Skip to main content

Advertisement

Log in

Identification of seasonal variation of water turbidity using NDTI method in Panchet Hill Dam, India

  • Review Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

Abstract

Sedimentation in reservoir is a common problem in any multipurpose river valley project and is effective upon the performance of it. Every dam has an estimated volume of water holding capacity at the time of inception, but it gradually reduces by siltation. The presence of sediments in water causes turbidity and it slowly precipitates on the floor of the reservoir. Normalized difference turbidity index (NDTI) is a remote sensing technique widely used to identify the water turbidity, which is the ratio of red and green bands of solar spectrum. In Panchet Dam actual rate of siltation exceeds the assumed rate that fills up the entire reservoir area at a faster rate. In this situation, the study has been conducted to explain the variation of water turbidity of the dam throughout the year 2015. Major findings of the study indicate that the turbidity level jumps from 60 NTU to 700 NTU in the monsoon. High turbid water covers 50.57%, 64.22% and 52.79% area of the reservoir in July, August and September, respectively. In contrast, coverage of low turbid water is more than 71.68% in the months of February and June. The medium turbid water covers less than 35.82% area throughout the year except the months of September and October. High level of correlation exists (R2 = 0.900) between NDTI values and total suspended sediments concentration in mg/L (N = 15, p < 0.05) with minimal RMSE (13.59). Variation of seasonal turbidity and different types of turbidity are significant at 95% (p < 0.05) level of significance. The paper is an attempt to probe into the seasonal variation of water turbidity of the dam with the application of NDTI method and related statistical measures.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Aguirre-Gomez R (2000) Detection of total suspending sediments in the North Sea using Avhrr and ship data. Int J Remote Sens 21:1583–1596

    Article  Google Scholar 

  • Alka S, Sushma P, Singh TS, Patel JG, Tanwar H (2014) Wetland information system using remote sensing and GIS in Himachal Pradesh, India. Asian J Geoinform 14(4):13–22

    Google Scholar 

  • Bhattacharya BK, Chakraborti BR, Sen NN, Mukherji S, Ray P, Sengupta S, Sengupta KS, Sen NN, Maity T (1985) West Bengal District Gazetteers. Puruliya, pp 2–22

  • Bid S (2016) Change detection of vegetation cover by NDVI technique on catchment area of the Panchet Hill Dam, India. Int J Res Geogr 2(3):11–20, ISSN 2454-8685. http://dx.doi.org/10.20431/24548685.0203002

  • Bin Omar AF, Bin MatJafri MZ (2009) Turbidimeter design and analysis: a review on optical fiber sensors for the measurement of water turbidity. In: Sensors (Basel). Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3292109/

    Article  Google Scholar 

  • Bonakdari H, Zaji AH, Binns AD, Gharabaghi B (2019) Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals. J Hydrol 572:75–95. https://doi.org/10.1016/j.jhydrol.2019.02.027

    Article  Google Scholar 

  • Borland WM, Miller CR (1958) Distribution of sediment in large reservoir. J Hydraul Div 84(2):1587.1–1587.10

    Google Scholar 

  • Campbell JB (1996) Introduction to remote sensing, 4th ed. Guilford Publications, New York, ISBN 1-59385-319-X

  • Chalov S, Bazilova V, Tarasov M (2017a) Modeling suspended sediment distribution in the Selenga River Delta using landsat data. Proc IAHS 375:19–22. https://doi.org/10.5194/piahs-375-19-2017

    Article  Google Scholar 

  • Chalov S, Golosov V, Tsyplenkov A, Zakerinejad R, Marker M, Samokhin M (2017b) A toolbox for sediment budget research in small catchments. Geogr Environ Sustain 10(4):43–68. https://doi.org/10.24057/2071-9388-2017-10-4-43-68

    Article  Google Scholar 

  • Chen Z, Hanson JD, Curran PJ (1991) The form of the relationship between suspended sediment concentration and spectral reflectance: its implication for the use of Daedalus 1268 data. Int J Remote Sens 12(1):215–222

    Article  Google Scholar 

  • Chen CH, Fang L, Zhang L, Huang W (2009) Remote sensing of turbidity in seawater intrusion reaches of Pearl River Estuary—a case study in Modaomen water way, Estuarine, Coastal and Shelf Science

  • Chesapeake Bay Program (2012) Water clarity. In: The Bay Ecosystem, Retrieved from https://www.chesapeakebay.net/discover/bayecosystem/waterclarity

  • Collins AL, Walling DE (2004) Documenting catchment suspended sediment sources: problems, approaches and prospects. Prog Phys Geogr 28:159–196

    Article  Google Scholar 

  • Doxaran D, Froidefond JM, Lavender S, Castaing P (2002) Spectral signature of highly turbid water application with SPOT data to quantify suspended particulate matter concentration. Remote Sens Environ 81(1):149–161

    Article  Google Scholar 

  • EPA (2012) Turbidity. In: Water: monitoring & assessment. Retrieved from http://water.epa.gov/type/rsl/monitoring/vms55.cfm

  • Fink JC (2005) Chapter 4—establishing a relationship between sediment concentrations and turbidity. In: The effects of urbanization on Baird creek, Green Bay, WI (Thesis). Retrieved from http://www.uwgb.edu/watershed/fink/Fink_Thesis_Chap4.pdf

  • Fondriest Environmental Inc. (2014) Turbidity, total suspended solids and water clarity, fundamentals of environmental measurements. http://www.fondriest.com/environmental-measurements/parameters/water-quality/turbidity-total-suspended-solids-water-clarity

  • Gardelle J, Hiernaux P, Kergoat L, Grippa M (2010) Less rain, more water in ponds: a remote sensing study of the dynamics of surface waters from 1950 to present in pastoral Sahel (Gourma region, Mali). Hydrol Earth Syst Sci 14:309–324

    Article  Google Scholar 

  • Garg V, Kumar AS, Aggarwal SP, Kumar V, Dhote PR, Thakur PK, Nikam BR, Sambare RS, Siddiqui A, Muduli PR, Rastogi G (2017) Spectral similarity approach for mapping turbidity of an inland waterbody. J Hydrol 550:527–537

    Article  Google Scholar 

  • Geological Survey of India (1991) Government of India

  • Ghosh S, Islam A (2016) Quaternary alluvial stratigraphy and Palaeoclimatic reconstruction in the Damodar River Basin of West Bengal. In: Das BC et al (eds) Neo-thinking on ganges Brahmaputra basin geomorphology. Springer, Switzerland, pp 1–18. https://link.springer.com/chapter/10.1007/978-3-319-26443-1_1

    Google Scholar 

  • Grade RJ (2006) River morphology. New Age International Ltd, New Delhi

    Google Scholar 

  • Guchhait SK, Islam A, Ghosh S, Das BC, Maji NK (2016) Role of hydrological regime. In: Channel and floodplain sediments in channel instability of Meandering Bhagirathi River, Ganga—Brahmaputra Delta, India, Physical Geography. Taylor & Francis, USA. https://www.tandfonline.com/doi/abs/10.1080/02723646.2016.1230986

  • Guyot G (1989) Signatures spectrales des surfaces naturelles. Télédétection satellitaire, 5, Col. SAT, Ed. Paradigme, 178

  • He GK, Shao MH, Gao BS, Liu RY (1994) The variable relations between the turbidity and suspended matter in the sea water during the dredging process of the channel in Dayao Bay. Mar Environ Sci 13:76–82 (in Chinese with English Abstract)

    Google Scholar 

  • Islam M, Sado K (2006) Analyses of ASTER and Spectroradiometer data with in situ measurements for turbidity and transparency study of lake Abashri. Int J Geoinf 2:31–45

    Google Scholar 

  • Issa IE, Ansari NA, Sherwany G, Knutsson S (2017) Evaluation and modification of some empirical and semi-empirical approaches for prediction of area-storage capacity curves in reservoirs of dams. Int J Sedim Res 32:127–135

    Article  Google Scholar 

  • Jain SK, Singh VP (2003) Water resources systems planning and management. Elsevier, Amsterdam

    Google Scholar 

  • Jensen JR (2015) Introductory digital image processing: a remote sensing perspective. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  • Kaveh K, Hosseinjanzadeh H, Hosseini K (2013) A new equation for calculation of reservoir’s area-capacity curves. KSCE J Civ Eng 17(5):1149–1156. https://doi.org/10.1007/s12205-013-0230-3

    Article  Google Scholar 

  • Kratzer S, Bowers D, Tett PB (2000) Seasonal changes in colour ratios and optically active constituents in the optical case-2 waters of the Menai Strait, North Wales. Int J Remote Sens 21:2225–2246

    Article  Google Scholar 

  • Lacaux JP, Tourre YM, Vignolles C, Ndione JA, Lafaye M (2007) Classification of ponds from high-spatial resolution remote sensing: application to Rift Valley Fever epidemics in Senegal. Remote Sens Environ 106:66–74

    Article  Google Scholar 

  • Mano V, Némery J, Belleudy P, Poirel A (2009) Assessment of suspended sediment transport in four alpine watersheds (France): influence of the climatic regime. Hydrol Process. https://doi.org/10.1002/hyp.7178

    Article  Google Scholar 

  • Martínez-Carreras N, Udelhoven T, Krein A, Gallart F, Iffly JF, Ziebel J, Hoffmann L, Pfister L, Walling DE (2010) The use of sediment colour measured by diffuse reflectance spectrometry to determine sediment sources: application to the Attert River catchment (Luxembourg). J Hydrol 382:49–63

    Article  Google Scholar 

  • Meybeck M, Laroche L, Dürr HH, Syvitski JP (2003) Global variability of daily total suspended solids and their fluxes. Glob Planet Changes 39:65–93

    Article  Google Scholar 

  • Moeeni H, Bonakdari H (2017) Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model. Stoch Environ Res Risk Assess 31(8):1997–2010. https://doi.org/10.1007/s00477-016-1273-z

    Article  Google Scholar 

  • Moeeni H, Bonakdari H, Ebtehaj I (2017) Integrated SARIMA with neuro-fuzzy systems and neural networks for monthly inflow prediction. Water Resour Manag 31(7):2141–2156. https://doi.org/10.1007/s11269-017-1632-7

    Article  Google Scholar 

  • Mohammadzadeh-Habili J, Heidarpour M (2010) New empirical method for prediction of sediment distribution in reservoirs. J Hydrol Eng 15(10):813–821. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000259

    Article  Google Scholar 

  • Mohammadzadeh-Habili J, Heidarpour M, Mousavi SF, Haghiabi AH (2009) Derivation of reservoir’s area-capacity equations. J Hydrol Eng 9:1017–1023. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000074

    Article  Google Scholar 

  • Molo VD, Piccazzo M, Ramella A, Giusto DD, Vernazza G (1989) Monitoring of coastal water quality through integration between ‘in situ’ measurements and remote sensing data. In: Hallikainen M (ed) Proceedings of the ninth EARSeL symposium, Espoo, 27 June–1 July 1989. Luxembourg: commission of the European Communities, Directorate General for Science Research and Development, pp 86–91

  • Morris GL, Fan J (1998) Reservoir sedimentation handbook, design and management of dams, reservoirs and watersheds for sustainable use. McGraw-Hill, New York

    Google Scholar 

  • Novo EMM, Steffen CA, Braga ZE (1991) Results of a laboratory experiment relating spectral reflectance to total suspended solids. Remote Sens Environ 36:67–72

    Article  Google Scholar 

  • Olmanson LG, Brezonik PL, Bauer ME (2013) Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: the Mississippi River and its tributaries in Minnesota. Remote Sens Environ 130:254–256

    Article  Google Scholar 

  • Papoutsa C, Retalis A, Toulios L, Hadjimitsis DG (2014) Defining the landsat Tm/Etm+ and Chris/Proba spectral regions in which turbidity can be retrieved in inland waterbodies using field spectroscopy. Int J Remote Sens 35:1674–1692

    Article  Google Scholar 

  • Perlman H (2014) Turbidity, the USGS Water Science School. Retrieved from http://water.usgs.gov/edu/turbidity.html

  • Qu Y, Qi H, Ayhan B, Kwan C, Kidd R (2017) Does multispectral/hyperspectral pansharpening improve the performance of anomaly detection? In: IEEE international geoscience and remote sensing symposium (IGARSS)

  • Rahaman KR, Hassan QK, Ahmed MR (2017) Pan-sharpening of Landsat-8 images and its application in calculating vegetation greenness and canopy water contents. Int J Geo-Inf 6(168):1–15. https://doi.org/10.3390/ijgi6060168

    Article  Google Scholar 

  • Ritchie JC, Schiebe FR (1986) Monitoring suspended sediments with remote sensing techniques. In: Hydrologie application of space technology, (Proceedings of the Cocoa Beach Workshop, FL, August 1985), pp 233–242. IAHS Pubf. No. 160

  • Shaharum NSN, Shafri HZM, Gambo J, Abidin FAZ (2018) Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms. Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2018.01.002

    Article  Google Scholar 

  • Sharma A, Panigrahy S, Singh TS, Patel JG, Tanwar H (2014) Wetland information system using remote sensing and GIS in Himachal Pradesh, India. Asian J Geoinform 14(4):13–22

    Google Scholar 

  • Siddique G, Bid S (2017) Ecological impact of the Panchet Dam: a review. Res World J Arts Sci Commer VIII(1(1)):104–112, ISSN 2231-4172

  • Singh S, Banerji P (eds) (2002) Large dams in India: environmental, social and economic impacts. Indian Institute of Public Administration, New Delhi

    Google Scholar 

  • Somvanshi S, Kunwar P, Singh NB, Kachhwaha TS (2011) Water turbidity assessment in part of Gomti River using high resolution Google Earth’s Quickbird satellite data. Geospatial World Forum, Hyderabad

    Google Scholar 

  • Spate OHK, Farmer BH (1954) India and Pakistan—a regional geography. Methuen & Co., Ltd., London

    Google Scholar 

  • Teodoro AC, Veloso-Gomes F, Goncalves H (2008) Statistical techniques for correlating total suspended matter concentration with seawater reflectance using multispectral satellite data. J Coast Res 24:40–49

    Article  Google Scholar 

  • Townshend JR, Justice CO (1986) Analysis of dynamics of African vegetation using the normalised difference vegetation index. Int J Remote Sens 7:1435–1445

    Article  Google Scholar 

  • Trinh LH, Zablotskii RV, Le TH, Dinh TTH, Le TT, Trinh TT, Nguyen TTN (2018) Estimation of suspended sediment concentration using VNREDSat—1A multispectral data, a case study in Red River, Hanoi, Vietnam. Geogr Environ Sustain 11(3):49–60. https://doi.org/10.24057/2071-9388-2018-11-3-49-60

    Article  Google Scholar 

  • Tucker CJ, Sellers PJ (1986) Satellite remote sensing of primary productivity. Int J Remote Sens 7:1395–1416

    Article  Google Scholar 

  • Tyler AN, Svab E, Presing M, Kovacs WA (2006) Remote sensing of the water quality of shallow lakes: a mixture modeling approach to quantifying phytoplankton in water characterized by high-suspended sediment. Int J Remote Sens 27:1521–1537

    Article  Google Scholar 

  • Valdiya KS (2016) The making of India—geodynamic evolution, 2nd edn. Springer, Switzerland, p 418, ISBN 978-3-319-25029-8 (eBook)

  • Verbyla DL (1995) Satellite remote sensing of natural resources. Lewis Publishers/CRC Press LLC, Boca Raton, p 224

    Google Scholar 

  • Wood MS (2014) Estimating suspended sediment in rivers using acoustic Doppler meters. In: U.S. Geological Survey Fact Sheet 2014-3038. N.p.: U S Geological Survey

  • Zaji AH, Bonakdari H, Gharabaghi B (2018) Reservoir water level forecasting using group method of data handling. Acta Geophys 66(4):717–730. https://doi.org/10.1007/s11600-018-0168-4

    Article  Google Scholar 

  • Zaji AH, Bonakdari H, Gharabaghi B (2019) Applying upstream satellite signals and a two-dimensional error minimization algorithm to advance early warning and management of flood water levels and river discharge. IEEE Trans Geosci Remote Sens 57(2):902–910. https://doi.org/10.1109/TGRS.2018.2862640

    Article  Google Scholar 

Download references

Acknowledgements

The corresponding author wishes to acknowledge the University Grand Commission, New Delhi, India, for the financial support [JRF Award Letter No. F.15-6(DEC.2013)/2014(NET), UGC-Ref. No.: 3154/(NET-DEC.2013)] to carry out this research work. The authors would like to thank two anonymous reviewers for their comments and suggestions to enrich the work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sumanta Bid.

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

Bid, S., Siddique, G. Identification of seasonal variation of water turbidity using NDTI method in Panchet Hill Dam, India. Model. Earth Syst. Environ. 5, 1179–1200 (2019). https://doi.org/10.1007/s40808-019-00609-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-019-00609-8

Keywords

Navigation