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Journal articles on the topic 'Mahanadi River (India)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Debata, Subrat, Tuhinansu Kar, Kedar Kumar Swain, and Himanshu Shekhar Palei. "The Vulnerable Indian Skimmer Rynchops albicollis Swainson, 1838 (Aves: Charadriiformes: Laridae) breeding in Odisha, eastern India." Journal of Threatened Taxa 9, no. 11 (November 26, 2017): 10961. http://dx.doi.org/10.11609/jott.3445.9.11.10961-10963.

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The Indian Skimmer is a globally threatened bird native to Bangladesh, India, Myanmar, Nepal, Pakistan, and Vietnam. In India, it is more confined to the north, from Punjab through Uttar Pradesh, Madhya Pradesh to West Bengal, extending up to Odisha. Earlier, the bird was known to breed only in Uttar Pradesh and Madhya Pradesh, we confirm here the breeding of the Indian Skimmer along the river Mahanadi near Mundali, Odisha, eastern India. So, further monitoring at the breeding site and survey along the entire Mahanadi River are essential to understand the status of the Indian skimmer in Odisha. The information will also aid in reassessing its global status and formulating conservation plans.
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2

Chandra, Sulekh, Arendra Singh, Praveen Kumar Tomar, and Adarsh Kumar. "Evaluation of Physicochemical Characteristics of Various River Water in India." E-Journal of Chemistry 8, no. 4 (2011): 1546–55. http://dx.doi.org/10.1155/2011/430232.

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Physicochemical parameters of water samples collected from various rivers in India. Water samples under investigations were collected from Krishana Vijaywada, Gomti Lucknow, Hoogali Kolkata, Ganga Kasi, Mahanadi Katak, Cauveri river Tiruchirapalli station during (July - August) seasons in the year 2009. The different sites show significant enrichment with Zn, Fe, Ni, Cr, Ca and Mg indicating input from industrial sources. The observed values of different physicochemical characteristics like pH, temperature, turbidity, total hardness (TH), iron, chloride, total dissolved solids(TDS), Ca2+, Mg2+, SO42-, F- total alkalinity (TA), COD, BOD, phosphate, FRC (Free residual chlorine), total silica and hydrazine of samples were compared with standard values recommended by Bureau of Indian standard (BIS).
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3

Ganguly, Ishita, Lipika Patnaik, and Sushree Nayak. "Macroinvertebrates and its impact in assessing water quality of riverine system: A case study of Mahanadi river, Cuttack, India." Journal of Applied and Natural Science 10, no. 3 (August 21, 2018): 958–63. http://dx.doi.org/10.31018/jans.v10i3.1817.

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The aim of this study was to identify the diverse macroinvertebrates present in river Mahanadi, Cuttack in India and to evaluate the role of macroinvertebrates in assessing river water quality and pollution level. We conducted field study of the river at Cuttack (85°46’21.29” E 20°28’15.81” N & 85°49’45.23” E 20°30’50.00” N) during 2013-2014 and collected aquatic invertebrate samples from 12 stations on river basin. The samples were analysed to explore the various families of Macroinvertebrates communities present in river Mahanadi, to examine the status of water quality of the river using biological indicators, to determine whether there are relationships between water chemistry and presence of typical macroinvertebrates and to develop a Macroinvertebrates based index to bio-assessment of Mahanadi River. A total of 484 taxa were identified and about 244 taxa of bivalves and 184 taxa of gastropods were collected. Presence of high number of pollution tolerant taxa and pollution sensitive taxa (Ephemeroptera, Plecoptera, Tricoptera and Chironomidae) indicated increased risk of water pollution and calculated biotic score (8), biological monitoring working party (BMWP) score (52), average score per taxa (ASPT) score (4) and macroinvertebrate-based index (MBI) value indicated moderate pollution level in the river. We recorded pH, total hardness, dissolved oxygen (DO), biological oxygen demand (BOD), total nitrite, chloride and total phosphate of water and physico-chemical parameters supported the values of biological assessment of water quality. Studying macaroinvertebrates helped to gain knowledge about aquatic faunal biodiversity in river Mahanadi and to develop a method for diagnosis of the health of river ecosystem and for measuring water pollution level.
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4

Ray, S. B., M. Mohanti, and B. L. K. Somayajulu. "Uranium Isotopes in the Mahanadi River-Estuarine System, India." Estuarine, Coastal and Shelf Science 40, no. 6 (June 1995): 635–45. http://dx.doi.org/10.1006/ecss.1995.0043.

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5

Sahu, Netrananda, Arpita Panda, Sridhara Nayak, Atul Saini, Manoranjan Mishra, Takahiro Sayama, Limonlisa Sahu, Weili Duan, Ram Avtar, and Swadhin Behera. "Impact of Indo-Pacific Climate Variability on High Streamflow Events in Mahanadi River Basin, India." Water 12, no. 7 (July 9, 2020): 1952. http://dx.doi.org/10.3390/w12071952.

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The potential impact of climate variability on the hydrological regime in the Mahanadi river basin is of great importance for sustainable water resources management. The impact of climate variability on streamflow is analyzed in this study. The impact of climate variability modes on extreme events of Mahanadi basin during June, July, and August (JJA), and September, October, and November (SON) seasons were analyzed, with daily streamflow data of four gauge stations for 34 years from 1980 to 2013 found to be associated with the sea surface temperature variations over Indo-Pacific oceans and Indian monsoon. Extreme events are identified based on their persistent flow for six days or more, where selection of the stations was based on the fact that there was no artificially regulated streamflow in any of the stations. Adequate scientific analysis was done to link the streamflow variability with the climate variability and very significant correlation was found with Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO), El Nino Modoki Index (EMI), and Indian monsoon. Agriculture covers major portion of the basin; hence, the streamflow is very much essential for agriculture as well as population depending on it. Any disturbances in the general flow of the river has subjected an adverse impact on the inhabitants’ livelihood. While analyzing the correlation values, it was found that all stations displayed a significant positive correlation with Indian Monsoon. The respective correlation values were 0.53, 0.38, 0.44, and 0.38 for Andhiyarkore, Baronda, Rajim, and Kesinga during JJA season. Again in the case of stepwise regression analysis, Monsoon Index for the June, July, and August (MI-JJA) season (0.537 for Andhiyarkore) plays significant role in determining streamflow of Mahanadi basin during the JJA season and Monsoon Index for July, August, and September (MI-JAS) season (0.410 for Baronda) has a strong effect in affecting streamflow of Mahanadi during the SON season. Flood frequency analysis with Weibull’s plotting position method indicates future floods in the Mahanadi river basin in JJA season.
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6

Prusty, Rabiranjan, and Trinath Biswal. "Assessment of Pollution Load in Terms of Water Quality Index and Modelling of Taladanda Canal and Mahanadi River in Paradip Area, Odisha, India." Asian Journal of Water, Environment and Pollution 17, no. 4 (October 31, 2020): 59–72. http://dx.doi.org/10.3233/ajw200052.

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The modelling of water quality is an integrated source of good management, which benefits the environment and its people. In the present study, the quality of water was measured in terms of physicochemical analysis and WQI. This analysis facilitates the eco-management study of the water. In this article, we have measured the quality of the water in Taladanda canal and river Mahanadi nearby Paradip area in terms of WQI for the year 2017. Five different sampling stations were selected from Taladanda canal and nine sampling points were selected from river Mahanadi. It was found that the water quality index in most of the areas was much higher, however, the water is of poor quality. But in PPL site areas, the quality of water was found to be very poor and not suitable for human use. The pollution load was found to be much higher in the Taladanda canal and moderate in Mahanadi River near the Paradip area.
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7

Chakrapani, G. J., and V. Subramanian. "Factors controlling sediment discharge in the Mahanadi River Basin, India." Journal of Hydrology 117, no. 1-4 (September 1990): 169–85. http://dx.doi.org/10.1016/0022-1694(90)90091-b.

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8

Thakur, P. K., A. Chouksey, P. Kalura, S. Ghosh, P. Dhote, A. Swain, M. Kalia, et al. "INDIAN INLAND WATER AND PARTS OF ANTARCTIC ICE SHEET ELEVATION AND ICE SHEET VELOCITY MONITORING USING ALTIMETRY AND SAR BASED DATASETS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 367–73. http://dx.doi.org/10.5194/isprs-archives-xlii-5-367-2018.

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<p><strong>Abstract.</strong> The monitoring of inland water and continental ice sheets is very important from water management and global climate related studies. The current study utilizes the SGDR data from Saral-Altika during 2013–2017 to estimate and monitor water level in 24 major reservoirs of India. The R<sup>2</sup> value for majority of reservoirs was more than 0.99 and RMSE error value also was less than 0.40<span class="thinspace"></span>m. In addition, wide rivers of India such as Mahanadi River, was also monitored using Altika data covering part of Mahanadi River from Khairmal to Naraj gauging sites during 2013–2016 time period. One dimensional hydro-dynamic (1D-HD) model was setup for this part of river to generate river Discharge at virtual gauge. The part of Antarctic ice sheet South of Indian research station Maitri, East Antarctica, was studied for ice sheet elevation changes using ground based stake network as well as space based altimeter/LIDAR datasets during 2003–2017 time period. 2003–2009 time was used for getting elevation changes using Icesat-1 level 2 altimetry product, and Geophysical Data Record (GDR) data from Altika was used with slope correction from 2013–2016 time period. An extensive network of ground based stake networks were used for validating the derived elevation changes. The ice sheet and glacier line of site velocity was estimated using Sentinel-1 based InSAR data with 6 to 12 day time interval data sets for year 2016 and 2017. The derived glacier velocity was comparable with optical image (Landsat-8) based glacier velocity for same year and also with historical Radarsat-1 based glacier velocity results.</p>
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9

Tripathy, Biplab, and Tanmoy Mondal. "Socioeconomic Challenges faced by Basin’s People in India." Think India 22, no. 2 (October 31, 2019): 296–304. http://dx.doi.org/10.26643/think-india.v22i2.8730.

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India is a subcontinent, there huge no of people lived in river basin area. In India there more or less 80% of people directly or indirectly depend on River. Ganga, Brahamputra in North and North East and Mahanadi, Govabori, Krishna, Kaveri, Narmoda, Tapti, Mahi in South are the major river basin in India. There each year due to flood and high tide lots of people are suffered in river basin region in India. These problems destroy the socio economic peace and hope of the people in river basin. There peoples are continuously suffered by lots of difficulties in sort or in long term basis. Few basin regions are always in high alert at the time of monsoon seasons. Sometime due to over migration from basin area, it becomes empty and creates an ultimate loss of resources in India and causes a dis-balance situation in this area.
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10

Rajput, Preeti, and Manish Kumar Sinha. "Geospatial evaluation of drought resilience in sub-basins of Mahanadi river in India." Water Supply 20, no. 7 (August 7, 2020): 2826–44. http://dx.doi.org/10.2166/ws.2020.178.

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Abstract Development is said to be sustainable in respect of drought if the effect has been absorbed by the existing system. Occurrence of drought depends on physiographical, climatic factors and optimum utilization of available resources of the river basin. This study aims to evaluate the vulnerability and resilience of river basin systems for the identification of priority areas under drought susceptibility for three different river basins, namely Arpa, Kharun and Upper Seonath of Mahanadi river in central India, as a pilot area for this study. The study represents an approach to evaluate the drought susceptibility of river basins based on physiographical factors and anthropogenic activities. A model proposed for vulnerability assessment based on variables of exposure, sensitivity and adaptive capacity, and a geospatial database of basin characteristics contributing to vulnerability, was generated using remote sensing and a geographic information system. Multi-criteria decision analysis was done to evaluate the influence of river basin characteristics, population load and land-use/cover on drought susceptibility for assessing the drought vulnerability of the river basin and suggest the solution for the optimum utilization of natural resources according to the river basin characteristics. The result of this study demarcates the area in four categories of Extremely vulnerable, Moderately vulnerable, Vulnerable and Not vulnerable. On the analysis, only 3.86% of Upper Seonath is Not vulnerable, followed by Kharun basin having 15.59% as Not vulnerable area and 48.23% of the area of Arpa river basin identified as Not vulnerable. Arpa river basin is least affected by drought due to its lower population density and high coverage of forest and agriculture area.
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11

REDDY, YENUMULA RANGA, and DANIELLE DEFAYE. "Parastenocarididae (Crustacea, Copepoda, Harpacticoida) of India: description of Parastenocaris mahanadi n. sp., and redescription of P. curvispinus Enckell, 1970 from hyporheic habitats." Zootaxa 1580, no. 1 (September 10, 2007): 1–26. http://dx.doi.org/10.11646/zootaxa.1580.1.1.

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Only four species of the genus Parastenocaris Kessler, 1913, were previously known from India: P. curvispinus Enckell, 1970, P. gayatri Ranga Reddy, 2001, P. savita Ranga Reddy, 2001, and P. sandhya Ranga Reddy, 2001. A fifth species, Parastenocaris mahanadi n. sp., is here described from the hyporheic habitat of the River Mahanadi near Raipur city in the State of Chhatisgarh, central India. This is the first Indian representative of the minuta species-group. It can be distinguished from the other members of this group by a unique combination of morphological characters, especially of the male: fifth antennular segment bearing an extraordinarily long aesthetasc and seventh segment without apophysis; caudal rami as long as anal somite and over four times as long as their maximum width; lateral setae inserted at about distal third of rami; anal operculum smooth; leg 4 bearing two chitinized, claw-like spines at distal inner corner of basis; endopodite small, 0.4 as long as first exopodite segment and ornamented with two groups of long spinules; leg 5 produced into smooth, slender, acute, spinous process at distal inner corner, and with only three setae. Parastenocaris curvispinus, which was originally incompletely described from Sri Lanka by Enckell (1970), is redescribed from several hyporheic habitats. This highly variable species, which was found co-occurring, inter alia, with P. mahanadi n. sp., is the dominant and most wide-spread parastenocaridid in the hyporheic habitats of peninsular India. Following the recent resurrection of the genus Remaneicaris Jakobi, 1972, the taxonomic position of this species is reviewed. Furthermore, the distribution of the Indian parastenocaridids is briefly discussed.
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12

Samantaray, Sandeep, and Abinash Sahoo. "Estimation of flood frequency using statistical method: Mahanadi River basin, India." H2Open Journal 3, no. 1 (January 1, 2020): 189–207. http://dx.doi.org/10.2166/h2oj.2020.004.

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Abstract Estimating stream flow has a substantial financial influence, because this can be of assistance in water resources management and provides safety from scarcity of water and conceivable flood destruction. Four common statistical methods, namely, Normal, Gumbel max, Log-Pearson III (LP III), and Gen. extreme value method are employed for 10, 20, 30, 35, 40, 50, 60, 70, 75, 100, 150 years to forecast stream flow. Monthly flow data from four stations on Mahanadi River, in Eastern Central India, namely, Rampur, Sundargarh, Jondhra, and Basantpur, are used in the study. Results show that Gumbel max gives better flow discharge value than the Normal, LP III, and Gen. extreme value methods for all four gauge stations. Estimated flood values for Rampur, Sundargarh, Jondhra, and Basantpur stations are 372.361 m3/sec, 530.415 m3/sec, 2,133.888 m3/sec, and 3,836.22 m3/sec, respectively, considering Gumbel max. Goodness-of-fit tests for four statistical distribution techniques applied in the present study are also evaluated using Kolmogorov–Smirov, Anderson–Darling, Chi-squared tests at critical value 0.05 for the four proposed gauge stations. Goodness-of-fit test results show that Gen. extreme value gives best results at Rampur, Sundergarh, and Jondhra gauge stations followed by LP III, whereas LP III is the best fit for Basantpur, followed by Gen. extreme value.
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13

Chakrapani, G. J., and V. Subramanian. "Rates of erosion and sedimentation in the Mahanadi river basin, India." Journal of Hydrology 149, no. 1-4 (August 1993): 39–48. http://dx.doi.org/10.1016/0022-1694(93)90098-t.

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14

Guru, Nibedita, and Ramakar Jha. "Flood estimation in Mahanadi river system, India using partial duration series." Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 10, no. 2 (November 26, 2015): 135–45. http://dx.doi.org/10.1080/17499518.2015.1116013.

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15

Chakrapani, G. J., and V. Subramanian. "Preliminary studies on the geochemistry of the Mahanadi River basin, India." Chemical Geology 81, no. 3 (January 1990): 241–53. http://dx.doi.org/10.1016/0009-2541(90)90118-q.

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16

Sahoo, Abinash, and Dillip K. Ghose. "Flood Frequency Analysis for Menace Gauging Station of Mahanadi River, India." Journal of The Institution of Engineers (India): Series A 102, no. 3 (June 2, 2021): 737–48. http://dx.doi.org/10.1007/s40030-021-00544-x.

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17

Parhi, Prabeer Kumar, R. N. Sankhua, and G. P. Roy. "Calibration of Channel Roughness for Mahanadi River, (India) Using HEC-RAS Model." Journal of Water Resource and Protection 04, no. 10 (2012): 847–50. http://dx.doi.org/10.4236/jwarp.2012.410098.

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18

Bastia, Fakira, and Sk Md Equeenuddin. "Chemical weathering and associated CO2 consumption in the Mahanadi river basin, India." Journal of Asian Earth Sciences 174 (May 2019): 218–31. http://dx.doi.org/10.1016/j.jseaes.2018.12.010.

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19

Hill, Christopher V. "Ideology and public works: “Managing” the Mahanadi river in colonial North India∗." Capitalism Nature Socialism 6, no. 4 (December 1995): 51–64. http://dx.doi.org/10.1080/10455759509358650.

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20

Rao, Pitta Govinda. "Effect of Climate Change on Streamflows in the Mahanadi River Basin, India." Water International 20, no. 4 (January 1995): 205–12. http://dx.doi.org/10.1080/02508069508686477.

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21

Routray, Niranjan, and A. K. Patra. "Studies on seasonal variations in primary production of river Mahanadi, Banki, Odisha, India." International Journal of Bioassays 5, no. 02 (January 31, 2016): 4779. http://dx.doi.org/10.21746/ijbio.2016.02.002.

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The primary productivity of the river Mahanadi at Banki has been estimated from January 2012 to December 2012 at three different stations. The seasonal variation of primary productivity revealed that maximum and minimum values of GPP was associated with summer and winter seasons respectively. The minimum values of NPP were recorded during rainy season and maximum during summer or winter for different study stations. The community respiration showed a systematic seasonal pattern where the maximum value was observed during summer and minimum value during winter. The ratio between NPP and GPP was lowest during rainy season and highest in summer.
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22

D'Souza, Rohan. "Damming the Mahanadi river: The emergence of multi-purpose river valley development in India (1943-46)." Indian Economic & Social History Review 40, no. 1 (January 2003): 81–105. http://dx.doi.org/10.1177/001946460304000104.

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23

Janhabi, Meher, and Jha Ramakar. "Time-series analysis of monthly rainfall data for the Mahanadi River Basin, India." Sciences in Cold and Arid Regions 5, no. 1 (2013): 73. http://dx.doi.org/10.3724/sp.j.1226.2013.00073.

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24

Behera, Bikash Chandra, Rashmi Ranjan Mishra, Santosh Kumar Singh, Sushil Kumar Dutta, and Hrudayanath Thatoi. "Cellulase fromBacillus licheniformisandBrucellasp. isolated from mangrove soils of Mahanadi river delta, Odisha, India." Biocatalysis and Biotransformation 34, no. 1 (January 2, 2016): 44–53. http://dx.doi.org/10.1080/10242422.2016.1212846.

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25

Das, M., and T. Panda. "Water Quality and Phytoplankton Population in Sewage Fed River of Mahanadi, Orissa, India." Journal of Life Sciences 2, no. 2 (December 2010): 81–85. http://dx.doi.org/10.1080/09751270.2010.11885156.

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26

Panda, Dileep K., A. Kumar, S. Ghosh, and R. K. Mohanty. "Streamflow trends in the Mahanadi River basin (India): Linkages to tropical climate variability." Journal of Hydrology 495 (July 2013): 135–49. http://dx.doi.org/10.1016/j.jhydrol.2013.04.054.

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27

Chakrapani, G. J., and V. Subramanian. "Heavy metals distribution and fractionation in sediments of the Mahanadi River basin, India." Environmental Geology 22, no. 1 (September 1993): 80–87. http://dx.doi.org/10.1007/bf00775288.

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28

Kar, Tuhinansu, Himanshu Shekhar Palei, and Subrat Debata. "Breeding reports and conservation implications of the Endangered Black-bellied Tern Sterna acuticauda J.E. Gray, 1831 (Aves: Charadriiformes: Laridae) in Odisha, eastern India." Journal of Threatened Taxa 10, no. 13 (November 26, 2018): 12840–43. http://dx.doi.org/10.11609/jott.4106.10.13.12840-12843.

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The Black-bellied Tern is an endangered species and its population is declining severely due to the loss and degradation of its foraging and breeding habitats because of increasing anthropogenic activities. We report the breeding of Black-bellied Tern from different localities along the Mahanadi River in Odisha, eastern India. We recommend the protection and conservation of its breeding sites along with regular community outreach activities for the long-term conservation of this globally threatened species.
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29

Matli, Chandra Sekhar, and Nivedita. "Water Quality Modelling of River Mahanadi using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR)." International Journal of Environment 10, no. 1 (July 23, 2021): 83–98. http://dx.doi.org/10.3126/ije.v10i1.38417.

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Surface water quality is one of the critical environmental concerns of the globe and water quality management is top priority worldwide. In India, River water quality has considerably deteriorated over the years and there is an urgent need for improving the surface water quality. The present study aims at use of multivariate statistical approaches for interpretation of water quality data of Mahanadi River in India. Monthly water quality data pertaining to 16 parameters collected from 12 sampling locations on the river by Central Water Commission (CWC) and Central Pollution Control Board (CPCB) is used for the study. Cluster analysis (CA), is used to group the sampling locations on the river into homogeneous clusters with similar behaviour. Principal component analysis (PCA) is quite effective in identifying the critical parameters for describing the water quality of the river in dry and monsoon seasons. PCA and Factor Analysis (FA) was effective in explaining 69 and 66% of the total cumulative variance in the water quality if dry and wet seasons respectively. Industrial and domestic wastewaters, soil erosion and weathering, soil leaching organic pollution and natural pollution were identified as critical sources contribution to pollution of river water. However, the quantitative contributions were variable based on the season. Results of multiple linear regression (MLR) are effective in explaining the factor loadings and source contributions for most water quality parameters. The study results indicate suitability of multivariate statistical approaches to design and plan sampling and sampling programs for controlling water quality management programs in river basins.
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30

SHANGNINGAM, BUNGDON, SHIBANANDA RATH, ASHA KIRAN TUDU, and LAISHRAM KOSYGIN. "A new species of Osteobrama (Teleostei: Cyprinidae) from the Mahanadi River, India with a note on the validity of O. dayi." Zootaxa 4722, no. 1 (January 10, 2020): 68–76. http://dx.doi.org/10.11646/zootaxa.4722.1.6.

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A new species of the genus Osteobrama is described from the Mahanadi River, Tikarpada, Angul District, Odisha state, India. Osteobrama tikarpadaensis, new species, differs from its congeners in having two pairs of minute barbels; iii–iv unbranched dorsal-fin rays with 25–33 serrae on the last unbranched ray; 15–16 branched pectoral-fin rays, and 25–27 branched anal-fin rays. The status of Osteobrama dayi is discussed and shown to be a valid species. A key to the species of the genus is provided.
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31

Sahoo, Bibhuti Bhusan, Ramakar Jha, Anshuman Singh, and Deepak Kumar. "Bivariate low flow return period analysis in the Mahanadi River basin, India using copula." International Journal of River Basin Management 18, no. 1 (February 19, 2019): 107–16. http://dx.doi.org/10.1080/15715124.2019.1576698.

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32

Asokan, Shilpa M., and Dushmanta Dutta. "Analysis of water resources in the Mahanadi River Basin, India under projected climate conditions." Hydrological Processes 22, no. 18 (August 30, 2008): 3589–603. http://dx.doi.org/10.1002/hyp.6962.

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33

Bastia, Fakira, and Sk Md Equeenuddin. "Spatio-temporal variation of water flow and sediment discharge in the Mahanadi River, India." Global and Planetary Change 144 (September 2016): 51–66. http://dx.doi.org/10.1016/j.gloplacha.2016.07.004.

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34

Sundaray, Sanjay Kumar, Binod Bihari Nayak, Tapan Kumar Kanungo, and Dinabandhu Bhatta. "Dynamics and quantification of dissolved heavy metals in the Mahanadi river estuarine system, India." Environmental Monitoring and Assessment 184, no. 2 (April 27, 2011): 1157–79. http://dx.doi.org/10.1007/s10661-011-2030-x.

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35

Panigrahy, B. K., and B. C. Raymahashay. "River water quality in weathered limestone: A case study in upper Mahanadi basin, India." Journal of Earth System Science 114, no. 5 (October 2005): 533–43. http://dx.doi.org/10.1007/bf02702029.

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36

Debata, Subrat. "Impact of cyclone Fani on the breeding success of sandbar-nesting birds along the Mahanadi River in Odisha, India." Journal of Threatened Taxa 11, no. 14 (November 26, 2019): 14895–98. http://dx.doi.org/10.11609/jott.5480.11.14.14895-14898.

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Cyclonic storms have detrimental effect on birds particularly those inhabit and breed in open and exposed areas like sea and riverine habitats. This note reports the effect of ‘Fani’, one of the severe cyclones that affected Odisha in May 2019 on sandbar- nesting birds along the Mahanadi River. The study found significant relative decline in population, complete failure of nests and survival of chicks. As tropical cyclones are expected to be more frequent in future, it may emerge as new threats for survival of sandbar-nesting birds.
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37

Kneis, D., C. Chatterjee, and R. Singh. "Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)." Hydrology and Earth System Sciences 18, no. 7 (July 4, 2014): 2493–502. http://dx.doi.org/10.5194/hess-18-2493-2014.

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Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.
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38

Kneis, D., C. Chatterjee, and R. Singh. "Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)." Hydrology and Earth System Sciences Discussions 11, no. 1 (January 23, 2014): 1169–201. http://dx.doi.org/10.5194/hessd-11-1169-2014.

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Abstract. The paper examines the quality of satellite-based precipitation estimates for the Lower Mahanadi River Basin (Eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gage-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gage data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analyzing their performance in the context of rainfall-runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gage-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high intensity levels. The rainfall depth derived from rain gage data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day−1). At the same time, the remotely sensed rainfall rates frequently exceed the gage-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalization of rain gage data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gage data were used as model input (Nash–Sutcliffe Index of 0.76–0.88 at gages not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gage-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.
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39

Das Sarkar, Soma, Amiya Kumar Sahoo, Pranab Gogoi, Rohan Kumar Raman, Manas Hoshalli Munivenkatappa, Kavita Kumari, Bimal Prasanna Mohanty, and Basanta Kumar Das. "Phytoplankton biomass in relation to flow dynamics: the case of a tropical river Mahanadi, India." Tropical Ecology 60, no. 4 (November 27, 2019): 485–94. http://dx.doi.org/10.1007/s42965-019-00048-7.

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40

Guru, Nibedita. "Simulation of BOD-DO Modeling in Mahanadi River System lying in Odisha using ANN, India." IOSR Journal of Environmental Science, Toxicology and Food Technology 2, no. 4 (2013): 52–57. http://dx.doi.org/10.9790/2402-0245257.

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41

Swetapadma, Sonali, and C. S. P. Ojha. "Selection of a basin-scale model for flood frequency analysis in Mahanadi river basin, India." Natural Hazards 102, no. 1 (April 28, 2020): 519–52. http://dx.doi.org/10.1007/s11069-020-03936-7.

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42

Behera, M. D., P. Tripathi, P. Das, S. K. Srivastava, P. S. Roy, C. Joshi, P. R. Behera, et al. "Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985." Journal of Environmental Management 206 (January 2018): 1192–203. http://dx.doi.org/10.1016/j.jenvman.2017.10.015.

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43

Samantaray, Sandeep, Abinash Sahoo, and Ankita Agnihotri. "Assessment of Flood Frequency using Statistical and Hybrid Neural Network Method: Mahanadi River Basin, India." Journal of the Geological Society of India 97, no. 8 (August 2021): 867–80. http://dx.doi.org/10.1007/s12594-021-1785-0.

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44

Sahoo, Abinash, Sandeep Samantaray, and Siddhartha Paul. "Efficacy of ANFIS-GOA technique in flood prediction: a case study of Mahanadi river basin in India." H2Open Journal 4, no. 1 (January 1, 2021): 137–56. http://dx.doi.org/10.2166/h2oj.2021.090.

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Abstract Accurateness in flood prediction is of utmost significance for mitigating catastrophes caused by flood events. Flooding leads to severe civic and financial damage, particularly in large river basins, and mainly affects the downstream regions of a river bed. Artificial Intelligence (AI) models have been effectively utilized as a tool for modelling numerous nonlinear relationships and is suitable to model complex hydrological systems. Therefore, the main purpose of this research is to propose an effective hybrid system by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with meta-heuristic Grey Wolf Optimization (GWO) and Grasshopper Optimization Algorithm (GOA) for flood prediction in River Mahanadi, India. Robustness of proposed meta-heurestics are assessed by comparing with a conventional ANFIS model focusing on various input combinations considering 50 years of monthly historical flood discharge data. The potential of the AI models is evaluated and compared with observed data in both training and validation sets based on three statistical performance evaluation factors, namely root mean squared error (RMSE), mean squared error (MSE) and Wilmott Index (WI). Results reveal that robust ANFIS-GOA outperforms standalone AI techniques and can make superior flood forecasting for all input scenarios.
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45

Srinivasa Raju, K., and D. Nagesh Kumar. "Ranking general circulation models for India using TOPSIS." Journal of Water and Climate Change 6, no. 2 (August 18, 2014): 288–99. http://dx.doi.org/10.2166/wcc.2014.074.

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Eleven general circulation models/global climate models (GCMs) – BCCR-BCCM2.0, INGV-ECHAM4, GFDL2.0, GFDL2.1, GISS, IPSL-CM4, MIROC3, MRI-CGCM2, NCAR-PCMI, UKMO-HADCM3 and UKMO-HADGEM1 – are evaluated for Indian climate conditions using the performance indicator, skill score (SS). Two climate variables, temperature T (at three levels, i.e. 500, 700, 850 mb) and precipitation rate (Pr) are considered resulting in four SS-based evaluation criteria (T500, T700, T850, Pr). The multicriterion decision-making method, technique for order preference by similarity to an ideal solution, is applied to rank 11 GCMs. Efforts are made to rank GCMs for the Upper Malaprabha catchment and two river basins, namely, Krishna and Mahanadi (covered by 17 and 15 grids of size 2.5° × 2.5°, respectively). Similar efforts are also made for India (covered by 73 grid points of size 2.5° × 2.5°) for which an ensemble of GFDL2.0, INGV-ECHAM4, UKMO-HADCM3, MIROC3, BCCR-BCCM2.0 and GFDL2.1 is found to be suitable. It is concluded that the proposed methodology can be applied to similar situations with ease.
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46

Panda, Unmesh Chandra, Sanjay Kumar Sundaray, Prasant Rath, Binod Bihari Nayak, and Dinabandhu Bhatta. "Application of factor and cluster analysis for characterization of river and estuarine water systems – A case study: Mahanadi River (India)." Journal of Hydrology 331, no. 3-4 (December 2006): 434–45. http://dx.doi.org/10.1016/j.jhydrol.2006.05.029.

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47

Yadav, Arvind, Snehamoy Chatterjee, and Sk Md Equeenuddin. "Suspended sediment yield modeling in Mahanadi River, India by multi-objective optimization hybridizing artificial intelligence algorithms." International Journal of Sediment Research 36, no. 1 (February 2021): 76–91. http://dx.doi.org/10.1016/j.ijsrc.2020.03.018.

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48

Samantaray, Dibyendu, Chandranath Chatterjee, Rajendra Singh, Praveen Kumar Gupta, and Sushma Panigrahy. "Flood risk modeling for optimal rice planning for delta region of Mahanadi river basin in India." Natural Hazards 76, no. 1 (November 11, 2014): 347–72. http://dx.doi.org/10.1007/s11069-014-1493-9.

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49

Sundaray, Sanjay Kumar, Binod Bihari Nayak, Byeong-Gweon Lee, and Dinabandhu Bhatta. "Spatio-temporal dynamics of heavy metals in sediments of the river estuarine system: Mahanadi basin (India)." Environmental Earth Sciences 71, no. 4 (August 29, 2013): 1893–909. http://dx.doi.org/10.1007/s12665-013-2594-6.

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50

Acharya, Aditya P., Annam Pavan-Kumar, Pathakota Gireesh-Babu, Chaitanya G. Joshi, Aparna Chaudhari, and Gopal Krishna. "Population genetics of Indian giant river-catfish, Sperata seenghala (Sykes, 1839) using microsatellite markers." Aquatic Living Resources 32 (2019): 4. http://dx.doi.org/10.1051/alr/2019002.

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The giant river-catfish Sperata seenghala is one of the commercially important freshwater catfishes of India with wide distribution in all major rivers and reservoirs. This fish has huge demand in domestic market due to high nutritional value and low number of intramuscular bones. Conversely, the culture practices for this fish have not yet been standardized and capture fisheries is the only source to meet the demand. This may lead to over exploitation of resources and subsequent population reduction. Knowledge on genetic structure of populations is prerequisite to formulate sustainable management and conservation measures. In the present study, 15 microsatellites were used to characterize population genetics of S. seenghala collected from river Brahmaputra, Ganga, Godavari, Mahanadi and Narmada. Locus-wise, the number of alleles varied from 8 to 19 with an average of 12 alleles per locus. The mean observed and expected heterozygosity values varied from 0.622 to 0.699 and 0.733 to 0.774, respectively. Several loci have shown deviation from Hardy–Weinberg equilibrium and no significant linkage disequilibrium between pairs of loci was detected. Pair-wise FST values between populations ranged from 0.135 (Brahmaputra–Ganga) to 0.173 (Brahmaputra–Narmada) and confirmed the moderate to high genetic differentiation among the populations. AMOVA, Structure and Principal Co-ordinate analyses showed significant genetic differentiation among the sampled populations of S. seenghala. A total of 65 private alleles were recorded across populations. This study confirmed the distinctiveness of each population of S. seenghala from five major rivers of India. These populations could be treated as distinct management units (MUs) for assessment and management purpose.
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