Whitebox Geospatial Analysis Tools Tutorial Series. Tutorial 3: Streams and Watershed Extraction

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1 1 Whitebox Geospatial Analysis Tools Tutorial Series Tutorial 3: Streams and Watershed Extraction

2 2 Tutorial version 1.0, March, 2010 Written by John Lindsay, Whitebox Geospatial Analysis Tools Project Lead Developer, The Centre for Hydrogeomatics, The University of Guelph, Guelph, Canada, This tutorial has been prepared by John Lindsay as part of Government of Canada contract #AM W.

3 3 Preamble This tutorial will cover the following topics: 1. DEM flow enforcement 2. Creating the flow-pointer grid 3. Creating the flow-accumulation grid 4. Extracting the stream network 5. Stream network analysis 6. Extracting watersheds Certain data files are necessary for completing this tutorial and should have been distributed with this document. The data files may have been distributed in a single compressed zip file, in which case the data will need to be uncompressed prior to commencing the tutorial. The data files include: I. Az DEM.dep and Az DEM.tas II. Hillshade.dep and Hillshade.tas III. Tutorial Map.wmc Please note that the following tutorial has been prepared with Whitebox GAT version Some of the material contained in this document may not be compatible with previous or future versions of the software. 1.0 Introduction Among of the most common applications for DEMs are the automated delineation of stream networks and watersheds. These features are fundamental units for environmental modelling and management because they partition landscapes in a way that is relevant to the geomorphical and hydrological

4 4 processes operating in an area. The stream network, for example, is the boundary between the concentrated processes acting in channels (e.g. channel incision) and the diffusive processes acting on the surrounding hillslopes (e.g. wash flow). From a hydrological point of view, precipitation falling on either side of a watershed divide will follow different flowpaths, with potential implications for water quality. Therefore, an understanding of the theoretical and technical aspects of stream network and watershed extraction is important. In this tutorial, you will follow the process involved in automated stream network and watershed extraction in Whitebox GAT. 2.0 DEM Flow Enforcement 2.1 The Flow-Routing Concept: Overland and near-surface water flow can be modeled using DEMs if we assume that surface topography is the sole factor in the distribution of water. One very simple model routes all water from a particular grid cell in a DEM to a single neighbouring cell (i.e., water is not partitioned between multiple neighbours). This D8 (8 direction) method (O'Callaghan and Mark, 1984), sets the flow direction toward the lowest of the eight neighbouring cells, in relation to the centre cell. Therefore, flow is allowed in one of eight possible directions, assuming that water will travel along the steepest downslope path. Based on the 3 x 3 cell neighbourhood in Figure 1, flow would be directed from the centre cell (elevation of 8) to the southwest cell (elevation of 4). grid cell Figure 1. A small grid of elevations. The arrow represents the direction of flow out of the centre 2.2 Interruptions to Drainage Networks:

5 5 Flow direction is undefined when a grid cell, or group of grid cells, is lower than all neighbouring cells. In essence, all water that enters a cell with no downslope neighbour is unable to get out and becomes stuck. We refer to these features as pits if they are a single cell in size, and depressions if they consist of groups of cells. Often, these features are artifacts of the data and should be removed during DEM preprocessing. Pre-processing involves altering the elevations of a DEM in a way that enforces continuous flow-paths. It is important to realize that sometimes these digital depressions reflect actual features in the landscape, and should be preserved during flow modelling. However, for our work, we will assume that all depressions in DEMs are artifacts and are justified in being removed. Several methods have been developed for removing depressions from DEMs. These methods vary greatly in terms of their sophistication and impact on the DEM. The two most common depressionremoval methods are depression filling and depression breaching. Depression filling raises cells within a depression to the elevation of the outlet cell (i.e. the lowest cell on the depressions catchment). Depression breaching digs a trench from a depression s bottom to some point downslope. At the time of writing, the only approach available in Whitebox is depression filling. Depression filling is probably the most commonly used approach to the remove of depressions from DEM, though some work has suggested that it is not as appropriate as depression breaching. Depression breaching will be available in future versions of the software. Not all interruptions to flow routing are caused by depression cells. Often, DEMs contain extensive flat regions (areas of equal elevation). Flat areas interrupt flow routing in the same way as depressions. Cells within a flat region do not have downslope neighbours, and therefore, flow routing is impossible on flat sites without pre-processing. Correction of flow direction on flat sites typically involves finding an outlet cell, forcing flow from cells adjacent to the outlet to the outlet, and continuing backwards in an iterative manner (e.g., Jenson and Dominique, 1988). Both topographic depressions and flat areas are usually removed in the same pre-processing step, often by the same algorithm, creating a 'hydrologically corrected' DEM with continuous flow-paths connecting each grid cell to some outlet cell along the edge of the grid. Open the Whitebox map composition (.wmc) file 'Tutorial Map' either by selecting Open Map from the File menu or by pressing the Open Map button from the toolbar. This file will be located in the directory that you downloaded and uncompressed the data files for this tutorial into (notice that it will require that all of the data files described in the preamble are located in the same directory). A

6 6 hillshaded DEM of an mountainous area in Arizona should be displayed. The data have been derived from the Shuttle Radar Topography Mission (SRTM) data set. Aside from changing the coordinate system from the original geographic coordinates (latitude and longitude) and filling in missing data holes, the DEM has not had substantial pre-processing, meaning that it likely contains topographic depressions and possibly flat areas, which will impede the delineation of surface flow-paths. Use the Fill Depressions tool contained in the DEM Pre-processing toolbox, itself contained in the Hydrological Tools toolbox (alternatively select FillDeps from the Tool Quick Launch), to remove the topographic depressions and flat areas from the DEM. Call the output DEM 'DEM filled'. The tool may take considerable time to complete depending on your computer's processing power and resources. When it is complete, the new DEM will be displayed. 3.0 Creating the Flow Pointer Grid Now that a DEM with all interruptions to flow removed has been created, we can continue to calculate the parameters needed to define the drainage network, i.e. the topological network describing how each cell in a DEM is connected to all others. The flow-pointer data layer stores the flow direction of each cell in a raster grid, and thus, the topology of the entire surface drainage network. The exact nature of these data depends on the method used to derive the flow directions. Whitebox GAT stores D8 pointers as binary numbers (Figure 2) such that: NW N NE W E SW S SE Figure 2: Possible flow direction pointers for the grid cell shaded in grey Grid cells that have no downslope neighbour are assigned a value of zero, though the only such grid cells that should occur in a hydrologically corrected DEM should be located along the edges of the

7 7 grid. Notice that the various numerical values contained in a D8 flow-pointer grid simply represent the direction (north, north-east, east, etc.) that water, or any other flow type, entering the grid cell will be directed to on its way through the cell. Flow-pointer grids are not necessarily useful for visualization purposes themselves, but rather, are used as inputs to a great many other tools, as we will see. The above example describes the simplest and most commonly used flow-routing method, D8. In fact, there are several more complicated, and often superior, methods for routing flow over a surface. Whitebox GAT offers several such methods in addition to D8, including the stochastic Rho8 method, FD8 (or multiple-flow direction method), and the popular D-infinity algorithm. The choice of which algorithm to use can be quite difficult for users that do not have experience with these various flow-routing techniques. Some methods are more suited to certain applications. For example, applications of flow-routing that involve extracting streams that are used in stream network analysis (i.e. the analysis of the topological and geometric characteristics of the stream network) or watershed (drainage basin) extraction will require a single-flow-direction (SFD) routing algorithm such as D8. By comparison modelling the redistribution of water, sediment, contaminants, and nutrients on hillslopes (i.e. the non-channelized part of watersheds) requires a routing algorithm that allows for dispersion of flow, such as FD8 or D-infinity. Use the D8 Flow Pointer tool (within the Flow Pointer toolbox, itself contained within the Hydrological Tools toolbox; alternatively select FlowPointerD8 from the Tool Quick Launch) to extract a flow-pointer raster grid from 'DEM filled'. Call the output raster 'D8 pointer'. When the tool has completed, the output raster will be automatically displayed. It is a colourful image. Zoom in and pan around the image, taking a look at the detailed patterns and values. You should find only the values displayed Figure 2. Remove the image from the map after you are done. 4.0 Creating the Flow-Accumulation Grid Once you have determined how each grid cell in a DEM is connected to its neighbours, it is possible to use this pointer information to calculate the number of grid cells that are connected to each cell in an upslope (catchment area) and downslope (dispersal area) direction. This is known as a flowaccumulation operation. These data describe the spatial pattern of upslope (or downslope) area draining to each pixel in a DEM. Measures of catchment area are often used for stream network extraction and

8 8 other terrain indices. There are three measures of catchment area, each of which can be specified as output from all of Whitebox GAT's flow-accumulation tools. These output formats include: 1. Specific catchment area (SCA): the TCA divided by the cell size or grid resolution; 2. Total catchment area (TCA): the number of upslope grid cells multiplied by the cell area; 3. Number of upslope grid cells: the number of cells draining to each grid cell. SCA characterizes the amount of flow across a unit length of contour (grid cell length) and is less affected by the grid resolution of the DEM than the other output types. Use the D8 and Rho8 Flow Accumulation tool to map the pattern of SCA from 'DEM filled'. Call the output raster 'D8 accum' and do not log-transform the output. This is a transformation that is often performed to better visualize the contributing area distribution. Because contributing areas tend to be very high along valley bottoms and relatively low on hillslopes, when a flow-accumulation image is displayed, the distribution of values on hillslopes tends to be 'washed out' because the palette is stretched out to represent the highest values. Log-transformation provides a means of compensating for this phenomenon. Importantly, however, log-transformed flow-accumulation grids must not be used to estimate other secondary terrain indices, such as the wetness index, or relative stream power index. A better alternative to log-transformation of flow-accumulation grid for visualization is to increase the Palette Non-linearity value under the Layer Properties tab. When the tool has completed and the image has been displayed, increase the Palette Non-linearity to 5.0, then zoom in and explore the spatial pattern of SCA in the study site. Notice that some grid cells, which are coincident with larger streams, have extremely high SCA values, while many more grid cells have much more modest values. This is the basis for automated stream network extraction from DEMs. Do not remove this layer from the active map when you have finished exploring the image. 5.0 Extracting the Stream Network There are several ways to extract a stream network from a DEM but the simplest method is to apply a threshold to the SCA data layer. Imagine for a moment that it is raining over a landscape and that as the

9 9 precipitation hits the ground it travel overland based on our derived flow network. Locations with higher SCA values will have larger catchment areas, will capture more flow, and therefore, will have higher discharges. At some point downslope of the catchment divide the discharge will be high enough to cause surface erosion. Diffusive processes, like mass wasting and surface wash, tend to fill these incised areas back in. However, at some point the erosion will be so great that the incisive processes outweigh the effects of mass wasting and a stable channel head occurs over the long term. This is implies that there is a constant critical support area for channels that mark the extent of the stream network. Let s see how this works. Open the Extract Streams tool in the Stream Network Analysis toolbox. Picking an appropriate value for the SCA threshold can be a difficult and highly subjective task. It requires that you preview the SCA layer and make a judgement about the extent of the stream network. This may be a trial and error process and will be different for every DEM. It is also best when the process is guided by knowledge of the existing stream network from either map or field data. For now, set the Channelization Threshold to and call the output raster 'Streams'. You will need to zoom into the map to see the stream network raster properly. At smaller scales (i.e. greater detail) it should be displayed as a single-grid cell wide red dendritic line network overlaying the flow-accumulation image, which shows through transparently because of the NoData valued background assigned to the 'Streams' raster. You may notice that in places there are what appear to be significant drainage features in the flow-accumulation images but that the streams image does not extend far enough. The extent of the extracted stream network (i.e. the location of the apparent channel heads) has, in this case, been determined entirely based on an SCA threshold. If we had selected a lower channelization SCA threshold the extracted stream network would have been more extensive, extending further into the headwaters. If the threshold were set higher, the network would have been less extensive, including only the largest of the streams. Again, selecting an appropriate threshold is a very challenging task and will depend on the region and the characteristics of the DEM, including its grid resolution. In many landscapes, channel head locations are better predicted by a threshold in a combined function of SCA and local slope gradient. For our purposes, we will assume that the extracted network is satisfactory at representing the actual network in the study area.

10 Stream Network Analysis Close any open maps and reopen the original 'Tutorial Map' that was opened at the beginning of this tutorial. The Stream Network Analysis toolbox contains a number of tools for analyzing raster stream networks such as the one extracted in the previous section. For example, each link in a stream network can be assigned the Horton-Strahler stream order, which is used to define the size of stream tributaries based on a hierarchy. The stream order of headwater links is one. When two links of the same order meet at a confluence, the downstream link's stream order is incremented by one. When two links of unequal stream order meet, the downstream link's stream order is the same as the larger of the tributaries. In fact, there are many methods for assigning a topological hierarchy to a stream network. In addition to the Horton-Strahler stream order, Whitebox GAT offers Shreve stream magnitude as a means of quantifying stream link relative size. Stream magnitude is simply the number of headwater stream links (magnitude one) upstream of each link in the network. Open the Horton-Strahler Stream Order tool from within the Stream Network Analysis toolbox. Specify 'Streams' as the Input Streams Raster File, 'D8 pointer' as the Input D8 Flow Pointer Raster File. Many of the stream network analysis tools require these same two inputs. The D8 flow-pointer raster is used by the algorithms to traverse each link in the stream network. Using these data, it is possible to move either upstream or downstream from any grid cell. Name the output raster 'Stream order'. The resulting raster should be displayed transparently overlaying the hillshaded DEM. Notice how each link in the network has been assigned a value that represents its Horton-Strahler stream order, with headwater stream each assigned an order of 1 and the highest order stream in the study area assigned an order of 6 (in the south-west area). In addition to hierarchical ordering, Whitebox GAT can be used to identify the main channel stem; assign each link in the network a unique indentifier; map channel heads, bifurfactions, and interior/exterior links; and estimate the length and slope of each stream link. Using the Extract Statistics tool within the Statistical Analysis toolbox, it is possible to extract even more information about each link in the stream network. For example, if a land-cover raster were available, one could determine what type of land-cover each stream link runs through by specifying the land-cover raster as the Data Image and the stream link identifier raster as the Feature Definition Raster. Using this same technique, one could easily determine the elevation drop associated with each link (i.e. the maximum minus the minimum elevation). Stream network analysis provides a powerful tool for examining a

11 11 catchment system. 7.0 Extracting Watersheds Watersheds are defined here as the set of grid cells that are connected to a special grid cell, known as an outlet cell or pour point, by an overland flow-path defined by the D8 drainage network. When the point of interest is along a major river, these features are often referred to as drainage basins, and when the outlet is in a less prominent area, we frequently refer to these watersheds as catchments, which tend to be much smaller, though this is an informal and ill-defined distinction. Automated watershed extraction based on DEM-derived flow-pointer grids is a very common application of GIS and is used in many areas of research and management. The major challenge in watershed mapping is in how you define your outlet point(s). There are two general cases that arise. First, it may be the case that you have specific sites for which you need to extract the drainage area, i.e. the watershed. You may have positioned hydrometric stations where you are measuring some aspect of water or sediment quality and/or quantity and now you need to extract the watershed areas of these sites to relate characteristics of these basins (predictors) to the measured hydrological output. This is a fairly common scenario. A second case arises when a researcher, or other individual, needs to carve the landscape up into hydrologically meaningful units, such that the exact position of the outlet points is not as important as the nature of the resulting basins. For example, frequently it is the case that we want to find each of the sub-basins draining to each of the links in the stream network. In this case, the outlets are effectively determined by the geometry of the stream network, i.e. the location of the network confluences. In other scenarios, the number and/or size of the watersheds is of much more significance, in which case, basin outlets must be located in positions to ensure a collection of a certain sized catchment. Whitebox GAT has tools to help with each of these scenarios, contained within the Watershed Tools toolbox, itself contained within the Hydrological Tools toolbox. Let's examine the first scenario, a situation when the researcher has specific sites for which they must identify the watersheds. There are two approaches to handling this problem both of which will ultimately use the Watershed tool. If you open this tool, you will find that it requires two inputs: 1) a D8 flow pointer grid, derived from a hydrologically corrected DEM, and 2) a pour-point raster. The D8 flow-pointer is used to traverse each of the possible surface flowpaths within the landscape contained

12 12 within the DEM; we have already described the process for obtaining this raster grid. The pour-point grid is a raster in which each of the outlet points (individual grid cells or contiguous groups of cells) have been assigned unique non-zero valued positive identifiers, whilst background, non-outlet grid cells are assigned either zeros or NoData values. There are two main ways in which you could create a pour-point raster. First, if you have GPS co-ordinates for the stations, you could use the Convert X,Y,Z Textfile to Raster tool in the Conversion Tools toolbox (XYZTextToRaster from the Tool Quick Launch) to create a new raster into which the pour points will be automatically positioned. There is an important issue here. The position of a point along the stream in the actual landscape, as described by a GPS-measured point, will not necessarily lie upon the 'digital' stream in the flow-pointer grid. If the measured point falls off the digital stream by even one grid cell, it will have an anomalously small watershed. As such, it is critical that these points be moved onto the digital stream network prior to running the watershed tool. One of the best ways to do this is to use the Jenson's Snap Pour Points tool, which moves each pour point in a pour-point raster to the nearest stream grid cell in a stream raster. If the pour point is already coincident with a stream cell, it will not be altered. This is a far superior method than the Snap Pour Points tool, which moves each grid cell to the site of highest flowaccumulation. This tool can actually be quite damaging to your dataset and has only been provided to be compatible with ArcGIS, which implements this rather dreadful tool (for a fuller comparison of these two tools see Lindsay, Rothwell, and Davies, 2008, Water Resources Research). Notice that this method of creating a pour-point raster assumes that your flow-point raster (and therefore DEM) use the same co-ordinate system as the GPS-measured points. An alternative to importing text GPS-measured X,Y,Z coordinates is to directly digitize pour-points into a blank raster. Add 'D8 accum' to the active map and change the Palette Non-linearity on the Layer Properties tab to 5.0. Open the Create Blank Outlet Raster tool from the Watershed toolbox (or select CreateBlankOutletRaster from the Tool Quick Launch). Specify the 'D8 pointer' as the base raster and call the output raster 'Pour points'. The image will be automatically displayed but because it is a grid that is completely filled with NoData values, it will be displayed transparently over the flowaccumulation raster image. Ensure that 'Pour points' is selected as the active layer and select the Modify Pixel Values tool in the Whitebox toolbar (Figure 3). Check the Save Changes checkbox to ensure that any modifications that are made to the raster grid are saved to file. Zoom very closely into each of the tributaries identified in Figure 4. It may be necessary to zoom into each of the tributary and confluence to the point where you can see individual pixels in the grid (Figure 5).

13 13 Figure 3. The use of the Modify Pixel Values tool to change individual pixel values. Figure 4. Three tributaries for which outlets will be digitized.

14 14 Figure 5. Assigning two outlet values to tributaries. When you have identified a grid cell to serve as a pour point, assign it a new value in the Modify Pixel Values grid (the pixel that has a cross-hair drawn over-top corresponds with the middle cell in the Modify Pixel Values grid). Assign each pour point a unique value from 1-3. Each time you modify a value, be sure to press the Update Map Display. The pixel should be re-coloured in the image. If it is not exactly overlaying a value of high SCA, i.e. It is not coincident with the digital stream network, move the pour point such that it is coincident. When you are satisfied with the pour-point raster, run the Watershed tool, calling the output raster 'Watersheds'. The image should look something like Figure 6, depending on the exact positioning on the pour points. Notice that each of the watersheds are contained completely within the raster grid, i.e. none of the watersheds intersect the edge of the grid. In the second scenario for watershed extraction, the location of pour points is determined by some other factor. For example, pour points can be located on the furthest downstream cell of each link within the stream network. This effectively finds the sub-basins for the network. Close the active map. Use the Sub-basins tool to extract the sub-basins from the 'Streams' raster, calling the output raster 'Subbasins'. The pour-point raster is calculated internally and is not saved. The resulting sub-basin raster is a very colourful map containing numerous irregular-shaped polygons. Overlay 'Streams' and zoom closely into the map. Notice the relation between the stream network and each of the sub-basins. Each subbasins contains exactly one link and new sub-basins start with each confluence in the network.

15 15 Figure 6. The watersheds for each of the three digitized pour points. The sub-basins that are derived from a less extensive stream network are comparably larger and vice versa. Within any group of sub-basins there is typically a wide range of individual sub-basin sizes. In particular, the sub-basins of interior stream links are on average far smaller than the sub-basins of exterior links. Many may be only a few pixels in size. In some instances, it is important to divide the landscape into a set of nearly equally sized units of hydrologically significance. This can be achieved using the Isobasin tool. The user can specify a the desired basin size, and the tool's algorithm will locate the pour points in optimal locations to ensure the greatest coverage of basins with areas as similar to the specified size as possible. Notice that, unlike sub-basins, the location of the pour points (i.e. the outlets of each of the isobasins) are not at all linked to the extent of the stream network. Hillslopes are similar to sub-basins except that they are more finely divided. There is one hillslope that drains to each side of a stream channel, and a third hillslope that drains to the channel head. Certain hydrological modelling applications require partitioning the landscape based on a hillslope scheme. Similar to sub-basins, the size and distribution of hillslopes is entirely dependent on the extent of the extracted stream network.

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