Climate change has significantly influenced regional precipitation patterns, affecting agricultural productivity and water-resource sustainability in monsoon-dependent regions. Reliable climate projections are therefore essential for climate impact assessment and adaptation planning. However, the performance of Regional Climate Models (RCMs) varies considerably across regions and climatic conditions, necessitating rigorous evaluation before their application in local-scale studies. In this study, eight CORDEX-CORE South Asia (0.22°) RCMs were evaluated for their ability to reproduce historical precipitation over Thanjavur district, Tamil Nadu, India, during 1976–2005 using observed rainfall data from 13 rain gauge stations. Model performance was assessed using seven statistical metrics integrated through the Compromise Programming Index (CPI), together with evaluations of precipitation occurrence, rainfall intensity occurrence, and cumulative precipitation distribution. The results revealed considerable variability among the RCMs. RegCM-based models generally exhibited wet biases, whereas REMO and CCLIM tended to underestimate seasonal rainfall. Based on the selected metrics and CPI framework, NorESM–RegCM, NorESM-CCLIM, and NorESM-REMO demonstrated comparatively better overall performance. However, analyses of precipitation occurrence and distribution showed that model performance depended on the precipitation characteristic considered, with no single RCM consistently outperforming the others across all evaluation criteria. These findings demonstrate that a multi-criteria evaluation framework provides a more comprehensive assessment of RCM performance than conventional statistical metrics alone and offers a robust basis for selecting suitable RCMs for future climate projections and agro-hydrological applications in the Cauvery Delta region.

Paddy cultivation forms the backbone of agriculture in the Cauvery Delta of Tamil Nadu, India; however, long-term monitoring of its spatial dynamics remains limited. This study employed multi-temporal Landsat imagery and machine learning techniques to analyse the spatiotemporal dynamics of paddy cultivation in Thanjavur district over a 23-year period (1995–96, 2007–08, and 2018–19), while an additional reference year (2015–16) was used for independent validation. Three machine learning classifiers, namely Random Forest (RF), Gradient Tree Boosting (GTB), and Support Vector Machine (SVM), were evaluated using Recall, Precision, Overall Accuracy (OA), F1 Score, and Kappa Coefficient (KC). The RF classifier achieved the highest classification accuracy during 1995–96 (OA = 0.84, KC = 0.68), 2007–08 (OA = 0.90, KC = 0.81), and 2015–16 (OA = 0.90, KC = 0.80), whereas GTB showed superior performance during 2018–19 (Recall = 0.85, OA = 0.82, F1 Score = 0.83, KC = 0.65). The classification results were validated using independently sourced Ground Control Points (GCPs) for 2015–16 and official Agriculture Department acreage statistics, demonstrating strong agreement with the reference data. The results revealed a 6.72% decline in paddy cultivation area (approximately 228 km²) between 1995–96 and 2018–19, accompanied by an expansion of non-paddy and urban land uses. Spatially, paddy cultivation became increasingly concentrated in the western and central parts of the district along the Cauvery River, primarily due to groundwater depletion, irrigation constraints, rapid urbanisation, and increasing climate variability. The findings demonstrate the effectiveness of ensemble-based machine learning approaches for large-scale agricultural monitoring and provide a transferable framework for assessing long-term land-use changes, thereby supporting food security, climate adaptation, and sustainable water resource management in monsoon-dependent deltaic regions.

Accurate precipitation estimation is critical for climate modelling, hydrological analysis, and agricultural planning. However, ground-based precipitation measurements are often geographically sparse or temporally inconsistent, especially in underdeveloped nations. This study compares the performance of four widely used precipitation datasets, including APHRODITE, IMD gridded (gauge-based), ERA5 (reanalysis), and CHIRPS (satellite-based), with independent in situ rain gauge data from Tamil Nadu’s agriculturally critical Cauvery Delta region over 20 years (1986–2005). The datasets were evaluated on an annual, seasonal, and monthly basis using eight statistical performance indicators (Mean Bias Error, Correlation Coefficient, Percent Bias, Root Mean Square Error, Index of Agreement, Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency, and Volumetric Efficiency) and ranked using the Compromise Programming Index. Results show that APHRODITE consistently outperforms others in replicating regional precipitation climatology across all temporal scales, despite a tendency to underestimate intensity. The IMD gridded and CHIRPS datasets demonstrated moderate reliability, with constant underestimation and minor overestimation, respectively, but ERA5 revealed the most significant variability and least dependability. These results are further confirmed with the Taylor diagram, which highlights the need for multi-timescale and multi-metric evaluation for region-specific applications. The work highlights APHRODITE’s potential to complement in situ observations in data-scarce areas, allowing for more precise planning for agricultural, water, and climate resilience in the Cauvery Delta and other monsoon-driven areas.

In the context of increasing urbanisation, it is vital to understand the factors driving urban expansion to ensure balanced and sustainable urban growth. However, obtaining precise data on the factors influencing urban expansion is difficult. Nonetheless, studying the patterns and intensities of urban growth can indirectly provide valuable insights. Hence, this study investigates urban growth patterns and intensities in Salem, India, over the last three decades by examining infill, sprawl, scattered, and ribbon development patterns through spatial analysis. The research addresses two critical gaps: the need for a simplified predictive model for urban growth in limited data scenarios and an appropriate methodology to map ribbon development in the Indian context. The results revealed a significant increase in built-up areas between 2011 and 2020, with ribbon development emerging as the most common type of pattern. The findings further demonstrate increasing urban growth on the city’s periphery, impacting agricultural land and damaging the local economy. The study also found that neighbouring towns like Omalur, Rasipuram, Sankari, and Vazhapadi influence Salem’s urban growth patterns. These changes are due to the dynamic interaction of population expansion, accessibility, agricultural land, and urban development issues. Urban Expansion Intensity Index (UEII) quantifies the intensity and provides additional information on the expansion rate. The study classifies growth rate into five categories: very slow, slow, medium, high, and very high. Between 2001 and 2011, high-intensity values were most common in the core regions of Salem and Omalur. However, these values dispersed over the subsequent decade, becoming more common in suburban areas. The study provides a new model for predicting growth based on patterns and intensity. A unique buffer tool for creating regions surrounding existing buildings generated areas with an error margin of less than 0.1 sq. km. This resulted in a spatial agreement of 74.11

A reconstruction of paleoflood stages reflects the magnitude and frequency of historic floods. Sediment-filled landforms store centuries-old paleoflood data, allowing examination of past events, though river changes and human activities can obscure these valuable records. Hence, identifying the plausible locale for collecting the sediment core is cumbersome. The present research proposes a methodological approach for precisely identifying the repositories. The Cauvery Delta, the largest sediment deposit on Tamil Nadu's eastern coast, is chosen for the study. The study's methodology is structured into: (1) reconstructing a catalogue of significant flood events using documentary records; (2) mapping fluvial geomorphic landforms using satellite images; (3) spatially correlating the records obtained from documentary sources with landforms, and (4) identifying flood geomorphic landforms (FGL) and demarcating promising prospective locales for future chronological studies. It has been observed that the Cauvery River has experienced recurrent instances of flooding throughout the past 8000 years. The FGL mapped using digitally processed satellite images displayed 17 types of landforms. Subsequently, the FGL are precisely identified by spatially integrating documentary data with landforms. Braided bar, channel bar, lateral bar, channel islands, natural levees, paleochannels, older flood plains, point bars, oxbow lakes, and water bodies are the most promising FGL for paleoflood research.

This research examines the significance of restoring efficient water management systems in India’s semiarid environment, with special emphasis on the role of traditional irrigation structures, such as tanks, in collecting and storing limited water resources. Assessing the benefits of any restoration program, especially when socioeconomic and environmental benefits are involved, is challenging. In the context of tank rehabilitation, a cost-benefit analysis will be conducted regarding economic and ecological returns in the post-desiltation phase. Since the restoration process requires a significant investment, assessing the project’s viability during the planning stage is better. The present study proposes a novel method to indirectly analyse the cost-benefit of the tank restoration process by correlating run-off and storage capacity of tanks before the planning phase. The Ambuliyar sub-basin, which covers an area of 930 square kilometres in Tamil Nadu, India, comprising 181 tanks (water bodies) of varying sizes and shapes, was taken for this study. This study employed the Soil Conservation Service Curve Number (SCS-CN) method, incorporating factors such as soil type, land cover, land use practices, and advanced remote sensing and Geographic Information System (GIS) tools to simulate surface run-off. Run-off volume and tank capacity were compared for all seasons at the micro-watershed level. The results demonstrated that the run-off volume in each micro-watershed significantly exceeded the tank capacity across all seasons. Even during the summer, the run-off volumes in the micro-watershed were considerably higher than the tank capacity. The findings suggest tank restoration can effectively store run-off and significantly fulfil agricultural and other essential needs throughout the year, thereby improving the local rural economy. This study also highlights the need for periodic maintenance and rehabilitation of these tank systems to retain their functionality.

A flood susceptibility assessment is crucial for identifying areas that are susceptible to flooding. This task usually uses models, but prior flood susceptibility assessment models focused on the frequency or duration of floods, not both. Integrating the frequency and duration of floods in susceptibility assessment could provide a more accurate picture of flood susceptibility. This study aimed to utilise and assess a novel integrated model that considers the frequency and duration of floods to categorise vulnerability/susceptibility zones. This study focuses on the multi-hazard zone between Cuddalore and Sirkazhi on the east coast of Tamil Nadu, India. Sentinel-1 A and RISAT-1 A Synthetic Aperture Radar (SAR) images were analysed using the Classification and Regression Tree (CART) classifier. Eight SAR images were used to study the persistence and temporal evolution of flooding over 49 days in 2015, along with multi-temporal datasets for 2015, 2018, and 2019. The classification of flood-susceptibility zones based on the frequency and duration of flooding yielded an accuracy of 0.87, whereas the integrated model scored 0.96 in all matrices. The hybrid integrated analysis provided a comprehensive understanding of the area’s flooding system, identifying the southern part of the study area as the most susceptible. The proposed model recommends a frequency-duration-based approach to demarcate flood susceptibility zones and potentially improve flood susceptibility assessments and management strategies.

Floods are highly destructive natural disasters. Climate change and urbanization greatly impact their severity and frequency. Understanding flood causes in urban areas is essential due to significant economic and social impacts. Hydrological data and satellite imagery are critical for assessing and managing flood effects. This study uses satellite images, climate anomalies, reservoir data, and cyclonic activity to examine the 2015 and 2023 floods in Chennai, Kanchipuram, and Thiruvallur districts, Tamil Nadu. Synthetic-aperture radar (SAR) satellite data were used to delineate flood extents, and this information was integrated with reservoir data to understand the hydrological dynamics of floods. The classification and regression tree (CART) model delineates flood zones in Chennai, Kanchipuram, and Thiruvallur during the flood years. The study region is highly susceptible to climatic events such as monsoons and cyclones, leading to recurrent flooding. The region’s reservoirs discharged floodwaters exceeding 35,000 cubic meters per second in 2015 and 15,000 cubic meters per second in 2023. Further, the study examines the roles of the Indian Ocean Dipole (IOD), which reached its peak values of 0.33 and 3.96 (positive IOD), and El Niño in causing floods here. The complex network of waterways and large reservoirs poses challenges for flood management. This research offers valuable insights for improving the region’s flood preparedness, response strategies, and overall disaster management.

Urbanization is one of the biggest challenges for developing countries, and predicting urban growth can help planners and policymakers understand how spatial growth patterns interact. A study was conducted to investigate the spatiotemporal dynamics of land use/land cover changes in Salem and its surrounding communities from 2001 to 2020 and to simulate urban expansion in 2030 using cellular automata (CA)–Markov and geospatial techniques. The findings showed a decrease in aerial vegetation cover and an increase in barren and built-up land, with a rapid transition from vegetation cover to bare land. The transformed barren land is expected to be converted into built-up land in the near future. Urban growth in the area is estimated to be 179.6 sq km in 2030, up from 59.6 sq km in 2001, 76 sq km in 2011, and 133.3 sq km in 2020. Urban sprawl is steadily increasing in Salem and the surrounding towns of Omalur, Rasipuram, Sankari, and Vazhapadi, with sprawl in the neighboring towns surpassing that in directions aligned toward Salem. The city is being developed as a smart city, which will result in significant expansion and intensification of the built-up area in the coming years. The study’s outcomes can serve as spatial guidelines for growth regulation and monitoring.

BACKGROUND:This study is based on blow fly samples collected from 8 medico-legal cases in Tamil Nadu, India. The fly life stages were identified and the consistency of minimum post-mortem intervals (PMImin) estimated by different thermal summation-based methods was assessed.METHODS:PMImin of 8 medico-legal cases was estimated using six different thermal summation constants and lower developmental temperatures that are based on C. megacephala and C. rufifacies developmental data. Limits of agreement (LoA), intra class correlation coefficient (ICC) between PMImin values and margin of error of mean of difference between PMImin values were calculated.RESULTS:Intra-class correlation between the PMImin values estimated using different thermal summation constants based on C. megacephala ranged between 0.89 and 0.98 and coefficient of determination ranged between 0.93 and 0.98. Intra-class correlation between the PMImin values estimated using different thermal summation constants based on C. rufifacies ranged between 0.91 and 0.99 and coefficient of determination ranged between 0.95 and 0.99. The mean difference of PMImin values estimated using different thermal summation methods based on C. megacephala ranged between 1.8 hr and 6.6hr and margin of error ranged between 2.51 and 6.93hr. The mean difference of PMImin values estimated using different thermal summation methods based on C. rufifacies ranged between 3.33 and 31.33hr and margin of error ranged between 4.66 and 32hr.CONCLUSION:Consistency of PMImin values estimated by different thermal summation methods was good to excellent. Thermal summation constants useful in estimation of PMImin with lowest mean difference and margin of error were described.

The use of regional climate models in climate change risk assessments is uncertain because of the threat of biases. Prior to using simulations, some corrections must be made. The aim of this study is to evaluate the effectiveness of simple and complex correction approaches on CORDEX (Coordinated Regional Climate Downscaling Experiment) precipitation datasets over Thanjavur in Tamil Nadu, India. The various bias correction approaches were assessed using the two-sample cross-validation technique, and the best method was chosen using statistical metrics such as the correlation coefficient, root mean square error, and percent bias for correcting the future scenario datasets. According to the findings, all methods significantly improved raw simulated estimations after correction. The Delta Change method was used to adjust the RCP2.6 and RCP8.5 scenarios datasets for this study region because it produced good agreement (0.98) and low wet bias (1

Changing climate leads to increased natural hazards like cyclone, flood and drought, in turn, cause severe damage to humanity and his dwellings. Thus, climate change projection is crucial for predicting natural disasters in advance and creating awareness and readiness among people. This research is aimed to project the precipitation using the Coordinated Regional Climate Downscaling Experiment (CORDEX) Regional Climate Model (RCM) simulations from 2021 to 2100 for Thanjavur district. The simulation’s biases hinder the usage of the model as a direct input data for climate change and impact studies. Hence, the bias correction was done using the Delta Change method with 30 years of historical data. The result shows that the intensity of precipitation gradually increases by 110.08 mm in near range, 115.76 mm in mid range and 187.69 mm in the far range, especially increases towards the east in the study area. The possibility of significant flood events during 2050, 2093 and 2098 and drought during 2025, 2055 and 2080 is envisaged. The study can be a useful tool for environmentalist, urban planners and policymakers of disaster management.

The utilisation of the regional climate models in climate change impact assessments is challenging owing to the threat of bias. Prior to using RCM simulations for developing future climate scenarios, some corrections must be performed. This study aims to evaluate the performance of the bias correction techniques (linear scaling, delta change, variance scaling and distribution mapping methods) using CORDEX (Coordinated Regional Climate Downscaling Experiment) simulated temperature datasets over Thanjavur district. Various statistical metrics are used to assess the performance of the bias correction methods against the observed temperature data. After bias correction, all methods greatly improved the raw RCM estimations. However, the distribution mapping exhibits good agreement than others since it corrects average, standard deviation and quantiles. The future minimum and maximum temperatures are projected from 2025 to 2100 under both RCP4.5 and 8.5 scenarios. Comparing with observed data, the results show that in the twenty-first century, the annual mean minimum temperatures are expected to rise about 1.06–1.38 °C, 1.58–2.12 °C, 1.99–2.32 °C (RCP4.5), and 1.59–2.16 °C, 2.65–2.71 °C, 3.73–3.87 °C (RCP8.5) in the near, mid, and far ranges, respectively. Also, the annual mean maximum temperatures are estimated to rise by around 0.54–1.11 °C, 0.91–1.07 °C, 0.64–1.42 °C (RCP4.5), and 0.67–1.25 °C, 1.41–1.63 °C, 2.28–2.51 °C (RCP8.5) in the near, mid, and far ranges, respectively. Due to the projected higher temperatures in both RCP scenarios, the state’s agricultural production, food security, ecosystems and the environment would be affected, and acute weather events such as heat waves and droughts would occur more frequently with higher severity. Thus, this study provides unambiguous details on future temperatures to the environmentalists, policy-makers of water resource and disaster management.

Urban dynamics refers to a phenomenon wherein certain factors contribute to imparting changes to an urban area. These factors can aid in either growth or deterioration of the city. One important factor that acts as a threat to the urban environment is rapid urbanization. To monitor and control such urban expansion, prediction is a necessity. It will throw some light on how the city grows and how it will affect the environment and living conditions. A precise and accurate dataset on land use/land cover (LULC) is a must for such analysis. Several methods exist to classify satellite data based on spectral reflectance with advancements in satellite technology and the means to process it. One problem is that there is no single classifier that can produce accurate results on LULC predictions. Each classifier varies in its performance based on several factors. Hence this research was carried out by choosing three classifiers, namely maximum likelihood (MLC), random forest algorithm (RFA) as well as support vector machines (SVM), which are commonly used. Its performance is then evaluated for a pan-sharpened Landsat 8 data of the study area, i.e., Salem and its surrounding urban agglomerations. From the results, it was inferred that the overall accuracies of MLC, SVM and RFA are 0.885, 0.930 and 0.945, respectively. Though the MLC accuracy is acceptable, it had many misclassifications of other classes into built-up classes. SVM and RFA performances were found good overall, but SVM had fewer misclassifications compared to RFA. SVM produced results close to reality and was concluded as the best classification method for pan-sharpened Landsat 8 data for Salem and its surrounding area.

Background: The present study is based on the necrophagous fly samples collected from 24 medico-legal cases between the year 2011 and 2018 in Tamil Nadu State, India. The fly life stages were identified based on morphological features. Pre-autopsy condition of the human corpse colonized by necrophagous flies and indoor/outdoor occurrence of the flies were recorded. Results: Chrysomya megacephala, Chrysomya rufifacies, Sarcophaga spp, and Musca domestica life stages were collected from the human corpses. Chrysomya megacephala was the most prevalent (70.8%) insects of forensic importance and found both in indoor and outdoor environments. Drowned and burnt human corpses were found to be only colonized by C. megacephala. Chrysomya rufifacies was found only in outdoor environments and Sarcophaga spp was found only in indoor environment. There was a fair agreement between the percentage occurrence of necrophagous flies in human corpse in the present study in Tamil Nadu State and percentage historical occurrence of necrophagous flies in human corpse in India. Conclusions: Chrysomya megacephala was the predominant blowfly species found to colonize corpses in Tamil Nadu State, India. Chrysomya megacephala was the only blowfly species found to colonize both burnt and floating corpses and corpses located indoor and outdoor.

In recent years, groundwater has become the major source of drinking water in both rural and urban India. But overexploitation and nonrejuvenation are threatening groundwater resources. Precision quantitative assessment based on generally valid scientific principles is critical for long-term development. In recent times, indirect proxies like remote sensing and geographic information system techniques are extensively utilized were used to determine the potentiality. For quantitative assessment, analytical methods are employed, and amongst Analytical Network method proven effective especially where multiple parameters interact. The present study aims to identify the potential groundwater zones of The Ramanathapuram, Tirupulani & Mandapam block of the Ramanathapuram district, Tamil Nadu, India, using the ANP modeling technique. Surface features such as geomorphology, soils, land use/land cover, and slope which act as indicators of groundwater existence were considered for analysis. To identify the groundwater potential zone, the analytical hierarchy process (AHP) method is utilized to establish the weights of various themes and their classes. The derived weights were assigned to their respective parameters. Then all the layers were integrated, thereby surface parameters-based groundwater potential zone map was prepared. The results showed that only 47.07% of the land was categorized as having very excellent groundwater potential, 33.56% as having moderate groundwater potential, and 13.87% as having low groundwater potential.

Growing agricultural, industrial, and residential needs have increased the demand for groundwater resources. Targeting groundwater has become a challenging endeavour because of the complex interplay between varying climatic, geological, hydrological, and physiographic elements. This study proposes a hybrid RS, GIS, and ANP method to delineate groundwater zones. The resource was evaluated using seven surface hydrological and six subsurface aquifer parameters. The analytic network process model was used to determine the global priority vectors of each subclass. Surface and subsurface groundwater potential maps were created by assigning the resulting weights and spatially integrating them. Later, an integrated potential map was created by combining them. The validation of the obtained results using water level data demonstrates that the integrated map accurately predicted the zones. The area under study has 172.94 km(2) of good groundwater potential. An area of 393.01 km(2) is classified as having a moderate potential, and an area of 410 km(2) is classified as having low potential. These findings will be beneficial to regional policymaking and long-term groundwater management. The results show that an integrated approach using ANP can better determine the groundwater potential zones in semi-arid zones.

The tanks of South India have a higher demand in agricultural, industrial and domestic sectors. Notably, in the state of Tamil Nadu, there are around 39,000 tanks with varying size and capacity, meant for storing and supplying water towards multifunctional needs of the people. But, the recent statistics show a decreasing trend in their effective utilization. Amongst various reasons for declination, the loss in their storage capacity seems to be the prime cause. But for estimating the storage capacity, extensive field measurements are mandate. However, studies shows that a broad correlation can be established between the capacity and the water spread area of tanks. The water spread area can be precisely mapped using remotely sensed images. By periodically analyzing the changes, the loss in their water spread area can be qualitatively assessed and also the deteriorated tanks can be identified. Accordingly, the Ambuliyar watershed falling in parts of Tamil Nadu is studied. Despite having 809 tanks, the area is now designated under the semi-critical category. The actual extent of the tanks during the year 1972 was mapped using the Survey of India topographic sheets. Subsequently, their water spread area during 1988, 1995 and 2015 was mapped using remote sensing data. The perusal of the overall data shows that almost all the tanks have significantly reduced in their extent, and the reduction varies from 3.13% to 91.36%. Based on their percentage of shrinkage, they were categorized into high, moderate and low deteriorated tanks and accordingly the tanks can be prioritized for reclamation.

Groundwater is significant in satisfying domestic and agricultural needs.Besides scarcity, the groundwater resource is degrading drastically around the world. The Ambuliyar watershed falling in parts of Tamil Nadu also faces similar problems. To decipher the quality degradation, pre-monsoon and post-monsoon data on various physical and chemical parameters was collected for 29 wells for the year 2014 from Public Works Department. Spatial maps were generated on the above geochemical parameters and categorized into five classes using GIS software. Weights were assigned for each parameter based on their relative importance in with each other parameters. Finally, quality index map was generated by integrating them, and subsequently their aerial extent in monsoons was worked out. During the post-monsoon period, 18% of the study area represents “excellent”, 46% “good”, 25% shows “moderate” and 11% shows “poor” quality. While during pre-monsoon period, 34% of the area exhibits “excellent”, 43% “ moderate”, and the remaining 23% of “poor”.