Faculty Publications
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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 Long-Term Climate Variability and Drought Characteristics in Tropical Region of India(American Society of Civil Engineers (ASCE), 2021) Vijay, A.; Sivan, S.D.; Mudbhatkal, A.; Mahesha, A.This work reports climate change signals and long-Term trend analysis of climate variables, meteorological drought, and extreme climate indexes over the tropical state of Kerala in India. The trend analysis reveals statistically significant decrease of annual and southwest monsoon rainfall (as much as 63 mm and 55 mm per decade, respectively). A decrease in number of annual rainy days (up to 2.8 days/decade) is also reported. Temperature trend analysis indicates an increasing trend with as high as 1.3°C/decade. The spatio-Temporal variation of extreme climate indexes across Kerala shows a decreasing trend of extreme precipitation indexes and an increasing trend of extreme temperature indexes. R95 and R95p decreased in northern and southern Kerala whereas R5 index increased in central and southern regions. Warm days have significantly increased whereas cold days exhibit a decreasing trend across the state. The increase in warmer nights is statistically significant whereas colder nights are decreasing in central and southern regions. Meteorological drought using Standardized Precipitation Index (SPI) reveals increasing occurrence of droughts in Kerala with higher frequencies over southern and central Kerala. © 2021 American Society of Civil Engineers.Item Copula-Based Frequency and Coincidence Risk Analysis of Floods in Tropical-Seasonal Rivers(American Society of Civil Engineers (ASCE), 2021) Muthuvel, D.; Mahesha, A.The conventional method of univariate flood frequency analysis based solely on peak flow (Q) overlooks the influence of other characteristic flood variables, such as the accumulated volume (V) of the flood and the duration (D) of flood events. A copula-based multivariate model that represents the joint behavior of these dependent flood variables could aid in computing joint return periods of flood events in tropical, seasonal rivers of India. In connection with the potential locations of high flood risk among west-flowing rivers, multivariate flood frequency analysis was performed on the Bharatapuzha, Periyar, and Chaliyar Rivers of the state of Kerala, India. A comparison of univariate return periods with multivariate return periods reveals that the intersection of flood variables corresponding to a 20-year univariate return period yields a trivariate return period of 91 years at Bharatapuzha and 144 years at Periyar and Chaliyar. The return period by the union of such flood variables is 10 years. The choice of flood variables and their combination depend on the problem at hand. Additionally, basinwise confluence flood frequency models are built with the peak flow at each stream as the random variables show their spatial interdependencies using conditional probabilities and return periods. The copula-based flood coincidence risk model captures the temporal aspect of the co-occurrence of flood peaks in a basin's streams. The co-occurrence of annual flood peaks between the stream pairs of the Bharatapuzha, Periyar, and Muvathapuzha basins is the highest toward the end of July with probabilities of approximately 2.2×10-4 (at the Kumbidi and Mankara stations), 3×10-4, and 1×10-3, respectively. A trio of copula-based multivariate flood frequency, confluence flood frequency, and flood coincidence risk models could be used to design safe and economic hydrologic infrastructure. © 2021 American Society of Civil Engineers.Item 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.