Faculty Publications
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Publications by NITK Faculty
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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.Item Machine learning-based ensemble of Global climate models and trend analysis for projecting extreme precipitation indices under future climate scenarios(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, G.P.; Dwarakish, G.S.Monitoring changes in climatic extremes is vital, as they influence current and future climate while significantly impacting ecosystems and society. This study examines trends in extreme precipitation indices over an Indian tropical river basin, analyzing and ranking 28 Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) based on their performance against India Meteorological Department (IMD) data. The top five performing GCMs were selected to construct multi-model ensembles (MMEs) using Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), Multiple Linear Regression (MLR), and the Arithmetic Mean. Statistical metrics reveal that the application of an RF model for ensembling performs better than other models. The analysis focused on six IMD-convention indices and eight indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). Future projections were examined for three timeframes: near future (2025–2050), mid-future (2051–2075), and far future (2076–2100) for SSP245 and SSP585 scenarios. Statistical trend analysis, the Mann-Kendall test, Sen’s Slope estimator, and Innovative Trend Analysis (ITA), were applied to the MME to assess variability and detect changes in extreme precipitation trends. Compared to SSP245, in the SSP585 scenario, Total Precipitation (PRCPTOT) shows a significant decreasing trend in the near future, mid-future, and far future and Moderate Rain (MR) shows a decreasing trend in the near future and far future of monsoon season. The findings reveal significant future trends in extreme precipitation, impacting Sustainable Development Goals (SDGs) achievement and providing crucial insights for sustainable water resource management and policy planning in the Kali River basin. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.