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Time traveling through the floodscape: assessing the spatial and temporal probability of floods and susceptibility zones in the Lower Damodar Basin

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Abstract

The flood of Damodar river is a well-known fact which is used to the whole riverine society of the basin as well as to the eastern India. The study aims to estimate the spatio-temporal probability of floods and identify susceptible zones in the Lower Damodar Basin (LDB). A flood frequency analysis around 90 years hydrological series is performed using the Log-Pearson Type III model. The frequency ratio model has also been applied to determine the spatial context of flood. This reveals the extent to which the LDB could be inundated in response to peak discharge conditions, especially during the monsoon season. The findings indicate that 36.64% of the LDB falls under high to very high flood susceptibility categories, revealing an increasing downstream flood vulnerability trend. Hydro-geomorphic factors substantially contribute to the susceptibility of the LDB to high magnitude floods. A significant shift in flood recurrence intervals, from biennial occurrences in the pre-dam period to decadal or vicennial occurrences in the post-dam period, is observed. Despite a reduction in high-magnitude flood incidents due to dam and barrage construction, irregular flood events persist. The effect of flood in the LDB region is considered to be either positive as well as negative in terms of wholistic sense and impact. The analytical results of this research could serve to identify flood-prone zones and guide the development of flood resilience policies, thereby promoting sustainability within the LDB floodplain.

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The data supporting the findings of this study are available upon request from the corresponding author, subject to reasonable requests.

References

  • Ali, S. A., Khatun, R., Ahmad, A., & Ahmad, S. N. (2019). Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Modeling Earth Systems and Environment, 5(3), 1083–1102. https://doi.org/10.1007/s40808-019-00593-z

    Article  Google Scholar 

  • Askar, S., Zeraat Peyma, S., Yousef, M. M., Prodanova, N. A., Muda, I., Elsahabi, M., & Hatamiafkoueieh, J. (2022). Flood susceptibility mapping using remote sensing and integration of decision table classifier and metaheuristic algorithms. Water, 14(19), 3062. https://doi.org/10.3390/w14193062

    Article  Google Scholar 

  • Bagchi, K. (1977). The Damodar valley development and its impact on the region. In: Indian urbanization and planning. Tata McGraw Hill, NewDelhi

  • Bhattacharyya, K. (1999). Floods, flood hazards and hazard reduction measures: A model – The case in the Lower Damodar River. Indian Journal of Landscape System and Ecological Studies, 22(1), 57–58.

    Google Scholar 

  • Bhattacharyya, K. (2011). The lower Damodar River, :Understanding the human role in changing fluvial environment. Springer.

    Book  Google Scholar 

  • Chandra, S. (2003). India: Flood management- Damodar River basin. https://www.apfm.info/pdf/case_studies/cs_india.pdf. Accessed Dec 2003

  • Chapi, K., Singh, V. P., Shirzadi, A., Shahabi, H., Bui, D. T., Pham, B. T., & Khosravi, K. (2017). A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software, 95, 229–245. https://doi.org/10.1016/j.envsoft.2017.06.012

    Article  Google Scholar 

  • Chow, V. T., Maidment, D. R., & Mays, L. W. (1988). Applied hydrology. McGraw-Hill.

    Google Scholar 

  • Chowdhuri, I., Pal, S. C., & Chakrabortty, R. (2020). Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Advances in Space Research, 65(5), 1466–1489. https://doi.org/10.1016/j.asr.2019.12.003

    Article  Google Scholar 

  • Chung, C.-J.F., & Fabbri, A. G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30(3), 451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b

    Article  Google Scholar 

  • Costache, R. (2019). Flood susceptibility assessment by using bivariate statistics and machine learning models - a useful tool for flood risk management. Water resources management: an international journal, Published for the European Water Resources Association (EWRA), 33(9), 3239–3256. https://ideas.repec.org//a/spr/waterr/v33y2019i9d10.1007_s11269-019-02301-z.html. Accessed 9 Apr 2024

  • Dano, U. L., Balogun, A.-L., Matori, A.-N., Wan Yusouf, K., Abubakar, I. R., Said Mohamed, M. A., Aina, Y. A., & Pradhan, B. (2019). Flood susceptibility mapping using GIS-based analytic network process: A case study of Perlis, Malaysia. Water, 11(3), 615. https://doi.org/10.3390/w11030615

    Article  Google Scholar 

  • Das, S. (2018). Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India. Arabian Journal of Geosciences, 11(19), 576. https://doi.org/10.1007/s12517-018-3933-4

    Article  Google Scholar 

  • Das, S. (2019). Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sensing Applications: Society and Environment, 14, 60–74. https://doi.org/10.1016/j.rsase.2019.02.006

    Article  Google Scholar 

  • Elkhrachy, I. (2015). Flash flood hazard mapping using satellite images and GIS tools: A case study of Najran City, Kingdom of Saudi Arabia (KSA). The Egyptian Journal of Remote Sensing and Space Science, 18(2), 261–278. https://doi.org/10.1016/j.ejrs.2015.06.007

    Article  Google Scholar 

  • Ghosh, D., Mandal, M., Banerjee, M., & Karmakar, M. (2020). Impact of hydro-geological environment on availability of groundwater using analytical hierarchy process (AHP) and geospatial techniques: A study from the upper Kangsabati river basin. Groundwater for Sustainable Development, 11, 100419. 

  • Ghosh, M., Paul, S., Karmakar, S., & Ghosh, S. (2021). Near-real-time flood forecasting for an urban coastal catchment: An approach in combination of numerical weather and 3-way coupled hydrodynamic flood modelling (No. EGU21-12834). In Copernicus Meetings

  • Ghosh, D., Sheet, S., Banerjee, M., Karmakar, M., & Mandal, M. (2022). Flood characteristics and dynamics of sediment environment during Anthropocene: Experience of the lower Damodar river, India. Sustainable Water Resources Management, 8(2), 58. https://doi.org/10.1007/s40899-022-00644-x

    Article  Google Scholar 

  • Glass, E. (1924). Floods of the Damodar River and rainstorms producing them. Minutes of the Proceedings, p 33–346

  • Guo, Q. (2023). Strategies for a resilient, sustainable, and equitable Mississippi River basin. River, 2(3), 336–349. https://doi.org/10.1002/rvr2.60

    Article  Google Scholar 

  • Haghizadeh, A., Siahkamari, S., Haghiabi, A. H., & Rahmati, O. (2017). Forecasting flood-prone areas using Shannon’s entropy model. Journal of Earth System Science, 126(3), 39. https://doi.org/10.1007/s12040-017-0819-x

    Article  Google Scholar 

  • Hammami, S., Zouhri, L., Souissi, D., Souei, A., Zghibi, A., Marzougui, A., & Dlala, M. (2019). Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (Ahp) in the flood susceptibility mapping (Tunisia). Arabian Journal of Geosciences, 12(21), 653. https://doi.org/10.1007/s12517-019-4754-9

    Article  Google Scholar 

  • Hoque, M., Tasfia, S., Ahmed, N., & Pradhan, B. (2019). Assessing spatial flood vulnerability at Kalaparaupazila in Bangladesh using an analytic hierarchy process. Sensors, 19(6), 1302. https://doi.org/10.3390/s19061302

    Article  Google Scholar 

  • Jahangir, M. H., Mousavi Reineh, S. M., & Abolghasemi, M. (2019). Spatial predication of flood zonation mapping in Kan River Basin, Iran, using artificial neural network algorithm. Weather and Climate Extremes, 25, 100215. https://doi.org/10.1016/j.wace.2019.100215

    Article  Google Scholar 

  • Jenks, G. F. (1967). The data model concept in statistical mapping. International Yearbook of Cartography, 7, 186–190.

    Google Scholar 

  • Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H. R. (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. Natural Hazards, 83(2), 947–987. https://doi.org/10.1007/s11069-016-2357-2

    Article  Google Scholar 

  • Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences, 67(1), 251–264. https://doi.org/10.1007/s12665-011-1504-z

    Article  Google Scholar 

  • Kirk, W. (1950). The Damodar Valley – Valley Opima. Eographical Review, 40(3), 415–443.

    Article  Google Scholar 

  • Knighton, D. (1998). Fluvial forms and processes: A new perspective. Hodder Arnold

  • Kumar, D. (2017). River Ganges – Historical, cultural and socioeconomic attributes. Aquatic Ecosystem Health & Management, 20(1–2), 8–20. https://doi.org/10.1080/14634988.2017.1304129

    Article  Google Scholar 

  • Leopold, L. B. (1968). Hydrology for urban land planning: A guidebook on the hydrologic effects of urban land use. Government Printing Office.

    Google Scholar 

  • Liuzzo, L., Sammartano, V., & Freni, G. (2019). Comparison between different distributed methods for flood susceptibility mapping. Water Resources Management, 33(9), 3155–3173. https://doi.org/10.1007/s11269-019-02293-w

    Article  Google Scholar 

  • Pandey, M., Arora, A., Arabameri, A., Costache, R., Kumar, N., Mishra, V. N., Nguyen, H., Mishra, J., Siddiqui, M. A., Ray, Y., Soni, S., & Shukla, U. (2021). Flood susceptibility modeling in a subtropical humid low-relief alluvial plain environment: Application of novel ensemble machine learning approach. Frontiers in Earth Science, 9. https://doi.org/10.3389/feart.2021.659296

  • Pawar, U., Try, S., Muttil, N., Rathnayake, U., & Suppawimut, W. (2023). Frequency and trend analyses of annual peak discharges in the Lower Mekong Basin. Heliyon, 9(9), e19690. https://doi.org/10.1016/j.heliyon.2023.e19690

    Article  Google Scholar 

  • Periyasamy, P., Yagoub, M. M., & Sudalaimuthu, M. (2018). Flood vulnerable zones in the rural blocks of Thiruvallur district, South India. Geoenvironmental Disasters, 5(1), 21. https://doi.org/10.1186/s40677-018-0113-5

  • Phrakonkham, S., Kazama, S., Komori, D., & Sopha, S. (2019). Distributed hydrological model for assessing flood hazards in Laos. Journal of Water Resource and Protection, 11(08), 937–958. 

  • Pradhan, B. (2009). Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. Journal of Spatial Hydrology, 9(2), 1–18.

    Google Scholar 

  • Purnawali, H. S., Hariyanto, T., Pratomo, D. G., & Hidayati, N. (2017). Flood vulnerability analysis using remote sensing and GIS: A case study of Sidoarjo Regency. IPTEK Journal of Proceedings Series, 3(6). https://doi.org/10.12962/j23546026.y2017i6.3305

  • Qasimi, A. B., Isazade, V., & Berndtsson, R. (2023). Flood susceptibility prediction using MaxEnt and frequency ratio modeling for Kokcha River in Afghanistan. Natural Hazards. https://doi.org/10.1007/s11069-023-06232-2

    Article  Google Scholar 

  • Rahman, M., Ningsheng, C., Islam, M. M., Dewan, A., Iqbal, J., Washakh, R. M. A., & Shufeng, T. (2019). Flood susceptibility assessment in bangladesh using machine learning and multi-criteria decision analysis. Earth Systems and Environment, 3(3), 585–601. https://doi.org/10.1007/s41748-019-00123-y

    Article  Google Scholar 

  • Rahmati, O., Zeinivand, H., & Besharat, M. (2016). Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomatics, Natural Hazards and Risk, 7(3), 1000–1017. https://doi.org/10.1080/19475705.2015.1045043

    Article  Google Scholar 

  • Reed, T., Mason, L. R., & Ekenga, C. C. (2020). Adapting to climate change in the upper mississippi river basin: Exploring stakeholder perspectives on river system management and flood risk reduction. Environmental Health Insights, 14, 117863022098415. https://doi.org/10.1177/1178630220984153

    Article  Google Scholar 

  • Roy, D., Mukherjee, S., & Bose, B. (1995). Regulation of a multipurpose reservoir system: Damodar Valley, India. http//iahs.info/redbooks/a230/iahs_230_0095

  • Saha, S. (1979). River-basin planning in the Damodar Valley of India. Geographical Review, 69(3), 273–287.

    Article  Google Scholar 

  • Samanta, S., Pal, D. K., & Palsamanta, B. (2018). Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Applied Water Science, 8(2), 66. https://doi.org/10.1007/s13201-018-0710-1

    Article  Google Scholar 

  • Sarkar, D., & Mondal, P. (2019). Flood vulnerability mapping using frequency ratio (Fr) model: A case study on Kulik river basin Indo-Bangladesh Barind Region. Applied Water Science, 10(1), 17. https://doi.org/10.1007/s13201-019-1102-x

    Article  Google Scholar 

  • Satheeshkumar, S., Venkateswaran, S., & Kannan, R. (2017). Rainfall–runoff estimation using SCS–CN and GIS approach in the Pappiredipatti watershed of the Vaniyar sub basin, South India. Modeling Earth Systems and Environment, 3(1), 24. https://doi.org/10.1007/s40808-017-0301-4

    Article  Google Scholar 

  • Sen, P. K. (1985). The genesis of floods in the lower Damodar catchment. In The concepts and methods in geography (pp. 71–85). Burdwan: The University of Burdwan

  • Sen, P. K. (1991). Flood hazards and river bank erosion in the Lower Damodar Basin. Indian Geomorphology, p 95–108

  • Sengupta, S. (2001). Rivers and floods (with special reference to floods in West Bengal). Breakthrough, 9(2), 2–8.

    Google Scholar 

  • Sheet, S., Banerjee, M., Karmakar, M., Mandal, D., & Ghosh, D. (2023). Evaluation of flood risk at the river reach scale using Shannon’s Entropy Model: A case study of the Damodar River. Safety in Extreme Environments, 5(2), 91–107. https://doi.org/10.1007/s42797-023-00072-w

    Article  Google Scholar 

  • Swain, S., Mishra, S. K., Pandey, A., Pandey, A. C., Jain, A., Chauhan, S. K., & Badoni, A. K. (2022). Hydrological modelling through SWAT over a Himalayan catchment using high-resolution geospatial inputs. Environmental Challenges, 8, 100579. https://doi.org/10.1016/j.envc.2022.100579

  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2015). Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environmental Research and Risk Assessment, 29(4), 1149–1165. https://doi.org/10.1007/s00477-015-1021-9

    Article  Google Scholar 

  • Tehrany, M. S., Shabani, F., Jebur, M. N., Hong, H., Chen, W., & Xie, X. (2017). GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomatics, Natural Hazards and Risk, 8(2), 1538–1561. https://doi.org/10.1080/19475705.2017.1362038

    Article  Google Scholar 

  • Tehrany, M. S., & Kumar, L. (2018). The application of a Dempster–Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods. Environmental Earth Sciences, 77(13), 490. https://doi.org/10.1007/s12665-018-7667-0

    Article  Google Scholar 

  • Tehrany, M. S., Kumar, L., & Shabani, F. (2019). A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia. PeerJ, 7, e7653. https://doi.org/10.7717/peerj.7653

    Article  Google Scholar 

  • Tien Bui, D., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J. F., Melesse, A. M., et al. (2019). Flood spatial modeling in northern Iran using remote sensing and GIS: A comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sensing, 11(13), 1589. 

  • 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), e0229153. https://doi.org/10.1371/journal.pone.0229153

  • Un (2015). Transforming our world the 2030 agenda for sustainable development. Resolution adopted by the general assembly on 25 september 2015, 42809, 1–13

  • Van Alphen, J. (2016). The Delta Programme and updated flood risk management policies in the Netherlands. Journal of Flood Risk Management, 9(4), 310–319. https://doi.org/10.1111/jfr3.12183

    Article  Google Scholar 

  • Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: The kappa statistic. Family Medicine, 37(5), 360–363.

  • Vojtek, M., & Vojteková, J. (2019). Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water, 11(2), 364. 

  • Wang, Y., Fang, Z., Hong, H., & Peng, L. (2020). Flood susceptibility mapping using convolutional neural network frameworks. Journal of Hydrology, 582, 124482. 

  • Ya, R., Wu, J., Tang, R., & Zhou, Q. (2023). Increased flood susceptibility in the Tibetan Plateau with climate and land use changes. Ecological Indicators, 156, 111086. https://doi.org/10.1016/j.ecolind.2023.111086

    Article  Google Scholar 

  • Zhang, K., Dong, Z., Guo, L., Boyer, E. W., Mello, C. R., Shen, J., Lan, P., Wang, J., & Fan, B. (2022). Allocation of flood drainage rights in the middle and lower reaches of the Yellow River based on deep learning and flood resilience. Journal of Hydrology, 615, 128560. https://doi.org/10.1016/j.jhydrol.2022.128560

    Article  Google Scholar 

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Acknowledgements

We would like to express our gratitude to Mr. Manas Karmakar, Mr. Susanta Mandi, and Mr. Subhadip Pal for their assistance during the fieldwork phase of this study. This work is part of the doctoral research undertaken by Sambit Sheet, who extends his appreciation to Dr. Mrinal Mandal, Assistant Professor at Sidho-Kanho-Birsha University, for his support during the development of the work.

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Sambit Sheet—data collection, field work, data table preparation, and technical work; Monali Banerjee—study conceptualization, manuscript preparation, and mapping; Dayamoy Mandal—FRM preparation using GIS software and field work; Debasis Ghosh—manuscript preparation and finalization, model application and total guidance.

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Correspondence to Debasis Ghosh.

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Sheet, S., Banerjee, M., Mandal, D. et al. Time traveling through the floodscape: assessing the spatial and temporal probability of floods and susceptibility zones in the Lower Damodar Basin. Environ Monit Assess 196, 482 (2024). https://doi.org/10.1007/s10661-024-12563-9

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