Abstract
Among the common hazards related to alluvial fans, flooding is one of the most important. Nonetheless, in populated alluvial fans, not only the natural processes are effective in floods, but the artificial structures and modifications can change the behavior of flooding and its potential risks. This study aims to indicate the flood-prone landforms in a set of populated alluvial fans in an arid region and investigate the role of anthropogenic activities in controlling/exacerbating flooding in alluvial fans. To achieve this goal, 15 Ground Range Detected in High resolution (GRDH) SAR Sentinel-1 images, covering a total of 24 alluvial fans, were acquired. Pre-processing and post-processing steps were applied to all images to identify flood-prone sections of the studied alluvial fans. The results showed that feeder channels, spreading sites, and wide interfluves are the most flood-prone landforms of alluvial fans. In terms of anthropogenic modifications to mitigate flooding, a rampart system is distributed in the study area. Ramparts are levee like structures formed from the fan material. They aim to mitigate flood hazard on residential areas, infrastructures, and agricultural lands of the study area. Results show that the rampart system can reduce the danger of floods, but it cannot be considered a long-term solution. Therefore, a comprehensive flood protection system distributed throughout the whole alluvial fan system is needed. Results also reveal that in regions where flood monitoring is challenging, SAR images can be used as a freely available data set to monitor and identify flooding hotspots.
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References
Ahmed KR, Akter S (2017) Analysis of landcover change in southwest Bengal delta due to floods by NDVI, NDWI and K-means cluster with Landsat multi-spectral surface reflectance satellite data. Remote Sens Appl Soc Environ 8: 168–181. https://doi.org/10.1016/j.rsase.2017.08.010
Arnesen S, Silva T, Hess L, et al. (2013) Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images. Remote Sens Environ 130: 51–61. https://doi.org/10.1016/j.rse.2012.10.035
Arzani N (2005) The fluvial megafan of Abarkoh Basin (Central Iran): an example of flash-flood sedimentation in arid lands J Geol Soc 251(1): 41–59. https://doi.org/10.1144/GSL.SP.2005.251.01.04
Bahrami S, Aghda SMF, Bahrami K, et al. (2015) Effects of weathering and lithology on the quality of aggregates in the alluvial fans of Northeast Rivand, Sabzevar, Iran. Geomorphology 241: 19–30. https://doi.org/10.1016/j.geomorph.2015.03.028
Bahrami S, Ghahraman K (2019) Geomorphological controls on soil fertility of semi-arid alluvial fans: A case study of the Joghatay Mountains, Northeast Iran. Catena 176: 145–158. https://doi.org/10.1016/j.catena.2019.01.016
Bai J, Chen X, Dobermann A, et al. (2010) Evaluation of NASA satellite - and model - derived weather data for simulation of maize yield potential in China. Agron J 102(1): 9–16. https://doi.org/10.2134/agronj2009.0085
Bioresita F, Puissant A, Stumpf A, et al. (2018) A method for automatic and rapid mapping of water surfaces from sentinel-1 imagery. Remote Sens 10(2): 217. https://doi.org/10.3390/rs10020217
Boni G, Ferraris L, Pulvirenti L, et al. (2016) A prototype system for flood monitoring based on flood forecast combined with COSMO-SkyMed and Sentinel-1 data. IEEE J Sel Top Appl Earth Obs Remote Sens 9(6): 2794–2805. https://doi.org/10.1109/JSTARS.2016.2514402
Boschetti M, Nutini F, Manfron G, et al. (2014) Comparative analysis of normalised difference spectral indices derived from MODIS for detecting surface water in flooded rice cropping systems. PloSone 9(2): e88741. https://doi.org/10.1109/JSTARS.2016.2514402
Bowman D (2019) Principles of Alluvial Fan Morphology. Springer. p:151.
Bull WB (1977) The alluvial-fan environment. Prog Phys Geogr 1(2): 222–270.https://doi.org/10.1177/030913337700100202
Cao L, Zhang K, Zhang W (2009) Detachment of road surface soil by flowing water. Catena 76(2): 155–162. https://doi.org/10.1016/j.catena.2008.10.005
Chandler WS, Hoell JM, Westberg D, et al. (2013) NASA prediction of worldwide energy resource high resolution meteorology data for sustainable building design (No. NF1676L-16456).
Chini M, Hostache R, Giustarini L, et al. (2017) A hierarchical split-based approach for parametric thresholding of SAR images: Flood inundation as a test case. IEEE Trans Geosci Remote Sens 55(12): 6975–6988. https://doi.org/10.1109/Tgrs.2017.2737664
Clarke LE (2015) Experimental alluvial fans: Advances in understanding of fan dynamics and processes. Geomorphology 244: 135–145. https://doi.org/10.1016/j.geomorph.2015.04.013
Clevis Q, de Boer P, Wachter M (2003) Numerical modelling of drainage basin evolution and three-dimensional alluvial fan stratigraphy. Sediment Geol 163(1–2): 85–110. https://doi.org/10.1016/S0037-0738(03)00174-X
DeVries B, Huang CQ, Armston J, et al. (2020) Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sens Environ 240: 111664. https://doi.org/10.1016/j.rse.2020.111664
Diakakis M, Andreadakis E, Nikolopoulos EI, et al. (2019) An integrated approach of ground and aerial observations in flash flood disaster investigations. The case of the 2017 Mandra flash flood in Greece. Int J Disaster Risk Reduct 33: 290–309.https://doi.org/10.1016/j.ijdrr.2018.10.015
Field J (2001) Channel avulsion on alluvial fans in southern Arizona. Geomorphology 37(1–2): 93–104. https://doi.org/10.1016/S0169-555x(00)00064-7
Filipponi F (2019) Sentinel-1 GRD preprocessing workflow. MDPI. https://doi.org/10.3390/ECRS-3-06201
Gao CL, Ji YL, Wu CL, et al. (2020) Facies and depositional model of alluvial fan dominated by episodic flood events in arid conditions: An example from the Quaternary Poplar Fan, north-western China. Sedimentology 67(4): 1750–1796. https://doi.org/10.1111/sed.12684
Geravand F, Hosseini SM, Ataie-Ashtiani B (2020) Influence of river cross-section data resolution on flood inundation modeling: Case study of Kashkan river basin in western Iran. J Hydrol 584: 124743. https://doi.org/10.1016/j.jhydrol.2020.124743
Greifeneder F, Wagner W, Sabel D, et al. (2014) Suitability of SAR imagery for automatic flood mapping in the Lower Mekong Basin. Int J Remote Sens 35(8): 2857–2874. https://doi.org/10.1080/01431161.2014.890299
Harvey AM, Mather AE, Stokes M (2005) Alluvial fans: geomorphology, sedimentology, dynamics - introduction. A review of alluvial-fan research. J Geol Soc 251(1): 1–7. https://doi.org/10.1144/Gsl.Sp.2005.251.01.01
Henry JB, Chastanet P, Fellah K, et al. (2006) Envisat multi-polarized ASAR data for flood mapping. Int J Remote Sens 27(9–10): 1921–1929. https://doi.org/10.1080/01431160500486724
Hong H, Tsangaratos P, Ilia I, et al. (2018) Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Sci Total Environ 625: 575–588. https://doi.org/10.1016/j.scitotenv.2017.12.256
Jahangir MH, Reineh SMM, Abolghasemi M (2019) Spatial predication of flood zonation mapping in Kan River Basin, Iran, using artificial neural network algorithm. Weather Clim Extrem 25: 100215. https://doi.org/10.1016/j.wace.2019.100215
Kain CL, Rigby EH, Mazengarb C (2018) A combined morphometric, sedimentary, GIS and modelling analysis of flooding and debris flow hazard on a composite alluvial fan, Caveside, Tasmania. Sediment Geol 364: 286–301. https://doi.org/10.1016/j.sedgeo.2017.10.005
Kastridis A, Kirkenidis C, Sapountzis M (2020) An integrated approach of flash flood analysis in ungauged Mediterranean watersheds using post-flood surveys and unmanned aerial vehicles. Hydrol Process 34(25): 4920–4939. https://doi.org/10.1002/hyp.13913
Khosravi K, Pourghasemi HR, Chapi K, et al. (2016) Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environ Monit Assess 188(12): 1–21. https://doi.org/10.1007/s10661-016-5665-9
Koenig TA, Bruce JL, O’Connor J, et al. (2016) Identifying and preserving high-water mark data, US Geological Survey.
Köppen W (1900) Versuch einer Klassifikation der Klimate, vorzugsweise nach ihren Beziehungen zur Pflanzenwelt. (Schluss). Geogr Z 6(12.H): 657–679. https://www.jstor.org/stable/27803939
Le Hooke RB, Rohrer WL (1979) Geometry of alluvial fans: Effect of discharge and sediment size. Earth Surf Process Landf 4(2): 147–166.https://doi.org/10.1002/esp.3290040205
Lee J-S, Jurkevich L, Dewaele P, et al. (1994) Speckle filtering of synthetic aperture radar images: A review. Int J Remote Sens 8(4): 313–340.https://doi.org/10.1080/02757259409532206
Li Y, Martinis S, Plank S, et al. (2018) An automatic change detection approach for rapid flood mapping in Sentinel-1 SAR data. Int J Appl Earth Obs Geoinf 73: 123–135. https://doi.org/10.1016/j.jag.2018.05.023
Liang JY, Liu DS (2020) A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery. ISPRS J Photogramm Remote Sens 159: 53–62. https://doi.org/10.1016/j.isprsjprs.2019.10.017
Lloyd SP (1982) Least-Squares Quantization in Pcm. IEEE Trans Inf Theory 28(2): 129–137. https://doi.org/10.1109/tit.1982.1056489
Magura LM, Wood DE (1980) Flood hazard identification and flood plain management on alluvial fans. JAWRA 16(1): 56–62. https://doi.org/10.1111/j.1752-1688.1980.tb02331.x
Martinis S (2017) Improving flood mapping in arid areas using Sentinel-1 time series data. IGARSS, IEEE. pp: 193–196. https://doi.org/10.1109/IGARSS.2017.8126927
Martinis S, Kersten J, Twele A (2015) A fully automated TerraSAR-X based flood service. ISPRS J Photogramm Remote Sens 104: 203–212. https://doi.org/10.1016/j.isprsjprs.2014.07.014
Martinis S, Plank S, Cwik K (2018) The Use of Sentinel-1 Time-Series Data to Improve Flood Monitoring in Arid Areas. Remote Sens 10(4): 1–13. https://doi.org/10.3390/rs10040583
Martinis S, Twele A, Voigt S (2010) Unsupervised extraction of flood-induced backscatter changes in SAR data using Markov image modeling on irregular graphs. IEEE Trans Geosci Remote Sens 49(1): 251–263. https://doi.org/10.1109/TGRS.2010.2052816
Mason DC, Garcia-Pintado J, Cloke HL, et al. (2015) The potential of flood forecasting using a variable-resolution global digital terrain model and flood extents from synthetic aperture radar images. Front Earth Sci 3: 43. https://doi.org/10.3389/feart.2015.00043
McFeeters SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17(7): 1425–1432. https://doi.org/10.1080/01431169608948714
Melrose RT, Kingsford RT, Milne AK (2012) Using radar to detect flooding in arid wetlands and rivers. IGARSS, IEEE. pp: 5242–5245. https://doi.org/10.1109/IGARSS.2012.6352427
Memon AA, Muhammad S, Rahman S, et al. (2015) Flood monitoring and damage assessment using water indices: A case study of Pakistan flood-2012. Egypt J Remote Sens Space Sci 18(1): 99–106. https://doi.org/10.1016/j.ejrs.2015.03.003
Munasinghe D, Cohen S, Huang YF, et al. (2018) Intercomparison of satellite remote sensing-based flood inundation mapping techniques. J Am Water Resour Assoc 54(4): 834–846. https://doi.org/10.1111/1752-1688.12626
NASA (2007). NASA Surface Meteorology And Solar Energy: Methodology.https://power.larc.nasa.gov/data-access-viewer/ (Accessed on 2022)
Ohki M, Watanabe M, Natsuaki R, et al. (2016) Flood area detection using ALOS-2 PALSAR-2 data for the 2015 heavy rainfall disaster in the Kanto and Tohoku Area, Japan. J Meteorol Soc Japan 36(4): 348–359. https://doi.org/10.11440/rssj.36.348
Pachauri RK, Allen MR, Barros VR, et al. (2014) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. IPCC.
Paola C, Straub K, Mohrig D, et al. (2009) The “unreasonable effectiveness” of stratigraphic and geomorphic experiments. Earth Sci Rev 97(1–4): 1–43. https://doi.org/10.1016/j.earscirev.2009.05.003
Psomiadis E (2016) Flash flood area mapping utilising SENTINEL-1 radar data. Earth Resources and Environmental Remote Sensing/GIS Applications VII, IJOP. https://doi.org/10.1117/12.2241055
Schumann G, Bates PD, Horritt MS, et al. (2009) Progress in Integration of Remote Sensing-Derived Flood Extent and Stage Data and Hydraulic Models. Rev Geophys 47(4): 1–20. https://doi.org/10.1029/2008rg000274
Schumm SA, Mosley MP, Weaver W (1987) Experimental Fluvial Geomorphology. John Wiley, New York and Chichester.pp413.
Shen XY, Anagnostou EN, Allen GH, et al. (2019) Near-realtime non-obstructed flood inundation mapping using synthetic aperture radar. Remote Sens Environ 221: 302–315. https://doi.org/10.1016/j.rse.2018.11.008
Sivanpillai R, Jacobs KM, Mattilio CM, et al. (2021) Rapid flood inundation mapping by differencing water indices from pre- and post-flood Landsat images. Front Earth Sci 15: 1–11. https://doi.org/10.1007/s11707-020-0818-0
Stock JD, Schmidt KM, Miller DM (2008) Controls on alluvial fan long-profiles. Geol Soc Am Bull 120(5–6): 619–640. https://doi.org/10.1130/B26208.1
Tariq A, Yan JG, Ghaffar B, et al. (2022) Flash flood susceptibility assessment and zonation by integrating analytic hierarchy process and frequency ratio model with diverse spatial data. Water 14(19): 3069. https://doi.org/10.3390/w14193069
Tavus B, Kocaman S, Gokceoglu C, et al. (2018) Considerations on the use of sentinel-1 data in flood mapping in urban areas: Ankara (Turkey) 2018 Floods. ISPRS Archives XLII-5: 575–581. https://doi.org/10.5194/isprs-archives-XLII-5-575-2018
Torres R, Snoeij P, Geudtner D, et al. (2012) GMES Sentinel-1 mission. Remote Sens Environ 120: 9–24. https://doi.org/10.1016/j.rse.2011.05.028
Twele A, Cao WX, Plank S, et al. (2016) Sentinel-1-based flood mapping: a fully automated processing chain. Int J Remote Sens 37(13): 2990–3004. https://doi.org/10.1080/01431161.2016.1192304
Uddin K, Matin MA, Meyer FJ (2019) Operational flood mapping using multi-temporal Sentinel-1 SAR images: A case study from Bangladesh. Remote Sens 11(13): 1581. https://doi.org/10.3390/rs11131581
Waqas H, Lu LL, Tariq A, et al. (2021) Flash flood susceptibility assessment and zonation using an integrating analytic hierarchy process and frequency ratio model for the Chitral District, Khyber Pakhtunkhwa, Pakistan. Water 13(12): 1–18. https://doi.org/10.3390/w13121650
Wendleder A, Wessel B, Roth A, et al. (2012) TanDEM-X water indication mask: Generation and first evaluation results. IEEE J Sel Top Appl Earth Obs Remote Sens 6(1): 171–179. https://doi.org/10.1109/JSTARS.2012.2210999
Zhang X, Chan NW, Pian B, et al. (2021) Mapping flood by the object-based method using backscattering coefficient and interference coherence of Sentinel-1 time series. Sci Total Environ 794: 1–16. https://doi.org/10.1016/j.scitotenv.2021.148388
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We would like to express our sincere gratitude to the Stipendium Hungaricum Scholarship Programme. We would also like to extend our heartfelt appreciation to the anonymous reviewers whose valuable feedback and constructive criticism have helped to shape this article into its final form. Their insights and suggestions have been instrumental in improving the quality and clarity of the work.
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Ghahraman, K., Nagy, B. Flood risk on arid alluvial fans: a case study in the Joghatay Mountains, Northeast Iran. J. Mt. Sci. 20, 1183–1200 (2023). https://doi.org/10.1007/s11629-022-7635-8
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DOI: https://doi.org/10.1007/s11629-022-7635-8