Relevant bibliographies by topics / BAITARANI RIVER

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Academic literature on the topic 'BAITARANI RIVER'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "BAITARANI RIVER"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "BAITARANI RIVER"

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Talukdar, Gaurav. "Flood Inundation Mapping of Baitarani River Basin Using Hydrologic and Hydraulic Model." Thesis, 2016. http://ethesis.nitrkl.ac.in/9129/1/2016_MT_GTalukdar.pdf.

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Flood is one of the repeatedly occurring natural disaster that brings on the detrimental impact on humans, property and environment. Frequent flood has been a major issue in almost all states of India. The importance of flood maps in governing strategies for flood risk management is of prime importance. Maps are not just for defining and communicating flood risks, but also for regulating them and for rationalizing the inevitable limits and failures of those controls. In this paper a continuous hydrological and hydraulic model has been implemented for mapping the flood caused by Baitarani River of Odisha, India. A long rainfall time series is fed into the continuous rainfall- runoff model producing discharge, which is given as an input into the 2-Dimensional hydraulic model, FLO-2D. SRTM 90 DEM data, Land use/Land cover map (LULC), soil texture data of the basin area are used to compute the topographic and hydraulic parameters. Flood inundation was simulated using the FLO-2D model and based on the flow depth, Hazard zones are specified. The result of the study shows that FLO-2D model is an utile tool for creating the inundation maps. An approach has been made for assessing the risk of flooding and proper management will help in mitigating the flood. However the current results have not been compared with literatures since much work with the model was not found. The automated procedure for mapping and the details of the study can be used for planning flood preparedness in worst affected area.
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SINGH, ANURAG. "ENVIRONMENTAL FLOW ASSESSMENT FOR ECOSYSTEM HEALTH IN A REACH OF BAITARANI RIVER." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16376.

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Rivers have always been vital to mankind as well as for other living beings infact, most civilizations started from the bank of Rivers. Since Rivers have been a source of food and home for variety of aquatic plants and animals and sustains an ecosystem, the alteration of the water flow is one of the most stressful factors influencing the aquatic and riverine ecosystem. Salubrious ecosystems help preserving the food, maintain the cycle of symbiotic relationship and keeps water quality healthy, therefore the condition of aquatic ecosystem is a measure for environment which depends on the flow in the stream. This essential flow is termed as Environmental Flow. Rivers need Environmental Flow to maintain the ecology of the systems and also sometimes rivers have to face water related challenges including increasing issue of the water scarcity and distribution for water among different sectors and states are being faced presently at global level. The present study aims to estimate the Environmental Flow of Baitarani River upto Anandapur, Odisha from its origin and analysis of eco-hydro relationships in time by environmental flow assessment. WMS, HMS, DSSvue and EFM software were used for assessing the Environmental Flow employing the DEM (Digital Elevation Model), precipitation, soil, Land use land cover (LULC), discharge, gauge and ecosystem data for the aquatic species in the present study. The environmental indicators employed for the research are Small Fishes, Maggar, Benthic Macro-Invertebrate Biodiversity, Wetland, Catla-Catla, Rohu, Hilsa and their corresponding assessed E-flow are 148, 6, 7, 122, 267, 254 and 272 cumecs respectively for observed flow of 17 years viz. 2000 to 2016. Through confiscate such as dams, barrages and weirs which are constructed for irrigation, drinking water, hydro-project and for other industrial demand. This does not only make restriction on the flow of water but also degrade the water quality. An attempt has also been made to estimate the E-flow which could only be provided upto certain values in case of climate change and future development flow regime for the above mentioned species and their corresponding values are 129, 5, 6, 106, 232, 221, 236 and 52, 2, 3, 43, 93, 89, 95 cumecs respectively.
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Prusty, Raunak Manoranjan. "Climate Change Impact Assessment under CORDEX South-Asia RCM Scenarios on Brahmani-Baitarani River Basin." Thesis, 2018. http://ethesis.nitrkl.ac.in/9508/1/2018_MT_216CE4090_RMPrusty_Climate.pdf.

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This study attempts to assess the impact of climate change on Brahmani and Baitarini river basin using a GIS-based semi-distributed model Soil and Water Analysis Tool (SWAT). The SWAT model uses various physiographic features such as slope, soil and land use classes to estimate the various water balance components of the river basin for the baseline period (1980-2010) and future climate scenarios (2071-2100). Sensitivity analysis has been carried out to identify the most critical parameters of themodel. The model was calibrated (1980-2000) and validated (2001-2010) using the observed average daily discharge data. The model performance was evaluated using the coefficient of determination (R2), Nash-Sutcliffe efficiency (ENS). The data from CORDEX South Asia RCM model for RCP 4.5 and 8.5 scenarios developed by IITM was used in the SWAT model to evaluate changes in various water balance components. Overall the SWAT model performed satisfactorily having Nash-Sutcliffe efficiency value of 0.72 and 0.65 for calibration and validation respectively. Results show an increase in average annual temperature (3.1°C), average rainfall (+10.7 mm/year).This corresponds to the increase in in the annual streamflow (110%-117%), evapotranspiration (48%) and water yield (159%)
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Das, Aparna. "Impact of Land use Land Cover Change on Streamflow of upper Baitarani River Basin using SWAT." Thesis, 2018. http://ethesis.nitrkl.ac.in/9554/1/2018_MT_216CE4080_ADas_Impact.pdf.

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Change in land use and land cover(LULC)pattern influences the hydrology of a watershed by altering its stream flow and groundwater characteristics. This study focuses on LULC change impact on streamflow of the Upper Baitarani river basin in India using Soil and Water Assessment Tool (SWAT). Initially,SWAT is run on daily time step using land use data of 1995 and 2013, slope and soil data for 1979-2013 with a warm-up period of five years to obtain the simulated streamflow. Sensitivity analysis is performed to identify the most influential parameters for streamflow. Calibration and validation are carried out using global sensitivity approach of Sequential Uncertainty Fitting (SUFI2) algorithm in SWAT Calibration and Uncertainty Procedure (SWAT-CUP) software. The simulated streamflow resulted from 1995 land use are calibrated for the period 1986-1992 and validated for 1993 -1995 using observed daily streamflow data at Anandapur hydrological gauge station. On the other hand, the simulated streamflow which are obtained from 2013 land use, are calibrated for the period 2004-2010 and validated for 2011-2013. The objective functions in terms of R2, NSE, PBIAS and RSR in SWAT-CUP shows the SWAT model has worked satisfactorily. Results show in eighteen years (1995-2013) the built-up area has increased by 8.26% whereas forest cover, barren land, and agricultural area have decreased by 0.59%, 1.62%,and6.18% respectively. To assess the impact of this LULC change on streamflow, calibrated SWAT model is run under two scenarios (climate of 2008-2013 with 1995 LU data and climate of 2008-2013 with 2013 LU data)on daily time step. The comparison between streamflow resulted from 1995 and 2013 LU data, shows due to LULC change (mainly urbanization) 2013 LU scenario has more annual average streamflow than 1995 LU scenario. The average increment in annual streamflow for 2008-2013 in the Upper Baitarani basin is 6.73%.
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Book chapters on the topic "BAITARANI RIVER"

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Sinam, Rebati. "Flood Frequency Analysis of Baitarani River Using Three Probability Distributions." In Monitoring and Managing Multi-hazards, 153–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15377-8_11.

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Bardhan, Avijit, and Chintalacheruvu Madhusudana Rao. "Hydrologic Response Estimation Using Different Descriptors for Upper Baitarani River Basin." In Lecture Notes in Civil Engineering, 219–33. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4629-4_16.

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Kumar, Aman, and K. C. Patra. "Flood Hazard Assessment of Baitarani River Basin Using One-Dimensional Hydrodynamic Model." In Lecture Notes in Civil Engineering, 157–71. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4629-4_12.

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Prabhakar, Ajay Krishna, K. K. Singh, Anil Kumar Lohani, and Surendra Kumar Chandniha. "Hydrological Water Balance Study in Champua Watershed of the Baitarani River Basin Using SWAT Model." In Advances in Hydrology and Climate Change, 255–71. Boca Raton: Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003282365-12.

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Prusty, Raunak M., Aparna Das, and Kanhu Charan Patra. "Impact of Land Use and Land Cover Change on Streamflow of Upper Baitarani River Basin Using SWAT." In Water Management and Water Governance, 227–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58051-3_16.

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Sethi, Ranu Rani, S. K. Jena, and D. K. Panda. "Time Series Analysis of Climate Variables for Baitarini River Basin of Odisha." In Transforming Coastal Zone for Sustainable Food and Income Security, 963–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95618-9_70.

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