Floods are one of the most disastrous and dangerous catastrophes faced by humanity for ages. Though mostly deemed a natural phenomenon, floods can be anthropogenic and can be equally devastating in modern times. Remote sensing with its non-evasive data availability and high temporal resolution stands unparalleled for flood mapping and modelling. Since floods in India occur mainly in monsoon months, optical remote sensing has limited applications in proper flood mapping owing to lesser number of cloud-free days. Remotely sensed microwave/synthetic aperture radar (SAR) data has penetration ability and has high temporal data availability, making it both weather independent and highly versatile for the study of floods. This study uses space-borne SAR data in C-band with VV (vertically emitted and vertically received) and VH (vertically emitted and horizontally received) polarization channels from Sentinel-1A satellite for SAR interferometry-based flood mapping and runoff modeling for Rupnagar (Punjab) floods of 2019. The flood maps were prepared using coherence-based thresholding, and digital elevation map (DEM) was prepared by correlating the unwrapped phase to elevation. The DEM was further used for Soil Conservation Service-curve number (SCS-CN)-based runoff modelling. The maximum runoff on 18 August 2019 was 350 mm while the average daily rainfall was 120 mm. The estimated runoff significantly correlated with the rainfall with an R2 statistics value of 0.93 for 18 August 2019. On 18 August 2019, Rupnagar saw the most devastating floods and waterlogging that submerged acres of land and displaced thousands of people.
This is a preview of subscription content, access via your institution.
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
Would be provided on a reasonable request.
Abdeldayem, O. M., Eldaghar, O., Mostafa, M. K., Habashy, M. M., Hassan, A., Mahmoud, H., & Peters, R. W. (2020). Mitigation plan and water harvesting of flashflood in arid rural communities using modeling approach: A case study in Afouna village. Egypt. MDPI Water, 12(9), 1–24. https://doi.org/10.3390/W12092565.
Ahluwalia, R. S., Rai, S. P., Gupta, A. K., Dobhal, D. P., Tiwari, R. K., Garg, P. K., & Kesharwani, K. (2016). Towards the understanding of the flash flood through isotope approach in Kedarnath valley in June 2013, Central Himalaya, India. Natural Hazards, 82(1), 321–332. https://doi.org/10.1007/s11069-016-2203-6.
Alexander, L. V., & Arblaster, J. M. (2009). Assessing trends in observed and modelled climate extremes over Australia in relation to future projections. International Journal of Climatology, 29(3), 417–435. https://doi.org/10.1002/joc.1730.
Amutha, R., & Porchelvan, P. (2009). Estimation of surface run-off in Malattar sub-watershed using SCS-CN method. Journal of the Indian Society of Remote Sensing, 37(2), 291. https://doi.org/10.1007/s12524-009-0017-7.
Behzad, A., Sarvati, M., & Moghimi, E. (2012). Estimating flood potentia l emphasizing on Geomorphologic characteristics in Tarikn Basin using the SC S method. European Journal of Experimental Biology, 2(5), 1928–1935.
Bézy, J., Sierk, B., Caron, J., Veihelmann, B., Martin, D., Langen, J., & Zhu, A. G. N. (2014). The Copernicus Sentinel-5 mission for operational atmospheric monitoring : Status and developments. SPIE Remote Sensing, 9241, 1–11. https://doi.org/10.1117/12.2068177.
Boerner, W. M. (2007). Recent advancements of radar remote sensing; air- and space-borne multimodal SAR remote sensing in forestry, Agriculture, Geology, Geophysics (Volcanology and Technology): Advances in P0L-SAR, IN-SAR, POLinSAR and POL-DIFF-IN-SAR Sensing and Ima. 2007 Asia-Pacific Microwave Conference. https://doi.org/10.1109/APMC.2007.4555164.
Borah, S. B., Sivasankar, T., Ramya, M. N. S., & Raju, P. L. N. (2018). Flood inundation mapping and monitoring in Kaziranga National Park, Assam using Sentinel-1 SAR data. Environmental Monitoring and Assessment, 190(9), 520. https://doi.org/10.1007/s10661-018-6893-y.
Boughton, W. C. (1989). A review of the USDA SCS curve number method. Soil Research, 27(3), 511–523. https://doi.org/10.1071/SR9890511.
Bronstert, A. (2003). Floods and Climate Change: Interactions and Impacts. Risk Analysis, 23(3), 545–557. https://doi.org/10.1111/1539-6924.00335.
Cheng, Q., Ko, C., Yuan, Y., Ge, Y., & Zhang, S. (2006). GIS modeling for predicting river runoff volume in ungauged drainages in the Greater Toronto Area. Canada. Computers & geosciences, 32(8), 1108–1119. https://doi.org/10.1016/j.cageo.2006.02.005.
Chini, M., Pelich, R., Pulvirenti, L., Pierdicca, N., Hostache, R., & Matgen, P. (2019). Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and hurricane Harvey as a test case. Remote Sensing, 11(2), 1–20. https://doi.org/10.3390/rs11020107.
Chung, H. W., Liu, C. C., Cheng, I. F., Lee, Y. R., & Shieh, M. C. (2015). Rapid response to a typhoon-induced flood with an SAR-derived map of inundated areas: Case study and validation. Remote Sensing, 7(9), 11954–11973. https://doi.org/10.3390/rs70911954.
Clauss, K., Ottinger, M., & Kuenzer, C. (2018). Mapping rice areas with Sentinel-1 time series and superpixel segmentation. International Journal of Remote Sensing, 39(5), 1399–1420. https://doi.org/10.1080/01431161.2017.1404162.
Cosgrove, W. J., & Loucks, D. P. (2015). Water management: Current and future challenges and research directions. Water Resources Research, 51(6), 4823–4839. https://doi.org/10.1002/2014WR016869.
Damodaran, H. (2008). The Paradox of Northern Farming Communities BT - India’s New Capitalists: Caste, Business, and Industry in a Modern Nation. In H. Damodaran (Ed.) (pp. 259–296). London: Palgrave Macmillan UK. https://doi.org/10.1057/9780230594128_8.
Dongchen, E., Zhou, C., & Liao, M. (2004). Application of SAR Interferometry on DEM Generation of the Grove Mountains. Photogrammetric Engineering & Remote Sensing, 70(10), 1145–1149. https://doi.org/10.14358/PERS.70.10.1145.
Frederick, S. E., Cebula, R. P., & Heath, D. F. (1986). Instrument characterization for the detection of long-term changes in stratospheric ozone: An analysis of the SBUY/2 radiometer. Journal of Atmospheric and Oceanic Technology, 3(3), 472–480. https://doi.org/10.1175/1520-0426(1986)003%3c0472:ICFTDO%3e2.0.CO;2.
Geetha, K., Mishra, S. K., Eldho, T. I., Rastogi, A. K., & Pandey, R. P. (2008). SCS-CN-based continuous simulation model for hydrologic forecasting. Water Resources Management, 22(2), 165–190. https://doi.org/10.1007/s11269-006-9149-5.
Geetha, M., & Rastogi, & Pandey. . (2007). Modifications to SCS-CN method for long-term hydrologic simulation. Journal of Irrigation and Drainage Engineering, 133(5), 475–486. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:5(475).
Grandin, R., Klein, E., Métois, M., & Vigny, C. (2016). Three-dimensional displacement field of the 2015 Mw8.3 Illapel earthquake (Chile) from across- and along-track Sentinel-1 TOPS interferometry. Geophysical Research Letters, 43(6), 2552–2561. https://doi.org/10.1002/2016GL067954.
Gupta, S., Javed, A., & Datt, D. (2003). Economics of flood protection in India BT - flood problem and management in South Asia. In M. M. Q. Mirza, A. Dixit, & A. Nishat (Eds.) (pp. 199–210). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-017-0137-2_10.
Heino, R., Brázdil, R., Forland, E., Tuomenvirta, H., Alexandersson, H., Beniston, M., & Wibig, J. (1999). Progress in the study of climatic extremes in northern and central Europe. Climatic Change, 42(1), 151–181. https://doi.org/10.1023/A:1005420400462.
Hoomehr, S., Schwartz, J. S., Yoder, D. C., Drumm, E. C., & Wright, W. (2013). Curve numbers for low-compaction steep-sloped reclaimed mine lands in the Southern Appalachians. Journal of Hydrologic Engineering, 18(12), 1627–1638. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000746.
Hughes, L. (2000). Biological consequences of global warming: Is the signal already apparent? Trends in Ecology & Evolution, 15(2), 56–61. https://doi.org/10.1016/S0169-5347(99)01764-4.
Jayraman, V., Chandrasekhar, M., & Rao, U. (1997). Managing the natural disasters from space technology inputs. Acta Astronautica, 40(2), 291–325. https://doi.org/10.1016/S0094-5765(97)00101-X.
Jung, J., Kim, D., Member, S., Lavalle, M., & Yun, S. (2016). Coherent change detection using InSAR temporal decorrelation model: A case study for volcanic ash detection. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 5765–5775. https://doi.org/10.1109/TGRS.2016.2572166.
Kadam, A. K., Kale, S. S., Pande, N. N., Pawar, N. J., & Sankhua, R. N. (2012). Identifying potential rainwater harvesting sites of a semi-arid, basaltic region of Western India Using SCS-CN Method. Water Resources Management, 26(9), 2537–2554. https://doi.org/10.1007/s11269-012-0031-3.
Kale, V. S., Ely, L. L., Enzel, Y., & Baker, V. R. (1994). Geomorphic and hydrologic aspects of monsoon floods on the Narmada and Tapi Rivers in central India. Geomorphology and Natural Hazards: Proceedings of the 25th Binghamton Symposium in Geomorphology, Held 24th September–25,1994 at SUNY, Binghamton, USA, 10, 157–168. https://doi.org/10.1016/B978-0-444-82012-9.50015-3.
Katimon, A., Zulkifli, M., & Yunos, M. (2003). Flood potential estimation of two small vegetated watersheds. Malaysian Journal of Civil Engineering, 15(1), 1–15. https://doi.org/10.11113/mjce.v15.103.
Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T. V., & Dech, S. (2011). Remote sensing of mangrove ecosystems: A review. Remote Sensing (Vol. 3). https://doi.org/10.3390/rs3050878.
Kuenzer, C., Guo, H., Huth, J., Leinenkugel, P., Li, X., & Dech, S. (2013). Flood mapping and flood dynamics of the mekong delta: ENVISAT-ASAR-WSM based time series analyses. Remote Sensing, 5(2), 687–715. https://doi.org/10.3390/rs5020687.
Kumar, A., Gupta, A. K., Bhambri, R., Verma, A., Tiwari, S. K., & Asthana, A. K. L. (2018). Assessment and review of hydrometeorological aspects for cloudburst and flash flood events in the third pole region (Indian Himalaya). Polar Science, 18, 5–20. https://doi.org/10.1016/j.polar.2018.08.004.
Lanari, R., Fornaro, G., Riccio, D., Migliaccio, M., Papathanassiou, K. P., Moreira, J. R., & Coltelli, M. (1996). Generation of digital elevation models by using SIR-C/X-SAR multifrequency two-pass interferometry: the Etna case study. IEEE Transactions on Geoscience and Remote Sensing, 34(5), 1097–1114. https://doi.org/10.1109/36.536526.
Latha, M., Rajendran, M., & Murugappan, A. (2012). Comparison of GIS-based SCS-CN and Strange table Method of Rainfall-Runoff Models for. International Journal of Scientific & Engineering Research, 3(10), 3–7. Retrieved from https://www.ijser.org/paper/Comparison-of-GIS-based-SCS-CN-and-Strange-table-Method-of-Rainfall-Runoff.html.
Lloyd, G. E. (1987). Atomic number and crystallographic contrast images with the SEM: a review of backscattered electron techniques. Mineralogical Magazine, 51(359), 3–19. https://doi.org/10.1180/minmag.1987.051.359.02.
Mishra, S. K., Jain, M. K., & Singh, V. P. (2004). Evaluation of the SCS-CN-based model incorporating antecedent moisture. Water Resources Management, 18(6), 567–589. https://doi.org/10.1007/s11269-004-8765-1.
Mishra, S. K., & Singh, V. P. (1999). Another look at SCS-CN method. Journal of Hydrologic Engineering, 4(3), 257–264. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:3(257).
Mishra, S. K., & Singh, V. P. (2004). Validity and extension of the SCS-CN method for computing infiltration and rainfall-excess rates. Hydrological processes, 18(17), 3323–3345. https://doi.org/10.1002/hyp.1223.
Mishra, S. K., Pandey, R. P., Jain, M. K., & Singh, V. P. (2008). A rain duration and modified AMC-dependent SCS-CN procedure for long duration rainfall-runoff events. Water Resources Management, 22(7), 861–876. https://doi.org/10.1007/s11269-007-9196-6.
Mishra, S., Mazumdar, S., & Suar, D. (2010). Place attachment and flood preparedness. Journal of Environmental Psychology, 30(2), 187–197. https://doi.org/10.1016/j.jenvp.2009.11.005.
Murmu, P., Kumar, M., Lal, D., Sonker, I., & Kumar, S. (2019). Groundwater for Sustainable Development Delineation of groundwater potential zones using geospatial techniques and analytical hierarchy process in Dumka district , Jharkhand , India. Groundwater for Sustainable Development, 9(October 2018), 100239. https://doi.org/10.1016/j.gsd.2019.100239.
Naab, F. Z., Dinye, R. D., & Kasanga, R. K. (2013). Urbanization and its impact on agricultural lands in growing cities in developing countries: a case study of Tamale in Ghana. Modern Social Science Journal, 2(2), 256–287.
Nishida, K., Nemani, R. R., Glassy, J. M., & Running, S. W. (2003). Development of an evapotranspiration index from Aqua/MODIS for monitoring surface moisture status. IEEE Transactions on Geoscience and Remote Sensing, 41(2), 493–501. https://doi.org/10.1109/TGRS.2003.811744.
Pandit, A., & Heck, H. H. (2009). Estimations of soil conservation service curve numbers for concrete and asphalt. Journal of Hydrologic Engineering, 14(4), 335–345. https://doi.org/10.1061/(ASCE)1084-0699(2009)14:4(335).
Peters, G., McCall, M. K., & Westen, C. (2012). Coping strategies and risk manageability: using participatory geographical information systems to represent local knowledge. Disasters, 36(1), 1–27. https://doi.org/10.1111/j.1467-7717.2011.01247.x.
Phalkey, R., Dash, S., Mukhopadhyay, A., Runge-Ranzinger, S., & Marx, M. (2012). Prepared to react? Assessing the functional capacity of the primary health care system in rural Orissa, India to respond to the devastating flood of September 2008. Global Health Action, 5(1), 10964. https://doi.org/10.3402/gha.v5i0.10964.
Ramakrishnan, D., Bandyopadhyay, A., & Kusuma, K. N. (2009). SCS-CN and GIS-based approach for identifying potential water harvesting sites in the Kali Watershed, Mahi River Basin, India. Journal of Earth System Science, 118(4), 355–368. https://doi.org/10.1007/s12040-009-0034-5.
Revi, A. (2008). Climate change risk: An adaptation and mitigation agenda for Indian cities. Environment and Urbanization, 20(1), 207–229. https://doi.org/10.1177/0956247808089157.
Saini, P., Saini, P., Kaur, J. J., Francies, R. M., Gani, M., Rajendra, A. A., & Chauhan, S. S. (2020). Molecular approaches for harvesting natural diversity for crop improvement BT - Rediscovery of genetic and genomic resources for future food security. In R. K. Salgotra & S. M. Zargar (Eds.) (pp. 67–169). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-0156-2_3.
Sandwell, D. T., & Price, E. J. (1998). Phase gradient approach to stacking interferograms. Journal of Geophysical Research: Solid Earth, 103(B12), 30183–30204. https://doi.org/10.1029/1998JB900008.
Sanyal, J., & Lu, X. X. (2004). Application of remote sensing in flood management with special reference to Monsoon Asia: A review. Natural Hazards, 33(2), 283–301. https://doi.org/10.1023/B:NHAZ.0000037035.65105.95.
Sanyal, J., & Lu, X. X. (2005). Remote sensing and GIS-based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India. Hydrological Processes, 19(18), 3699–3716. https://doi.org/10.1002/hyp.5852.
Satheeshkumar, S., Venkateswaran, S., & Kannan, R. (2017). Rainfall–run-off 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), 1–8. https://doi.org/10.1007/s40808-017-0301-4.
Schwartz, S. S. (2010). Effective curve number and hydrologic design of pervious concrete storm-water systems. Journal of Hydrologic Engineering, 15(6), 465–474. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000140.
Sharma, S. B., & Singh, A. K. (2014). Assessment of the flood potential on a lower Tapi basin tributary using SCS-CN method integrated with remote sensing & GIS data. Journal of Geography & Natural Disasters, 4(2), 1–7. https://doi.org/10.4172/2167-0587.1000128.
Sharma, S., & Singh, A. (2015). Assessment of the flood potential on a lower tapi basin tributary using SCS- CN method integrated with remote sensing & GIS data. Journal of Geography & Natural Disasters, (August 2014). https://doi.org/10.4172/2167-0587.1000128.
Shen, X., Wang, D., Mao, K., Anagnostou, E., & Hong, Y. (2019). Inundation extent mapping by synthetic aperture radar: A review. Remote Sensing, 11(7), 1–17. https://doi.org/10.3390/RS11070879.
Sivakumar, M. V. K. (2007). Interactions between climate and desertification. Agricultural and Forest Meteorology, 142(2), 143–155. https://doi.org/10.1016/j.agrformet.2006.03.025.
Small, D., Pasquali, P., & Fuglistaler, S. (1996). A comparison of phase to height conversion methods for SAR interferometry. In IGARSS ’96. 1996 International Geoscience and Remote Sensing Symposium (Vol. 1, pp. 342–344 vol.1). https://doi.org/10.1109/IGARSS.1996.516334.
Soulis, K. X., & Valiantzas, J. D. (2012). SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds – the two-CN system approach. Hydrology and Earth System Sciences, 16(3), 1001–1015. https://doi.org/10.5194/hess-16-1001-2012.
Tapete, D. (2018). Appraisal of opportunities and perspectives for the systematic condition assessment of heritage sites with copernicus Sentinel-2 high-resolution multispectral imagery. MDPI Remote Sensing, 1–22. https://doi.org/10.3390/rs10040561.
Tripathi, A., & Kumar, S. (2019). Effect of phase filtering on interferometry based displacement analysis of cultural heritage sites. In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1–5). IEEE. https://doi.org/10.1109/UPCON.2018.8597027.
Tripathi, A., & Tiwari, R. K. (2019 a). C-band SAR Interferometry based flood inundation mapping for Gorakhpur and adjoining areas. In 2019 International Conference on Computer, Electrical & Communication Engineering (ICCECE) (pp. 1–6). https://doi.org/10.1109/ICCECE44727.2019.9001870.
Tripathi, A., & Tiwari, R. K. (2019 b). Utilization of space-borne C-band SAR data for analysis of flood impact on agriculture and its management. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(3/W6). https://doi.org/10.5194/isprs-archives-XLII-3-W6-521-2019.
Tripathi, A., & Tiwari, R. K. (2019 c). Mapping of deflection caused due to hydrostatic pressure using Differential SAR Interferometry ( DInSAR ) on Bhakhra dam. IEEE XPLORE. https://doi.org/10.1109/UPCON47278.2019.8980117.
Tripathi, A., & Tiwari, R. K. (2020). Synergetic utilization of sentinel-1 SAR and sentinel-2 optical remote sensing data for surface soil moisture estimation for Rupnagar, Punjab, India. Geocarto International, 1–22.https://doi.org/10.1080/10106049.2020.1815865.
Tsai, Y. L. S., Dietz, A., Oppelt, N., & Kuenzer, C. (2019). Remote sensing of snow cover using spaceborne SAR: A review. Remote Sensing, 11(12). https://doi.org/10.3390/rs11121456.
Wałęga, A., & Rutkowska, A. (2015). Usefulness of the modified NRCS-CN method for the assessment of direct runoff in a mountain catchment. Acta Geophysica, 63(5), 1423–1446. https://doi.org/10.1515/acgeo-2015-0043.
Wright, R. (2009). The Ancient Indus - Urbanism, Economy and Society. In Ancient Pakistan (Vol. XX, p. 2009). Retrieved from http://journals.uop.edu.pk/papers/AP_v20_249to249.pdf.
Xu, F., & Jin, Y. (2007). Automatic reconstruction of building objects from multiaspect meter-resolution SAR images. IEEE Transactions on Geoscience and Remote Sensing, 45(7), 2336–2353. https://doi.org/10.1109/TGRS.2007.896614.
Zhang, M., Chen, F., Liang, D., Tian, B., & Yang, A. (2020). Use of sentinel-1 grd SAR images to delineate flood extent in Pakistan. Sustainability (Switzerland), 12(14), 1–19. https://doi.org/10.3390/su12145784.
Zhu, L., Suomalainen, J., Liu, J., Hyyppä, J., Kaartinen, H., & Haggren, H. (2018). A review: remote sensing sensors. Multi-purposeful application of geospatial data, 19-42.https://doi.org/10.5772/intechopen.71049.
This study was supported by the ESRI ArcGIS team, SNAP Software team, the European Space Agency (ESA), Alaska Satellite Facility, and the Department of Civil Engineering IIT Ropar.
Conflict of interest
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Tripathi, A., Attri, L. & Tiwari, R.K. Spaceborne C-band SAR remote sensing–based flood mapping and runoff estimation for 2019 flood scenario in Rupnagar, Punjab, India. Environ Monit Assess 193, 110 (2021). https://doi.org/10.1007/s10661-021-08902-9
- SAR interferometry