Faculty Publications
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Item Rainfall trend analysis in coastal region of karnataka(Springer Science and Business Media Deutschland GmbH, 2021) Ashwin, S.; Kemmannu, K.; Doranalu Chandrashekar, D.C.Climate can be defined as the weather conditions or the weather patterns that is present in the particular geographical area for a very lengthy period. It can be assessed by the important factors like temperature, humidity, wind, precipitation. The climate of the region also depends on the latitude, terrain, water bodies, etc. Coastal Karnataka receives an average rainfall of 3456 mm; at summer, the temperature lies between 33.5 and 40 °C and the minimum temperature of 23.3 and 27.9 °C. Tropical monsoon climate covers whole coastal places of Karnataka and other nearby places too. Rainfall time series is divided into four periods. This region has a very hot climate with extreme rainfall in monsoon season, i.e., June to September. These drastic changes in the climate severely affect the various activities throughout the coastal area of Karnataka. Understanding the variability of climate in the region is essential. In this study, the variation in the climate for a period of 1984–2017 is observed and investigated and changes in the trend in the grid points can be noticed. Mann–Kendall trend test is applied for the precipitation to find trend patterns, and the magnitude of the trend is determined by Sen’s slope estimator. Increase in trend was found in the grid point 3 and grid point 4. © Springer Nature Singapore Pte Ltd 2021.Item Tropical, Seasonal River Basin Development: Hydrogeological Analysis(2011) Shetkar, R.V.; Mahesha, A.This study presents a hydrogeological analysis of a humid tropical, seasonal river in the context of climate change, increasing demand for water, and uneven distribution of rainfall. We investigate the Netravathi basin, a tropical river basin of south India. The climate change effect on the basin was evident in terms of increasing trend in temperature by about 0.7°C/100 years and decreasing trend in the river flow during the monsoon by about 0.8% of average annual flow per year using the Mann-Kendall trend test. Even though rainfall was found to be decreasing, no significant trend could be established. From the trend analysis of the river flow, it was found that there is an overall declining trend with longer scarcity periods. In addition, the trends of magnitude and frequency of high flows are declining. Even though the region receives an average annual rainfall of about 3,930 mm, it has nonuniform distribution with most of the rainfall confining to a few months of a year. In view of this, the region suffers from a prolonged dry period during February to May. The projected domestic water demand of the region for the next 25 years is estimated to be increasing from the present 0.09 mm3 to 0.25 mm3 per day because of rapid urbanization and industrialization. The purpose of this investigation is to highlight the effects of climate change and uneven distribution of rainfall in the river basin. This may assist in proper planning of the basin through strategies such as river water harvesting, which is investigated in the companion paper. Because the Netravathi River is a seasonal and tidal river, and saltwater intrusion along the river during the summer months is affecting the development of the basin. It was found that the river water is affected up to distance of about 22,000 m from the Arabian sea and the wells on the banks of the river are found to be highly vulnerable to saltwater intrusion during the summer period (March to May). © 2011 American Society of Civil Engineers.Item Impacts of climate change on varied River-Flow regimes of southern india(American Society of Civil Engineers (ASCE) onlinejls@asce.org, 2017) Mudbhatkal, A.; Raikar, R.V.; Venkatesh, B.; Mahesha, A.This paper assesses the possible impact of climate change on the hydrology of the subhumid and perhumid river regimes originating from the western mountain range (Western Ghats) of India. The modified Mann-Kendall test evaluates the trend of observed data (1975-2004) and RCP 4.5 data (2006-2070) of climatic variables. The results indicate a decreasing trend for annual rainfall over the Malaprabha River catchment (26 mm per year at the 5% significance level), whereas no trend is observed over the Netravathi River catchment at the 10% level. Indian southwestern monsoon rainfall shows a decreasing trend from 84 to 80% of total rainfall in the Malaprabha River catchment and from 80 to 77% in the Netravathi River catchment. Summer rains are found to be increasing in the Malaprabha River catchment (3-4.5% of total rainfall), whereas there is no significant trend for the Netravathi River catchment. Furthermore, the postmonsoon rainfall also shows a significant increase in the Malaprabha catchment (40 mm per decade at the 5% significance level) and the Netravathi catchment (30 mm per decade at the 10% significance level). The Netravathi River shows a decreasing trend for annual flow (0.22 Mm3 per year at the 10% significance level). However, for both catchments the temperature is found to be increasing by 0.2-0.8°C per decade. The soil and water assessment tool (SWAT) model is used to simulate the river catchments and exhibits a Nash-Sutcliffe efficiency of 0.831 and 0.857 for the Malaprabha and Netravathi River catchments, respectively. In addition, a decreasing trend in the high flow is estimated for Netravathi, whereas the trend is increasing for Malaprabha. Thus the impacts of climate change over the Western Ghats are very evident, but the flow of each river responds differently. © 2017 American Society of Civil Engineers.Item Bias Correction and Trend Analysis of Temperature Data by a High-Resolution CMIP6 Model over a Tropical River Basin(Korean Meteorological Society, 2022) Jose, D.M.; Dwarakish, G.S.Technological advancements like increase in computational power have led to high-resolution simulations of climate variables by Global Climate Models (GCMs). However, significant biases exist in GCM outputs when considered at a regional scale. Hence, bias correction has to be done before using GCM outputs for impact studies at a local/regional scale. Six bias correction methods, namely, delta change (DC) method, linear scaling (LS), empirical quantile mapping (EQM), adjusted quantile mapping (AQM), Gamma-Pareto quantile mapping (GPQM) and quantile delta mapping (QDM) were used to bias correct the high-resolution daily maximum and minimum temperature simulations by Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 (MRI-AGCM3–2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6), of Netravati basin, a tropical river basin on the south-west coast of India. The quantile-quantile (Q–Q) plots and Taylor diagrams along with performance indicators like Nash–Sutcliffe efficiency (NSE), the Root-Mean Square Error (RMSE) or Root-Mean Square Deviation (RMSD), the Mean Absolute Error (MAE), the Percentage BIAS (PBIAS) and the correlation coefficient (r) were used for the evaluation of the performance of each bias correction method in the validation period. Considerable reduction in the bias was observed for all the bias correction methods employed except for the LS method. The results of QDM method, which is a trend preserving bias correction method, was used for analysing the trend of future temperature data. The trend of historical and future temperature data revealed an increasing trend in the annual temperature. An increase of 0.051 °C and 0.046 °C is expected for maximum and minimum temperature annually during the period 2015 to 2050 as per RCP 8.5 scenario. This study demonstrates that the application of a suitable bias correction is needed before using GCM projections for climate change studies. © 2021, Korean Meteorological Society and Springer Nature B.V.Item Machine learning–based assessment of long-term climate variability of Kerala(Springer Science and Business Media Deutschland GmbH, 2022) Vijay, A.; Varija, K.Studies on historical patterns of climate variables and climate indices have attained significant importance because of the increasing frequency and severity of extreme events worldwide. While the recent events in the tropical state of Kerala (India) have drawn attention to the catastrophic impacts of extreme rainfall events leading to landslides and loss of human lives, a comprehensive and long-term spatiotemporal assessment of climate variables is still lacking. This study investigates the long-term trend analysis (119 years) of climate variables at 5% significance level over the state using gridded datasets of daily rainfall (0.25° × 0.25° spatial resolution) and temperature (1° × 1° spatial resolution) at annual and seasonal scales (south-west monsoon, north-east monsoon, winter and summer). Five trend analysis techniques including the Mann–Kendall test (MK), three modified Mann–Kendall tests and innovative trend analysis (ITA) test were performed in the study. It is evident from the trend analysis results that more than 83% of grid points were showing negative trends in annual and south-west monsoon season rainfall series (at a mean rate of 39.70 mm and 28.30 mm per decade respectively). All the trend analysis tests identified statistically significant increasing trends in mean and maximum temperature at annual and seasonal scales (0.10 to 0.20 °C/decade) for all grids. The K-means clustering algorithm delineated 59 grid points into five clusters for annual rainfall, illustrating a clear geographical pattern over the study area. There is a clear gradient in rainfall distribution and concentration inside the state at annual as well as seasonal scales. The majority of annual rainfall is concentrated in a few months of the year. That may lead the state vulnerable to water scarcity in non-rainy seasons. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Regional Trends and Spatiotemporal Analysis of Rainfall and Groundwater in the West Coast Basins of India(American Society of Civil Engineers (ASCE), 2022) Krishnan, C.; Mahesha, M.The present study investigates the spatiotemporal variabilities of long-term (1950-2016) rainfall and regional groundwater levels for annual and seasonal periods over the west coast of India. The study area is a narrow strip of land between Western Ghats (mountainous terrain) and the Arabian Sea, extending over 1,500 km from south to north. The Mann Kendall (MK) and Sen's slope estimator established the long-term trend and magnitude of rainfall and groundwater. The nature of trends in the time series of hydroclimatic variables was identified through singular spectrum analysis (SSA). The SSA extracted nonlinear trends along with the shape for both increasing and decreasing trends. Annual and southwest monsoon rainfall exhibited prominent decreasing trends. The percentage departure analysis of rainfall revealed that earlier decades (1950-1980) were the wettest, followed by the drier decades (1980-2016) for Periyar, Varrar, and Netravati and vice versa for Vasishti and Bhatsol. The wavelet spectra for rainfall indicated short- and long-term modulations. The long-term groundwater level trends of 725 wells on the entire west coast showed a significant decline in 13% of wells, and 6% of wells indicated increasing trends. The Monte Carlo-based numerical investigations on the modified MK (mMK) test power indicated the influence of parent distributions on trend detection. The field significance of trends at a 5% significance level was examined using the bootstrap test. The precipitation data were then compared with groundwater level variation at each site, and correlations were established. The declining southwest monsoon rains and their uneven spatial distribution could be attributed to a subsequent decline in the region's postmonsoon groundwater levels. © 2022 American Society of Civil Engineers.Item Trend Analysis of Rainfall and Meteorological Drought Indices over India During 1958–2017(Springer Nature, 2023) Kikon, A.; Dodamani, B.M.Rainfall plays a very vital role and its deficit causes a huge impact on the environment. Understanding the pattern of rainfall and drought trends has become increasingly crucial in many regions due to climate change. In this study, using the rainfall data from 1958 to 2017 for thirty-four meteorological subdivisions of India, trend analysis is performed for annual and seasonal rainfall. Along with the rainfall trend analysis, the study is also performed for meteorological drought indices, i.e., Effective Drought Index (EDI), Standardized Precipitation Index-9 (SPI-9), and Standardized Precipitation Index-12 (SPI-12). The results obtained from the Mann–Kendall test show that the rainfall patterns in the area under investigation are changing over time. As evidenced by the decrease in rainfall, the study region has been experiencing a lack of water supply in numerous subdivisions. The drought frequency for the meteorological drought indices has also been investigated, and it has been observed that the region is experiencing drought from extremely dry conditions to normal dry conditions. The findings in this study will help us to better comprehend the changes in rainfall and drought severity over the study region. This study may also benefit effective disaster management and preparedness strategies for this catastrophe, which is wreaking havoc on the environment. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Item Large-scale atmospheric teleconnections and spatiotemporal variability of extreme rainfall indices across India(Elsevier B.V., 2024) Vinod, D.; Mahesha, A.Identifying trends in hydrometeorological time series during extreme weather events and their interactions with large-scale atmospheric teleconnections is crucial for climate change research. This study evaluates 14 precipitation-based indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) across seven climatic zones of India using gridded daily rainfall data from the India Meteorological Department (IMD) for 120 years (1902–2021) utilised. Trend analysis was carried out using the Mann-Kendall (MK) test, Theil-slope Sen's estimator, Innovative Trend Analysis (ITA), and other statistical tools. Change point detection is established using the Pettitte test and Cumulative Sum algorithm. The relationships between large-scale atmospheric teleconnections and ETCCDI indices are also found, and Multiple Linear Regression (MLR) models are developed between them. The results show significant increasing trends in extreme rainfall indices in India's Ladakh region, located in the arid desert-cold climatic zone. The annual, Southwest Monsoon (SW-Monsoon), Northeast Monsoon (NE-Monsoon), and summer rainfall trends were positive, while winter rainfall had a negative trend across most climatic zones. Significant associations between large-scale atmospheric teleconnections, including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Global Temperature Anomaly (GTA), Southern Oscillation Index (SOI), SST of Niño 3.4 region, Oceanic Niño Index (ONI), and Dipole Mode Index (IOD) and ETCCDI indices were established across multiple climatic zones. Using MLR analysis, this study attempts to establish, for the first time, the relationship between teleconnections and ETCCDI indices across India. Extreme rainfall indices are influenced by climate change during the SW-Monsoon across most of the climatic zones of India. During the previous El Niño event (2014–2016), average annual rainfall decreased by 19.5%, SW-Monsoon rainfall decreased by 25.2%, and NE-Monsoon rainfall decreased by 64.1% in India. The findings may provide valuable insights into mitigation strategies to sustain the adverse effects of extreme weather conditions and enhance climate resilience. © 2023 Elsevier B.V.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.