Suitability of open-access elevation models for micro-scale watershed planning
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Watershed planning is a major issue in Turkey and other parts of the world. Surrounded by seawater on almost three-quarters of its international borders and by sheer mountains along the coastal regions and throughout the country, Turkey experiences a range of climatic changes, which constantly shape its topography. Recently, the occurrences of floods, landslides, and torrents have increased, forcing decision-makers to come up with solutions to manage and rehabilitate the upper watersheds in order to stop or limit the impact of disasters on downstream areas. Possible solutions should reduce flow coefficients, erosion, and sedimentation and increase reservoir capacities. It is expected that torrent volumes will decrease, drainage regimes on slopes will be better organized and adjusted, thawing snow will be better deposited and delayed, evapotranspiration will increase, surface runoffs will be delayed, and water regimes will be better managed, meaning that flood and torrent control will be achieved. For the reasons mentioned above, watershed parameters need to be firmly set. In the scope of this study, the elevation, slope acreage, and reservoir capacity of a small watershed, as extracted from open-access elevation models, were compared to a real-time kinematic (RTK) global positioning system (GPS)-generated point cloud and the resulting elevation model through various geospatial and analytical means. The Shuttle Radar Topography Mission (SRTM) C-band digital elevation model (DEM) (version 3) proved to be a satisfactory method in making residual, correlation, mean, and reservoir capacity comparisons. An L-band Advanced Land Observing Satellite (ALOS) phased-array-type synthetic aperture radar (PALSAR) and an X-band DLR_SRTM ASTER were slightly superior methods in terms of defining a greater number of slope categories than the other models. Finally, DLR_SRTM and SRTMv3 could match a greater number of slope façades than the other models. Seventeen years after its acquisition, SRTM and its derivatives have continued leading the topographic definition of the Earth.
KeywordsWatershed parameters Elevation models GIS
- Anornu, G. K., & Kortatsi, B. K. (2012). Comparability studies of high and low resolution digital elevation models for watershed delineation in the tropics: Case of Densu River basin of Ghana. International Journal of Cooperative Studies., 1(1), 9–14.Google Scholar
- Aronof, S. (2005). Remote sensing for GIS managers. First. Redlands: ESRI Press 487 p.Google Scholar
- Aster Global DEM Validation (2009). Summary report. 28 p. Available online at https://lpdaac.usgs.gov/sites/default/files/public/aster/docs/ASTER_GDEM_Validation_Summary_Report.pdf, last accessed 28 July 2017.
- Bolt, B. A., Horn, W. L., MacDonald, G. A. & Scott, R. F. (2013). Geological hazards: earthquakes - tsunamis - volcanoes - avalanches - landslides - floods. Revised, Second edition. Springer Science & Business Media. 332 p.Google Scholar
- Boydak, M. & Caliskan, S. (2015). Afforestation in arid and semi-arid regions. 68 p. Available online at: http://www.cem.gov.tr/erozyon/Files/yayinlarimiz/AFFORESTATIONINARIDANDSEMIARIDREGIONS.pdf, last accessed 25 July 2018.
- Ceylan, A., Alan, I. & Ugurlu, A. (2007). Causes and effects of flood hazards in Turkey. The proceedings of International Congress River Basin Management. Antalya, Turkey: 415–423.Google Scholar
- Deniz, R., Celik, R. N., Kutoglu, H., Ozludemir, M. T., Demir, C. & Kinik, I. (2008). Buyuk Olcekli Harita ve Harita Bilgileri Uretim Yonetmeligi. 86 p. Available online at: http://www.hkmo.org.tr/resimler/ekler/7VST_ff3e350028d0cfc_ek.pdf, last accessed 16 July 2018.
- Duncan, J. M., Wright, S. G., & Brandon, T. L. (2014). Soil strength and slope stability (2nd ed.). John Wiley and Sons, Inc.: Hoboken 317 p.Google Scholar
- Elkhrachy, I. (2016). Vertical accuracy assessment for SRTM and ASTER Digital Elevation Models: A case study of Najran city, Saudi Arabia. Ain Shams Engineering Journal, 2, 1–11.Google Scholar
- EM-DAT (2009). Center for Research on the epidemiology of disasters. Available online at: http://www.emdat.be/database, last accessed 16 Mar 2017.
- ESRI (2018). What is raster data?—Help | ArcGIS for desktop. Available online at: http://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/what-is-raster-data.htm, last accessed 16 July 2018.
- FAO (2006). Guidelines for soil description. Fourth. Food and Agriculture Organization of the United Nations, Rome. 109 p. Available online at: http://www.fao.org/3/a-a0541e.pdf, last accessed 16 July 2018.
- Grussenmeyer, P., Landes, T., Voegtle, T., & Ringle, K. (2008). Comparison methods of terrestrial laser scanning, photogrammetry and tachometry data for recording of cultural heritage buildings. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI, 213–218.Google Scholar
- Kang, M., Wang, M., & Du, Q. (2015). A method of DTM construction based on quadrangular irregular networks and related error analysis. PLoS One, 10(5), 1–17.Google Scholar
- López-Moreno, J. I., Revuelto, J., Gilaberte, M., Morán-Tejeda, E., Pons, M., Jover, E., Esteban, P., García, C., & Pomeroy, J. W. (2013). The effect of slope aspect on the response of snowpack to climate warming in the Pyrenees. Theoretical and Applied Climatology, 117(1), 207–219.Google Scholar
- Mei, Y., Chang, C., Dong, Z., & Wei, L. (2016). Stream, Lake, and reservoir management. Water Environment Research, 87(10), 1515–1550.Google Scholar
- Mispan, M. R., Zamir, M., Rasid, A., Faiza, N., Rahman, A., Haron, S. H., & Ahmad, N. (2015). Assessment of ASTER and SRTM derived digital elevation model for highland areas of peninsular Malaysia region. Concepts Journal of Applied Research., 02(09), 316–320.Google Scholar
- Mukundan, R., Pradhanang, S. M., Schneiderman, E. M., Pierson, D. C., Anandhi, A., Zion, M. S., Matonse, A. H., Lounsbury, D. G., & Steenhuis, T. S. (2013). Suspended sediment source areas and future climate impact on soil erosion and sediment yield in a New York City water supply watershed, USA. Geomorphology., 183, 110–119.CrossRefGoogle Scholar
- Pradhan, B. & Abdulwahid, W. M. (2017). Landslide risk assessment using multi-hazard scenario produced by logistic regression and LiDAR-based DEM in Laser Scanning Applications in Landslide Assessment: 253–275.Google Scholar
- Sturm, T., & Podobnikar, T. (2017). A probability model for long term forest fire occurrence in the karst forest management area of Slovenia. International Journal of Wildland Fire., 26, 399–412.Google Scholar
- Turker, A. & Acikgoz, T. (2006). Orman Isletmelerinin Etkinliklerine Iliskin Finansal Cozumlemeler in the Proceedings of “Ormancilikta Sosyo Ekonomik Sorunlar Kongresi” _2006. Cankiri: 163–173. Available online at: http://www.foresteconomics.org/congress/proceedings_2006.pdf, last accessed 25 July 2018.
- Turner, K. G., Anderson, S., Gonzalez, M., Costanza, R., Courville, S., Dominati, E. & Ogilvy, S. (2014). Toward an integrated ecology and economics of land degradation and restoration: Methods, data, and models. Report to the ELD Project Data and Methodology Working Group: 1–60.Google Scholar
- Yang, R., Chang, Z. & Xue, T. (2011). 3D terrain visualization for Mountain Taishan. ICSDM 2011 - Proc. 2011 IEEE Int. Conf. Spat. Data Min. Geogr. Knowl. Serv:285–290. Available online at: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5969048, last accessed 25 July 2018.
- Zhou, J., Li, Z., & Xing, Q. (2011). Monitoring thickness changes of mountain glacier by differential interferometry of ALOS PALSAR data. IEEE International Geoscience and Remote Sensing Symposium, 24-29 July 2011, Vancouver, BC, CANADA, 3649–3652.Google Scholar