Faculty Publications
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Item Global water quality indices for river Gurpur, Karnataka State, India(2010) Rajagopal, B.; Shrihari, S.; Dwarakish, G.S.Three water quality indices viz. Drinking Water Quality Index (DWQI), Health Water Quality Index (HWQI) and Acceptability Water Quality Index (AWQI) were developed by United Nations Environment Programme (UNEP) under the Global Environment Monitoring System (GEMS)/Water for global comparison of quality of water sources. In this paper these three global water quality indices were determined for River Gurpur, in Karnataka state of India. Gurpur is one of the important west flowing rivers of India and is the source for industrial needs of fast developing Mangalore city. River water samples were collected from Gurpur River at Gurpur Bridge on National Highway -13 near Mangalore monthly from November 2006 to October 2007. The samples were analyzed for sixteen physico-chemical and bacteriological parameters. The global water quality indices determined for river Gurpur can be designated as 'Fair' during the study period. The seasonal variation in global water quality indices ranged from 'Marginal' to 'Excellent'. © 2010 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Hydrological effects of land use /land cover changes on stream flow at Gilgel Abay River Basin, Upper Blue Nile, Ethiopia(CAFET INNOVA Technical Society 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2016) Mulu, A.; Dwarakish, G.S.Water is the most important resource for the survival of living things and it is the most essential resource associated with land use/ land cover (LU/LC) changes. Therefore, it is very important to make evaluations of the expected impact on the hydrology and water resources due to expected changes. The main objective of this study is to assess the hydrological effect of land use/ land cover changes on stream flow at GilgelAbay river basin using Precipitation Runoff Modeling System (PRMS) model. System inputs are daily time-series values of precipitation, minimum and maximum air temperature, and parameter files which are generated from GIS Weasel. To identify effect of changes in LU/LC, vegetation type and vegetation density on stream flow, LU/LC, vegetation type and vegetation density data from 1990-2000 and 2001-2010 years were considered. This different period LU/LC, vegetation type and vegetation density with soil data and DEM were given to GIS Weasel to generate different parameters for PRMS model. These generated parameters together with time series data (daily minimum and maximum air temperature, daily precipitation and daily stream flow) feed to PRMS model to simulate stream flow for the years 1993-2000 and 2001-2008. From the time series data, climate changes (daily maximum and minimum temperature and daily precipitations) were kept the same as baseline period (1993-2000). The stream flow of 2001-2008 compared with baseline period (1993-2000) and the effect of LU/LC, vegetation type and vegetation density was identified using calibrated and simulated PRMS model. Hence, as LU/LC, vegetation type and vegetation density changed from 1993-2000 period to 2001-2010 period, stream flow increased from 7.8% (128.4 Mm3) to 25.3% (432 Mm3) and ET decreased from 4.2% (75 Mm3) to 20% (524 Mm3) from baseline period. For the whole simulation periods (2001-2008) stream flow increased by 10.9% (784 Mm3), but ET decreased 6.7% (43 Mm3) related to baseline periods. © 2016 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Effect of disturbed river sediment supply on shoreline configuration: A case study(Taylor and Francis Ltd., 2022) Yadav, A.; Dodamani, B.M.; Dwarakish, G.S.The magnitude of river sediment supply and its distribution play a significant role in coastal sediment dynamics, especially in erosion and deposition. Due to the construction of the dam, obstruction in the natural flow of water occurs, and part of the sediment is trapped. In the present study, the Kali river catchment and its river-mouth at Karwar, Devbagh, and Ravindranath Tagore beaches are considered as the study area, to assess the impact of dams on coastal processes. Landsat data for 42 years, from 1975 to 2017, were collected and analyzed using DSAS, an ArcGIS extension. The sediment yield estimated at the Kali river basin outlet, without the dam is 4.19 t/ha/yr and with the dam, it is estimated to be 1.42 t/ha/yr. Similarly, for the Aghanashini river basin outlet, the sediment yield was found to be 4.58 t/hr/yr. From the results of shoreline analysis, it is found that after the construction of the dam, Devbagh beach is under erosion at the rate of ?0.93 m/yr End Point Rate (EPR) and ?0.47 m/yr Linear Regression Rate (LRR). Ravindranath Tagore beach also has undergone erosion, which is ?0.75 m/yr (EPR) and ?0.97 m/yr (LRR). Further, both the beaches have been changed to the erosion zone. © 2021 Indian Society for Hydraulics.Item Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques(Nature Research, 2022) Jose, D.M.; Vincent, A.M.; Dwarakish, G.S.Multi-Model Ensembles (MMEs) are used for improving the performance of GCM simulations. This study evaluates the performance of MMEs of precipitation, maximum temperature and minimum temperature over a tropical river basin in India developed by various techniques like arithmetic mean, Multiple Linear Regression (MLR), Support Vector Machine (SVM), Extra Tree Regressor (ETR), Random Forest (RF) and long short-term memory (LSTM). The 21 General Circulation Models (GCMs) from National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and 13 GCMs of Coupled Model Inter-comparison Project, Phase 6 (CMIP6) are used for this purpose. The results of the study reveal that the application of a LSTM model for ensembling performs significantly better than models in the case of precipitation with a coefficient of determination (R2) value of 0.9. In case of temperature, all the machine learning (ML) methods showed equally good performance, with RF and LSTM performing consistently well in all the cases of temperature with R2 value ranging from 0.82 to 0.93. Hence, based on this study RF and LSTM methods are recommended for creation of MMEs in the basin. In general, all ML approaches performed better than mean ensemble approach. © 2022, The Author(s).Item The Influence of Land Use and Land Cover Transitions on Hydrology in a Tropical River Basin of Southwest India(Springer Nature, 2024) Kumar, G.P.; Sreejith, K.S.; Dwarakish, G.S.The Kali River basin in Karnataka, India, is a vital hydropower resource, crucial to the state’s economy. Understanding the region’s hydrological processes and the factors influencing water availability is essential, with land use and land cover (LULC) change being a significant driver of these impacts. This study focuses on detecting LULC changes in the Kali River basin and assessing their effects on hydrological processes within the Supa Dam catchment area. Using satellite images from 1992, 2002, 2013, and 2022 and the ERDAS imagine tool, LULC classification was done with a supervised classification algorithm. The analysis revealed that from 1992 to 2022, the basin experienced a 5.97% decline in dense forest and a 5.64% decrease in open forest cover, while agricultural land expanded by 7.03%, and tree plantations increased by 1.49%. Water bodies increased by 1.44%, built-up areas and barren land rose by 0.97% and 0.76%, respectively, with grassland remaining stable. The impact of these LULC changes on hydrological processes was evaluated using the Soil and Water Assessment Tool (SWAT) model. Between 1992 and 2013, the model, which showed a surface flow increase of 212.83 mm, a water yield decrease of 46.10 mm, an increase in lateral flow by 37.95 mm, and a decrease in groundwater flow by 180.90 mm, with R2 and NSE values exceeding 0.60 for both calibration and validation, demonstrates satisfactory model performance. These findings underscore the importance of understanding LULC change impacts on streamflow to guide effective land management strategies and mitigate adverse effects on the watershed’s hydrology. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.Item Comparison of the Multiple Imputation Approaches for Imputing Rainfall Data: A Humid Tropical River Basin Case Study(Springer Nature, 2025) Kumar, G.P.; Dwarakish, G.S.Accurate rainfall data is crucial for agriculture, hydrology, and climate research as it guides water management, crop planning, and disaster preparedness. Missing data affects reliability, requiring effective imputation. The purpose of this study is to address the critical challenge of imputing missing daily rainfall data, which is especially important given rainfall’s nonlinear distribution and variability in missingness patterns. This research aims to develop and evaluate advanced imputation algorithms to improve data completeness and integrity in humid tropical regions. This study evaluates ten imputation algorithms: K-nearest neighbors (KNN), classification and regression trees (CART), predictive mean matching (pmm), random forest (rf), mean method, and Bayesian methods (norm. boot, lasso. norm, norm, norm. nob, midastouch) for addressing missing daily rainfall data. Using 37 years of data from thirteen stations in the Kali River Basin, the methods leverage descriptive statistics to enhance accuracy in humid tropical regions. The proposed algorithms incorporate descriptive statistics of the rainfall time series and are evaluated using 37 years of daily data from thirteen selected rainfall stations in the Kali River Basin, a humid tropical region. Model performance was assessed at four missingness levels (1%, 5%, 10%, and 20%) and evaluated with accuracy metrics root mean square error (RMSE), mean absolute error (MAE), index of agreement (d), and RMSE-observations standard deviation ratio (RSR). Among the evaluated methods, KNN consistently demonstrated superior performance across all levels of missingness (RMSE = 13.22 to 15.42; MAE = 4.68 to 6.08; d = 0.87 to 0.90; RSR = 0.57 to 0.61), followed closely by CART (RMSE = 16.48 to 20.77; MAE = 6.20 to 8.31; d = 0.81 to 0.86; RSR = 0.71 to 0.79). Overall, KNN, CART, pmm, and rf emerged as reliable methods for imputing missing rainfall data of varying lengths, contributing to more accurate weather forecasting and climate change analyses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.