Recent Advances in Continuum Mechanics, Hydrology and Ecology

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1 Effect of DEM Type and Resolution in Extraction of Hydro- Geomorphologic Parameters Vahid Nourani 1, Safa Mokhtarian Asl 2 and Maryam Khosravi Sorkhkolaee 3, Elnaz Sharghi 4 1 Associate Prof., 2,3 M.Sc. Student, 4 Ph.D. Student Department of Water Resources Engineering, University of Tabriz, University of Tabriz, 29 bahman Ave., Tabriz, Iran vnourani@umn.edu, vnourani@yahoo.com Abstract: - In distributed hydrological models, geomorphologic parameters extracted from DEM (Digital Elevation Model), play fundamental role either in modelling rainfall-runoff-sediment process or flood management. This paper compares application of two different types of DEMs (Digital Elevation Models) for extracted geomorphologic characteristics of watersheds. For this purpose, USGS (US geological survey) and LIDAR (light detection and ranging) DEMs were utilized. LIDAR DEMs are high resolution compared with USGS DEMs. Although such a high resolution DEM can be a useful tool to terrain analysis, vast amount of noise is expected involved in such a mega-data set. Furthermore the DEM resolution and noise effect on the geomorphic parameters of watershed was investigated. Watershed slope, specific catchment area and topographic index (TI) are the geomorphologic parameters that were evaluated in this study using different DEMs at different resolutions. Obtained results indicate that, the DEM resolution affects geomorphologic parameters of watershed. in addition, it is concluded that the existing noises can affect geomorphologic parameters. Key-Words: - Digital Elevation Model; LIDAR; GIS; Topographic Index; the Elder Creek River. 1 Introduction Watershed studies are the foundation of various hydrological analyses and hydrological parameters can be extracted by studying through the watershed geomorphologic properties. Current progresses in computer, geographic information system (GIS) and remote sensing (RS) technology have led to develop different generation of the digital elevation models (DEMs). During recent years, DEMs have been widely applied in hydro-geomorphological researches. Owing to usage of geomorphological parameters in hydrological models, it can be indicated that DEMs are used in hydrological modelling (e.g., [1]). The availability of digital elevation data has made it easy to compute topographic characteristics such as slope, specific catchment area, topographic index and main channel length. Topography also affects the transfer of chemicals and sediments from the ground level through the stream- channel system [2]. The topographic properties are categorized as initial and secondary characteristics; initial characteristics are calculated directly from elevation data, and secondary characteristics are extracted from the initial characteristics. Two initial characteristics that are used in this paper are slope (S) and specific catchment area (As), that are combined and produced the secondary characteristic of topographic index (TI), as Eq. (1): TI Ln( A / s) (1) s The (TI) has been used in several watershed models such as TOPMODEL (e.g.,[3]). [4] investigate effect of DEM resolution on TI distribution, application of continuous wavelet transform. Accessibility to continental and global DEMs can provide extraction of topographic characteristics. To this end, TI extracted from coarse-resolution DEMs is an example of such a topographic characteristics. There are various algorithms via GIS to determined the initial topographic characteristics of slope and specific catchment area, that these characteristics are composed together to generate the secondary topographic characteristic of (TI). The earliest and simplest algorithm for determining flow direction and accumulation area is the D8 ( eight flow direction) method [5]. Thereupon, some other methods such as multiple flow direction (MFD) were proposed by [6]. This method was modified by [7]. In addition, other methods such as infinite ISBN:

2 possible flow directions (D ) [8] and hybrid MFD- D [9] have also been suggested. Although, topographic characteristics such as the TI can be computed from coarse-resolution DEM, a number of researches have shown that topographic characteristics are affected by DEM grid-cell resolution. [10] studied an area in the north-eastern USA and concluded that, mean slope value computed from a 30 arc- second DEM is smaller than mean slope value that is computed from 3 arcsecond DEM. The US Geological survey (USGS) provides (1/3 arc-second~10met) and (1 arcsecond~10met) resolution DEMs for many parts of the USA ( According to many studies, often DEMs with small cell size have not shown satisfactory results [11]. [12] exhibited that specific catchment area is longer for the coarseresolution DEMs by comparing DEMs with resolution ranging from 15 to 90-m. [1] compared SRTM ( Shuttle Radar Topography Mission ) DEM with USGS (US Geological Survey ) DEM to determine the noise and the effect of type of DEM on hydrogeomorphological parameters. The research presented in this paper compares slope, specific catchment area and topographic index extracted from different DEMs. For this purpose, USGS and LIDAR DEMs at resolution 10- and 30-m are utilized. In this way 1-m high resolution LIDAR DEM is aggregated into 10- amd 30-m DEMs. Three topographic characteristics, slope, TI, specific catchment area, and main channel length, were selected for this investigation because of their extensively implications use in several distributed hydrological models. 2 Study Area The South Fork Eel River basin in northern California, USA drains an area of approximately 1,800 km 2. The climate is generally Mediterranean type and characterized by long, warm summers and cool, wet winters. Mean annual rainfall ranges from 1,500 mm to 1,800 mm most of which falls between October and April [14]. Approximately 20 percent of the basin is owned by State Parks and the Bureau of Land Management and a small portion is owned by large timber industries, while the remainder is owned by small landholders, ranchers and residential communities [15]. This research focuses on a sub-basin of the South Fork Eel River, it is called, Elder Creek that is a largely undisturbed basin. This sub-basin is located between the to and to North latitude and West longitude, respectively. The length of the main channel is about 8 km with an average stream gradient of 80 m/km. This basin with beautiful landscape from narrow canyons and steep hills has km 2 area. LIDAR DEM on 1-m resolution and USGS DEM on 10- and 30-m resolution for this sub-basin were retrieved from and respectively. 3 Methodology USGS DEM of Elder creek sub-basin was retrieved from on 10- and 30-m resolutions and LIDAR DEM was obtained from in 1-m resolution. Then, 10- and 30-m resolution DEMs were extracted from 1-m resolution LIDAR DEM. LIDAR is an optical remote sensing technology that can measure the long distances, or other properties of target by illuminating the target with light, often using laser pulses. Slope, specific catchment area, TI and main channel length were computed from USGS and LIDAR DEMs with 10- and 30-m resolutions. LIDAR DEM with 1-m resolution was aggregated into 10- and 30-m resolution DEMs. To this end, it was used the GIS software. In export part of the GIS software, size of resolution was entered to produce the target resolutions DEMs. The effects of DEM type and resolution on the topographic characteristics is determined by comparing topographic characteristics obtained by USGS and LIDAR DEMs on different resolutions. Each DEMs first were processed to fill sinks (i.e. If the height of eight pixels around central pixel were higher than central pixel, in this case, water would fall into the trap and could not continue to flow). Most of the time, the DEMs have sinks. If the process of filling sinks do not place on DEMs, via sink pixels will be faced no data. These pixels without data disrupt the DEM processing. Downslope directions also were assigned to grid cells in flat areas using Jenson and Domingue (1988) algorithm. The magnitude of the slope (S), in the steepest downslope direction was calculated for each grid cell as: S Z / L (2) where Z is the change in elevation between neighbouring cells (i.e. Each pixel has eight pixels around itself, one of the eight pixels has the highest elevation. This pixel is selected to determine the Z, and L is the horizontal distance between the centre points of grid cells. ISBN:

3 Also, it is necessary determine the flow direction. Flow direction displays the downward slope of the flow. Specific catchment area (As), is then computed for each grid cell of the DEM as : A ( n 1) A C (3) s cell cell / where n cell is the number of upslope grid cells, A cell is the area of a single grid cell, and C is the grid-cell length. Eq. (3) simplifies as: A ( n 1) C (4) s cell since A cell = C C. Spatial distributions of A s, S and Ln(A s /S) were computed for each DEMs. Then maximum, minimum, mean and Standard deviation (SD) were calculated for extracted topographic characteristics. This process is performed by ArcHydro tool in GIS software ( 4 Result and Discussion In hydrological modelling, GIS is used for information storage in raster pixels, due to its ability for spatial analysis. The DEM is the most common types of raster data, that is used to describe the topography. In this paper USGS and LIDAR DEMs were utilized. To extract topographic characteristics, it is necessary to prepare DEM. In so doing, it was used of ArcHydro tool. ArcHydro is the geospatial data model utilized for water resource problems works in ArcGIS software ( This tool is used to derive the hydrogeomorphologic characteristics of watersheds. 4.1 Extraction of drainage networks Drainage network as a set of discharge channels in the basin plays critical role in the hydrological studies. In this study, drainage network is extracted by D8 (eight flow direction) method using ArcHydro tool. In this method, flow point is defined as a pixel raster that is surrounded by eight other pixels. The flow point, based on steep direction is poured to one of eight peripheral pixels. The Flow direction raster has been used for watershed extraction. The extracted drainage network of watershed using USGS and LIDAR DEMs have been presented in (Figs. 1) in 10-m resolutions. With comparison drainage networks, it is observed that the number of extracted subbasins are obtained differently by two DEMs. 4.2 Supplying the hypsometry curve of elevation To derive the hypsometry curve of elevations, the watershed elevation is reclassified via GIS software. Then the hypsometry curve of elevation is plotted based on area (Fig. 2). In (Fig. 2) it is observed that the hypsometry curve of elevation for USGS and LIDAR DEMs on 10- and 30-m resolutions are consistent with high approximation. But the hypsometry curve of elevation is somewhat different for LIDAR DEM on 1-m resolution. In this way, it seems that LIDAR DEM on 1-m resolution is high resolution DEM as a mega data set. High amount of noise is expected involved in a mega data set. 4.3 Calculated the mean slope Slope is the rate of height change, that commonly is expressed by percent or degree. This characteristic has a significant impact on hydrological and geomorfological studies. The slope affects on surface and subsurface flow velocity and sedimentation rate. Slope is calculated based on Eq. 2. The GIS software calculates slope for each cell then the mean slope for basin is calculated through weighed mean. The values of mean slope are shown in Table 2. Mean value of slope computed from USGS DEM with 30-m resolution is smaller than those computed from similar DEM with 10-m resolution. Also, mean value of slope computed from LIDAR DEM with 30-m resolution is smaller than those computed from similar DEM with 10-m resolution. It seems that, increasing of cell size acts like a filter and with increasing the cell size, the mean value of slope is decreased. But this process is not observed for LIDAR DEM with 1-m resolution. In this way, it is stated LIDAR DEM with 1-m resolution is high resolution DEM as a mega data set. High amount of noise is expected involved in a mega data set. AS well as, mean value of slope for LIDAR DEM is bigger than USGS DEM in the same resolution. ISBN:

4 4.4 Supplying the hypsometry curve of topographic index For this purpose the TI is calculated by Eq. (1). Then the TI layer is classified and the hypsometry curve of TI is plotted based on area (Fig. 3). The Maps of spatial distributed TI are displayed in (Fig. 4). In (Fig. 3) it is observed that (TI) changes is related on the changes of cell size and DEM type. Mean values of TI are larger for USGS DEM with 30-m resolution compared with the USGS DEM with 10-m resolution and also, mean values of TI are larger for LIDAR DEM with 30-m resolution compared with the LIDAR DEM with 10-m resolution. These results are consistent with previous researches on the effects of DEM resolution on topographic characteristics about slope and topographic index [10], [12], [6], [7], [3]. As well as, it is observed that the TI value is negative for LIDAR DEM with 1-m resolution which makes the hydrological modelling based on it would not have the satisfactory results. With the comparison results in (Fig. 4) it is observed that spatial distributed (TI) depends on resolution and source of the product DEM. The Standard deviation (SD), maximum, minimum, mean, for TI, slope, specific catchment area Table 1; Table 2; Table 3; respectivley. The results are indicated that mean values of specific catchment area for 30-m resolution is not larger than 10-m resolution for USGS DEM. The same process is repeated for LIDAR DEM. Fig. 1 (a) Drainage networks for LIDAR DEM (10m) and (b) Drainage networks for USGS DEM (10m) Fig. 2 Hypsometry curve of Elevation Fig. 3 Hypsometry curve of topographic index ISBN:

5 Fig. 4 spatial distribution of Topographic index Table 1. statistical of Topographic Index Table 2. statistical of Slope DEM Cell Length (m) Max (TI) Mean (TI) Min (TI) SD DEM Cell Length (m) Max slope o Mean slope 0 Min slope o SD USGS USGS USGS USGS LIDAR LIDAR LIDAR LIDAR LIDAR LIDAR Table 3. statistical of Specific Catchment Area (SCA) DEM Cell Length (m) Max SCA (m) Mean SCA (m) Min SCA (m) SD USGS USGS LIDAR LIDAR LIDAR Conclusion This research has shown that type of DEMs and their resolution can affect on computed values of topographic characteristics. Also, has displayed that because of mega data set the noise is generated on DEM with high resolution. It should be noted that as the resolution of DEM becomes finer and finer, some artificial characteristics such as buildings, parking lots, roads, and bridges and other ISBN:

6 canopy structures could be calculated as topographic characteristics. Therefore, it is recommended to use denoising methods such as wavelet to eliminate the noise. Then it is extracted different resolution DEM of denoised DEM. So, in hydrological modelling base on extracted hydrological parameters of various DEM type with different resolution should not expect to obtain similar results. At this point a question arises: what kind of DEM with which resolution is suitable for extracting the hydrogeomorphologic parameters? In response to this question, It is suggested to extract the topographic characteristics for using in watershed modelling processes, various type of DEM with different resolution be considered. Also, initially it is used of denoising tools such as wavelet transform for DEM with high resolution. Then, different resolutions are prepared via denoising DEM. Finally the topographic characteristics can be extracted from these DEMs. Extracted topographic characteristics such as TI can be used in hydrogeomorphologic modelling such as TOPMODEL. Finally, it can be estimated that what kind of DEM with which resolution is suitable, based on results of the hydrological modelling. References: [1] Falorni, G., Teles, V., Vivoni, E.R., Bras R.L., and Amaratunga, K.S., Analysis and characterization of the vertical accuracy of digital elevation models from the Shuttle Radar Topography Mission, Journal of Geophisical Research, VOL. 110, F02005, doi: /2003jf000113, [2] Moore, I.D., Grayson, R.B., and Ladson, A.R., Digital terrain modeling: a review of hydrological, geomorphological and biological applications. Hydrological Processes, Vol.5, 1991, pp [3] Nourani, V. and Zanardo, s., Wavelet-based regularization of the extracted topographic index from high resolution topography for hydrogeomorphic applications, Hydrological Processes, 2013, doi: /hyp.9665 [4] Nourani, V., Application of continues wavelet transform to investigate the resolution effect of digital elevation model on topographic index distribution, International Journal of Soft Computing and Engineering, Vol.1, No.5, 2011, pp [5] O Callaghan, J.F., and Mark, D.M., The extraction of drainage networks from digital elevation data, Computer Vision, Graphics, and Image processing, Vol.28, 1984, pp [6] Quinn, P.F.,. Beven, K.J, Chevallier P., and Planchon O., The prediction of hill slope flow paths for distributed modeling using digital terrain models, Hydrological processes, Vol.5, 1991, pp [7] Quinn, P.F., Beven, K.J., and Lamb R., The ln (a/tanb) index: How to calculate it and how to use it within the TOPMODEL framework, Hydrological processes, Vol.9, 1995, pp [8] Tarboton, D.G., A new method for the determination of flow directions and upslope areas in grid digital elevation models, Water resources research, Vol.33, 1997, pp [9] Seibert, J., and McGlynn, B.L., A new triangular multiple flow direction algorithm for computing upslope areas from gridded digital elevation models, Water resources research, Vol.43, 2007, 04501, doi: /2006wr [10] Jenson, S.K., Applications of hydrologic information automatically extracted from digital elevation models, Hydrological Processes, Vol.5, 1991, pp [11] Jiang, L., Wong W.S., Effects of DEM source on hydrologic applications. Computers, Environment and urban system, Vol.34, 2010, pp [12] Panuska, J.C., Moore, I.D., and Kramer, L.A., Terrain analysis: integration into the agriculture nonpoint source (AGNPS) pollution model, Journal of Soil and Water Conservation, Vol.46, 1991, pp : [13] James, S.M., South Fork Eel watershed erosion investigation, Northern District:California Department of Water Resources, 1983, 95 p. [14] Allen, D., Dietrich, W., Baker, P., Ligon, F., and orr, B., Development of a Mechanistically Based, Basin-Scale Stream Temperature Model: Applications to Cumulative Effects Modeling, United States Department Of Agriculture, 2007, pp ISBN: