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Author: Grafiati

Published: 11 September 2023

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1

Sihag, P., M. R. Sadikhani, V. Vambol, S. Vambol, A. K. Prabhakar, and N. Sharma. "Comparative study for deriving stagedischarge– sediment concentration relationships using soft computing techniques." Journal of Achievements in Materials and Manufacturing Engineering 2, no. 104 (February 1, 2021): 57–76. http://dx.doi.org/10.5604/01.3001.0014.8489.

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Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.

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2

Pati, Ramesh Chandra, and Swoyam P. Rout. "Water Quality Status of Baitarani River Basin." Asian Journal of Research in Chemistry 10, no. 6 (2017): 732. http://dx.doi.org/10.5958/0974-4150.2017.00124.9.

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3

Sinam, Rebati. "RAINFALL TREND ANALYSIS OF BAITARANI RIVER SUB-BASIN, ODISHA." International Journal of Advanced Research 7, no. 10 (October 31, 2019): 569–75. http://dx.doi.org/10.21474/ijar01/9868.

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4

Dahm, R. J., F. C. Sperna Weiland, U. K. Singh, M. Lal, M. Marchand, S. K. Singh, and M. P. Singh. "Assessment of future rainfall for the Brahmani-Baitarani river basin – practical implications of limited data availability." Journal of Water and Climate Change 10, no. 4 (April 16, 2018): 782–98. http://dx.doi.org/10.2166/wcc.2018.004.

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Abstract Severe floods are common in the Brahmani-Baitarani river basin in India. Insights into the implications of climate change on rainfall extremes and resulting floods are of major importance to improve flood risk analysis and water system design. A wide range of statistical and dynamical downscaling and bias-correction methods for the generation of local climate projections exists. Yet, the applicability of these methods highly depends on availability of meteorological data. In developing countries, data availability is often limited, either because data do not exist or because of restrictions on use. We here present a climate change analysis for the Brahmani-Baitarani river basin focusing on changes in rainfall using data from three GCMs from the Fifth Coupled Model Intercomparison Project (CMIP5) that were selected based on their performance. We apply and compare two widely used and easy to implement bias-correction methods. These were selected because reliable open historical meteorological datasets required for advanced methods were not available. The results indicate likely increases in monsoon rainfall especially in the mountainous regions and likely increases in the number of heavy rain days. We conclude with a discussion on the gap between state-of-the-art downscaling techniques and the actual options in regional climate change assessments.

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5

Maitra, Shibani. "Landforms and Geomorphological Classification of Part of The Upper Baitarani River Basin." Journal of the Indian Society of Remote Sensing 27, no. 3 (September 1999): 175–84. http://dx.doi.org/10.1007/bf02991571.

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6

R S, Sharma, Mandal B K, and Das G K. "DIAGNOSTIC ANALYSIS OF CATASTROPHIC FLOOD OVER EASTERN INDIA IN JULY 2017 - A CASE STUDY." MAUSAM 71, no. 3 (August 3, 2021): 513–22. http://dx.doi.org/10.54302/mausam.v71i3.53.

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Floods are very common in eastern India during southwest monsoon season. It brings a lot of misery to the people of this region. Every year eastern Indian states namely West Bengal, Odisha and Bihar witness such types of flood during monsoon period. Major river basins in eastern India are Ganga river basin in Bihar and West Bengal area, Odisha has three river basins namely Mahanadi, Subarnarekha, Brahmani and Baitarani [Fig. 1(a)]. As majority of tributary rivers of Ganga passing through Bihar and West Bengal; these two states are more prone to massive flood during monsoon season. The abnormal occurrence of rainfall generally causes floods. It occurs when surface runoff exceeds the capacity of natural drainage. The heavy rainfall is frequently occurring event over the area during South-West Monsoon (SWM) every year. The geographical location of the area, orography and its interaction with the basic monsoon flow is considered as one of prime factors of these heavy rainfall activities. Synoptically, the latitudinal oscillation of eastern end of the Monsoon Trough and the synoptic disturbances formed or passing over the eastern India region and / or its neighbourhood that brings moisture laden Easterly or South-Easterly winds over the area are the main causes responsible for heavy rainfall in this area.

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7

K., Sindhu, and Durga Rao K. H. V. "Hydrological and hydrodynamic modeling for flood damage mitigation in Brahmani–Baitarani River Basin, India." Geocarto International 32, no. 9 (May 2, 2016): 1004–16. http://dx.doi.org/10.1080/10106049.2016.1178818.

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8

Samantaray, Sandeep, Sambit Sawan Das, Abinash Sahoo, and Deba Prakash Satapathy. "Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm." Ain Shams Engineering Journal 13, no. 5 (September 2022): 101732. http://dx.doi.org/10.1016/j.asej.2022.101732.

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9

Sucharita, K., E. Shiva Kumar, Ch Sasikala, B. B. Panda, S. Takaichi, and Ch V. Ramana. "Marichromatium fluminis sp. nov., a slightly alkaliphilic, phototrophic gammaproteobacterium isolated from river sediment." International Journal of Systematic and Evolutionary Microbiology 60, no. 5 (May 1, 2010): 1103–7. http://dx.doi.org/10.1099/ijs.0.013284-0.

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An anoxygenic, phototrophic gammaproteobacterium designated strain JA418T was isolated from a sediment sample collected from the Baitarani River, Orissa, India. The bacterium was a Gram-negative, motile rod with a single polar flagellum. Bacteriochlorophyll a and rhodopin were the major photosynthetic pigments. The organism grew best at slightly alkaline pH (8–8.5) and lacked chemotrophic growth. The major fatty acids were C16 : 0, C16 : 1 ω7c/C16 : 1 ω6c and C18 : 1 ω7c. A phylogenetic tree based on 16S rRNA gene sequence analysis showed that strain JA418T clusters with species of the genus Marichromatium belonging to the class Gammaproteobacteria. The highest 16S rRNA gene sequence similarities of strain JA418T were found with the type strains of Marichromatium gracile (95.9 %), Marichromatium indicum (95.6 %), Marichromatium purpuratum (95.5 %) and Marichromatium bheemlicum (95.6 %). The DNA base composition of strain JA418T was 71.4 mol% G+C (by HPLC). Based on the 16S rRNA gene sequence analysis and physiological and chemotaxonomic characteristics, strain JA418T is sufficiently different from other Marichromatium species to merit the description of a novel species, Marichromatium fluminis sp. nov., to accommodate it. The type strain is JA418T (=KCTC 5717T =NBRC 105221T).

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10

Khatai, Amit Kumar. "To Assess the Impact of Land Use and Climate change on Streamflow of Upper Baitarani River Basin." International Journal for Research in Applied Science and Engineering Technology 8, no. 4 (April 30, 2020): 119–32. http://dx.doi.org/10.22214/ijraset.2020.4019.

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11

Sharma, Satyabrata Das, Debasish Mahapatro, Mitali Priyadarsini Pati, Sangeeta Mishra, and Chitta Ranjan Panda. "Range Extension of Barnea Candida (Linnaeus, 1758) and Habitat Loss in a Tropical Mangrove Estuarine System, Bay of Bengal, India." Research Journal of Chemistry and Environment 27, no. 6 (May 15, 2023): 31–35. http://dx.doi.org/10.25303/2706rjce031035.

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The most specified wood borer Barnea candida’s has been recorded during three consecutive years from 2015 to 2018 as live form in the river belt of Brahmani and Baitarani mangrove dominated areas of Dhamra estuary. The carcasses of the observed wood borer were found in huge number in and around of Dhamra Port. Present study further declared that the range extension of Barnea candida occurred from Subarnarekha estuary to Dhamra estuary by showing a southward migration. During the survey, other molluscan dead shells were also observed along with the wood borer which could be the consequences of port and harbour related activities. It is well known that Barnea candida is an efficient substrate destructor of wooden boats, shipwrecks, jetty pillars and also cemented structures of fishing harbours in the marine and brackish water ecosystem. Therefore, the observed species might be a threat for the Dhamra ecosystem in future which needs long-term integrated research in future.

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12

Mishra, Siba Prasad, Jasmini Nayak, Kamal Kumar Barik, and Kumar Ch Sethi. "The Delineation of Watersheds in the Dhenkanal District, Odisha, India; Using Arc-GIS." Journal of Scientific Research and Reports 29, no. 5 (April 21, 2023): 1–18. http://dx.doi.org/10.9734/jsrr/2023/v29i51743.

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The watersheds of the Dhenkanal district of Odisha, India with the centrally flowing, river Brahmani identified and explored. The watersheds added with anthropogenic stresses are unattended that affect the land, hydrology, ecology, vegetation, and fertility of the soil. Population growth, migration to cities, challenges, continuous disasters, and anthropogenic stresses block the growth. The Arc GIS software and its Hydrology toolset were utilized. The Digital Elevation Model is used to scale various maps like flow direction, stream order, flow accumulation, Stream features, Pour points, and watershed maps of the district. The Rengali Dam, and Samal barrage of the Brahmani system and its left and right canal system have a positive impact on watersheds on both sides of the river Brahmani but the surface and groundwater are polluted by industrial effluents. The watersheds in transition ridges of the Mahanadi and the Baitarani basin of the Brahmani basin have been poorly attended in Dhenkanal. Construction of farm ponds, groundwater recharge units, soil conservation activities, renovation, and new construction of waterbodies can cater to and care for the land, water, and vegetation of the district. It is high time to prepare large-scale maps by modern survey techniques (UAV, drones, GIS, and GPS) and the observed data used to identify, delineate, prioritize, and plan for sustainable environment management considering availability v/s demand of water through Integrated Watershed Management in the district.

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13

Rai, Pawan Kumar, C. T. Dhanya, and B. R. Chahar. "Coupling of 1D models (SWAT and SWMM) with 2D model (iRIC) for mapping inundation in Brahmani and Baitarani river delta." Natural Hazards 92, no. 3 (April 9, 2018): 1821–40. http://dx.doi.org/10.1007/s11069-018-3281-4.

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14

Padhiary, Jagadish, Kanhu Charan Patra, Sonam Sandeep Dash, and A. Uday Kumar. "Climate change impact assessment on hydrological fluxes based on ensemble GCM outputs: a case study in eastern Indian River Basin." Journal of Water and Climate Change 11, no. 4 (November 1, 2019): 1676–94. http://dx.doi.org/10.2166/wcc.2019.080.

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Abstract The present study assessed the impact of climate change in the Anandapur catchment of Baitarani River basin, India, using the Soil and Water Assessment Tool (SWAT) hydrological model. The future climatic alterations under two Representative Concentration Pathways (RCPs), i.e. 4.5 and 8.5 scenarios, are quantified by an ensemble of two different CMIP5 models, i.e. CNRM-CM5.0, GFDL-CM3.0. The outcomes of this study reveal that the future rainfall and temperature may experience an increasing trend with gradual shifting of monsoon from mid-June to mid-May. The average annual streamflow experienced the highest increase during the period 2071–2095, whereas the highest average annual evapotranspiration (ET) is observed for the period 2046–2070 under both the RCPs and resulting in comparatively slower groundwater recharge (GWR) over the basin. In order to implement suitable adaptation strategies for a possible flood scenario on the concerned study basin, three critical sub-basins, namely, sub-basin 1, 4, and 5, were identified. Furthermore, the altered streamflow and ET dynamics may result in a significant shifting in the conventional agricultural practice in the coming future time scales. Conclusively, the outcomes of this study have potential implications for policy makers in formulating the policies related to sustainable water resources management in future scenarios.

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15

Banerjee, Kakoli, Gobinda Bal, and Rakesh Paul. "Total Biomass and Carbon Estimates in Mangrove Species of Bhitarkanika Wildlife Sanctuary (BWLS), Odisha." INTERNATIONAL JOURNAL OF PLANT AND ENVIRONMENT 4, no. 02 (July 31, 2018): 27–34. http://dx.doi.org/10.18811/ijpen.v4i02.3.

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We estimated total biomass and total carbon in 18 selected woody mangrove species based on the IVI values along with physico-chemical parameters of sea water and soil. AGB and BGB values were calculated as per the standard protocol in BWLS in five sampling stations namely Dangmal, Bhitarkanika, Habalikhati, Ekakula and Gupti. The highest values of IVI were noted for E. agallocha followed by A. officinalis, A. marina, H. fomes and C. decandra. Total biomass of species range was found to be varying between 0.69 tha-1 in X. molluccensis and 2122.17 tha-1 in A. officinalis. Total site-wise biomass ranged between 0.22 tha-1 in B. cylindrica at Gupti and 1419.26 tha-1 in A. officinalis at Bhitarkanika. Total carbon was considered to be 50% of total dry biomass which varied from 188.45±21.87 tha-1 at Ekakula to 941.31±201.17 tha-1 at Bhitarkanika, respectively. The higher biomass and carbon in A. officinalis proves it to be highly adaptive to the environmental changes in the present geographical locale. The maximum biomass and carbon of all the mangrove species in station Bhitarkanika is due to the fact that this station receives the fresh water discharge of Baitarani river thereby the adequate lower salinity is maintained. The present study reveals that mangroves of Bhitarkanika Wildlife Sanctuary can store substantial amount of atmospheric carbon and therefore needs to be conserved and sustainably managed.

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16

Chintalacheruvu, Madhusudana Rao, Avijit Bardhan, Santosh Murlidhar Pingale, and Jagadish Prasad Patra. "Water footprint analysis for the upper Baitarani River basin, India." Sustainable Water Resources Management 8, no. 6 (October 22, 2022). http://dx.doi.org/10.1007/s40899-022-00769-z.

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17

Sinam, Rebati. "At Site Flood Frequency Analysis of Baitarani River at Champua Watershed, Odisha." International Journal of Scientific Research in Science and Technology, November 1, 2019, 54–64. http://dx.doi.org/10.32628/ijsrst19665.

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For any development of hydraulic structures and dam modelling, flood frequency analysis is an effective tool to determine the appropriate measures and strategy. Flood frequency analysis has been conventionally used in hydraulic engineering and floodplain management. The present study is an attempt to estimate the expected flood using two probability distributions: Gumbel distribution and Log Pearson III distribution at Champua watershed, Upper Baitarani River Basin, Odisha. The analysis is based on annual maximum flood time series from 1991 to 2018 (28 years) obtained from Water Resources Information System at the Champua gauging station. Three Goodness of fit methods namely Kolmogorov Smirnov, Anderson Darling and Chi Squared tests are used to choose the better model. From the analysis, expected flood for return period 2, 10, 25, 50, 100 and 1000 years are calculated. Gumbel give an expected flood 521.72 cumecs while Log Pearson III give an expected flood of 493.17 cumecs for 2 years return period. It is observed that Gumbel estimated a higher values for all the said return period except for 1000 years where Log Pearson III predicted a much higher values. Goodness of test show inconsistent results. While Chi-squared test indicate Gumbel Method as the better model, the other two tests show that Log Pearson III is the better fitting model for the given dataset. Therefore, Log Pearson III is chosen as the best model. However, the results from both the distributions can be referred for storm management.

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18

Marchand, Marcel, Ruben Dahm, Joost Buurman, Subbiah Sethurathinam, and Chris Sprengers. "Flood protection by embankments in the Brahmani–Baitarani river basin, India: a risk-based approach." International Journal of Water Resources Development, April 12, 2021, 1–20. http://dx.doi.org/10.1080/07900627.2021.1899899.

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19

Talukdar, Gaurav, Janaki Ballav Swain, and Kanhu Charan Patra. "Flood inundation mapping and hazard assessment of Baitarani River basin using hydrologic and hydraulic model." Natural Hazards, June 16, 2021. http://dx.doi.org/10.1007/s11069-021-04841-3.

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20

Sethi, Binod Kumar, Siba Prasad Mishra, Kabir Sethi, and Kamal Barik. "Deltaic Expansions of the Mahanadi Tri-delta and the Chilika Lagoon: Geospatial Approach." Current Journal of Applied Science and Technology, November 9, 2020, 46–65. http://dx.doi.org/10.9734/cjast/2020/v39i3431036.

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Prelude: The major river deltas are sinking, shrinking which accommodate ≈40% of global population. The Mahanadi tri-delta along the east coast of India consists of three river deltas i.e. the Mahanadi, the Bramhani and the Baitarani. The tri-delta encompasses many ecological hubs and the 2nd largest lagoon of Asia, the Chilika Lagoon lies in the southern corner of the delta. Presently the Mahanadi tri-delta and its coastal environments are under threat due to coastal vulnerability, population growth, urbanization and industrialization but the expansion of the delta towards south is observed. Scope: Present research is ubiquitous to know the quantity of sinking, shrinking and subsidence of the Mahanadi tri-delta under congregation of different geological setting, damming, LULC changes, soil erosion, regional sea level rise, and anthropogenic pressure. Also geospatial geomorphic changes in the anastomosis of the rivers and drainage channels are studied along with expansion of the delta to its southern fringe Methodology: The causes of vulnerability of the delta are estimated from sediment inflow and marine transgression is evaluated. The land use and land cover changes has been evaluated and analyzed by using satellite imageries, remote sensing, GIS tools and ERDAS softwares. Results: The study revealed that due to effect of relative sea-level rise, paucity of sediment influx @ 66.7% to the delta are the main causes for the delta vulnerability accompanied by geo-mining and urbanization. The extention of delta to the south is due to emergence of the Makara river system which carries more flood flow than the existing Daya river. Prominent changes in LU & LC are observed from the satellite imageries of two different period. Changes in the LU &LC of the tri-delta has shown the extension of the Mahanadi tri-delta towards south and shifting of deltaic boundary from 20 m to 50 m contour line in l landward direction during last four to five decades (recasted).

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21

Samantaray, Sandeep, Sambit Sawan Das, Abinash Sahoo, and Deba Prakash Satapathy. "Evaluating the application of metaheuristic approaches for flood simulation using GIS: A case study of Baitarani river Basin, India." Materials Today: Proceedings, December 2021. http://dx.doi.org/10.1016/j.matpr.2021.11.561.

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22

Bhuyan, Mahendra K., Asutosh Dash, Joygopal Jena, Pradeep K. Bhunya, and Akshay Kumar Bhuyan. "Design Flood Computation at Ungauged Catchments of Baitarani River Basin using Scaling Concept and Probabilistic Time Distribution of Design Rainfall." International Journal of River Basin Management, April 10, 2022, 1–46. http://dx.doi.org/10.1080/15715124.2022.2064482.

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23

Verma, Arbind Kumar, and Madan Kumar Jha. "Evaluation of a GIS-Based Watershed Model for Streamflow and Sediment-Yield Simulation in the Upper Baitarani River Basin of Eastern India." Journal of Hydrologic Engineering 20, no. 6 (June 2015). http://dx.doi.org/10.1061/(asce)he.1943-5584.0001134.

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24

PANDA, B. S., S. NAYAK, S. S. PATI, S. S. MAHALA, and S. MISHRA. "SEASONAL ASSESSMENT OF HEAVY METALS ALONG THE ESTUARIAN REGION OF DHAMRA, EAST COAST OF INDIA: A SPECIAL APPROACH THROUGH DISTRIBUTION MAPS AND CORRELATION MATRIX." Journal of Global Ecology and Environment, March 2, 2022, 39–65. http://dx.doi.org/10.56557/jogee/2022/v14i37440.

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Dhamra estuary region is located at latitude 20046’09.74’’N and longitude 86059’26.90’’E. Heavy metals and metalloids, while happening at higher fixations, become serious toxic substances for every single living life form including human. For example, an extreme amount of Mercury (Hg), Arsenic (As), Lead (Pb), and Cadmium (Cd) components could be harmful to the living cells, and a drawn-out presentation to the body can cause illness of death. This study aimed to determine the concentrations of the heavy metals viz., Mercury (Hg), Vanadium (V), Chromium (Cr), Manganese (Mn), Iron (Fe), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Arsenic (As), Lead (Pb), Molybdenum (Mo), and Cadmium (Cd) and also their distribution in the water collected from the eastern part of Bay of Bengal from four different estuary locations of Dhamra coast, Odisha. Overall, we found that heavy metal dominance in Dhamra estuary region was in order of Zn>Cu>Fe> V>Cr> Ni> Co> As>Mo> Mn>Pb> Hg> Cd. As most of the heavy metals were found in the surface water of Dhamra estuary,It may be due to basically by two factors; the first one is the riverine influence through both Baitarani and Brahmani river. The second one is Dhamra port activities and fishing harbour.

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25

Madan Kumar Jha, Bhumika Uniyal. "Assessing Climate Change Impact on Water Balance Components of Upper Baitarni River Basin using SWAT Model." Journal of Earth Science & Climatic Change 06, no. 03 (2015). http://dx.doi.org/10.4172/2157-7617.1000267.

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