Delineating the major landforms of catchments using an objective hydrological terrain analysis method

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1 WATER RESOURCES RESEARCH, VOL. 41, W12416, doi: /2005wr004013, 2005 Delineating the major landforms of catchments using an objective hydrological terrain analysis method G. K. Summerell, 1,2,3 J. Vaze, 4,3 N. K. Tuteja, 4,3 R. B. Grayson, 2,3 G. Beale, 1 and T. I. Dowling 3,5 Received 7 February 2005; revised 3 August 2005; accepted 19 August 2005; published 14 December [1] The location and distribution of landform shape and size describe and categorize many features of a catchment. Landforms indicate soil types, arability of land, geological features, hydrological influences, and even shallow groundwater systems. This paper describes an objective method for delineating major landforms of a catchment on the basis of hydrological terrain analysis. It allows comparisons to be made within and between catchments. The method uses the UPNESS index from the Fuzzy Landscape Analysis Geographic Information System (FLAG) model (Roberts et al., 1997) that is derived from digital elevation data. UPNESS was developed as an index of surface and shallow subsurface water accumulation. In the method presented in this paper, we fit a five-parameter sigmoidal function to the cumulative distribution function (cdf) of the natural log (ln) of UPNESS. The point of inflection of the cdf of the UPNESS index is defined from the first derivative of the five-parameter sigmoidal function as the point of maximum concavity. The second and third points are defined by determining the maximum upward concavity and minimum downward concavity from the second derivative of a five-parameter sigmoidal function (referred to as break points). The inflection and break points from the UPNESS index are used to segment the cdf into three regions that represent four different landform elements. Landform categories based on these points represent ridge tops and upper slopes, midslopes, lower slope, and in-filled valley/alluvial deposits. The shape of the cdf curve indicates the dominance of major landforms within a catchment, providing an objective means for classifying this catchment characteristic. Examples are given showing how landform discrimination compares to geological maps. The landforms index presented in this study offers a useful technique for differentiating complex landforms from a landscape using terrain analysis that attempts to represent dominant hydrological soil formation processes. Citation: Summerell, G. K., J. Vaze, N. K. Tuteja, R. B. Grayson, G. Beale, and T. I. Dowling (2005), Delineating the major landforms of catchments using an objective hydrological terrain analysis method, Water Resour. Res., 41, W12416, doi: /2005wr Introduction [2] Physical descriptions of catchments at a land management scale are conveniently broken down into different landforms based on landscape toposequence. Luo et al. [2004] state that landform evolution is a vital aspect of the Earth because these are the simplest natural features we observe and they provide important clues to past and present processes operating within a landscape. Many 1 Department of Natural Resources, Wagga Wagga, New South Wales, Australia. 2 Department of Civil and Environmental Engineering, University of Melbourne, Parkville, Victoria, Australia. 3 Cooperative Research Center for Catchment Hydrology, Canberra, ACT, Australia. 4 Department of Natural Resources, Queanbeyan, New South Wales, Australia. 5 CSIRO Land and Water, Canberra, ACT, Australia. Copyright 2005 by the American Geophysical Union /05/2005WR W12416 landform classification systems exist and in Australia, most are based around the work of Speight [1990]. The landforms of catchments have been used to define landscape features to aid in soil and land capability mapping [Northcote, 1978; Emery, 1985]. Landform shape and patterns are used to develop geological maps. More recently, Murphy et al. [2003] and Vaze et al. [2004] have used the landforms along with soil landscape mapping and pedotransfer functions to parameterize soil hydraulic properties for large catchments. Summerell [2001] and Dowling et al. [2003] used the distribution of alluvial landforms within catchments for determining areas that may have been influenced by shallow local groundwater systems. A toposequences in this study reflects a regular pattern of soil down slope as soil-forming processes, soil profile drainage and sometimes parent material change. Landforms are defined as areas within the toposequence identified down and across slope by the land surface shape and pattern. [3] Simple terrain based modeling techniques can be used efficiently to define different landforms. One such technique involves using a slope index derived from a digital elevation 1of12

2 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 model (DEM). By classifying a slope index, a representation of the major landforms within a catchment can be obtained. However, selection of the categories to be used is usually subjective and invariably further processing is needed to create and refine a landform index. For example, if a slope class of 0 2% is used to represent flat alluvial or in-filled valley landforms, then the tops of ridges also fall in this slope class. Many other landform classification indices derived as secondary attributes from DEMs exist, for example landform element classifications by Pennock et al. [1987, 1994], Blaszczynski [1997], and H. I. Reuter (Analyzing digital elevation models using relief analysis within ArcInfo, Ph.D. thesis in progress, Department of Soil Landscape Research, Muncheberg, Germany, 2003). These landform classification systems aim to predict detailed soil properties by defining back slopes, foot slopes, shoulders, convergent and divergent hillslope characteristics from the DEM. This style of landform index has been used in various studies such as conservation planning based on vegetation community types within different landform elements [Carlson et al., 2004] and classification schemes for remote sensing frameworks [Bocco et al., 2001; Bolstad, 1992]. The success of such methods is dependent on the quality of the DEM, which needs to be high resolution to depict these features. [4] In this study, the UPNESS index [Roberts et al., 1997] derived from raster DEMs is used to generate the major landforms of catchments. The UPNESS index represents the dominant hydrological subsurface processes that influence soil pedogenesis and groundwater distributions [Summerell et al., 2004]. The resulting landforms index is a generalized but hydrologically process-based classification of landforms when compared to the existing landform delineation methods (as discussed above). The method is also less sensitive to DEM quality and hydrologic imperfections (i.e., the effects of a sink is limited to the extent of that sink and the UPNESS algorithm uses raw elevations making it less affected by the hydrologic disconnectedness than flow directional indices), making it relevant for hydrological studies of large catchments where DEM quality is less assured. When larger catchment ( km 2 ) studies are being undertaken, to many landform categories (some of the other methods discussed can create >10 different categories at a hillslope scale) complicate the interpretation of the landscape. To reduce complexity in these larger catchments landform categories are lumped together. When lumping occurs, decisions for combining categories are often made subjectively. [5] Landform analysis has been commonly used in catchment-scale water balance modeling [Vaze et al., 2004; Tuteja et al., 2003]. The representation of landforms for these studies was detailed enough to represent the dominant hydrological processes but simplified in a way that reduces the complexity of the problem. These studies undertook detailed model runs for each combination of landform, soil type (20 30 soil types), climate zone (4 6 zones based on rainfall) and vegetation (4 6 land uses). As such, it is very important to keep the number of landforms to a minimum. The approach proposed in this paper provides a simplified, objectively defined but hydrological based landform classification that compliments the requirements of catchment based water balance modeling studies. 2. FLAG Landform Conceptualization [6] Summerell et al. [2004] demonstrated that the UPNESS index correlates well with depth to a shallow water table and groundwater electrical conductivity (EC) for a hillslope groundwater aquifer compared to the compound topographic index (CTI) of Beven and Kirkby [1979]. The main differences between the CTI and UPNESS is that CTI calculates the upslope contributing area using more common flow direction algorithms such as D8 (steepest descent [O Callaghan and Mark, 1984]) or D1 (proportional descent [Tarboton, 1997]). In the case of the D1 algorithm, upslope area is calculated by apportioning flow between down slope cells proportional to how close this flow direction is to the steepest descent direction to the downhill cell. This method is based on surface flow directions whereas the UPNESS index is based on absolute elevations difference (discussed in detail below) and can be considered as an everything-monotonic ascent algorithm. Some landscape features can be affected by groundwater distributions which are not always dependant on surface conditions. The UPNESS index measures the position in the landscape relative to the total landscape represented on the DEM, not just the surface defined upslope area. [7] The relationship between UPNESS and groundwater EC for a single groundwater system developed by Summerell et al. [2004] was supported by the concept that as groundwater accumulates down slope, secondary weathering and concentrations of rock and soil minerals occurs, thereby increasing the concentration of salts in the unconfined to semiconfined hillslope aquifer. These processes influence soil formation and it was shown that over larger areas, with more complex groundwater systems, predictions of seasonally wet to waterlogged, saline and sodic soils could occur. On the basis of this process it was then demonstrated that this index could be used to represent various soils within a catchment. When classifying the UPNESS index into discrete areas, smoothed boundaries are derived which match more closely the line work drawn (conceptualized) by soil surveyors. This contrasts with the classification of other indices based on surface drainage networks, which tend to be discretized and highly fragmented. For this reason the UPNESS index was chosen for further investigations. [8] UPNESS is derived from digital elevation data and is defined as the accumulation of upslope area at any given point, i.e., by the set of points that are connected by a continuous monotonic uphill path. It is assumed that, for subsurface flow, all points that are connected in this way exert some hydrologic effect on the down slope location. Although the UPNESS index is considered to be a type of contributing area, measuring relative height in the landscape, it is not restricted by flow direction and any number of topographic catchment boundaries can be crossed, provided the adjacent uphill cells in the next catchment are monotonically higher [Dowling, 2000; Laffan, 2002]. Figure 1 illustrates how UPNESS is calculated from a raster DEM. The values in the cells represent 2of12

3 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 elevations. For cell A1, the UPNESS count is 11, which is derived from the cells marked with a black dot. Cell C4 represents a hill top with an elevation of 5. Cells B5, C5 and D5, which are effectively behind the hill with respect to A1, are included as contributing to cell A1 because they are connected by the monotonic uphill path through cell A4. Therefore, although cells B5, C5 and D5 are outside the topographic or surface water catchment boundary, they have subsurface potential to affect cell A1 hydrologically. The following pseudocode gives the logic of this algorithm. For a cell... process_cell(target, current): mask current =1; UPNESS target = UPNESS target +1; for i = each cell adjacent to current if elev i equals nodata then mask i =1; else if elev i greater than or equals elev current then if mask i equals 0 then call process_cell(target, i); endif endif endfor end; where maskcurrent is a temporary mask grid that tracks which cells have been processed for each target UPNESS cell (UPNESS target ); UPNESS target is the output cell for the sum of all cells monotonically uphill; and elevcurrent is the input elevation grid cell corresponding to UPNESS current. For the whole DEM... for n = each cell in grid if elevn not_equals nodata for i = each cell in grid maski = 0; endfor UPNESSn = 0; process_cell(n, n); endif endfor; end; [9] Figure 2 demonstrates the differences between UPNESS and flow accumulation indices. It compares the UPNESS index with conventional surface flow direction based contributing area and shows the distinct attributes of the UPNESS index. While the two surface catchments shown are of similar size, the UPNESS indices are larger, have significantly different areas, reach beyond the watersheds for the respective pour points, and share a common area of overlap. The nature of the UPNESS index leads very rapidly to large values for cells down slope. It is the difference, or relative height in the landscape, that is important in the UPNESS index. [10] Landforms within toposequences are often defined by concave and convex breaks of slope. At these locations in the toposequence, a significant difference in soil properties commonly occurs due to different soil depths, pedogenesis and hydrological properties. The assumption is made that specific changes in soil materials and soil forming processes are dependent on the landscape evolution processes and can be related to the UPNESS index. These break of slope positions also significantly affect the contributing cells in accumulation algorithms. Therefore Figure 1. A diagrammatic representation of a raster DEM and how UPNESS is calculated for cell A1. Note that UPNESS contributing cells (B5, C5, and D5) occur behind the surface water catchment boundary. From Summerell et al. [2004]. the UPNESS index should conceptually be able to delineate major landform types of a given toposequence by identifying these changes by major changes in cell accumulation contributions. 3. FLAG Landform Delineation Methodology [11] The UPNESS index was calculated for the Tarcutta, Billabung, Goulburn, Wollombi, Little River, and West Hume catchments (Figure 3). These catchments range between 800 and 5000 km 2, and a 25 m resolution DEM supplied by the New South Wales Land Information Centre (Statewide digital elevation model data, 1999) was used for calculating UPNESS. The UPNESS index was calculated using a threshold of 0.0 specifying that any neighboring cell with a height difference greater than or equal to zero will be included in the UPNESS area computation. The resulting UPNESS areas for each grid cell were normalized between 0 and 1 with 0 (hilltops) having the least accumulation and 1 (catchment outlet) the most. [12] The UPNESS index is converted to natural log (ln) and plotted against the number of grid cells corresponding to each UPNESS value, referred to as count in this study. Cell counts at an UPNESS value of 0 are excluded from this analysis so natural log values can be plotted. Leaving out the values at 0 does not effect the data distribution. The five-parameter sigmoidal function (equation (1)) is fitted to the cdf) of the ln of UPNESS (Figure 3). a f ¼ y o þ c ð1þ 1 þ exp x x0 b 3of12

4 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 Figure 2. An illustration of the UPNESS index compared to conventional topographic catchment contributing area. Dashed polygons are watershed catchments for pour points A and B. UPNESS index for the top catchment A is the sum of the black and light gray, while for catchment B it is the sum of the medium and light gray. The light gray overlap represents areas that are monotonically uphill from both A and B. 4of12

5 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 Figure 3. Catchment locations. West Hume (973 km 2 ), Tarcutta (1640 km 2 ), Billabung (841 km 2 ), Wollombi (1664 km 2 ), Little River (1999 km 2 ) and Goulburn (4946 km 2 ). where x is the UPNESS index, f is the count and a, b, c, x 0 and y 0 are the five-parameters. The parameter a is the maximum value of cdf count and is therefore set to this value. The parameters b, c, x 0, and y 0 can be set to initial values of 1,1, 1 and 1 respectively to aid the solver in finding a quick solution. The optimization package, solver within Microsoft Excel is used to optimize the sum of the square of errors between the observed and estimated values using the R 2 efficiency criterion of Nash and Sutcliffe [1970]. [13] Using the above procedure the five-parameter sigmoidal function was fitted to the cdf of the ln of UPNESS for six catchments (Figure 4). The six catchments chosen have different distributions of hilly and flat landforms. The fiveparameter sigmoidal function has the most difficulty fitting values of the cdf near an UPNESS value of 0 or ln Close fits occur throughout the remaining sections of the data. The three- and four-parameter sigmoidal function was also tested but the five-parameter function obtained the best fit with the values of 0 or ln Other functions tested also included the four-parameter logistic function, piecewise continuous function and various polynomials. [14] In the areas where the fit was not as close, the actual point of maximum positive second derivative (described in the next section) only varies slightly if a manual adjustment is made to the five parameters of the sigmoidal curve to force a better fit. It was therefore deemed not necessary to try and manually improve the fit determined objectively by automatic optimization (solver). [15] The point of inflection of the cdf of the UPNESS index corresponds to the first derivative of the five-parameter sigmoidal function (the point of maximum concavity in Figure 5). It can also be defined by the second derivative where the function changes from concavity upward to concavity downward and is equal to zero (Figure 6). The second and third points are defined by determining the maximum upward concavity and minimum downward concavity (referred to as break points), which corresponds with the second derivative of the five-parameter sigmoidal, function (Figure 6). [16] These three points of the UPNESS index cdf allow for a four category subdivision which broadly represent the following landforms: ridge tops and upper slopes (LF4), mid slopes (LF3), lower slope (LF2) and in-filled Figure 4. Five-parameter sigmoidal fits to the cdf of natural log of UPNESS for the (a) Tarcutta, (b) Billabung, (c) Goulburn, (d) Wollombi, (e) Little River, and (f) West Hume catchments. Note that for visual presentation to help convey the concepts, the x axis is plotted in reverse as plotting in this way actually presents as a hillslope with the ridge top category occurring a the top of the cdf and the alluvial flats at the bottom. 5of12

6 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 Figure 5. First derivative of the five-parameter sigmoidal function showing the point of inflection. valley/alluvial deposits (LF1) (Figure 7). These four categories create the FLAG landforms from the UPNESS index. [17] Summerell et al. [2003] proposed that these inflection points could be obtained from the probability distribution function (pdf). Attempts to fit the cdf of the ln UPNESS index distribution using the log Pearson and log normal distributions were made however these methods did not perform well. The UPNESS distribution has a highly left skewed distribution that begins with a high cell count at 0, rapidly increases, very quickly decreases and then turns sharply at a very low cell count. The log Pearson and log normal distributions are unable to match either the rapidly increasing distribution that starts at a high count or the sharp turn at the very low cell count. An example of the log Pearson fit is given in Figure 8. As a fit to an analytical function could not be found it was decided to fit an empirical function (the five-parameter sigmoidal function discussed above) for the purpose of fitting an optimized solution to the observed data. The five parameters themselves have not been explored for a physical basis (as a solution on the pdf was not found) and are simply used as an empirical fit. However once the optimized best fit of the function is determined the first, second, and third inflection and breakpoints from the first and second derivatives appear Figure 7. Tarcutta ln UPNESS index cdf showing the point of inflection (first point), the point of maximum upward concavity (second point), and the maximum downward concavity (third point), delineating the four landform elements. to have a physical basis, or at least indicate points in the landscape of significance. Field validation from Murphy et al. [2003] and Wild et al. [2005] has shown that these break points represent the breaks in slope that define landforms from hillslope toposequences in steep and undulating landscapes. In landscapes that have terrace formations, such as basalt flows, the inflection and break points reflect these features. Therefore as hypothesized earlier, changes in cell accumulation contributions in the UPNESS algorithms indicate where major landform types occur. The sensitivity of this change will be reflected in the detail and scale of the DEM. The effects of DEM resolution and scale on the location of these inflection and break points are discussed later. [18] For most catchments, taking values of the cdf up to an UPNESS value of provides enough data to show the first, second, and third inflection and breakpoints and therefore derive a good fit to the five-parameter sigmoidal function. The value of was an arbitrary choice to allow for the setting up of this process in a spreadsheet template, and is specific to a 25 m DEM for catchments between km 2. More or less data can be used as long as the first, second, and third inflection and breakpoints are generated. In flatter catchments, extending the Figure 6. Second derivative of the five-parameter sigmoidal function. The maximum upward concavity gives the second break point, and the minimum negative concavity gives the third break point. The inflection point in the first derivative corresponds to where the function changes from concavity upward to concavity downward where the second derivative is zero. 6of12 Figure 8. Log Pearson fit to the UPNESS index distribution.

7 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 Figure 9. (a, d, g) FLAG landforms, (b, e, h) catchment geology [Kingham, 1998], and (c, f, i) catchment cdf of UPNESS, shown as the solid line for the catchment of interest and shaded lines for the remaining five catchments. 7of12

8 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 Figure 10. (a, d, g) FLAG landforms, (b, e, h) catchment geology [Kingham, 1998], and (c, f, i) catchment cdf of UPNESS, shown as the solid line for the catchment of interest and shaded lines for the remaining five catchments. 8of12

9 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 Figure 11. FLAG landforms on different geologies at a hillslope scale showing how the distribution and patterns reflect the geomorphic characteristics that shape the landforms of the landscape. Vertical exaggeration is threshold out slightly beyond may be necessary to obtain the maximum downward concavity (third break point). Sometimes extra break points in the cdf occur at values very close to 1. If this happens, it is necessary to only include enough data to generate the third breakpoint as extra break points will stop the fitting of the fiveparameter sigmoidal function. The Billabung catchment is an example where the data range was extended beyond (Figure 4b) to allow the maximum downward concavity to occur. 4. Field Validation of the FLAG Landform Methodology [19] The distributions of landforms in a landscape closely reflect major geological changes within catchments. There- 9of12

10 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 fore, to test the FLAG landforms method at a catchment scale it was hypothesized that this method should be able to discriminate major geological changes that reflect different weathering, drainage and relief patterns in a landscape. [20] Figures 9 and 10 show comparisons of the FLAG landforms to major geological changes in the study catchments. For the Tarcutta catchment (Figures 9d and 9e) hard weathering metasediments form hilly sloped landscapes, which are represented by the dominance of landform LF3 and LF4. However, to the south east of the catchment, the landforms become more dominated by lower slopes represented by an increase in distribution of the landform LF2. At this location, the geology changes to granitic, forming the undulating gentle sloped landscape in this area. The Goulburn catchment (Figures 9g and 9h) provides another example. The Tertiary Basalts have weathered to landscapes dominated by narrow flat ridge tops (LF4) with steep upper/ mid slopes (LF3) leading into long gentle lower slopes. The Triassic Sediments become dominated by narrow ridge tops with long upper/mid slopes. Other examples as described for Tarcutta and Goulburn can be seen in the other four catchments with different geological structures (Figures 9 and 10). Figure 11 shows, at a hillslope scale, the FLAG landforms over three different geologies. On the Meta sediment geology (Figure 11a) the ridges and steep hillslopes dominate. For the Granite geology (Figure 11b) the long lower slopes of the undulating landscape occur and for the Basalt (Figure 11c), the short ridge tops followed by steep dominant upper/mid and long gentle lower slopes are shown. 5. Determining the Dominance of Landforms Within a Catchment [21] Developing an understanding of the dominant landforms within a catchment provides insight into the expected soils types and hydrological processes that occur within a catchment. The key development with this dominant landform classification is that it is based on objective criteria using the cdf distribution of the UPNESS index. To allow direct comparisons of the UPNESS cdf between different catchments, the UPNESS index for all the six catchments is plotted as a normalized (cdf) on a log scale (Figures 9c, 9f, 9i, 10c, 10f, and 10i). The shape of the UPNESS index cdf varies depending on the distribution of landforms found within a catchment. Generally, catchments dominated by steep sloping toposequences will have a cdf being plotted to the left of the chart and flatter toposequences to the right. This is the case for the Tarcutta and Wollombi catchments as they plot to the left indicating that these catchments have a dominance of ridge tops and upper slopes. The inflection (first points) for these catchments was and respectively. Whereas the Goulburn and Little River catchments plot further to the right indicating that they are progressively dominated by lower slope landforms, with the inflection (first points) values at and respectively. As the catchments become progressively flatter the curve continues to move to the right as shown for the Billabung and West Hume catchments where the inflection (first point) values were and respectively. With further research Figure 12. The cdf of natural log of UPNESS for the Little River catchment at different DEM resolutions resampled from the 25 m DEM and the AUSLIG 9 s (250 m) DEM (Australian Surveying and Land Information Group, AUSLIG 9 Second DEM, version 1, 1996). and development using more study catchments the shape of the cdf can be used as an objective measure or indicator to describe the dominance of landforms within a catchment. General observations using this method found that the measure of landform dominance is sensitive to relative catchment size. For example, hillslope catchments (i.e., <1 km 2 ) plot very differently to larger study catchments (i.e., km 2 ). Hillslope catchments needed to be of similar area to allow comparisons of landform dominance, however as catchments get progressively larger (as is the case for the catchments used in this study) the sensitivity of the method to catchment size reduces. This is because more opportunity for correct representation of the different landform features found within the landscape occurs. It is also likely that the method will not work in completely flat or very steep cliff landscapes as the shape of the cdf curve may not be representable by the five-parameter sigmoidal curve. 6. Effects of DEM Resolution on the FLAG Landforms Methodology [22] The effects of DEM scale were tested using the objective landforms method. The 25m DEM was resampled to 100 m and 250 m for the Little River Catchment, and the AUSLIG 9 s DEMs (Australian Surveying and Land Information Group, AUSLIG 9 Second DEM, version 1, 1996) was also used as an independent DEM. The Little River was chosen for this analysis as this catchment has an even distribution of landforms based on the results of Figure 10c when compared to the other catchments. The UPNESS cdf and the objectively determined inflection and break points were different for the changes in DEM resolution (Figure 12 and Table 1). However, the effects of DEM scale appear to have little effect on the landform grids generated (Figure 13) or the percentage of area occurring within each landform class (Table 2). The results are similar for all the resampled and AUSLIG 9 s DEMs demonstrating the robustness of method. The similarity of the results for the resampled 250 m and AUSLIG 9 s DEMs, exhibiting visually different structure, indicates that the method is also robust across varying quality DEMs and source data. It is noted as with all DEMs, scale will affect interpretations of results 10 of 12

11 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 Table 1. Objectively Determined Inflection and Break Points for the Little River Catchment at Four Different DEM Resolutions Resampled From the 25 m DEM and the AUSLIG 9 s (250 m) DEM Inflection and Break Points 25 m 100 m 250 m 250 m 9s First Second Third Table 2. Difference in Landform Percent for the Little River Catchment Between DEM Resolution Landform Percent of Area 25 m DEM 100 m DEM 250 m DEM 250 m 9s DEM (i.e., the generalization of information in 250 m relative to 25 m cells). 7. Discussion and Conclusions [23] An objective terrain analysis technique has been presented that enables landforms to be identified from landscape toposequences. The technique has been applied on six different catchments across NSW, Australia. The FLAG landform index closely represented major changes in the geology (which relates to landform due to different weathering and formation processes). Even though only four landform types are identified, the major geological changes are expressed by different patterns within the landscape caused by location and extent of these landforms. Field validation of modeled to observed landforms has been carried out in the Little River catchment in the Macquarie basin, Australia [Murphy et al., 2003]. The field validation shows a good match between the modeled and field mapped landforms. This index is best used to value add to existing soils information where soil toposequences are described from a broad-scale soil survey but not spatially represented. By combining the broad soil survey data with the FLAG landforms, spatially represented soil types of the landscape toposequence can be obtained. This is an area where more work and field validation has been currently undertaken in the Bombala catchment in the Murrumbidgee basin, Australia. It was identified by Summerell et al. [2004] that UPNESS often under represented alluvial areas. Future work will also see the FLAG landform combined with the multiresolution index of valley bottom flatness [Gallant and Dowling, 2003], as this index is specifically designed to map depositional areas within the landscapes. It is envisaged that by combining both methods, the strength of MRVBF in valley floors and FLAG landforms in the hillslopes will create an overall better landform delineation procedure. [24] Another strength of this method is the way the cdf plots indicating the dominance of landforms within a catchment. The shape of the cdf provides an objective measure of the dominant landforms within a catchment. This can be used as an indicator of expected hydrological and storage responses within a catchment. The method presented in this paper could potentially be used for any accumulation index analogous to the FLAG UPNESS index. The method has also shown to be robust at various scales. [25] Acknowledgments. The authors wish to thank John Lous for mathematical advice and providing the ideas on how to objectively determine the inflection and break points on the cdf of ln UPNESS curve. Peter Barker, Dugald Black, and Ross Williams are thanked for providing the opportunity to undertake this study. Brian Murphy and John Gallant are thanked for helping with the field validation of the method and supporting these ideas as well as John contributing to the pseudocode of UPNESS. Darryl Lindner is thanked for helping to prepare Figure 11. Senlin Zhou is thanked for aiding in attempts to fit the pdf to the FLAG UPNESS data. This work was funded by the NSW State Salinity Strategy, Department of Natural Resources. Figure 13. Effects of different DEM resolutions resampled from the 25 m DEM and the AUSLIG 9 s (250 m) DEM resolution on the FLAG landforms methodology for the Little River catchment. References Beven, K. J., and M. J. Kirkby (1979), A physically-based variable contributing area model of basin hydrology, Hydrol. Sci. Bull., 24, Blaszczynski, J. S. (1997), Landform characterization with geographical information systems, Photogramm. Eng. Remote Sens., 63(2), Bocco, G., M. Mendoza, and A. Vekazquez (2001), Remote sensing and GIS based regional geomorphological mapping A tool for land use planning in developing countries, Geomorphology, 39, of 12

12 W12416 SUMMERELL ET AL.: DELINEATING MAJOR LANDFORMS OF CATCHMENTS W12416 Bolstad, P. V. (1992), Improved classification of forested vegetation in northern Wisconsin through a rule-based combination of soils, terrain and Landsat Thematic Mapper data, For. Sci., 1, Carlson, B., D. Wang, D. Capen, and E. Thompson (2004), An evaluation of GIS-derived landscape diversity units to guide landscape-level mapping of natural communities, J. Nat. Conserv., 12, Dowling, T. I. (2000), FLAG analysis of catchments in the Wellington region of NSW, Consult. Rep. 12/00, CSIRO Land and Water, Canberra, Feb. Dowling, T. I., G. K. Summerell, and J. Walker (2003), Soil wetness as an indicator of stream salinity, Environ. Manage. Software, 18, Emery, K. A. (1985), Rural land capability mapping, scale 1: , Soil Conserv. Serv. of N. S. W., Sydney, Australia. Gallant, J. C., and T. I. Dowling (2003), A multi-resolution index of valley bottom flatness for mapping depositional areas, Water Resour. Res., 39(12), 1347, doi: /2002wr Kingham, R. A. (1998), Geology of the Murray-Darling Basin Simplified lithostratigraphic groupings, map, scale 1: , Aust. Geol. Surv. Organ., Canberra. Laffan, S. W. (2002), Using process models to improve spatial analysis, Int. J. Geogr. Inf. Sci., 16(3), Luo, W., K. L. Duffin, P. Edit, J. A. Stravers, and M. H. George (2004), A Web-based interactive landform simulation model (WILSIM), Comput. Geosci., 30, Murphy, B., G. Geeves, M. Miller, G. Summerell, P. Southwell, and M. Rankin (2003), The application of pedotransfer functions with existing soil maps to predict soil hydraulic properties for catchment-scale hydrologic and salinity modelling, in MODSIM, edited by D. A. Post, pp , Modell. and Simul. of Aust. and N. Z., Townsville, Queensl. Nash, J. E., and J. Sutcliffe (1970), River flow forecasting through conceptual models, part 1, A discussion of principles, J. Hydrol., 10, Northcote, K. H. (1978), Atlas of Australian Resources, vol. 1, Soils and Land Use, Div. of Natl. Mapp., Canberra. O Callaghan, J. F., and D. M. Mark (1984), The extraction of drainage networks from digital elevation data, Comput. Vision Graphics Image Process., 28, Pennock, D. J., B. J. Zebarth, and E. De Jong (1987), Landform classification and soil distribution in hummocky terrain, Saskatchewan, Canada, Geoderma, 40, Pennock, D. J., D. W. Anderson, and E. De Jong (1994), Landscape-scale changes in indicators of soil quality due to cultivation in Saskatchewan, Canada, Geoderma, 64, Roberts, D. W., T. I. Dowling, and J. Walker (1997), FLAG: A fuzzy landscape analysis GIS method for dryland salinity assessment, Tech. Rep. 8/97, CSIRO Land and Water, Canberra. Speight, J. G. (1990), Landform, in Australian Soil and Land Survey Field Handbook, 2nd ed., edited by R. C. Mcdonald et al., pp. 8 43, Inkata, Melbourne, Vic., Australia. Summerell, G. K. (2001), Exploring mechanisms of salt delivery to streams within the Kyeamba Valley Catchment, New South Wales, Australia, in MODSIM, edited by F. Ghasseemi et al., pp , Modell. and Simul. of Aust. and N. Z., Canberra. Summerell, G. K., J. Vaze, N. K. Tuteja, R. B. Grayson, and T. I. Dowling (2003), Development of an objective terrain analysis based method for delineating the major landforms of catchments, in MODSIM, edited by D. A. Post, pp , Modell. and Simul. of Aust. and N. Z., Townsville, Queensl. Summerell, G. K., T. I. Dowling, J. A. Wild, and G. Beale (2004), FLAG UPNESS and its application for determining seasonally wet and waterlogged soils, Aust. J. Soil Res., 42(2), Tarboton, D. G. (1997), A new method for the determination of flow directions and upslope areas in grid digital elevation models, Water Resour. Res., 33, Tuteja, N. K., et al. (2003), Predicting the effects of landuse change on water and salt balance A case study of a catchment affected by dryland salinity in NSW, Australia, J. Hydrol., 283, pp Vaze, J., P. Barnett, G. T. H. Beale, W. Dawes, R. Evans, N. K. Tuteja, B. Murphy, G. Geeves, and M. Miller (2004), Modelling the effects of landuse change on water and salt delivery from a catchment affected by dryland salinity in south-east Australia, Hydrol. Processes, 18, Wild, J. A., G. Summerell, S. Grant, M. Miller, and J. Young (2005), An infrastructure hazard map derived from soil landscape mapping with digital landform analysis, paper presented at Infrastructure and Planning Symposium, Dep. of Infrastructure Plann. and Nat. Resour., Wollongong, N. S. W., Australia. G. Beale and G. K. Summerell, Department of Natural Resources, P.O. Box 5336, Wagga Wagga, NSW 2650, Australia. (gregory.summerell@ dipnr.nsw.gov.au) T. I. Dowling, CRC for Catchment Hydrology, PO Box 1666, Canberra, ACT 2601, Australia. R. B. Grayson, Department of Civil and Environmental Engineering, University of Melbourne, Parkville, VIC 3100, Australia. N. K. Tuteja and J. Vaze, Department of Natural Resources, P.O. Box 189, Queanbeyan, NSW 2620, Australia. 12 of 12