River Morphodynamics Modelling through Suitability Analysis of Geomatic Methods

  1. Jose-Luis Molina 1
  2. Pablo Rodríguez-Gonzálvez 1
  3. M-Carmen Molina 2
  4. Diego González-Aguilera 1
  5. Luis Balairon 3
  6. Fernando Espejo 1
  7. Jose María Montejo 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

  2. 2 SENER Ingeniería y Sistemas. Oficina Sevilla
  3. 3 Laboratorio de Hidráulica del Centro de Estudios Hidrográficos del CEDEX
Actas:
IAHR World Congress

Año de publicación: 2013

Congreso: 2013 IAHR World Congress

Tipo: Aportación congreso

Resumen

The modelling of river morphodynamics requires a multidisciplinary approach due to its wide range of dimensions and applications such as extreme events and flood analysis, erosion and soil loss estimation, river hydraulics, sediment transport modelling, reservoir sedimentation, among others. In order to minimize the initial uncertainty, all these studies require an accurate and reliable field measures for the subsequent analysis. Acquisition of spatial data is a key factor as it is the base for further calculations and analysis. However, the use of the most suitable geospatial technique for each case study becomes essential due to the numerous types of studies. This study comprises a comparative analysis of geomatic methods for river morphodynamics modelling through three types of applications: soil loss and erosion estimation models, sediment transport modelling and reservoir sedimentation. This analysis is driven by six decision variables grouped in two general groups, depending on their nature as geometric or radiometric. Geometric variables are Spatial Resolution, Terrain Elevation Resolution, Bathymetry measurement capacity, and Infrastructure Elevation Resolution (for hydraulic infrastructures). On the other hand, radiometric variables included are Vegetation Resolution, Land Uses Resolution and Dynamic Behavior. After making a review of the most used and latest geomatic methods for the aforementioned applications, the selected methods for soil loss and erosion estimation models are Terrestrial Laser Scanner (TLS), Close-range Photogrammetry, Airborne Light Detection and Ranging (LiDAR) and Aerial Photogrammetry. Then, for sediment transport modelling the Bathymetric LiDAR and Aerial Photogrammetry are considered; finally, for reservoir sedimentation, methods included in the analysis are Bathymetric LiDAR, and the combination between Sonar Bathymetry and Surveying. Each method is scored depending on the level of suitability for each decision variable. Final suitability score is defined as an average result from each partial suitability score assigned to decision variables. This study aims to provide the reader with a tool for evaluating the suitability of different geomatic methods when it comes to modelling river morphodynamics

Referencias bibliográficas

  • Allouis T., Bailly J.-S., Pastol Y. and Le Roux C., 2010. Comparison of LiDAR waveform processing methods for 10 very shallow water bathymetry using Raman, near-infrared and green signals. Earth Surface Processes and Landforms, 35(6), 640-650.
  • Arabi M., Frankenberger J.R., Enge B.A., Arnold J.G., 2008. Representation of agricultural conservation practices with SWAT. Hydrol Process. 22,3042–55.
  • Bagnold R.A., 1977. Bed load transport by natural rivers. Water Resour Res.13,303–12.
  • Belmont P., Gran K. B., Schottler S. P., Wilcock P. R., Day S. S., Jennings C., Lauer J. W., Viparelli E., Willenbring J. K., Engstrom D. R. and Parker G., 2011. Large Shift in Source of Fine Sediment in the Upper Mississippi River. Environmental Science & Technology, 45(20), 8804-8810.
  • Betrie G.D., Mohamed Y.A., van Griensven A., Srinivasan R., 2011. Sediment management modelling in the Blue Nile Basin using SWATmodel. Hydrol Earth Syst Sci. 15:807–18.
  • Bhuyan S.J., Kalita P.K., Janssen K.A., Barnes P.L,. 2002. Soil loss predictions with three erosion simulation models. Environ Model Softw 17,137–146.
  • Bladé, E., Cea L.,. Corestein G., Escolano E., Puertas J., Vázquez-Cendón E., Dolz J., Coll A., 2012. Iber: herramienta de simulación numérica del flujo en ríos. Rev. int. métodos numér. cálc. diseño ing. http://dx.doi.org/10.1016/j.rimni.2012.07.004.
  • Bodulski, J., Górski, J., 2007. Evaluation and prediction of silting in reservoir of Cedzyna on the Lubrzanka River. Journal of Water and Land Development 10, 133–149.
  • Caputo M. and Carcione J.M., 2013. A memory model of sedimentation in water reservoirs. Journal of Hydrology 476, 426–432.
  • Chen, Y.H., Richardson, E.V., Lopez, J.L., 1978. Mathematical modeling of sediment deposition in reservoirs. Journal of the Hydraulics Division 104, 1605–1616.
  • Chen Z., Hu C. and Muller-Karger F., 2007. Monitoring Turbidity in Tampa Bay using MODIS/Aqua 250-m imagery. Remote Sensing of Environment, 109(2), 207-220.
  • Cogle A.L., Lane L.J., Basher L., 2003. Testing the hillslope erosion model for application in India, New Zealand and Australia. Environ Model Softw 18, 825–830.
  • Cogollo, P.R.J., Villela, S.M., 1988. Mathematical model for reservoir silting. In: Proceedings of the 1988 Porto Alegre Symposium, 174 IASH Publication. 174, 43–51.
  • D’Oleire-Oltmanns S., Marzolff I., Peter K. D., Ries J. B. and Hssaïne A. A., 2011. Monitoring soil erosion in the Souss basin, Morocco, with a multiscale object-based remote sensing approach using UAV and satellite data. Proceedings 1st World Sustain. Forum.
  • Day S. S., Gran K. B., Belmont P. and Wawrzyniec T., 2012. Measuring bluff erosion part 1: terrestrial laser scanning methods for change detection. Earth Surface Processes and Landforms.
  • Davies-Colley R.J and Smith D.G., 2001. Turbidity suspended sediment, and water clarity: a review. Journal of the American Water Resources Association. American Water Resources Association. 37, 5.
  • De Asis A. M. and Omasa K., 2007. Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data. ISPRS Journal of Photogrammetry and Remote Sensing, 62(4), 309-324.
  • De Rose R. C. and Basher L. R., 2011. Measurement of river bank and cliff erosion from sequential LIDAR and historical aerial photography. Geomorphology, 126(1–2), 132-147.
  • Desmet P.J.J. and Govers G., 1996. A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units. J Soil Water Conserv 51(4), 427–433.
  • Dorninger P., Nothegger C., Pfeifer N. and Molnár G., 2008. On-the-job detection and correction of systematic cyclic distance measurement errors of terrestrial laser scanners. Journal of Applied Geodesy, 2(4), 191-204.
  • Dowling T., Read A. and Gallant J., 2009. Very high resolution DEM acquisition at low cost using a digital camera and free software. Proceedings 18th World IMACS/MODSIM Congress., p. 2479-2485.
  • Dugdale S. J., Carbonneau P. E. and Campbell D., 2010. Aerial photosieving of exposed gravel bars for the rapid calibration of airborne grain size maps. Earth Surface Processes and Landforms, 35(6), 627-639.
  • Easton Z.M., Fuka D.R., White E.D., Collick A.S., Ashagre B.B., McCartney M., 2010. A multi basin SWAT model analysis of runoff and sedimentation in the Blue Nile, Ethiopia. Hydrol Earth Syst Sci.14,1827–41.
  • Eitel J. U. H., Williams C. J., Vierling L. A., Al-Hamdan O. Z. and Pierson F. B., 2011. Suitability of terrestrial laser scanning for studying surface roughness effects on concentrated flow erosion processes in rangelands. CATENA, 87(3), 398-407.
  • Ernstsen V., Noormets R., Hebbeln D., Bartholomä A. and Flemming B., 2006. Precision of high-resolution multibeam echo sounding coupled with high-accuracy positioning in a shallow water coastal environment. Geo-Marine Letters, 26(3), 141-149.
  • Gao J., 2009. Bathymetric mapping by means of remote sensing: methods, accuracy and limitations. Progress in Physical Geography, 33(1), 103-116.
  • Graf W.L., Wohl E., Sinha T., Sabo J.L., 2010. Sedimentation and sustainability of western American reservoirs. Water Resources Research, 46, doi:10.1029/2009WR008836.
  • Grauso S., Fattoruso G., Crocetti C., Montanari A., 2008. Estimating the suspended sediment yield in a river network by means of geomorphic parameters and regression relationships. Hydrol Earth Syst Sci.12,177–91.
  • Guo Q.C. and Jin Y.C., 2001. Estimating coefficients in one-dimensional depth-averaged sediment transport model. Can J Civ Eng. 28, 536–40.
  • Guo Q.C. 2006. Sediment-carrying capacity in natural rivers (in Chinese). J Sediment Res. 45–51
  • Hernández-López D., Felipe-García B., Sánchez N., González-Aguilera D. and Gomez-Lahoz J., 2012. Testing the Radiometric Performance of Digital Photogrammetric Images: Vicarious vs. Laboratory Calibration on the Leica ADS40, a Study in Spain. Photogrammetrie - Fernerkundung - Geoinformation, 2012(5), 557-571.
  • Hernandez-Lopez D., Felipe-Garcia B., Gonzales-Agullera D. and Arias-Perez B., 2013. An Automatic Approach to UAV Flight Planning and Control for Photogrammetric Applications: A Test Case in the Asturias Region (Spain). Photogrammetric Engineering and Remote Sensing, 79(1), 87-98.
  • Hilldale R. C. and Raff D., 2008. Assessing the ability of airborne LiDAR to map river bathymetry. Earth Surface Processes and Landforms, 33(5), 773-783.
  • Hirschmuller H., 2008. Stereo Processing by Semiglobal Matching and Mutual Information. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(2), 328-341.
  • Hodge R., Brasington J. and Richards K., 2009. Analysing laser-scanned digital terrain models of gravel bed surfaces: linking morphology to sediment transport processes and hydraulics. Sedimentology, 56(7), 2024-2043.
  • Hydrographic Confederation of the Duero.2013. http://www.chduero.es/ (accessed 25/04/2013) Jha K.S. amd Bombardelli F.A., 2011. Theoretical/numerical model for the transport of non-uniform suspended sediment in open channels. Advances in Water Resources. 34, 577–591.
  • Kaur R., Singh O., Srinivasan R., Das S.N., Mishra K., 2004. Comparison of a subjective and a physical approach for identification of priority areas for soil and water management in a watershed: a case study of Nagwan watershed in Hazaribagh District of Jharkhand, India. Environ Model Assess. 9,115–27.
  • Kinzel P. J., Legleiter C. J. and Nelson J. M., 2013. Mapping River Bathymetry With a Small Footprint Green LiDAR: Applications and Challenges1. JAWRA Journal of the American Water Resources Association, 49(1), 183-204.
  • Kirk, J. T. 0., 1994. Light and Photosynthesis in Aquatic Ecosystems. (Second Edition). Cambridge University Press, New York, New York. 509 pp.
  • Konz M., Chiari M., Rimkus S., Turowski J.M., Molnar P., Rickenmann D., 2011. Sediment transport modelling in a distributed physically based hydrological catchment model. Hydrol Earth Syst Sci.15, 2821–37.
  • Kouli M., Vallianatos F., Soupios P., Alexakis D., 2007. A GIS example of morphometric analysis in two major watersheds of Western Crete, Greece. J Environ Hydrol 15(1),1–17.
  • Lejot J., Delacourt C., Piégay H., Fournier T., Trémélo M. L. and Allemand P., 2007. Very high spatial resolution imagery for channel bathymetry and topography from an unmanned mapping controlled platform. Earth Surface Processes and Landforms, 32(11), 1705-1725.
  • Lenhart T., Van Rompaey A., Steegen A., Fohrer N., Frede H.G., Govers G., 2005. Considering spatial distribution and deposition of sediment in lumped and semi-distributed models. Hydrol Process.19,785–94.
  • Lewis J., (1996). Turbidity-controlled suspended sediment sampling for runoff-event load estimation. Water Resources Research, 32 (7), 2299–2310.
  • Li X., Gunturk B. and Zhang L., 2008. Image demosaicing: a systematic survey. Proceedings Visual Communications and Image Processing 2008, p. 68221J-68221J.
  • Li T., Wang G., Xue H., Wang K., 2009. Soil erosion and sediment transport in the gullied Loess Plateau: scale effects and their mechanisms. Sci China Ser E-Tech Sci. 52, 1283–92.
  • Liang A.Z., Zhang X.P., Yang X.M., Mclaughlin N.B., Shen Y., Li W.F., 2009. Estimation of total erosion in cultivated Black soils in northeast China from vertical profiles of soil organic carbon. European Journal of Soil Science, 60, 223–229.
  • Lin P.N., Huan J.Q., Li X.Q., 1983. Unsteady transport of suspended-load at small concentrations. J Hydraul Eng ASCE. 109, 86–98.
  • Liu, Z. and Todini, E. 2002. Towards a comprehensive physically-based rainfall-runoff model, Hydrol. Earth Syst. Sci., 6, 859–881, doi:10.5194/hess-6-859-2002.
  • Lopez, S.J.L., 1978. Mathematical Modelling of Sediment Deposition in Reservoirs. Hydrology Papers. Colorado State University, Fort Collins, Colorado.
  • Lowe D., 1999. Object recognition from local scale-invariant features. Proceedings International Conference on Computer Vision, p. 1150-1157.
  • Morgan R.P.C., Morgan D.D.V., Finney H.J., 1984. A predictive model for the assessment for soil erosion risk. Journal of Agricultural Engineering Research 30, 245–253.
  • Naik M.G., Rao E.P., Eldho T.I., 2009. Finite element method and GIS based distributed model for soil erosion and sediment yield in a watershed. Water Resour Manag 23(2), 553–579.
  • Nearing M.A., Foster G.R., Lane L.J., Finkner S.C., 1989. A process-based soil erosion model for USDA–Water Erosion Prediction Project Technology. Trans ASAE 32(4):1587–1593.
  • Neitsch S.L., Arnold J.G., Kiniry J.R., Williams J.R., King K.W., 2005. Soil and water assessment tool theoretical documentation. Temple, TX: Grassland, soil and research service.
  • O'Neal M. A. and Pizzuto J. E., 2011. The rates and spatial patterns of annual riverbank erosion revealed through terrestrial laser-scanner surveys of the South River, Virginia. Earth Surface Processes and Landforms, 36(5), 695-701.
  • Pandey A., Chowdary VW., Mal BC., 2007. Identification of critical erosion prone areas in the small agricultural watershed using USLE, GIS and remote sensing. Water Resour Manage 21(10), 729-746.
  • Parajuli P.B., Mankin K.R., Barnes P.L., 2008. Applicability of targeting vegetative filter strips to abate fecal bacteria and sediment yield using SWAT. Agric Water Manage. 95:1189–200.
  • Pavanelli D. and Pagliarani A., 2002. Monitoring Water Flow, Turbidity and Suspended Sediment Load, from an Apennine Catchment Basin, Italy. Biosystems Engineering, 83 (4), 463–468.
  • Potes M., Costa M. J. and Salgado R., 2012. Satellite remote sensing of water Turbidity in Alqueva reservoir and implications on lake modelling. Hydrol. Earth Syst. Sci., 16(6), 1623-1633.
  • Rãdoane, M. and Rãdoane, N., 2005. Dams, sediments sources and reservoir silting in Romania. Geomorphology 71, 112–125.
  • Rakhmatullaev S., Marache A., Huneau F., Coustumer P., Bakiev M. and Motelica-Heino M., 2011. Geostatistical approach for the assessment of the water reservoir capacity in arid regions: a case study of the Akdarya reservoir, Uzbekistan. Environmental Earth Sciences, 63(3), 447-460.
  • Rieke-Zapp D. H. and Nearing M. A., 2005. Digital close range photogrammetry for measurement of soil erosion. The Photogrammetric Record, 20(109), 69-87.
  • Santhi C., Arnold J.G., Williams J.R., Dugas W.A., Srinivasan R., Hauck L.M. 2001. Validation of the SWAT model on a large river basin with point and nonpoint sources. J Am Water Resour Assoc. 37,1169–88.
  • Syahrezaab S., Beha B.C., Lima H.S., MatJafria M.Z., Abdullaha K., 2010. Monitoring the Total Suspended Solids (TSS) using High Spatial Resolution Satellite of THEOS.
  • Seitz S. M., Curless B., Diebel J., Scharstein D. and Szeliski R., 2006. A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. Proceedings IEEE Conference on Computer Vision and Pattern Recognition, p. 519-528.
  • Sekellick A. J. and Banks W. S., 2010, Water Volume and Sediment Accumulation in Lake Linganore, Frederick County, Maryland, 2009, US Department of the Interior, US Geological Survey.
  • Sharma, D.C. and Dubey, O.P., 2001. Remote sensing data made for estimating sediment delivery ratio. In: Kaushish, S.P., Naidu, B.S.K. (Eds.), Silting Problems in Hydroelectric Plants. Swets and Zeitlinger Publishers, 100–106.
  • Snavely N., Seitz S. M. and Szeliski R., 2006. Photo tourism: exploring photo collections in 3D. Proceedings SIGGRAPH 2006, p. 835-846.
  • Song J.-H., Han S.-H., Yu K. and Kim Y.-I., 2002. Assessing the possibility of land-cover classification using lidar intensity data. Proceedings Photogrammetric Computer Vision (PCV'02), p. 259-262.
  • Steegen A., Govers G., Takken I., Nachtergaele J., Poesen J., Merckx R., 2001. Factors controlling sediment and phosphorus export from two Belgian agricultural catchments. J Environ Qual. 30,1249–58.
  • Taconet O. and Ciarletti V., 2007. Estimating soil roughness indices on a ridge-and-furrow surface using stereo photogrammetry. Soil and Tillage Research, 93(1), 64-76.
  • Tajbakhsh T., 2012. CIE chromaticity, Planckian locus, and correlated color temperature estimation from raw image data using color checker training images. Proceedings SPIE 8292, Color Imaging XVII: Displaying, Processing, Hardcopy, and Applications,, p. 82920L-82920L.
  • Tamene L., Park S.W., Dikau R., Vlek P., 2006. Analysis of factors determining sediment yield variability in the highlands of northern Ethiopia. Geomorphology.76,76–91.
  • Todini, E. and Ciarapica, L.2001. The TOPKAPI model, in: Mathematical models of large watershed hydrology, edited by: Singh, V. P., Water Resources Publications, Littleton, Colorado, USA.
  • Tosic R., Dragisevic S.S., Bikit I.S., Forkapic S., Mrdja D., Torodovic N., Blagojevi B., 2012. Estimating the soil erosion and de position rate using 137CS tracer method in the catchment of Drenova reservoir (B&H). Nuclear Technology & Radiation Protection. 27, (3), 247-253.
  • U.S. Environmental Protection Agency, 1998, Report of the Federal Advisory Committee on the Total Maximum Daily Load (TMDL) Program: The National Advisory Council for Environmental Policy and Technology. EPA 100-R-98-006, 97 p., 7 appendixes.
  • Verstraeten G. and Poesen J., 1999. The nature of small-scale flooding, muddy floods and retention pond sedimentation in central Belgium. Geomorphology. 29, 275–92.
  • Verstraeten G. and Poesen J,. 2000. Using sediment deposits in small ponds to quantify sediment yield from small catchments: possibilities and limitations. Meeting on the linkage of hillslope erosion to sediment transport and storage in river and floodplain systems. Almeria, Spain: John Wiley & Sons Ltd. 1425–39.
  • Voegtle T. and Wakaluk S., 2009. Effects on the measurements of the terrestrial laser scanner HDS 6000 (Leica) caused by different object materials. Proceedings Laser Scanning 2009, p. 68-74.
  • Wang C.K. and Philpot W. D., 2007. Using airborne bathymetric lidar to detect bottom type variation in shallow waters. Remote Sensing of Environment, 106(1), 123-135.
  • Wang G., Wu B., Li T., 2007. Digital Yellow River model. J Hydro Environ Res.1:1-11.
  • Williams J.R., 1980. SPNM, a model for predicting sediment, phosphorus, and nitrogen yields from agricultural basins. Water Resour Bull.16,843–8.
  • Williams J.R., Jones C.A., Dyke P.T., 1984. Amodeling approach to determine the relationship between erosion and soil productivity. Trans ASAE 27(1), 129–144.
  • Winterbottom S. J. and Gilvear D. J., 1997. Quantification of channel bed morphology in gravel-bed rivers using airborne multispectral imagery and aerial photography. Regulated Rivers: Research & Management, 13(6), 489-499.
  • Young R.A., Onstad C.A., Bosch D.D., Anderson W.P., 1989. Agricultural nonpoint source pollution model for evaluating agricultural watersheds. J Soil Water Conserv 44(1),168–173.
  • Zhang R., 1959. Investigation on sediment-carrying capacity in the middle stream of Yangtz River (in Chinese). Sediment Research Group of Wuhan Water Resources and Hydropower College. J Sediment Re.4, 54–73