Navigating the impact of climate change in India: a perspective on climate action (SDG13) and sustainable cities and communities (SDG11)

Frontiers in Sustainable Cities, Journal Year: 2024, Volume and Issue: 5

Published: Jan. 11, 2024

Climate change is a global concern of the current century. Its rapid escalation and ever-increasing intensity have been felt worldwide, leading to dramatic impacts globally. The aftermath climate in India has brought about profound transformation India's environmental, socio-economic, urban landscapes. In 2019, ranked seventh, among most affected countries by extreme weather events caused due changing climate. This impact was evident terms both, human toll with 2,267 lives lost, economic damage, which accounted for 66,182 million US$ Purchasing power parities (PPPs). Over recent years, experienced significant increase number frequency events, causing vulnerable communities. country severe air pollution problems several metropolitan cities highlighted list world's polluted cities. Additionally, become populous nation globally, boasting population 1.4 billion people, equating ~18% population, experiencing an increased rate consumption natural resources. Owing country's scenario, various mitigation strategies, including nature-based solutions, must be implemented reduce such support target achieving Sustainable Development Goals (SDGs). review tries holistic understanding effects on different sectors identify challenges SDG 13 11. Finally, it also future recommendations change-related research from Indian perspective.

Language: Английский

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Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(2)

Published: Jan. 4, 2024

Language: Английский

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Hydrological Responses to Climate Change and Land-Use Dynamics in Central Asia's Semi-arid Regions: An SWAT Model Analysis of the Tuul River Basin

Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 8(2), P. 297 - 323

Published: Jan. 22, 2024

Language: Английский

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Ecological monitoring of urban thermal field variance index and determining the surface urban heat island effects in Lahore, Pakistan

Environmental Monitoring and Assessment, Journal Year: 2023, Volume and Issue: 195(10)

Published: Sept. 14, 2023

Language: Английский

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Assessment of Ground Water Quality of Lucknow City under GIS Framework Using Water Quality Index (WQI)

Water, Journal Year: 2023, Volume and Issue: 15(17), P. 3048 - 3048

Published: Aug. 25, 2023

Continuous groundwater quality monitoring is crucial for ensuring safe drinking and irrigation by mitigating risks from geochemical contaminants through appropriate treatment methods. Therefore, the primary objective of this study was to assess suitability collected Lucknow, India, both irrigation. Forty samples were different sites within area evaluate quality. Various parameters such as pH, turbidity, total dissolved solids (TDS), chlorides (Cl−), alkalinity, hardness, sulphate (SO42−), nitrate (NO3−), fluorides (F−), iron (Fe), arsenic (As), magnesium (Mg2+), calcium (Ca2+) analyzed. The weighted arithmetic water index (WAWQI), a vital rating system representing overall quality, employed classify into categories, very good, moderate, poor, unfit drinking. This classification invaluable public awareness decision-making make informed decisions regarding effective management, treatment, sustainable societal development on broader scale. A correlation matrix generated analyzed observe correlations between various parameters. Additionally, spatial distribution maps WQI prepared using inverse distance (IDW) method. found that values in ranged 2.64 168.68, indicating good most places except Kukrail region, where purposes. map shows 86% falls under category, 14.63% moderate 0.37% categorized Consequently, findings suggest studied suitable

Language: Английский

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Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Earth, Journal Year: 2023, Volume and Issue: 4(3), P. 728 - 751

Published: Sept. 15, 2023

The main aim of this study is to comprehensively analyze the dynamics land use and cover (LULC) changes in Bathinda region Punjab, India, encompassing historical, current, future trends. To forecast LULC, Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, cultural. For segmenting a combination ML models, support vector machine (SVM) Maximum Likelihood Classifier (MLC). empirical nature, it employs quantitative analyses shed light LULC variations through time. result indicates that barren expected shrink from 55.2 km2 1990 5.6 2050, signifying better management or increasing human activity. Vegetative expanses, other hand, are rise 81.3 205.6 reflecting balance between urbanization ecological conservation. Agricultural fields increase 2597.4 2859.6 2020 before stabilizing at 2898.4 2050. Water landscapes 13.4 providing possible issues for water resources. Wetland regions decrease, thus complicating irrigation groundwater reservoir sustainability. These findings confirmed by strong statistical indices, with study’s high kappa coefficients Kno (0.97), Kstandard (0.95), Klocation (0.97) indicating reasonable level accuracy CA prediction. From F1 score, significant issue found MLC vegetation, resolved SVM classification. can be inform policy plans sustainable development beyond.

Language: Английский

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Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques

Modeling Earth Systems and Environment, Journal Year: 2023, Volume and Issue: 10(2), P. 2393 - 2419

Published: Dec. 16, 2023

Abstract Climate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including Himalayan foothill region India. The Jia Bharali River basin, situated this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to environment local communities. Developing accurate reliable susceptibility models is crucial for effective prevention, management, adaptation strategies. In study, we aimed generate a comprehensive zone model catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, Frequency Ratio model, Fuzzy Logic (FL) Multi-criteria Decision Making based Analytical Hierarchy Process evaluate of basin. results revealed that approximately one-third basin area fell within moderate very high flood-prone zones. contrast, over 50% was classified as low demonstrated strong performance, ROC-AUC scores exceeding 70% MAE, MSE, RMSE below 30%. FL AHP were recommended application among areas similar physiographic characteristics due their exceptional performance training datasets. This study offers insights policymakers, regional administrative authorities, environmentalists, engineers working region. By providing robust research enhances prevention efforts thereby serving vital climate strategy regions. findings also implications disaster risk reduction sustainable development areas, contributing global towards achieving United Nations' Sustainable Development Goals.

Language: Английский

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Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(9), P. 1637 - 1637

Published: May 3, 2024

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), use cover (LULC) changes, identification urban heat island (UHI) (SUHI) phenomena. This research focuses on nexus between LULC alterations variations in LST air (Tair), with a specific emphasis intensified SUHI effect Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat MODIS satellites, alongside Tair records, utilizing machine learning techniques linear regression analysis. Key findings indicate statistically significant upward trend during summer months 1984 to 2023, notable positive correlation across both datasets. exhibit stronger (R2 = 0.879) compared 0.663). The application supervised classification through Random Forest algorithms vegetation indices reveals alterations: 70.3% increase decrement vegetative comprising 15.5% reduction dense 62.9% decrease sparse vegetation. Change detection analysis elucidates 24.6% conversion land, underscoring pronounced trajectory towards urbanization. Temporal seasonal different classes were analyzed using kernel density estimation (KDE) boxplot Urban areas had smallest average fluctuations, at 2.09 °C 2.16 °C, respectively, but recorded most extreme values. Water exhibited slightly larger fluctuations 2.30 2.24 bare class showing highest fluctuation 2.46 fewer extremes. Quantitative Kolmogorov-Smirnov tests various substantiated normality distributions p > 0.05 monthly annual Conversely, Shapiro-Wilk test validated normal distribution hypothesis exclusively data, indicating deviations data. Thresholded classifies lands as warmest 39.51 38.20 water 35.96 35.52 37.71 coldest, which is that consistent annually monthly. effects demonstrates UHI intensity, statistical trends growth values over time. comprehensive underscores role remote understanding addressing urbanization local climates, emphasizing need sustainable planning green infrastructure mitigate effects.

Language: Английский

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Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan

Agronomy, Journal Year: 2024, Volume and Issue: 14(2), P. 268 - 268

Published: Jan. 25, 2024

Changes occurring because of human activity in protected natural places require constant monitoring land use (LU) structures. Therefore, Korgalzhyn District, which occupies part the State Natural Reserve territory, is considerable interest. The aim these studies was to analyze changes composition use/land cover (LULC) District from 2010 2021 and predict LU transformation by 2030 2050. Landsat image classification performed using Random Forest on Google Earth Engine. combined CA-ANN model used LULC 2050, were carried out MOLUSCE plugin. results showed that 2021, there a steady increase share ploughable an adequate reduction grassland. It established that, this trend will continue. At same time, be no drastic other classes. obtained can helpful for development management plans policies District.

Language: Английский

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A review of urban heat island mapping approaches with a special emphasis on the Indian region

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(4)

Published: March 8, 2025

Language: Английский

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