2022, Volume 19, Issue 12
Engineering >> 2022, Volume 19, Issue 12 doi: 10.1016/j.eng.2021.03.027
Recognition of Fluvial Bank Erosion Along the Main Stream of the Yangtze River
a State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200000, China
b Nanjing Center, China Geological Survey, Nanjing 210000, China
c Institute of Eco-Chongming, Shanghai 200000, China
Next Previous
Abstract
Recognizing the risk of fluvial bank erosion is an important challenge to ensure the early warning and prevention or control of bank collapse in river catchments, including in the Yangtze River. This study introduces a geomorphons-based algorithm to extract river bank erosion information by adjusting the flatness from multibeam echo-sounding data. The algorithm maps 10 subaqueous morphological elements, including the slope, footslope, flat, ridge, peak, valley, pit, spur, hollow, and shoulder. Twenty-one flatness values were used to build an interpretation strategy for the subaqueous features of riverbank erosion. The results show that the bank scarp, which is the erosion carrier, is covered by slope cells when the flatness is 10°. The scour pits and bank scars are indicated by pit cells near the bank and hollow cells in the bank slope at a flatness of 0°. Fluvial subaqueous dunes are considered an important factor accelerating bank erosion, particularly those near the bank toe; the critical flatness of the dunes was evaluated as 3°. The distribution of subaqueous morphological elements was analyzed and used to map the bank erosion inventory. The analysis results revealed that the near-bank zone, with a relatively large water depth, is prone to form large scour pits and a long bank scarp. Arc collapse tends to occur at the long bank scarp to shorten its length. The varied assignment of flatness values among terrestrial, marine, and fluvial environments is discussed, concluding that diversified flatness values significantly enable fluvial subaqueous morphology recognition. Consequently, this study provides a reference for the flatness-based recognition of fluvial morphological elements and enhances the targeting of subaqueous signs and risks of bank failure with a range of multibeam bathymetric data.
Keywords
Multibeam echo-sounding data ; Morphological elements ; Bank erosion ; Bank scarp ; Scour pits ; Bank collapse
SupplementaryMaterials
Figures
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
References
[ 1 ] Sekely AC, Mulla DJ, Bauer DW. Streambank slumping and its contribution to the phosphorus and suspended sediment loads of the Blue Earth River, Minnesota. J Soil Water Conserv 2002;57(5):243–50. link1
[ 2 ] Evans DJ, Gibson CE, Rossell RS. Sediment loads and sources in heavily modified Irish catchments: a move towards informed management strategies. Geomorphology 2006;79(1–2):93–113. link1
[ 3 ] Wilson CG, Kuhnle RA, Bosch DD, Steiner JL, Starks PJ, Tomer MD, et al. Quantifying relative contributions from sediment sources in Conservation Effects Assessment Project watersheds. J Soil Water Conserv 2008;63(6): 523–32. link1
[ 4 ] Henshaw AJ, Thorne CR, Clifford NJ. Identifying causes and controls of river bank erosion in a British upland catchment. Catena 2013;100:107–19. link1
[ 5 ] Marteau B, Vericat D, Gibbins C, Batalla RJ, Green DR. Application of structurefrom-motion photogrammetry to river restoration. Earth Surf Process Landf 2017;42(3):503–15. link1
[ 6 ] Xia J, Zong Q, Deng S, Xu Q, Lu J. Seasonal variations in composite riverbank stability in the Lower Jingjiang Reach, China. J Hydrol 2014;519:3664–73. link1
[ 7 ] Konsoer KM, Rhoads BL, Langendoen EJ, Best JL, Ursic ME, Abad JD, et al. Spatial variability in bank resistance to erosion on a large meandering, mixed bedrock-alluvial river. Geomorphology 2016;252:80–97. link1
[ 8 ] Gilvear DJ, Davies JR, Winterbottom SJ. Mechanisms of floodbank failure during large flood events on the rivers Tay and Earn, Scotland. Q J Eng Geol Hydrogeol 1994;27(4):319–32. link1
[ 9 ] Kummu M, Lu XX, Rasphone A, Sarkkula J, Koponen J. Riverbank changes along the Mekon River: remote sensing detection in the Vientiane–Nong Khai area. Quat Int 2008;186(1):100–12. link1
[10] Sarkar A, Garg RD, Sharma N. RS-GIS based assessment of river dynamics of Brahmaputra River in India. J Water Resource Prot 2012;04(02):63–72. link1
[11] Winterbottom SJ, Gilvear DJ. A GIS-based approach to mapping probabilities of river bank erosion: regulated River Tummel, Scotland. Regul Rivers Res Manage 2000;16(2):127–40. link1
[12] Bandyopadhyay S, Ghosh K, De SK. A proposed method of bank erosion vulnerability zonation and its application on the River Haora, Tripura, India. Geomorphology 2014;224:111–21. link1
[13] Midgley TL, Fox GA, Heeren DM. Evaluation of the bank stability and toe erosion model (BSTEM) for predicting lateral retreat on composite streambanks. Geomorphology 2012;145–146:107–14. link1
[14] Deng S, Xia J, Zhou M, Lin F. Coupled modeling of bed deformation and bank erosion in the Jingjiang Reach of the middle Yangtze River. J Hydrol 2019;568:221–33. link1
[15] Hackney C, Best J, Leyland J, Darby SE, Parsons D, Aalto R, et al. Modulation of outer bank erosion by slump blocks: disentangling the protective and destructive role of failed material on the three-dimensional flow structure. Geophys Res Lett 2015;42(24):10663–70. link1
[16] Jugie M, Gob F, Virmoux C, Brunstein D, Tamisier V, Le Coeur C, et al. Characterizing and quantifying the discontinuous bank erosion of a small low energy river using structure-from-motion Photogrammetry and erosion pins. J Hydrol 2018;563:418–34. link1
[17] Twichell DC, Chaytor JD, ten Brink US, Buczkowski B. Morphology of late Quaternary submarine landslides along the US Atlantic continental margin. Mar Geol 2009;264(1–2):4–15. link1
[18] Puga-Bernabéu Á, Webster JM, Beaman RJ, Guilbaud V. Morphology and controls on the evolution of a mixed carbonate–siliciclastic submarine canyon system, Great Barrier Reef margin, north-eastern Australia. Mar Geol 2011;289 (1–4):100–16. link1
[19] McAdoo BG, Pratson LF, Orange DL. Submarine landslide geomorphology, US continental slope. Mar Geol 2000;169(1–2):103–36. link1
[20] Green A, Uken R. Submarine landsliding and canyon evolution on the northern KwaZulu-Natal continental shelf, South Africa, SW Indian Ocean. Mar Geol 2008;254(3–4):152–70. link1
[21] Wood J. The geomorphological characterisation of digital elevation models [dissertation]. Leicester: University of Leicester; 1996. link1
[22] MacMillan RA, Pettapiece WW, Nolan SC, Goddard TW. A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets Syst 2000;113(1):81–109. link1
[23] Adediran AO, Parcharidis I, Poscolieri M, Pavlopoulos K. Computer-assisted discrimination of morphological units on north–central Crete (Greece) by applying multivariate statistics to local relief gradients. Geomorphology 2004;58(1–4):357–70. link1
[24] Schmidt J, Hewitt A. Fuzzy land element classification from DTMs based on geometry and terrain position. Geoderma 2004;121(3–4):243–56. link1
[25] Dra˘gut L, Blaschke T. Automated classification of landform elements using object-based image analysis. Geomorphology 2006;81(3–4):330–44. link1
[26] Jasiewicz J, Stepinski TF. Geomorphons—a pattern recognition approach to classification and mapping of landforms. Geomorphology 2013;182:147–56. link1
[27] Cui X, Xing Z, Yang F, Fan M, Ma Y, Sun Yi. A method for multibeam seafloor terrain classification based on self-adaptive geographic classification unit. Appl Acoust 2020;157:107029. link1
[28] Di Stefano M, Mayer LA. An automatic procedure for the quantitative characterization of submarine bedforms. Geosciences 2018;8(1):28. link1
[29] Debese N, Jacq JJ, Garlan T. Extraction of sandy bedforms features through geodesic morphometry. Geomorphology 2016;268:82–97. link1
[30] Libohova Z, Winzeler HE, Lee B, Schoeneberger PJ, Datta J, Owens PR. Geomorphons: landform and property predictions in a glacial moraine in Indiana landscapes. Catena 2016;142:66–76. link1
[31] Campos Pinto L, de Mello CR, Norton LD, Owens PR, Curi N. Spatial prediction of soil–water transmissivity based on fuzzy logic in a Brazilian headwater watershed. Catena 2016;143:26–34. link1
[32] Kramm T, Hoffmeister D, Curdt C, Maleki S, Khormali F, Kehl M. Accuracy assessment of landform classification approaches on different spatial scales for the Iranian loess plateau. ISPRS Int J Geoinf 2017;6(11):366. link1
[33] Luo W, Liu CC. Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods. Landslides 2018;15(3): 465–74. link1
[34] Chea H, Sharma M. Residential segregation in hillside areas of Seoul, South Korea: a novel approach of geomorphons classification. Appl Geogr 2019;108:9–21. link1
[35] Flynn T, Rozanov A, de Clercq W, Warr B, Clarke C. Semi-automatic disaggregation of a national resource inventory into a farm-scale soil depth class map. Geoderma 2019;337:1136–45. link1
[36] Duan J, Duan W, Zhu J. Analysis of riverbank sloughing and stability. Eng J Wuhan Univ 2004;37(6):17–21. Chinese. link1
[37] Zhang X, Jiang C, Chen Q, Ying Q. Types and features of riverbank collapse. Adv Sci Technol Water Resour 2008;28(5):66–70. link1
[38] Xu Y, Liang Z, Wang X, Li W, Du Y. Analysis on bank failure and river channel changes. J Sediment Res 2001;4:41–6. link1
[39] Jin L, Wang N, Fu Q. Analysis of topography of bank-slides and its affecting factors in Mahu reach of the Yangtze River. J Sediment Res 1998;2:67–71. link1
[40] Peng Y, Xiong C, Yang C. Analysis of relationship between fluvial process and bank caving in the Jingjiang Reach of Yangtze River. J China Hydrol 2010;30(6): 29–36. link1
[41] Liao WH. Region description using extended local ternary patterns. In: Proceedings of the 20th International Conference on Pattern Recognition; 2010 Aug 23–26; Istanbul, Turkey. New York City: IEEE; 2010.
[42] Yokoyama R, Shirasawa M, Pike RJ. Visualizing topography by openness: a new application of image processing to digital elevation models. Photogramm Eng Remote Sensing 2002;68(3):257–66. link1
[43] Tang J, Deng J, You X, Wang F. Forecast method for bank collapse in middle and lower Yangtze River. J Sichuan Univ 2012;44(1):75–81. link1
[44] Wang YG, Kuang SF. Study on types and collapse modes of bank failures. J Sediment Res 2014;1:13–20. link1
[45] Zheng S, Cheng H, Wu S, Liu G, Lu X, Xu W. Discovery and implications of catenary-bead subaqueous dunes. Sci China Earth Sci 2016;59(3):495–502. link1
[46] Zheng S, Cheng H, Wu S, Shi S, Xu W, Zhou Q, et al. Morphology and mechanism of the very large dunes in the tidal reach of the Yangtze River. China. Cont Shelf Res 2017;139:54–61. link1
[47] Zheng S, Cheng H, Shi S, Xu W, Zhou Q, Jiang Y, et al. Impact of anthropogenic drivers on subaqueous topographical change in the Datong to Xuliujing Reach of the Yangtze River. Sci China Earth Sci 2018;61(7):940–50. link1
[48] Cheng HQ, Kostaschuk R, Shi Z. Tidal currents, bed sediments, and bedforms at the South Branch and the South Channel of the Changjiang (Yangtze) estuary, China: implications for the ripple-dune transition. Estuaries 2004;27(5): 861–6. link1
[49] Cheng H, Li J, Yin D, Li M, Wang B. Nearshore bedform instability in the eastern entrance to the Qiongzhou Strait, South China Sea. Front Earth Sci China 2008;2(3):283–91. link1
[50] Cheng H, Teng L, Chen W. Dune dynamics in coarse silt, sand and gravel along the main channel from the estuarine front of the Yangtze River to the Three Gorges Dam. In: Proceedings of the Marine and River Dune Dynamics; 2019 Apr 1–2; Bremen, Germany; 2019.
京公网安备 11010502051620号
