Skip to main content

Advertisement

SpringerLink
Log in

Remote sensing and GIS in the recent Chennai flood study

  • Original Paper
  • Published:
Nanotechnology for Environmental Engineering Aims and scope Submit manuscript

Abstract

Flood in Chennai is still indelible in the minds of the people. The most anguish and torment nature of affected people is colossal despair to the society and panic worldwide. This paper focuses on prediction of runoff in Chennai Adyar watershed, Tamil Nadu in India during the storm events in 2005 and 2015 floods. The runoff water available for artificial recharge in the basin is estimated through Soil Conservation Service (SCS) Curve number method. The land use/land cover and hydrologic soil classification are processed in ArcGIS for each of the 19 micro-watersheds and the volume of runoff is estimated. This paper represents the rainfall–runoff in that particular micro-watershed level, and curve fitting relationship of rainfall–runoff is shown. The remote sensing and GIS play a vital role in the study, and the results were obtained. The total volumes of rainfall drained as runoff are 326.67 million m3, 280.99 million m3 and 207.69 million m3 in October, November and December 2005 flood, respectively, and 71.33 million m3, 681.88 million m3 and 312.92 million m3 in October, November and December 2015 flood, respectively. This runoff results will create awareness to save the excess water flow during flood season and be helpful for planning the artificial groundwater recharge. Thus, some useful management measures have to be implemented at the earliest to avoid the impacts of Chennai floods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Babcock HM, Cushing EM (1942) Recharge to ground-water from floods in a typical desert wash Pinal County Arizona. Trans Am Geophys Union 52(1):49–56

    Article  Google Scholar 

  2. Todd DK (1959) Ground water hydrology. Chapman & Hall, London, p xii 336, 156 figures 23 tables 86s

    Google Scholar 

  3. Karanth KR (1987) Ground water assessment: development and management. Tata McGraw-Hill Education, New York, p 720

    Google Scholar 

  4. Kadam A et al (2012) Identifying potential rainwater harvesting sites of a semi-arid, Basaltic Region of Western India, using SCS–CN method. Water Resour Manag 26:2537–2554. https://doi.org/10.1007/s11269-012-0031-3

    Article  Google Scholar 

  5. James VB (1997) Determination of watershed curve number using derived distribution. J Irrig Drain Eng ASCE 123:28–36. https://doi.org/10.1061/(ASCE)0733-9437(1997)123:1(28)

    Article  Google Scholar 

  6. Zhan Xiaoyong, Huang Min-Lang (2004) ArcCN-Runoff: an ArcGIS tool for generating curve number and runoff maps. Environ Model Softw 19:875–879. https://doi.org/10.1016/j.envsoft.2004.03.001

    Article  Google Scholar 

  7. Anbazhagan S, Ramasamy SM, Das Gupta S (2005) Remote sensing and GIS for artificial recharge study, runoff estimation and planning in Ayyar basin, Tamil Nadu, India. Environ Geol 48(2):158–170. https://doi.org/10.1007/s00254-005-1284-4

    Article  Google Scholar 

  8. Anon (1973) A method for estimating volume and rate of runoff in small watersheds. Technical paper no. 149 soil conservation service USDA, SCS Washington DC

  9. Thomas A (2015) Modelling of spatially distributed surface runoff and infiltration in the Olifants River catchment/water management area using GIS. Int J Adv Remote Sens GIS 4(1):828–862

    Article  Google Scholar 

  10. Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69. https://doi.org/10.1080/02626667909491834

    Article  Google Scholar 

  11. Singh VP (1989) Watershed modeling: hydrologic systems, vol 2. Prentice Hall, Englewood Cliffs, NJ

    Google Scholar 

  12. Kaviya K, Ramalingam M (2014) Estimation of surface runoff using soil and water assessment tool (SWAT). Appl Mech Mater 622:89–96. https://doi.org/10.4028/www.scientific.net/AMM.622.89

    Article  Google Scholar 

  13. Mishra SK, Jain MK, Singh VP (2004) Evaluation of the SCS–CN-based model incorporating, antecedent moisture. Water Resour Manag 18:567–589

    Article  Google Scholar 

  14. Kumar D, Bhattacharjya RK (2011) Distributed rainfall runoff modeling. Int J Earth Sci Eng 04(06):270–275 (ISSN 0974-5904)

    Google Scholar 

  15. Bansode A, Patil KA (2014) Estimation of runoff by using SCS curve number method and arc GIS. Int J Sci Eng Res 5(7):1283–1287 (ISSN 2229–5518)

    Google Scholar 

  16. Jain A, Srinivasalu S (2004) Development of effective and efficient rainfall–runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques. Water Resour Res 40:1–12

    Article  Google Scholar 

  17. Fan F, Deng Y, Hu X, Weng Q (2013) Estimating composite curve number using an improved SCS-CN method with remotely sensed variables in Guangzhou China. Remote Sens 2013(5):1425–1438. https://doi.org/10.3390/rs5031425

    Article  Google Scholar 

  18. Kumar R, Singh RD, Sharma KD (2005) Water resources of India. Curr Sci 89(5):794–811

    Google Scholar 

  19. Gupta PK, Punalekar S, Panigrahy S, Sonakia A, Parihar JS (2012) Runoff modeling in an agro-forested watershed using remote sensing and GIS. J Hydrol Eng 17(11):1255–1267

    Article  Google Scholar 

  20. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290. https://doi.org/10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  21. Sahu RK, Mishra SK, Eldho TI (2012) Improved storm duration and antecedent moisture condition coupled SCS-CN concept-based model. J Hydrol Eng 17:1173–1179

    Article  Google Scholar 

  22. Suresh Babu P, Mishra SK (2012) Improved SCS–CN–inspired model. J Hydrol Eng 17:1164–1172. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000435

    Article  Google Scholar 

  23. Ajmal M, Moon G, Ahn J, Kim T (2017) Quantifying excess storm water using SCS-CN–based rainfall runoff models and different curve number determination methods. In: Garg V et al (eds) Development of water resources in India Water Science and Technology Library 75. Springer International Publishing AG. https://doi.org/10.1007/978-3-319-55125-8-22

  24. Adam EO, Elbasit Mohamed AM, Solomon AT, Ahmed F (2017) Integration of satellite rainfall data and curve number method for runoff estimation under semi-arid wadi system. Int Arch Photogramm Remote Sens Spat Inf Sci 42(4/w6):1–7

    Article  Google Scholar 

  25. Mohammad FS, Adamowski J (2015) Interfacing the geographic information system remote sensing and the soil conservation service-curve number method to estimate curve number and runoff volume in the Asir region of Saudi Arabia. Arab J Geosci 8:11093–11105. https://doi.org/10.1007/s12517-015-1994-1

    Article  Google Scholar 

  26. Saravanan S, Manjula R (2015) Geomorphology based semi-distributed approach for modeling rainfall–runoff modeling using GIS. Aquat Proc 4:908–916

    Article  Google Scholar 

  27. 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. Model Earth Syst Environ, South India. https://doi.org/10.1007/s40808-017-0301-4

    Book  Google Scholar 

  28. Yusuf SM, Guluda D, Jayanegara T (2017) Surface runoff estimation from various land use in Mapili watershed using SCS curve number and geographic information system. In: IOP conference series: earth and environmental science 54. http://iopscience.iop.org/1755-1315/54/1/012020. https://doi.org/10.1088/1755-1315/54/1/012022

    Article  Google Scholar 

  29. Bhura CS et al (2015) Estimation of surface runoff for Ahmedabad urban area using SCS–CN method and GIS IJSTE. Int J Sci Technol Eng 1(11):2349–2784

    Google Scholar 

  30. Jiao P, Xu D, Wang S, Yu Y, Han S (2015) Improved SCS–CN method based on storage and depletion of antecedent daily precipitation. Water Resour Manag 29:4753–4765

    Article  Google Scholar 

  31. Rawat KS, Singh SK (2017) Estimation of surface runoff from semi-arid ungauged agricultural watershed using SCS-CN method and earth observation data sets. Water Conserv Sci Eng 1:233–247. https://doi.org/10.1007/s41101-017-0016-4

    Article  Google Scholar 

  32. Yadav SK, Dubey A, Szilard S, Singh SK (2016) Prioritization of sub-watersheds based on earth observation data of agricultural dominated northern river basin of India. Geocarto Int. https://doi.org/10.1080/10106049.2016.1265592

    Article  Google Scholar 

  33. Patel J, Singh NP, Prakash I, Mehmood K (2017) Surface runoff estimation using SCSCN method—a case study on Bhadar Watershed, Gujarat, India. IJIR 3(5):1213–1218 (ISSN: 2454-1362)

    Google Scholar 

  34. Pani S, Chakrabarty A (2017) Runoff depth estimation using SCS–CN method in Jhargram community development block—a remote sensing and geographic information system approach. Int J Adv Remote Sens GIS 6(1):2306–2324. https://doi.org/10.23953/cloud.ijarsg.296 (ISSN 2320–0243)

    Article  Google Scholar 

  35. Mishra SK, Singh VP (1999) Another look at SCS-CN method. J Hydrol Eng 4(3):257–264. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:3(257)

    Article  Google Scholar 

  36. SCS, 1956, 1964, 1969, 1971, 1972, 1985, 1986, 1993. “Hydrology-national engineering handbook” Supplement A Section 4 Chapter 10 Soil Conservation Service USDA and Washington DC

  37. USDA (1986) Urban hydrology for small watersheds TR-55 United States Department of Agriculture 210-VI-TR-55, 2nd edn June 1986

  38. Chow VT, Maidment DK, Mays LW (1988) Applied hydrology. McGraw-Hill Book Company, New York

    Google Scholar 

  39. Kumar P, Tiwart KN, Pal DK (1991) Establishing SCS runoff curve number from IRS digital data base. J Indian Soc Remote Sens 19:24. https://doi.org/10.1007/BF03023971

    Article  Google Scholar 

  40. David RM (1993) Hand book of hydrology. Civil Engineering University of Texas at Austin, McGraw-Hill, New York, p 1424

    Google Scholar 

  41. Hawkins RH (1975) The importance of accurate curve numbers in the estimation of storm runoff. Water Resour Bull 11:887–891

    Article  Google Scholar 

  42. Oliveira PTS, Nearing MA, Hawkins RH, Stone JJ, Rodrigues DBB, Panachuki E, Wendland E (2016) Curve number estimation from Brazilian Cerrado rainfall and runoff data. J Soil Water Conserv 71(5):420–429. https://doi.org/10.2489/jswc.71.5.420

    Article  Google Scholar 

  43. Linsley Paulhus (1958) Hydrology for engineers. Mcgraw Hill, New York

    Google Scholar 

  44. Pike JG (1964) The estimation of annual run-off from meteorological data in a tropical climate. J Hydrol 2:116–123. https://doi.org/10.1016/0022-1694(64)90022-8

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, JSRREC, Anna University, Chennai, India

    A. D. Sheena

  2. Department of Civil Engineering, JCE Chennai, Chennai, India

    M. Ramalingam

  3. Department of Civil Engineering, MIET, Chennai, India

    B. Anuradha

Authors
  1. A. D. Sheena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Ramalingam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. Anuradha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. D. Sheena.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheena, A.D., Ramalingam, M. & Anuradha, B. Remote sensing and GIS in the recent Chennai flood study. Nanotechnol. Environ. Eng. 4, 3 (2019). https://doi.org/10.1007/s41204-018-0049-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41204-018-0049-8

Keywords

Search

Navigation