Godavari Research Articles

Integrative Taxonomy Clarifies the Historical Flaws in the Systematics and Distributions of Two Osteobrama Fishes (Cypriniformes: Cyprinidae) in India

The taxonomy and geographical distributions of Osteobrama species have historically posed challenges to ichthyologists, leading to uncertainties regarding their native ranges. While traditional taxonomy has proven valuable in classification, the utility of an integrated approach is restricted for this particular group due to limitations in combining information from biogeography, morphology, and genetic data. This study addresses the taxonomic puzzle arising from the recent identification of Osteobrama tikarpadaensis in the Mahanadi and Godavari Rivers, casting doubt on the actual distribution and systematics of both O. tikarpadaensis and Osteobrama vigorsii. The research reveals distinctions among specimens resembling O. vigorsii from the Krishna and Godavari riverine systems. Notably, specimens identified as O. vigorsii from the Indian Museum exhibit two pairs of barbels, while those from the Godavari River in this study are identified as O. tikarpadaensis. Inter-species genetic divergence and maximum likelihood phylogeny provide clear delineation between O. vigorsii and O. tikarpadaensis. The study suggests that O. vigorsii may be limited to the Krishna River system in southern India, while O. tikarpadaensis could potentially extend from the Mahanadi River in central India to the Godavari River in southern India. Proposed revision to morphological features for both species, accompanied by revised taxonomic keys, aim to facilitate accurate differentiation among Osteobrama congeners. The data generated by this research provide a resource for future systematic investigations into cyprinids in India and surrounding regions. Further, the genetic diversity information obtained from various riverine systems for Osteobrama species will be instrumental in guiding aquaculture practices and formulating effective conservation action plans.

Analysis of Historical and Future Rainfall Projections in India's Lower Godavari Basin

Changes in evaporation and precipitation in the climate are precursors to changes in the water cycle due to the increase in temperatures due to climate change. In recent years, enormous changes in precipitation patterns have led to an increase in extreme rainfall events, reflecting flash floods in smaller areas. To understand this, you must study the rainfall patterns of the district and the smaller watersheds. In the present study, we have observed the trend of rainfall in the lower Godavari basin, which was part of the Godavari River from 1970 to 2019, using IMD 0.25X0.25 ° gridded rainfall data. According to the study, during the winter season and before the monsoon, there is no significant increasing or decreasing trend. In the post-monsoon season they are showing a negative and decreasing trend with a magnitude of 4 mm/year. But in the monsoon season, one end (the right end near the river mouth) of the basin or watershed is showing an increasing trend, while the left part is showing a significantly decreasing trend. In addition to that, future projections of the CMIP5 two scenarios data from 2000 to 2100 to understand the precipitation patterns in future projections. Lower Godavari basin from 2020–2040 and 2040-2100 with the RCP 4.5 and RCP 8.5 scenarios. With the RCP 4.5 scenario, the first 40 years (2020–2040) of the future projected mean seasonal rainfall are 4.5 mm/year higher in the western part of the sub-basin than in the eastern and river mouth regions.

Integrating conceptual and machine learning models to enhance daily-Scale streamflow simulation and assessing climate change impact in the watersheds of the Godavari basin, India

This study examined and addressed climate change's effects on hydrological patterns, particularly in critical places like the Godavari River basin. This study used daily gridded rainfall and temperature datasets from the Indian Meteorological Department (IMD) for model training and testing, 70% and 30%, respectively. To anticipate future hydrological shifts, the study harnessed the EC-Earth3 data, presenting an innovative methodology tailored to the unique hydrological dynamics of the Godavari River basin. The Sacramento model provided initial streamflow estimates for Kanhargaon, Nowrangpur, and Wairagarh. This approach melded traditional hydrological modeling with advanced multi-layer perceptron (MLP) capabilities. When combined with parameters like lagged rainfall, lagged streamflow, potential evapotranspiration (PET), and temperature variations, these initial outputs were further refined using the Sac-MLP model. A comparison with Sacramento revealed the superior performance of the Sac-MLP model. For instance, during training, the Nash Sutcliffe efficiency (NSE) values for the Sac-MLP witnessed an improvement from 0.610 to 0.810 in Kanhargaon, 0.580 to 0.692 in Nowrangpur, and 0.675 to 0.849 in Wairagarh. The results of the testing further corroborated these findings, as evidenced by the increase in the NSE for Kanhargaon from 0.890 to 0.910. Additionally, Nowrangpur and Wairagarh experienced notable improvements, with their NSE values rising from 0.629 to 0.785 and 0.725 to 0.902, respectively. Projections based on EC-Earth3 data across various scenarios highlighted significant shifts in rainfall and temperature patterns, especially in the far future (2071–2100). Regarding the relative change in annual streamflow, Kanhargaon projections under SSP370 and SSP585 for the far future indicate increases of 584.38% and 662.74%. Similarly, Nowrangpur and Wairagarh are projected to see increases of 98.27% and 114.98%, and 81.68% and 108.08%, respectively. This study uses EC-Earth3 estimates to demonstrate the Sac-MLP model's accuracy and importance in climate change water resource planning. The unique method for region-specific hydrological analysis provides vital insights for sustainable water resource management. This research provides a deeper understanding of climate-induced hydrological changes and a robust modeling approach for accurate predictions in changing environmental conditions.

A stacking ANN ensemble model of ML models for stream water quality prediction of Godavari River Basin, India

The importance of water quality models has increased as their inputs are critical to the development of risk assessment framework for environmental management and monitoring of rivers. However, with the advent of a plethora of recent advances in ML algorithms better predictions are possible. This study proposes a causal and effect model by considering climatological such as temperature and precipitation along with geospatial information related to the agricultural land use factor (ALUF), the forest land use factor (FLUF), the grassland usage factor (GLUF), the shrub land use factor (SLUF), and the urban land use factor (ULUF). All these factors are included in the input data, whereas four Stream Water Quality parameters (SWQPs) such as Electrical Conductivity (EC), Biochemical Oxygen Demand (BOD), Nitrate, and Dissolved Oxygen (DO) from 2019 to 2021 are taken as outputs to predict the Godavari River Basin water quality. In the preliminary investigation, out of these four SWQPs, nitrate's coefficient of variation (CV) is high, revealing a close association with climate parameters and land use practices across the sampling stations. In the authors' earlier study, a model using a single-layer Feed-Forward Neural Network (FFNN) showed improved performance in predicting cause and effect factors linked to water quality metrics. To achieve better prediction, a stacked ANN meta-model and nine conventional machine learning (ML) models, including Extreme Gradient Boosting (XGB), Extra Trees (ET), Bagging (BG), Random Forest (RF), AdaBoost or Adaptive Boosting (ADB), Decision Tree (DT), Highest Gradient Boosting (HGB), Light Gradient Boosting Method (LGBM), and Gradient Boosting (GB), were compared in this study. According to the study's findings, Bagging and Boosting models outperformed stand-alone earlier FFNN for the same dataset and showed superior predictive capabilities in terms of accuracy in forecasting the variable of interest. For instance, during testing, the coefficient of determination (R2) of Biochemical Oxygen Demand (BOD) increased from 0.72 to 0.87. Furthermore, a stacked Artificial Neural Network (ANN) meta model that was reinforced using Extreme Gradient Boosting (XGB), Random Forest (RF), and Extra Trees (ET) as base models performed better than the individual ML models (from R2 = 0.87 to 0.91 for BOD in testing). By using this new framework, the effort for hyperparameter tuning can be minimized.

Dams and their safety under the extreme climate conditions: study of dams on Godavari River

Dams and their safety under the extreme climate conditions: study of dams on Godavari River

Characterization of phytoplankton functional types in the western Bay of Bengal: HPLC- and CHEMTAX-based approach.

This study investigates phytoplankton functional group variations in the western Bay of Bengal (WBoB) during the Spring Intermonsoon. Samples were collected from four cross-shore transects: Mahanadi (MN), Vamsadhara (VD), Godavari (GD), and Krishna (KS). East India Coastal Current and warm gyre influenced the southern transects (KS, GD), VD was experiencing moderate upwelling and MN was characterized by low salinity and oligotrophic conditions due to freshwater input. In response to hydrography, phytoplankton biomass and functional types differed within and between the transects. Chlorophyll-a (Chl-a) was spatially high in VD and low in MN. The subsurface Chlorophyll-a maxima (SSCM) was prominent and shallow in the MN and VD, compared to the southern transects. Total diagnostic pigments concentration was high in VD, followed by GD, KS and MN. Phytoplankton functional groups and each groups contribution to Chl-a was calculated through CHEmical Taxonomy (CHEMTAX). Diatoms and cyanophytes were the dominant functional types in the surface layers. Progressive shift from diatoms in the nearshore region to cyanophytes in the offshore was observed. The low saline and low-nutrient conditions were conducible for the growth of cyanophytes, while nutrient-rich optimum light layer of SSCM and upper layer of VD were favorable for diatoms. Cryptophytes contribution to Chl-a was higher in southern transects compared to the north. Prymnesiophytes and prasinophytes were high in the subsurface and deep layers could be due to their adaptions to light and nutrients. The present study highlights the significance of physical processes associated hydrography in structuring the phytoplankton functional types.

Study On Flood Management and Analysis Using Geographical Information Systems On Godavari, Konaseema District, India

The Konasema area in Andhra Pradesh, India is prone to flooding due to coastal storms, storm surges, and river overflows. Flooding can be dangerous and pose a threat to human life and property. Floods are unpredictable and can happen suddenly, causing significant damage. Therefore, planning for flood management is essential for rescuing flood victims, resolving flood issues, and implementing preventive measures. In recent years, flooding has become more frequent in the Konaseema area, especially in the Godavari river's and near surrounding areas. Heavy floods from the Godavari River have been caused severe flooding in nearby konaseema villages. Flood management by water resource planners using GIS (Geographic Information System) developed land use and land cover maps, Flood zone maps in the study region. This study aims to analyse impact of Godavari River floods in Konaseema and East Godavari district Andhra Pradesh, India. DEM (Digital Elevation Model) is generated and prepared various thematic maps of Flood prone regions. The GIS generated flood impacted maps are analyzed and these maps will help to rescue the people from flood regions and migrate them in time to the safe zones.

ZOOPLANKTON DIVERSITY OF GODAVARI RIVER AT MUDGAL DAM, PATHARI DIST. PARBHANI (M.S.), INDIA

Aquatic ecosystem has a vast diversity of micro-eukaryotes (rotifers, crustaceans and protists), and such diverse taxonomic groups play important roles in ecosystem functioning and services. Farming organisms such as fish receive nutrients other than minerals by taking food. In nature, most organisms survive by consuming live food such as plankton from the environment. Zooplankton are of great importance and basically essential in fish culture. The present work is carried out for the assessment of plankton diversity of Godavari river water in Mudgal dam area for a period of one year starting from March, 2019 to February, 2020. The zooplankton serves as a food for other higher organisms. During the exploration, authors recorded four groups of zooplankton namely Rotifera with 7 species, Cladocera with 5 species, Copepoda 3 species and Ostracoda 2 species.

Long-term spatial and temporal trend analysis of stream flow and suspended sediment transport of Godavari River Basin, India

Long-term spatial and temporal trend analysis of stream flow and suspended sediment transport of Godavari River Basin, India

Study of Freshwater fish diversity in Godavari River Flowing in Northern Western Ghats of India

Study of Freshwater fish diversity in Godavari River Flowing in Northern Western Ghats of India

Integrated Hydrological Modeling of the Godavari River Basin in Maharashtra Using the SWAT Model: Streamflow Simulation and Analysis

Integrated Hydrological Modeling of the Godavari River Basin in Maharashtra Using the SWAT Model: Streamflow Simulation and Analysis

Adaptive Neuro Fuzzy Inference System based Water Quality Index for Godavari River (India)

Adaptive Neuro Fuzzy Inference System based Water Quality Index for Godavari River (India)

Enhanced morphometric analysis for soil erosion susceptibility mapping in the Godavari river basin, India: leveraging Google Earth Engine and principal component analysis

ABSTRACT Morphometric analysis of a drainage basin along with its stream network is indispensable for analysing a river basin’s prevailing geomorphology, geo-hydrology, and erosional susceptibility. In this study, an enhanced morphometric evaluation method using Google Earth Engine (GEE) and Principal Component Analysis (PCA) was used to assess and prioritize 12 sub-basins of the Godavari River basin (GRB), India, based on their erosional susceptibility. The findings of erosional susceptibility were validated using the Sediment Yield (SY) model. Our findings highlight that the sub-basins of Indravati, Pranhita, and Manjira, i.e. 42.42% (132707.4 km2) of the entire GRB are at high risk of erosional susceptibility, requiring immediate implementation of soil conservation measures. Our findings also highlight that 52.71% of the GRB is in the mature stage of geological evolution, wherein erosional landform-building processes are still ongoing. Overall, GRB has a relatively low drainage density with dendritic drainage patterns, permeable soils, and elongated sub-basins of enhanced erosional susceptibility. Moreover, the results of the SY model exhibited 90% agreement with the results of the enhanced morphometric analysis. This study presents an efficient framework for enhanced morphometric analysis that can assist policymakers in the demarcation of priority zones for erosional susceptibility.

A novel partitioning of gross primary production and water use efficiency for sustaining water and food security using Budyko hypothesis

This study coupled the green water and blue water accounting with the existing standard Budyko framework and Fu's 1-parameter Budyko framework to diagnose the basin hydrological behavior. Both Budyko frameworks were employed to determine green water consumption (ETGreen) and blue water consumption (ETBlue) which, in turn, were used to map the blue water index (BWI) hotspots and green water index (GWI) bright spots. The relative contributions of green water and blue water were quantified for sustaining water and food security. A new methodology is proposed using BWI and GWI for partitioning the Gross Primary Production (GPP) and Water Use Efficiency (WUE) into GPPBlue, GPPGreen and WUEBlue and WUEGreen. The methodology was applied to five sub-basins of the Central Godavari River Basin (CGRB): Purna, Dhalegaon, GR Bridge, Yeli and Delta. Results showed that all five basins exhibited larger deviations from the theoretical Budyko curve of Fu's 1-parameter Budyko framework than did the standard Budyko framework and the Dhalegaon basin showed the largest deviations. The partitioning of GPP and WUE by the proposed methodology showed that the proportion of GPPGreen to the total GPP was much higher than that of the GPPBlue. Similarly, the proportion of WUEGreen to WUE was more than that of WUEBlue. The mapping of GPPBlue and GPPGreen, and WUEBlue and WUEGreen showed that the Delta and Yeli basins had the highest values of both GPPGreen & GPPBlue and WUEBlue and WUEGreen (bright spot basins) and the Dhalegaon and parts of GR Bridge basin had the lowest values (hot spot basins). The proposed partitioning of GPP and WUE will help identify the relative contributions of green water and blue water (for managing agricultural waters) and formulate agronomical and engineering practices for stakeholders and policy makers for increasing the overall WUE and GPP to sustain water and food security.

Length-weight Relationship and Condition Factor of Siluriformes Fish Species, Endangered Eutropiichthys vacha, Hamilton (1922) and Endemic Deccan Catfish Rita kuturnee (Sykes, 1839) of Godavari River Andhra Pradesh, South India

Background: The present study of the length-weight relationship and condition factors of two Eutropiichthys vacha and Rita kuturnee, from Godavari River Andhra Pradesh, from the year August 2021 to July 2023 at fortnightly intervals. Methods: The log transformation formula was employed to establish length-weight relationships (LWRs) [3]. The length-weight equation used was W = aLb, in the usual notations. Linear regression of the log-transformed equation, log(W) = log(a) + b log(L), was utilized to calculate the parameters ‘a’ (intercept) and ‘b’ (slope) of the relationship. Result: A total of 1453 specimens of E. vacha and 1273 specimens of R. kuturnee were collected by using gill nets. The collected specimens exhibited a total length range of 10 to 27 cm for E. vacha and 7 to 18.5 cm for R. kuturnee. Correspondingly, the body weight range was recorded as 12 to 140 gm for E. vacha and 8 to 50 gm for R. kuturnee. Statistical analysis revealed a negative allometric growth trend for both males and females of E. vacha and R. kuturnee, with b values ranging from 2.569 to 2.711 for E. vacha and 2.53 to 2.61 for R. kuturnee. The fluctuation in the relative condition factor ranged from 1.06 to 1.09 for E. vacha and 1.001 to 1.12 for R. kuturnee. Furthermore, the overall health conditions of these two species were determined to be favorable.

Mitigating El Niño impacts on hydro-energy vulnerability through identifying resilient run-of-river small hydropower sites

Study regionThe Godavari River basin (GRB), situated between the geographical coordinates of 73°21′ E to 83°09′ E and 16°07′ N to 22°50′ N, India Study focusWe developed an integrated framework combining the variable infiltration capacity (VIC) model and geospatial tools to identify potential sites for run-of-river small hydropower plants (RoR-SHP) capable of meeting the GRB's energy demands during El Niño events. This study utilized long-term (1951–2020) daily streamflow data, simulated with the VIC model for design discharge computation at 30%, 75%, and 90% flow dependability and provided a thorough assessment of the potential of RoR-SHP in the GRB. New hydrological insights for the regionThe analysis revealed the GRB's potential for RoR-SHP development, identifying 226 potential sites based on the head along the river, with a combined power and annual energy generation estimate of 92 MW and 0.4 TWh/yr, respectively, at 90% flow dependability. After meticulous screening, 11 potential sites based on the head and the power potential demonstrated a decline of 46.03%, 37.97%, and 17.77% in annual energy at 30%, 75%, and 90% flow dependability, respectively, leaving nine sites maintaining the firm power (90% flow dependability) even during El Niño years. Our findings underscore the increased risk of power shortages during El Niño years and emphasize the need to develop appropriate strategies to cope with the risks associated with El Niño events.

Threat of Water Scarcity and Pollution in the Godavari River Region – A Study Analysis

Threat of Water Scarcity and Pollution in the Godavari River Region – A Study Analysis

IMDLIB: An open-source library for retrieval, processing and spatiotemporal exploratory assessments of gridded meteorological observation datasets over India

Addressing a pressing need for streamlined access to observed meteorological data in India, we present IMDLIB, an open-source Python library that operates on gridded datasets from the India Meteorological Department (IMD). IMDLIB simplifies the retrieval, management, and processing of IMD's rainfall, maximum and minimum temperature data at varying scales (point, regional, catchment, and country scales). IMDLIB stands out by directly interfacing with IMD's database through robust APIs, streamlining data acquisition. It offers transformation of IMD's binary data into common formats (e.g., NetCDF/GeoTIFF/CSV) and advanced analytics, including trend analysis, spatiotemporal data interpolation, calculation of meteorological indices and extreme characteristics, showcasing superior capabilities compared to traditional meteorological data processing tool available for the region. To underline its robustness, IMDLIB's application was illustrated in two distinct regions: Rajasthan State and the Godavari River Basin. Through IMDLIB, we envision enhanced data accessibility, leading to more insightful hydro-climatic research and analysis.

An Assessment of Tourists’ Attitude on Environmental Degradation of the Godavari River Region

This article is a small portion of the broader research, it includes a collection of primary data. Generally, any tourist can feel happy and enjoy more if the river environment is good in all respects. Hence, an attempt is made to know the attitudes of tourists on the need for attention on river environment degradation. The tourist having any type of occupation reacted that there is a most needed attention on degradation of river region environment. Godavari is the second-longest river, containing huge river basin, stood first in India by culturally in rich heterogeneous inhabitants. The universe of the study is the end flowing area of 220 kilometers from ‘Eastern Ghats to Bay of Bengal’ covering two regions with in the Andhra Pradesh. The present study is focused on temple, eco and rural areas of tourism in the Godavari river region places. Estimated annual tourist population is six lacks and the aggregate sample size is 600. The interview schedule was used as an instrument for the collection of data during the field survey.

Investigating river water quality assessment through non‐parametric analysis: A case study of the Godavari River in India

AbstractThis study examines temporal variations in water quality parameters of Godavari River. The present study examines the concentrations of seven water quality parameters, namely total alkalinity (Alk), calcium (Ca), chlorine (Cl), total hardness (Hard), magnesium (Mg), sodium (Na), and sulfate (SO4), using data from 1981 to 2005 at seven gauging stations, that is, Mancherial, Pathagudem, Nowrangpur, Jagdalpur, Perur, Konta, and Polavaram. A seasonal analysis (pre‐monsoon, monsoon, and post‐monsoon) is conducted to detect trends in water quality parameters. The study also uses normality tests Kolmogorov–Smirnov (K–S) and Shapiro–Wilk (S–W) which reveals nonnormal distribution in the majority of datasets, necessitating non‐parametric analysis. The concentration of Alk, Ca, and Hard is found to be significantly increasing trend at some stations during all three seasons, along with significantly decreasing trend found in the concentrations of Cl, Na, and SO4 using non‐parametric Mann–Kendall's (M–K) and Sen's Slope test. The concentration of Mg depicts mixed trends at different stations which may be due the variation in agricultural practices or industrial activities surrounding the stations. It is evident that none of the parameter exhibits statistically significant trends across all seasons at any station. The results of Sen's Slope test are found to be in good agreement with Mann–Kendall's test statistics. A second order auto‐regressive model (AR (2)) is successfully employed to predict and forecast the water quality parameters in present and future time steps. The authenticity of AR (2) model is assessed using Nash‐Sutcliffe (NSE) efficiency and coefficient of determination (R2). The values of NSE and R2 are found to be more than 0.85 for all parameters during all the three seasons, along with, the values of mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The results of coefficient of variation (CV) and persistence‐criterion (PC) also depicts that AR (2) can successfully be utilized in forecasting the water quality parameters. The research contributes insights for effective water resource management in Godavari River basin.