and the occurrence of errors in data entry. Although watershed mapping is a very common GIS application, users rarely consider the implications of incorrectly repositioned basin outlets. The outlet repositioning preprocessing step of any drainage divide mapping exercise is critical to the accuracy of the final mapped watersheds. Therefore outlet repositioning can affect our ability to study basin responses to catchment characteristics and process functioning. As such, users need to be more aware of the implications of selecting various automated approaches to handling this problem. [42] Acknowledgments. 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Lindsay, Department of Geography, College of Social and Applied Human Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada. (jlindsay@uoguelph.ca) J. J. Rothwell, Department of Environmental and Geographical Sciences, Manchester Metropolitan University, Chester Street, Manchester M1 5GD, UK. 9of9