Sedimentation Study in a Reservoir Using Remote Sensing Technique

Merina, R. Ninija

doi:10.15666/aeer/1404_296304

Cited by 23 publications

(8 citation statements)

References 10 publications

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“…Shavkat, et al [3] put the global figure of annual sedimentation rate of reservoirs at around 1%. The sedimentation rate is, however believed to be higher in developing countries due to the poor land-use practices upstream of reservoirs [2,4].…”

Section: Discussionmentioning

confidence: 99%

“…A Red, Green and Blue (RGB) false colour composite was created to enhance water and vegetation visualisation [5]. The NDWI method using Equation [2] was used for identification of water pixels as suggested by Avinash & Chandramouli [8].…”

Section: Methodsmentioning

confidence: 99%

“…Climate change has increased water supply uncertainty thereby putting more emphasis on the damming of rivers to meet power, food, industry and agricultural water demand [1]. As such, dams are important in capturing flood water during the rainfall season for use during times of water deficit [2]. Sedimentation of reservoirs reduces the amount of water available for use and takes away important water sources from needy communities.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Reservoir Capacity and Sedimentation Rate Using Direct and Indirect Methods

Nyikadzino¹,

Gwate²

2021

Int J Earth Sci Geophys

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Dams are important in harnessing flood water to enhance water availability during the dry season. Globally, most dams no longer hold the amount of water they were designed to hold due to sedimentation. The study sought to monitor sedimentation of Chesa dam in Upper Ruya Subcatchment, Mazowe Catchment in Zimbabwe. Five Landsat 8 OLI datasets for the period 2015 to 2017 were used. Remotely sensed sedimentation data was analysed using the NDWI method. Results indicated that Chesa dam's current capacity was 374,535 m3 against the design capacity of 1,150,000 m3 in 1991, indicating a loss of 775,465 m3. The annual rate of sedimentation for Chesa was estimated to be 2.7% and this suggests that the dam might completely sediment by year 2028. The difference between observed and predicted sedimentation rate was 0.36% indicating that remote sensing can be used for reservoir capacity estimation. It was concluded that there is rapid environmental degradation upstream hence the high rates of sedimentation. There is need for drastic interventions to enhance sustainable landuse practices upstream of the dam. The study confirmed that remotely sensed data and GIS technology can be used in determining reservoir capacity. Future studies may need to use finer resolution data for similar studies.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Reservoir Capacity and Sedimentation Rate Using Direct and Indirect Methods

Nyikadzino¹,

Gwate²

2021

Int J Earth Sci Geophys

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“…Globally, the rivers in south East Asia have rivers with large deltas and their sediment yield are large and posing problems to the reservoirs [16,17,18,9]. Southeast Asia considered the region with the largest sediment yields [8].…”

Section: Review Of Literaturementioning

confidence: 99%

Estimation of Sediments in Rengali Reservoir, Odisha (India) Using Remote Sensing

Mishra

Kumar

Mishra

et al. 2021

IJECC

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Reservoir sedimentation is a regular process and sequential path of sedimentation in reservoirs comprising of erosion, entrainment, transference, deposition and compaction of dregs carried into artificial lakes formed behind the dams. India houses 5334 large dams in function (2329 numbers before 1980) and 411 dams are in pipeline. The Rengali dam, functioned from 1984, that traps 50% of the total sediment load of the Brahmani River continues to thwart the growth and buffering of the Brahmani delta. Remote sensing (RS) and Geographical Information System (GIS) have emerged as powerful tools to create spatial inventory on Hydro-Bio-geo resources and the state of the environment. The RS/GIS and process-based modelling employed in spatial and dynamic assessment of loss in live storage of the reservoir by developing contour, aspect and slope map by using data received from LANDSAT sources. The sedimentation of the Rengali reservoir (functional from 1984) studied for three decades 1990-2000; 2000-2010 and 2010- 2020 by constructing contour, aspect and water spread area maps by using web based data (satellite downloads). The web based water spread area data analysed by GIS tool for integration, spatial analysis, and visual presentations. The results revealed that the decadal rate of sedimentation of Rengali reservoir is reducing with age. An appropriate reservoir operation and management system as per defined protocols considering sediment related problems is essential for controlling the ageing processes that may diminish the safety and shorten the reservoir life.

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“…By using empirical and numerical techniques prediction of sediment deposition is done which requires large amounts of data and the results are still less accurate (Goel, Jain, & Agarwal, 2002). By using remote sensing it becomes very convenient to estimate sedimentation in reservoir (Ninija Merina, R. Sashi Kumar, M. C., Rizvana, N., Adlin, 2016). Remote sensing techniques are superior to the other conventional techniques (such as hydrographic surveys and inflow-outflow records), as it provides data availability over a long time span with a wide spectral range (Goel et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Revised Capacity in Gobind Sagar Reservoir Using Google Earth Engine and Gis

Singh

Dhasmana

Shrivastava

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Capacity studies of reservoirs are important to evaluate sedimentation and optimize reservoir operation schedule based on realistic assessment of available storage. Capacity study at regular interval provide information about rate and pattern of sedimentation between various levels, loss of capacity due to sedimentation, remaining time span of reservoir, etc. In the present study, evaluation of revised capacity of Gobind Sagar reservoir in Bilaspur district, Himachal Pradesh, India has been done using Google Earth Engine. Landsat 8 (OLI) data for September 2015 and for the period September 2017 to May 2018 covering full extent of Gobind Sagar reservoir is taken to compute the water spread area of this reservoir at different dates. Subsequently, the reservoir water level and volume of water stored on the corresponding dates is acquired from India-WRIS. By using trapezoidal formula capacity between two elevations is determined using water spread area obtained from Google Earth Engine analysis and elevation data obtained from India-WRIS. The comparison of water spread areas of different water levels, as obtained from remotely sensed data from September 2017 to May, 2018 with those from survey carried out during 1996/97, indicates a reduction in the capacity by 10.71% and sedimentation rate was estimated to be 14.24Mm3/year.

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Sedimentation Study in a Reservoir Using Remote Sensing Technique

Cited by 23 publications

References 10 publications

Estimation of Reservoir Capacity and Sedimentation Rate Using Direct and Indirect Methods

Estimation of Reservoir Capacity and Sedimentation Rate Using Direct and Indirect Methods

Estimation of Sediments in Rengali Reservoir, Odisha (India) Using Remote Sensing

Estimation of Revised Capacity in Gobind Sagar Reservoir Using Google Earth Engine and Gis

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