Abstract
Flash floods that occur suddenly, which cause damage to the weirs or embankments, immediately threaten human life. Identifying the causes of a flash flood is very important to reduce its negative impact. This paper examines changes in flash flood disasters in the Wonoboyo watershed based on estimates of flash flood hazard, land-use changes, and rainfall depth distribution patterns. The method of predicting susceptibility to flash flood hazards is based on various environmental factors that are integrated with GIS. Three bivariate statistics consisting of the Statistical Index (SI), Frequency Ratio (FR), and Predictor Rate (FP-PR) model are applied to select the best Flash Susceptibility Index (FFHSI) model. Changes in land use are then explored based on the conditioning factor for a flash flood. In the final stage, the estimation of areal rainfall uses Inverse Distance Weighting (IDW) to describe the position of rain and flash flood events. The best statistical bivariate statistical approach for the FFHSI is FR. Assessment of environmental factors using the FFHSI shows that 21% of the catchment area has moderate to high until very high vulnerability levels. Changes in land cover significantly affect flash floods, especially changes from forest to agricultural land or settlements. The distribution pattern and intensity of rainfall are closely related to the location of the flash flood. This study results can guide future flood mitigation measures.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Elkhrachy I (2015) Flash flood hazard mapping using satellite images and GIS tools: a case study of Najran City, Kingdom of Saudi Arabia (KSA). Egypt J Remote Sens Sp Sci 18(2):261–278. https://doi.org/10.1016/j.ejrs.2015.06.007
Hapuarachchi HAP, Wang QJ, Pagano TC (2011) A review of advances in flash flood forecasting. Hydrol Process 25(18):2771–2784. https://doi.org/10.1002/hyp.8040
Antarafoto (2008) Banjir Desa Wonoboyo, antarafoto
BPBD (2016) BPBD Jatim Bantu Korban Banjir dan Tanah Longsor di’, BPBD. https://web.bpbd.jatimprov.go.id/2016/02/01/bpbd-jatim-bantu-korban-banjir-dan-tanah-longsor-di-bondowoso/. Accessed Feb 27 2021
Beritaekspress (2017) Akibat Banjir, Akses ke Desa Wonoboyo Jatim Lumpuh, beritaekspres
Terbitannews (2020) Akibat Hujan Deras, Banjir dan Longsor Terjadi di Desa Leprak Bondowoso, Terbitannews
Faktualnews (2018) 3 Kecamatan di Bondowoso Diterjang Banjir Bandang, faktualnews. https://faktualnews.co/2018/01/28/3-kecamatan-bondowoso-diterjang-banjir-bandang/60730/. Accessed 27 Feb 2021
Gaume E, Borga M (2008) Post-flood field investigations in upland catchments after major flash floods: proposal of a methodology and illustrations. J Flood Risk Manag 1(4):175–189. https://doi.org/10.1111/j.1753-318x.2008.00023.x
Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8(9) (2016). https://doi.org/10.3390/su8090948
Pradhan (2009) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9(2):1–18
Jodar-Abellan A, Valdes-Abellan J, Pla C, Gomariz-Castillo F (2019) Impact of land use changes on flash flood prediction using a sub-daily SWAT model in five Mediterranean ungauged watersheds (SE Spain). Sci Total Environ 657:1578–1591. https://doi.org/10.1016/j.scitotenv.2018.12.034
Costache R, Hong H, Pham (2020) QB (711) Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models. Sci Total Environ 711:134514. https://doi.org/10.1016/j.scitotenv.2019.134514
Bui DT, Tsangaratos P, Ngo PTT, Pham TD, Pham BT (2019) Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ 668:1038–1054. https://doi.org/10.1016/j.scitotenv.2019.02.422
Popa MC et al (2020) Spatial assessment of flash-flood vulnerability in the Moldova river catchment (N Romania) using the FFPI. J Flood Risk Manag 2019:1–10. https://doi.org/10.1111/jfr3.12624
Khosravi K et al (2018) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, Northern Iran. Sci Total Environ 627:744–755. https://doi.org/10.1016/j.scitotenv.2018.01.266
Lappas I, Kallioras A (2019) Flood susceptibility assessment through GIS-based multi-criteria approach and analytical hierarchy process (AHP ) in a River Basin in Central Greece, IRJET, pp 738–751
Stefanidis S, Stathis D (2013) Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat Hazards 68(2):569–585. https://doi.org/10.1007/s11069-013-0639-5
Kazakis N, Kougias I, Patsialis T (2015) Assessment of flood hazard areas at a regional scale using an index-based approach and analytical hierarchy process: application in Rhodope-Evros region, Greece. Sci Total Environ 538:555–563. https://doi.org/10.1016/j.scitotenv.2015.08.055
Costache R (2019) Flash-flood potential assessment in the upper and middle sector of Prahova river catchment (Romania). A comparative approach between four hybrid models. Sci Total Environ 659:1115–1134. https://doi.org/10.1016/j.scitotenv.2018.12.397
Khosravi K, Nohani E, Maroufinia E, Pourghasemi HR (2016) A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Nat Hazards 83(2):947–987. https://doi.org/10.1007/s11069-016-2357-2
Khosravi K, Pourghasemi HR (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. https://doi.org/10.1007/s10661-016-5665-9
Costache R, Zaharia L (2017) Flash-flood potential assessment and mapping by integrating the weights-of-evidence and frequency ratio statistical methods in GIS environment—case study. Bâsca Chiojdului River Catchment (Romania). J Earth Syst Sci 126(4):1–19. https://doi.org/10.1007/s12040-017-0828-9
Efendi D, Hidayah E, Hasanuddin A (2020) U KaRsT, Mapping of landslide susceptible zones by using frequency. Ratios Bluncong 5(1):126–141. https://doi.org/10.30737/ukarst.v3i2
Tehrany MS, Pradhan B, Jebur MN (2015) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch Environ Res Risk Assess 29(4):1149–1165. https://doi.org/10.1007/s00477-015-1021-9
Beven K (1984) Infiltration into a class of vertically non-uniform soils, Hydrol Sci J 29(4):425–434. https://doi.org/10.1080/02626668409490960
Manfreda S, Di Leo M, Sole A (2011) Detection of flood-prone areas using digital elevation models. J Hydrol Eng 16(10):781–790. https://doi.org/10.1061/(asce)he.1943-5584.0000367
Duman TY, Can T, Gokceoglu C, Nefeslioglu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environ Geol 51(2):241–256. https://doi.org/10.1007/s00254-006-0322-1
Fernández DS, Lutz MA (2010) Urban flood Hazard zoning In Tucumán Province, Argentina. Using GIS and multicriteria decision analysis. Eng Geol 111(1–4):90–98. https://doi.org/10.1016/j.enggeo.2009.12.006
Glenn EP, Morino K, Nagler PL, Murray RS, Pearlstein S, Hultine KR (2012) Roles of Saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated Desert River. J Arid Environ 79:56–65. https://doi.org/10.1016/j.jaridenv.2011.11.025
Falahnsia AR (2014) Vegetasi dengan metode skoring menggunakan citra satelit di, pp 400–416
Ullah K, Zhang J (2020) GIS-based flood hazard mapping using relative frequency ratio method: a case study of Panjkora River Basin, Eastern Hindu Kush, Pakistan. PLoS One 15(3):1–18. https://doi.org/10.1371/journal.pone.0229153
Ullah K, Zhang J, Id JZ (2020) GIS-based flood hazard mapping using relative frequency ratio method: a case study of Panjkora River Basin, Eastern Hindu Kush, Pakistan. PLoS 15(3):1–18. https://doi.org/10.1371/journal.pone.0229153
Abdulwahid WM, Pradhan B (2017) Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR). Landslides 14(3):1057–1076. https://doi.org/10.1007/s10346-016-0744-0
Van Westen CJ, Rengers N, Terlien M (1997) Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation. https://doi.org/10.1007/s005310050149
Regmi AD et al (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7(2):725–742. https://doi.org/10.1007/s12517-012-0807-z
Bartier PM, Keller CP (1996) Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW). Comput Geosci 22(7):795–799. https://doi.org/10.1016/0098-3004(96)00021-0
Chen FW, Liu CW (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10(3):209–222. https://doi.org/10.1007/s10333-012-0319-1
Yang X, Xie X, Liu DL, Ji F, Wang L (2015) Spatial interpolation of daily rainfall data for local climate impact assessment over Greater Sydney Region. Adv Meteorol. https://doi.org/10.1155/2015/563629
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hidayah, E., Halik, G., Widiarti, W.Y. (2023). Assessment of the Conditioning Factor for Flash Flood Susceptibility Potential Based on Bivariate Statistical Approach in the Wonoboyo Watershed in East Java, Indonesia. In: Kristiawan, S.A., Gan, B.S., Shahin, M., Sharma, A. (eds) Proceedings of the 5th International Conference on Rehabilitation and Maintenance in Civil Engineering. ICRMCE 2021. Lecture Notes in Civil Engineering, vol 225. Springer, Singapore. https://doi.org/10.1007/978-981-16-9348-9_49
Download citation
DOI: https://doi.org/10.1007/978-981-16-9348-9_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9347-2
Online ISBN: 978-981-16-9348-9
eBook Packages: EngineeringEngineering (R0)