Browsing by Author "Kumar, G.P."

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A Comprehensive Review of Cutting-Edge Flood Modelling Approaches for Urban Flood Resilience Enhancement
(Springer Nature, 2025) Snikitha, S.; Kumar, G.P.; Dwarakish, G.S.
Urban environments face a growing threat from floods, driven by factors like excessive rainfall, inadequate drainage, rapid urbanization, and climate-amplified extreme weather events. These floods inflict significant economic losses, endanger public health, and impede disaster response. This review tackles a critical issue by exploring the most effective flood modelling approaches for urban areas. Through a comprehensive analysis, it examines various hydrologic and hydraulic models such as SWAT, HEC-HMS, VIC, TOPMODEL, HBV, ANUGA, LISFLOOD, HEC-RAS, SWMM, MIKE URBAN, and others, highlighting their strengths and limitations, and offering a structured comparison based on criteria for selecting the appropriate model. The research revealed that integrating HEC-HMS and HEC-RAS produced the most favourable outcomes for urban flood modelling when open channels are key components, where the spatial extent of the flood, water surface profile, and velocity are easily determined by the HEC-RAS model. Conversely, SWMM integrates surface runoff and underground drainage, providing comprehensive urban water management. This nuanced understanding underscores the importance of selecting appropriate modelling approaches based on the unique characteristics of urban environments, ensuring effective flood management strategies tailored to the specific challenges each area presents. These urban flood modelling techniques serve as indispensable tools in forecasting flood patterns, evaluating vulnerability levels, and formulating effective strategies to mitigate risks. By empowering informed decision-making in urban planning and disaster management, these models are critical for minimizing the devastating effects of floods on communities and infrastructure. This review provides valuable insights for developing resilient urban areas prepared to navigate the complex challenges of urban flooding. Â© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
A Comprehensive Review of Cutting-Edge Flood Modelling Approaches for Urban Flood Resilience Enhancement
(Springer Nature, 2025) Snikitha, S.; Kumar, G.P.; Dwarakish, G.S.
Urban environments face a growing threat from floods, driven by factors like excessive rainfall, inadequate drainage, rapid urbanization, and climate-amplified extreme weather events. These floods inflict significant economic losses, endanger public health, and impede disaster response. This review tackles a critical issue by exploring the most effective flood modelling approaches for urban areas. Through a comprehensive analysis, it examines various hydrologic and hydraulic models such as SWAT, HEC-HMS, VIC, TOPMODEL, HBV, ANUGA, LISFLOOD, HEC-RAS, SWMM, MIKE URBAN, and others, highlighting their strengths and limitations, and offering a structured comparison based on criteria for selecting the appropriate model. The research revealed that integrating HEC-HMS and HEC-RAS produced the most favourable outcomes for urban flood modelling when open channels are key components, where the spatial extent of the flood, water surface profile, and velocity are easily determined by the HEC-RAS model. Conversely, SWMM integrates surface runoff and underground drainage, providing comprehensive urban water management. This nuanced understanding underscores the importance of selecting appropriate modelling approaches based on the unique characteristics of urban environments, ensuring effective flood management strategies tailored to the specific challenges each area presents. These urban flood modelling techniques serve as indispensable tools in forecasting flood patterns, evaluating vulnerability levels, and formulating effective strategies to mitigate risks. By empowering informed decision-making in urban planning and disaster management, these models are critical for minimizing the devastating effects of floods on communities and infrastructure. This review provides valuable insights for developing resilient urban areas prepared to navigate the complex challenges of urban flooding. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
A Critical Review of the Soil Conservation Services – Curve Number Method in Hydrological Modelling
(Springer Science and Business Media B.V., 2024) Sreejith, K.S.; Kumar, G.P.; Dwarakish, G.S.
The Soil Conservation Service Curve Number (SCS-CN) method is popular for predicting surface runoff due to its simplicity, ease of application, and widespread acceptance. However, it has limitations, such as the neglect of storm duration, a lack of guidance on antecedent moisture conditions, and the assumption of a constant initial abstraction coefficient (λ = 0.2), leading to uncertainty. Its reliance on static land use classifications and empirical assumptions limits its accuracy across diverse geographic regions and complex hydrological scenarios, particularly under extreme weather conditions. Furthermore, selecting the most suitable watershed CN values remains a subject of global debate. Moreover, the model is widely applied beyond its originally intended purpose. Its basic assumptions, flexibility in dealing with different hydrological conditions, and susceptibility to variables including soil type, land use, and antecedent moisture conditions have all drawn criticism for the method. To overcome the original curve number method limitations, many studies have been made on improving the SCS-CN method. Despite these advancements, significant gaps remain, particularly in the method's applicability across diverse geographic regions and its accuracy in extreme weather events. This paper revisits the popular SCS-CN method, its history, development of methodology, limitations, and refinements that occurred to the original method with the progress of science and technology. It also explores the need for further research to improve its applicability, highlighting opportunities for more robust, flexible runoff estimation models. © The Author(s), under exclusive licence to Society of Wetland Scientists 2024.
A Critical Review of the Soil Conservation Services â€“ Curve Number Method in Hydrological Modelling
(Springer Science and Business Media B.V., 2024) Sreejith, K.S.; Kumar, G.P.; Dwarakish, G.S.
The Soil Conservation Service Curve Number (SCS-CN) method is popular for predicting surface runoff due to its simplicity, ease of application, and widespread acceptance. However, it has limitations, such as the neglect of storm duration, a lack of guidance on antecedent moisture conditions, and the assumption of a constant initial abstraction coefficient (Î» = 0.2), leading to uncertainty. Its reliance on static land use classifications and empirical assumptions limits its accuracy across diverse geographic regions and complex hydrological scenarios, particularly under extreme weather conditions. Furthermore, selecting the most suitable watershed CN values remains a subject of global debate. Moreover, the model is widely applied beyond its originally intended purpose. Its basic assumptions, flexibility in dealing with different hydrological conditions, and susceptibility to variables including soil type, land use, and antecedent moisture conditions have all drawn criticism for the method. To overcome the original curve number method limitations, many studies have been made on improving the SCS-CN method. Despite these advancements, significant gaps remain, particularly in the method's applicability across diverse geographic regions and its accuracy in extreme weather events. This paper revisits the popular SCS-CN method, its history, development of methodology, limitations, and refinements that occurred to the original method with the progress of science and technology. It also explores the need for further research to improve its applicability, highlighting opportunities for more robust, flexible runoff estimation models. Â© The Author(s), under exclusive licence to Society of Wetland Scientists 2024.
Analysis of Land Use Land Cover Change Detection Using Remotely Sensed Data for Kali River Basin
(Springer Science and Business Media Deutschland GmbH, 2024) Sreejith, K.S.; Kumar, G.P.; Dwarakish, G.S.
For the last two centuries, the Earth's land cover has undergone fast change, and all indications indicate that this trend will continue. This shift is being driven by economic development and population expansion. For the management of natural resources and the observation of environmental changes, land use and land cover (LULC) change has become a key element. Natural landscapes have undergone significant change as a result of anthropogenic activity, particularly in areas where population increase and climate change have a significant impact. To effectively manage the environment, especially water management, it is essential to understand how trends in land use and land cover (LULC) change. This study used remote sensing and geographic information systems (GIS) to examine changes in LULC patterns during a 20-year period in the Kali River Basin. LULC changes were mapped using multitemporal Landsat series satellite images. Landsat-5 image of 2002 and Landsat-8 image of 2022 were obtained for the purpose of the study. Maximum likely hood algorithm was used to detect areas of change with supervised classification, performed in ERDAS Imagine 2014 and took minimum of 100 samples and maximum of 250 samples of ground truth data for each class. The supervised classification produced good results with overall accuracies of 91.58% and 89.47% for the 2002 and 2022, respectively. The results of the change detection analysis conducted between 2002 and 2022 demonstrate the extent of LULC changes that have taken place in various LULC classes, while the majority of the river basin's grassland, barren land, and open forest have undergone intensive conversion to cultivated land and built-up areas. These modifications show that population growth was responsible for the rise in cultivated land and built-up areas. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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.
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.
Multi-criteria decision-making and machine learning-based CMIP6 general circulation model ensemble for climate projections in a tropical river basin in India
(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, G.P.; Vinod, D.; Dwarakish, G.S.; Mahesha, A.
General circulation models (GCMs) are vital for accurate climate prediction and informing strategic water resource planning. The investigation explores the performance of five machine learning (ML) algorithms for ensembling the GCMs for top-5 and least-5 ranked models in multi-criteria decision-making (MCDM) in addition to 28 GCMs applicable to a tropical river basin in India and the performance of their ensemble using statistical metrics. The gridded datasets from the India Meteorological Department (IMD) are used as observed data. From the statistical metrics, an entire 28 GCMs ensemble showed superiority over top-5 and least-5 ranked ensembles for three meteorological variables. The random forest (RF) algorithm consistently demonstrated high accuracy and reliability in ensembling the GCMs for the three meteorological variables, followed by support vector machine (SVM) and multiple linear regression (MLR). By implementing the proposed approach, researchers can minimize biases, enable resource-efficient modeling, and deliver practical insights through robust and reliable climate projections. These results highlight the importance of thoughtful ensemble design, advocating using multi-model ensembles (MMEs) in comprehensive climate studies to ensure accurate predictions across diverse climate indices. The findings provide valuable insights into local climate conditions, supporting ecosystem management and informing policy decisions. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.
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.