Skip to main content
Log in

Monitoring Turbid Plume Behavior from Landsat Imagery

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

A simple model based on available Landsat imagery data was used in order to characterize the dynamics of a rainstorm-induced turbid plume into a semi-arid, medium-sized reservoir. Additionally, a set of empirical expressions based on mouth river conditions was employed to estimate the spatial extent of the turbid plume, as well as the influence of the wind on the spreading and resuspension of sediment. This predicted plume behavior was then confirmed by the imagery data. Overall, the main contribution of the paper is a unified procedure of different approaches that can be applied to forecast turbidity effects during muddy inflow events in aquatic systems where minor field-measured information is available. The approach may be also considered as an assisting tool to provide managers with the capacity to develop real-time strategies to monitor flood events.

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

Similar content being viewed by others

References

  • Alcantara E, Stech J, Lorenzzetti J, Bonnet M, Casamitjana X, Assireu A, Novo E (2010) Remote sensing of water surface temperature and heat flux over a tropical hydroelectric reservoir. Remote Sens Environ 114(11):2651–2665

    Article  Google Scholar 

  • CERC (1984) Coastal Engineering Research Center. Shore protection manual. Waterways Experiment Station, US Army Corps of Engineers

  • Chavez P (1996) Image-based atmospheric corrections revisited and improved. Photogramm Eng Remote Sens 62:1025–1036

    Google Scholar 

  • Choubey V (1994) Monitoring water quality in reservoirs with IRS-1A-LISS-I. Water Resour Manag 8(2):121–136

    Article  Google Scholar 

  • Chung E, Hipsey M, Imberger J (2009) Modelling the propagation of turbid density inflows into a stratified lake: daecheong reservoir, Korea. J Environ Model Softw 24:1467–1482

    Article  Google Scholar 

  • Cohen W, Goward S (2004) Landsat’s role in ecological applications of remote sensing. Bioscience 54(6):535–545

    Article  Google Scholar 

  • Fernandez R, Lasso R, Furbatto C, Larrosa N (2010) “Modelación por la contaminación por fuentes difusas en la cuenca hídrica del embalse Los molinos” XXIV latinoamerican congress. Punta del Este, Uruguay

    Google Scholar 

  • Florsheim J, Keller E, Best D (1991) Fluvial sediment transport in response to moderate storm flows following chaparral wildfire, southern California. Geol Soc Am Bull 103:504–511

    Article  Google Scholar 

  • Ford D, Johnson M (1983) An assessment of reservoir density currents and inflow processes. Technical report E-83-7, U.S. Army Corps of Engineering Waterways Experiment Station, Vicksburg

  • Gao B, Montes M, Davis C, Goetz A (2009) Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sens Environ 113(1):517–524

    Google Scholar 

  • Garvine R (1995) A dynamical system for classifying buoyant coastal discharges. Cont Shelf Res 20:2067–2093

    Google Scholar 

  • Giardino C (2010) Application of remote sensing in water resource management : the case study of lake Trasimeno, Italy. Water Resour Manag 24(14):3885–3899

    Article  Google Scholar 

  • Giardino C, Pepe M, Brivio P, Ghezzi P, Zilioli E (2001) Detecting chlorophyll, secchi disk depth and surface temperature in a sub-alpine lake using landsat imagery. Sci Total Environ 268(1–3):19–29

    Article  Google Scholar 

  • Han Z, Jin Y, Yun C (2006) Suspended sediment concentrations in the Yangtze River estuary retrieved from the CMODIS data. Int J Remote Sens 27(19):4329–4336

    Article  Google Scholar 

  • Hawley N, Lesht BM (1992) Sediment resuspension in Lake St Clair. Limnol Oceanogr 37(8):1720–1737. doi:10.4319/lo.1992.37.8.1720

    Article  Google Scholar 

  • Hebbert B, Imberger J, Loh I, Petterson J (1979) Collie River underflow into the Wellington Reservoir. J Hydraul Div ASCE 105:533–545

  • Hellweger F, Miller W, Oshodi K (2006) Mapping turbidity in the Charles River, Boston using a high resolution satellite. Environ Monit Assess 132(1–3):311–320

    Google Scholar 

  • Herting A, Farmer T, Evans J (2004) Mapping of the evaporative loss from elephant butte reservoir using remote sensing and GIS technology. Technical report. States University (NMSU, CAGE), New Mexico

    Google Scholar 

  • Hu C, Muller-Karger F, Andrefouet S, Carder K (2001) Atmospheric correction and cross-calibration of LANDSAT-7/ETM+ imagery over aquatic environments: a multiplatform approach using sea WiFS/MODIS. Remote Sens Environ 78(1–2):99–107

    Article  Google Scholar 

  • Huang C, Zhang Z, Yang L, Luylie B, Homer C (2002) MRLC 2000. Image Preprocessing Procedure, USGS

  • Imberger J, Patterson JC (1990) Physical lumnology. Adv Appl Mech 27:303–475

    Article  Google Scholar 

  • Jirka GH, Adams EE, Stolzenbach KD (1981) Buoyant surface jets. J Hydr Div 10711:1467–1487

  • Jones G, Nash D, Doneker L, Jirka H (2007) Buoyant surface discharge into water bodies. I: flow classification and prediction methodology. J Hydraul Eng 133:1010–1020

  • Kloiber S, Brezonik P, Olmanson L, Bauer M (2002) A procedure for regional lake water clarity assessment using Landsat multispectral data. Remote Sens Environ 82(1):38–47

    Article  Google Scholar 

  • Luettich RA Jr et al (1990) Dynamic behavior of suspended sediment concentrations in a hallow lake perturbed by episodic wind events. Limnol Oceanogr 35(5):1050–1067. doi:10.4319/lo.1990.35.5.1050

    Article  Google Scholar 

  • Mian M, Yanful E (2004) Analysis of wind-driven resuspension of metal mine sludge in a tailings pond. J Environ Eng Sci 3:119–135

    Article  Google Scholar 

  • Miller J, Yool S (2002) Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data. Remote Sens Environ 82(2–3):481–496

    Article  Google Scholar 

  • Ostlund C, Flink P, Strombeck N, Pierson D, Lindell T (2001) Mapping of the water quality of lake Erken, Sweden, from imaging spectrometry and Landsat thematic mapper. Sci Total Environ 268(1–3):139–154

    Article  Google Scholar 

  • Ouaidrari H, Vermote E (1999) Operational atmospheric correction of Landsat data. Remote Sens Environ 70(1):4–15

    Article  Google Scholar 

  • Parker G, Toniolo H (2007) Note on the analysis of plunging of density flows. J Hydraul Eng 133(6):690–694

    Article  Google Scholar 

  • Pavelsky T, Smith L (2009) Remote sensing of suspended sediment concentration, flow velocity, and lake recharge in the Peace-Athabasca Delta, Canada. Water Resour Res 45:W11417. doi:10.1029/2008 WR007424

    Google Scholar 

  • Peckham S (2008) A new method for estimating suspended sediment concentrations and deposition rates from satellite imagery based on the physics of plumes. Compt Rendus Geosci 34(10):1198–1222

    Article  Google Scholar 

  • Reneau SL, Katzman D, Kuyumjian GA, Lavine A, Malmon DV (2007) Sediment delivery after a wildfire. Geol Soc Am 35(2):151–154

    Google Scholar 

  • Saville T (1954) The effect of Fetch width on wave generation. Journal Technical Memorandum, 70

  • Schmugge T, Kustas W, Ritchie J, Jackson T, Rango A (2002) Remote sensing in hydrology. Adv Water Resour 25(8–12):1367–1385

    Article  Google Scholar 

  • Shukla J, Misra A, Chandra P (2008) Modeling and analysis of the algal bloom in a lake caused by discharge of nutrients. Appl Math Comput 196(2):782–790

    Article  Google Scholar 

  • Smith V, Tilman G, Nekola J (1999) Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ Pollut 100(1–3):176–196, Oxford, U. K

    Google Scholar 

  • Song C, Woodcock C, Seto K, Lenney M, Macomber S (2001) Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sens Environ 75(2):230–244

    Article  Google Scholar 

  • Sriwongsitanon N, Surakit K, Thianpopirug S (2011) Influence of atmospheric correction and number of sampling points on the accuracy of water clarity assessment using remote sensing application. J Hydrol 401(3–4):203–220

    Article  Google Scholar 

  • Vicente-Serrano S, Pérez-Cabello F, Lasanta T (2008) Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images. Remote Sens Environ 112(10):3916–3934

    Article  Google Scholar 

  • Vogelmann J, Helder D, Morfitt R, Choate M, Merchant J, Bulley H (2001) Effects of landsat 5 thematic mapper and landsat 7 enhanced thematic mapper plus radiometric and geometric calibrations and corrections on landscape characterization. Remote Sens Environ 78(1–2):55–70

    Article  Google Scholar 

  • Whitney M, Garvine R (2005) Wind influence on a coastal buoyant outflow. J Geophys Res 110:C3014. doi:10.1029/22003JC002261

  • Wu G, de Leeuw J, Liu Y (2009) Understanding seasonal water clarity dynamics of Lake Dahuchi from in-situ and remote sensing data. Water Resour Manag 23(9):1849–1861

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. L. Fernandez.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fernandez, R.L., Bonansea, M. & Marques, M. Monitoring Turbid Plume Behavior from Landsat Imagery. Water Resour Manage 28, 3255–3269 (2014). https://doi.org/10.1007/s11269-014-0676-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-014-0676-1

Keywords

Navigation