Browsing by Author "Subbarayan, Saravanan"

Now showing 1 - 6 of 6
  • No Thumbnail Available
    Item
    Advancing Agricultural Land Suitability in Urbanized Semi-Arid Environments: Insights from Geospatial and Machine Learning Approaches
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-12-03) Sathiyamurthi, Subbarayan; Subbarayan, Saravanan; Ramya, Madhappan; Sivasakthi, Murugan; Gobi, Rengasamy; Qaysi, Saleh; Praveen Kumar, Sivakumar; Lee, Jinwook; Alarifi, Nassir; Wahba, Mohamed; M. Youssef, Youssef
    Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated by erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable agricultural productivity in such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms—Naïve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), and support vector machines (SVM)—alongside land-use/land-cover (LULC) considerations in the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors such as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, and calcium carbonate. The tuned ETC model showed the lowest root mean squared error (RMSE = 0.15), outperforming RF (RMSE = 0.18), NB (RMSE = 0.20), SVM (RMSE = 0.22), and KNN (RMSE = 0.23). The AgLS-ETC map identified 29.09% of the area as highly suitable (S1), 19.06% as moderately suitable (S2), 16.11% as marginally suitable (S3), 15.93% as currently unsuitable (N1), and 19.21% as permanently unsuitable (N2). By incorporating Landsat-8 derived LULC data to exclude forests, water bodies, and settlements, these suitability estimates were adjusted to 19.08% (S1), 14.45% (S2), 11.40% (S3), 10.48% (N1), and 9.58% (N2). Focusing on the ETC model, followed by land-use analysis, provides a robust framework for optimizing sustainable agricultural planning, ensuring the protection of ecological and social factors in developing countries. © 2024 by the authors.
  • No Thumbnail Available
    Item
    Evaluation of hydrological responses to decadal variability of land use/land cover patterns in tropical river basin of the Western Ghats, Southern India
    (Taylor and Francis Ltd., 2026-07-03) Gayen, Sudeshna; Seenipandi, Kaliraj; Srinivas, Reji; Devaraj, Suresh; Subbarayan, Saravanan; Loganathan, Parthiban
    LULC change is increasingly altering hydrological processes in the Karamana River Basin, which faces declining vegetation cover, increasing impervious surfaces, and growing drought vulnerability. This study evaluates decadal LULC transformations and their hydrological impacts using Landsat (2000, 2015 and 2025), MODIS-derived variables, and IMD rainfall data integrated within a GIS-SVM framework. Unlike previous studies that primarily assessed individual indicators, this research provides a comprehensive evaluation linking LULC dynamics with multiple hydrological and drought indicators. Built-up areas expanded by 68.2% between 2000 and 2025, accompanied by a 2.12 °C increase in LST. NDVI maximum values declined from 1 to 0.92, NDWI decreased from 0.79 to 0.48, ET reduced from 55.88 to 38.60 mm, and SPI ranged from −1.84–0.70 to −1.47–1.13, indicating localized severe drought hotspots (SPI < −1.5). The findings highlight the need for sustainable land-use planning, urban growth regulation, watershed restoration, and climate-resilient water resource management. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
  • No Thumbnail Available
    Item
    Integrating Multi-Temporal Land Use/Land Cover Dynamics into GALDIT-Based Seawater Intrusion Vulnerability Assessment for Sustainable Groundwater Management Along the Indian Coastline
    (Multidisciplinary Digital Publishing Institute (MDPI), 2026-08-06) Subbarayan, Saravanan; Ezhilarasu, Deepack; Đurin, Bojan; Seenipandi, Kaliraj; Gomaa, Ehab; Youssef, Youssef M.; Abd-Elmaboud, Mahmoud E.
    Seawater intrusion (SWI) represents an increasingly critical challenge for coastal groundwater systems, with particularly pronounced impacts observed along the Indian coastline. Coastal aquifers constitute a vital freshwater resource supporting domestic, agricultural, and industrial demands. This study evaluates SWI vulnerability along the entire Indian coast, extending from Gujarat to West Bengal, covering approximately 7517 km of shoreline and up to 100 km inland. The assessment applies the GALDIT vulnerability framework that combines several hydrogeological and hydrochemical criteria such as groundwater occurrence, aquifer hydraulic conductivity, depth to groundwater, distance from shoreline, hydrochemical data, and groundwater quality data. We also assessed the intrusion of existing seawater, shoreline location, and aquifer thickness. However, conventional vulnerability assessments are inherently static and often fail to capture anthropogenic influences. To address this limitation, the present study integrates multi-temporal land use and land cover (LULC) datasets derived from ESA WorldCover remote sensing data for the period 2017–2024. Incorporating LULC dynamics enables a more comprehensive evaluation of the impacts of urban expansion and agricultural intensification on coastal susceptibility to SWI. Accordingly, a modified GALDIT-LU framework is developed to assess the spatiotemporal evolution of coastal vulnerability. The outcomes suggest that huge parts of the Indian coastline are vulnerable to moderate or very high classes, with the very high vulnerability class growing from 13,295 km2 in 2017 to 38,257 km2 in 2024, a 188% increase in vulnerability over the course of seven years. Groundwater chloride concentrations from Central Ground Water Board (CGWB) monitoring well locations have been used for validation over the proposed assessment, and show good spatial agreement between areas identified as high vulnerability and the spatial distribution of groundwater salinity for all three assessment periods, lending support to the robustness and predictive power of the proposed groundwater salinity assessment. The findings carry direct implications for the United Nations 2030 Agenda, demonstrating that the identified vulnerability patterns intersect with critical targets related to clean water and sanitation, food security, public health, climate action, and poverty reduction along one of the world’s most densely populated coastlines. © 2026 by the authors.
  • No Thumbnail Available
    Item
    Irrigation water balance spatio temporal modelling using GEE and GIS in NSLBC command area South India
    (Springer Nature, 2026-05-23) Sankriti, Ramanarayan; Subbarayan, Saravanan
    This study presents a spatiotemporal assessment of soil water balance within the Nagarjunasagar Left Bank Canal (NSLBC) command area, aimed at evaluating irrigation water demand and supply dynamics over time. A distributed water balance simulation model was developed using the Google Earth Engine (GEE) cloud platform to analyse the geographic and temporal variability of irrigation water availability. The model integrates key hydrological components, including groundwater storage fluctuations, precipitation inputs, and water losses from runoff and evapotranspiration. Data sources include MODIS (evaporation), TerraClimate (soil moisture), GRACE (terrestrial water storage), and the SCS Curve Number method (runoff estimation). Evapotranspiration was calculated using the Surface Energy Balance Algorithm for Land (SEBAL). Simulation results from 2003 to 2013 indicate a generally favourable water balance, with only a minor deficit of 6 mm in January 2010 and a peak surplus of 477 mm in July 2010. Trend analysis using Sen’s Slope estimator and the Mann-Kendall test reveals consistent growth of water balance in the command area, accompanied by notable spatiotemporal variability. Monthly water balance values ranged from − 1.5 mm to 233 mm across the region. Model validation against actual canal flow data from 2003 yielded a root mean square error (RMSE) of 29 mm - less than 10% of the observed water balance range - demonstrating high reliability. These findings underscore the value of cloud-based platforms like GEE for large-scale hydrological modelling and offer actionable insights for sustainable irrigation water management in semi-arid agricultural regions.
  • No Thumbnail Available
    Item
    Mapping of Spatially Distributed Soil Erosion over the Tungabhadra River Sub-Basin (TRB) Using Satellite-Based Precipitation Products (SPPs) and RUSLE Modelling
    (Multidisciplinary Digital Publishing Institute (MDPI), 2026-06-05) Subbarayan, Saravanan; Sankriti, Ramanarayan
    In many developing regions, the lack of on-site weather data impedes the estimation of rainfall-driven processes, such as soil erosion. Satellite-based precipitation products (SPPs) can support hydrological modelling in gauge-sparse regions by providing continuous rainfall estimates. Accurate rainfall estimation is crucial to soil erosion modelling, particularly in data-scarce regions such as the TRB. In this study, seven satellite-based precipitation products—CHIRPS, IMERG, TRMM, ERA5, GLDAS, and PERSIANN-CDR, along with the IMD gridded dataset—were evaluated for their ability to represent rainfall patterns and support R-factor estimation in the RUSLE framework. This is the first comprehensive evaluation of multiple SPPs for RUSLE-based soil erosion modelling in the Tungabhadra river basin (TRB), providing insights for ungauged watersheds in India. CHIRPS and IMERG displayed relatively smooth and continuous patterns, while PERSIANN-CDR and TRMM exhibited fragmented rainfall zones. ERA5 and GLDAS demonstrated consistent but moderate values across the basin. IMD data served as the reference product for comparison. The findings reveal that the choice of precipitation dataset directly affects the accuracy of erosion estimation. Therefore, multi-dataset evaluation is recommended for reliable assessment of soil loss and watershed planning in ungauged or partially gauged catchments. © 2026 by the authors.
  • No Thumbnail Available
    Item
    Soil and Water Assessment Tool-Based Prediction of Runoff Under Scenarios of Land Use/Land Cover and Climate Change Across Indian Agro-Climatic Zones: Implications for Sustainable Development Goals
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-02-06) Subbarayan, Saravanan; Youssef, Youssef M.; Singh, Leelambar; Dąbrowska, Dominika; Alarifi, Nassir; Ramsankaran, RAAJ.; Visweshwaran, R; Saqr, Ahmed M.
    Assessing runoff under changing land use/land cover (LULC) and climatic conditions is crucial for achieving effective and sustainable water resource management on a global scale. In this study, the focus was on runoff predictions across three diverse Indian watersheds—Wunna, Bharathapuzha, and Mahanadi—spanning distinct agro-climatic zones to capture varying climatic and hydrological complexities. The soil and water assessment (SWAT) tool was used to simulate future runoff influenced by LULC and climate change and to explore the related sustainability implications, including related challenges and proposing countermeasures through a sustainable action plan (SAP). The methodology integrated high-resolution satellite imagery, the cellular automata (CA)–Markov model for projecting LULC changes, and downscaled climate data under representative concentration pathways (RCPs) 4.5 and 8.5, representing moderate and extreme climate scenarios, respectively. SWAT model calibration and validation demonstrated reliable predictive accuracy, with the coefficient of determination values (R2) > 0.50 confirming the reliability of the SWAT model in simulating hydrological processes. The results indicated significant increases in surface runoff due to urbanization, reaching >1000 mm, 600 mm, and 400 mm in southern Bharathapuzha, southeastern Wunna, and northwestern Mahanadi, respectively, especially by 2040 under RCP 8.5. These findings indicate that water quality, agricultural productivity, and urban infrastructure may be threatened. The proposed SAP includes nature-based solutions, like wetland restoration, and climate-resilient strategies to mitigate adverse effects and partially achieve sustainable development goals (SDGs) related to clean water and climate action. This research provides a robust framework for sustainable watershed management in similar regions worldwide. © 2025 by the authors.