Abstract Wetlands are often found in depressions or around rivers, lakes, and coastal seas, where they periodically flood. Hence, this study presents a new techno‐societal framework for quantifying the risk of heavy rainfall events (HREs) on wetland ecosystems and surrounding communities in India. By integrating advanced technological approaches such as Bayesian analysis, fuzzy logic, and remote sensing with societal considerations, we provide a comprehensive assessment of wetland vulnerability to climate change impacts. An effort has been made to understand the non‐stationarity of HREs, inundation patterns of wetlands, impact evaluation, and future precipitation trends (CMIP6). The overall assessment of the extreme precipitation indices indicated that return periods were highest for Thane Creek, followed by Bhoj Wetland. We also assessed the risk index based on the parameters of hazard, vulnerability, and exposure for all wetlands using the fuzzy logic approach. The overall risk index evaluation indicated that Bhoj Wetland, Thane Creek, Point Calimere, Deepor Beel, Sasthamkotta Lake, and Vembannur Wetland are at “very high” risk. The study also investigated inundation patterns of critical “very high” risk wetlands and conducted an impact evaluation for the Bhoj Wetland, highlighting the influence of HREs on infrastructures, human settlements, and ecosystems. Wetlands such as Point Calimere, Vembannur Wetland, Karikili Bird Sanctuary, Vendanthangal, and Vaduvur Bird Sanctuary showed a significantly increasing trend in precipitation for both historical and future SSP2‐4.5 scenario. These findings are useful in decision‐making for policymakers to adopt the best practices to manage the local wetlands wisely.
Potential competing interests: No potential competing interests to
No abstract is provided for this article.
The ecological significance of wetlands makes it imperative to study changes in their inundation extent and propose necessary conservation measures. Monitoring wetland dynamics and implementing strategies to protect these essential ecosystems is crucial for maintaining the balance of natural systems. This study used pre-processed Landsat imagery (1991–2020) to generate yearly composites and produce inundation maps based on an automated Short-Wave Infrared thresholding technique within the Google Earth Engine platform. The analysis was executed on individual wetlands to describe their typical condition owing to regional climatic and geographical circumstances. The Mann-Kendall test was used to understand the trends in the change of inundation extent. The thresholding method achieved an overall accuracy of 89.0 %, with average dry and wet Producer's accuracies of 90.6 % and 86.6 %, respectively. The accuracy was higher for open water lakes compared to wetlands with complex vegetation dynamics. The trend analysis revealed that 46 sites follow an increasing trend, while the remaining 43 sites were found to be decreasing. Among these 43, 12 sites were found to be significantly decreasing, with the Upper Ganga River showing a maximum decrease of about 59 % in the inundation extent. Factors such as elevation, precipitation, temperature, and climate type were found to influence the trends in wetland inundation. Wetlands at high altitudes (>4000 m) and those receiving less than 500 mm of annual precipitation were more likely to exhibit decreasing trends. Coastal wetlands showed varying trends, with five increasing and three significantly increasing. The findings of this study provide valuable insights into the relationship between sustainable development and wetland conservation, supporting the Ramsar Convention's goals and the UN's Sustainable Development Goals. The individualized analysis of Ramsar sites enables the development of localized management strategies, climate change adaptation, and informed policy-making, ultimately contributing to the sustainable use of these critical ecosystems in South Asia.
Abstract Solid waste management in developing countries such as India faces persistent challenges due to weak monitoring systems and the absence of reliable reporting mechanisms for landfill statistics. To address this gap, this study develops a remote sensing methodology that integrates Python programming with the Sentinel Application Platform (SNAP) to generate Digital Elevation Models (DEMs) from Sentinel-1 synthetic aperture radar (SAR) imagery for quantifying landfill characteristics. Key parameters, including waste height and volumetric estimates, were extracted from satellite observations and processed through Google Earth Engine (GEE), enabling efficient large-scale analysis. A total of 80 landfill sites distributed across India were examined, providing the first nationwide assessment of landfill volume using a uniform and replicable framework. Field validation was conducted at two representative sites, Gondiya Landfill and Ujjain Ring Road Trenching Ground, through drone surveys and Differential Global Positioning System (DGPS) measurements. The evaluation showed deviations of 21.12% and 0.12% in height, 0.7% and 0.65% in area delineation, and 20.21% and 0.8% in volume for Gondiya and Ujjain, respectively, confirming the reliability of the proposed approach. These results demonstrate that SAR-based DEMs offer a cost-effective and scalable solution for systematic, near real-time monitoring of landfills across large regions. The framework not only supports capacity planning, environmental assessments, and policy formulation but also provides a pathway for developing countries to transition toward data-driven waste management strategies in the context of rapid urbanization and increasing waste generation.
Flash droughts, characterized by their rapid onset and intensification, pose significant risks to biodiversity and local populations, with extensive impacts on ecosystems and agriculture. This study presents a comprehensive framework to quantify flash drought risk across Key Biodiversity Areas (KBAs) in India from 1979 − 2021. By combining hazard, vulnerability, and exposure components, we found that 45.8 % of KBAs fall within the high-risk category, with inland forest areas and national parks showing higher vulnerability than coastal regions. Analysis of 42 freshwater KBAs revealed that 73.8 % face moderate to high risk from flash droughts, while significant correlations were observed between risk index and trends in tree cover loss and wildfire events. The average flash drought duration across KBAs was 34 days, with October being the most frequent onset month (18 %). Our findings indicate that high-risk areas with extensive cropland, such as the Jawaharlal Nehru Bustard Sanctuary (8316 km2), face substantial agricultural vulnerability where flash droughts could lead to rapid crop failure, affecting food security and local livelihoods. The study also revealed that proximity to large water bodies provides some protection against flash droughts, as coastal and marine protected areas mostly showed lower risk levels. These results emphasize the urgent need for targeted conservation strategies and policy reforms to address the challenges posed by flash droughts. Our framework provides a valuable tool for prioritizing conservation efforts and developing resilient ecosystem management plans in the face of increasing climate-related challenges.
Wetlands are one of the most critical components of an ecosystem, supporting many ecological niches and a rich diversity of flora and fauna. The ecological significance of these sites makes it imperative to study the changes in their inundation extent and propose necessary measures for their conservation. This study analyzes all 64 Ramsar sites in China based on their inundation patterns using Landsat imagery from 1991 to 2020. Annual composites were generated using the short-wave infrared thresholding technique from June to September to create inundation maps. The analysis was carried out on each Ramsar site individually to account for its typical behavior due to regional geographical and climatic conditions. The results of the inundation analysis for each site were subjected to the Mann-Kendall test to determine their trends. The analysis showed that 8 sites exhibited a significantly decreasing trend, while 14 sites displayed a significantly increasing trend. The accuracy of the analysis ranged from a minimum of 72.0% for Hubei Wang Lake to a maximum of 98.0% for Zhangye Heihe Wetland National Nature Reserve. The average overall accuracy of the sites was found to be 90.0%. The findings emphasize the necessity for conservation strategies and policies for Ramsar sites.
The phenomena of climate change and increase in warming conditions across the globe causes changes in the frequency and severity of extreme weather events. The present study analyzed extreme precipitation weather events across four Indian cities in different climatic conditions. The study uses IMD precipitation datasets from 1900 to 2004 to analyze different atmospheric influencing parameters like ENSO, AMO, and IOD on future extreme precipitation conditions. The Bayesian analysis is carried out for nonstationary analysis of extreme indices like Rx1 Day, SDII, R10, and CWD with a 10, 20, 50, and 100 years return period. Significant outcomes of the comparative study of the stationary and nonstationary analysis showed an intensification in extreme precipitation across Indian cities for all return periods using the CWD indicator. The remaining three indicators of the nonstationary study suggested intensifying extreme rainfall across all Indian cities except Guwahati.
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Wetlands are often found in areas that undergo periodic flooding, such as coastal seas, lakes, and rivers. Coastal wetlands are particularly vulnerable to climate change effects, such as changes in precipitation patterns, risk of extreme rainfall, and cyclones/storms. This study assessed the uncertainties associated with extreme rainfalls in terms of return levels (RLs; 20 and 50 years) and quantified the potential risk level of these events in the future for coastal wetlands in India. The extreme precipitation indices (EPIs) were evaluated using a non-stationary approach, and the results showed that Thane Creek had the highest RLs, followed by Kolleru Lake. The risk level for each wetland was assessed using the fuzzy logic approach, which considered parameters such as exposure, vulnerability, and threat. The overall risk assessment showed that Thane Creek, Kolleru Lake, Pallikaranai Marsh Reserve Forest, and Tampara Lake are at a “High” risk level for both RLs of EPIs. Furthermore, the automated Shortwave Infrared (SWIR) thresholding technique was employed in Google Earth Engine to create inundation maps of wetlands. This study also indicated that Thane Creek is at risk of flooding based on the analysis of spatiotemporal changes. The impact evaluation of Thane Creek showed that rapid urbanization has encroached upon the creek's boundaries. Therefore, the variability of EPIs may be affected by climatic oscillations, leading to an upsurge in extreme rainfalls, causing the coastal wetlands to flood. Policymakers can use these findings to develop effective strategies for the proper management of coastal wetlands.
No abstract is provided for this article.
