Retrieved from įondriest Environmental Inc. In: The effects of urbanization on Baird creek, Green Bay, WI (Thesis). Retrieved from įink JC (2005) Chapter 4-establishing a relationship between sediment concentrations and turbidity. Prog Phys Geogr 28:159–196ĭoxaran D, Froidefond JM, Lavender S, Castaing P (2002) Spectral signature of highly turbid water application with SPOT data to quantify suspended particulate matter concentration. In: The Bay Ecosystem, Retrieved from Ĭollins AL, Walling DE (2004) Documenting catchment suspended sediment sources: problems, approaches and prospects. Int J Remote Sens 12(1):215–222Ĭhen 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 ScienceĬhesapeake Bay Program (2012) Water clarity. Ĭhen 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. Ĭhalov S, Golosov V, Tsyplenkov A, Zakerinejad R, Marker M, Samokhin M (2017b) A toolbox for sediment budget research in small catchments. Guilford Publications, New York, ISBN 1-59385-319-XĬhalov S, Bazilova V, Tarasov M (2017a) Modeling suspended sediment distribution in the Selenga River Delta using landsat data. īorland WM, Miller CR (1958) Distribution of sediment in large reservoir. Retrieved from īonakdari H, Zaji AH, Binns AD, Gharabaghi B (2019) Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals. īin Omar AF, Bin MatJafri MZ (2009) Turbidimeter design and analysis: a review on optical fiber sensors for the measurement of water turbidity. Puruliya, pp 2–22īid S (2016) Change detection of vegetation cover by NDVI technique on catchment area of the Panchet Hill Dam, India. Asian J Geoinform 14(4):13–22īhattacharya BK, Chakraborti BR, Sen NN, Mukherji S, Ray P, Sengupta S, Sengupta KS, Sen NN, Maity T (1985) West Bengal District Gazetteers. Int J Remote Sens 21:1583–1596Īlka S, Sushma P, Singh TS, Patel JG, Tanwar H (2014) Wetland information system using remote sensing and GIS in Himachal Pradesh, India. 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.Īguirre-Gomez R (2000) Detection of total suspending sediments in the North Sea using Avhrr and ship data. Variation of seasonal turbidity and different types of turbidity are significant at 95% ( p < 0.05) level of significance. High level of correlation exists ( R 2 = 0.900) between NDTI values and total suspended sediments concentration in mg/L ( N = 15, p < 0.05) with minimal RMSE (13.59). The medium turbid water covers less than 35.82% area throughout the year except the months of September and October. In contrast, coverage of low turbid water is more than 71.68% in the months of February and June. High turbid water covers 50.57%, 64.22% and 52.79% area of the reservoir in July, August and September, respectively.
Major findings of the study indicate that the turbidity level jumps from 60 NTU to 700 NTU in the monsoon. In this situation, the study has been conducted to explain the variation of water turbidity of the dam throughout the year 2015. In Panchet Dam actual rate of siltation exceeds the assumed rate that fills up the entire reservoir area at a faster rate. 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. The presence of sediments in water causes turbidity and it slowly precipitates on the floor of the reservoir. Every dam has an estimated volume of water holding capacity at the time of inception, but it gradually reduces by siltation. Sedimentation in reservoir is a common problem in any multipurpose river valley project and is effective upon the performance of it.
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