Manish Kumar Goyal

Inland wetlands across Central India represent a critical nexus between monsoon‐driven hydrology, climate variability, and intensifying anthropogenic pressures. Yet, their contribution to regional water security and climate resilience remains insufficiently quantified. This study presents a structured narrative synthesis of climate change impacts on wetlands in Madhya Pradesh and Chhattisgarh by integrating hydrological observations, biodiversity assessments, greenhouse gas (GHG) emissions, land‐use dynamics, and reservoir sedimentation studies. Regional warming and increasing variability in monsoon rainfall are altering wetland hydroperiods, intensifying evapotranspiration, and disrupting sediment–nutrient interactions that regulate wetland functioning. Empirical observations from Ramsar wetlands and major reservoirs reveal widespread ecological degradation. Methane emissions exceeding 160 mg m ⁻² h ⁻¹ have been reported in shallow wetlands, while reservoir systems exhibit significant storage losses due to sedimentation. Despite these pressures, wetlands such as Bhoj, Yashwant Sagar, Tawa, and Ravishankar Sagar continue to sustain domestic water supply, irrigation, fisheries, and migratory bird habitats. However, rapid urban expansion and catchment alteration increasingly undermine their buffering capacity. Strengthening wetland resilience through basin‐scale, integrated management approaches is essential to support water security and advance Sustainable Development Goals on clean water, climate action, and ecosystem conservation. These approaches should incorporate climate projections, continuous monitoring, and coordinated governance to improve decision‐making and long‐term sustainability.

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.

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.

Climate change, characterized by erratic rainfall patterns and rising temperatures, is enhancing the severity and intensity of drought across India. Considering this impact, assessment of these changes is critical for developing effective adaptation and mitigation strategies. This study employs a stochastic entropy-based approach to compute the inter- and intra-annual rainfall variability for multiple temporal scales. Considering this assessment, we identified winter, pre-monsoon, and post-monsoon seasons as key contributors to annual rainfall variability. Furthermore, using the entropy approach, we prepared a water resource availability map. It presents that 53.88% of the Betwa's water resources and 54.57% of the Kshipra's resources fall under Classes B and C, highlighting significant vulnerability to drought conditions in both basins. Subsequently, meteorological and hydrological droughts were evaluated using SPI, SPEI, and SRI indices. It showed that the Southern and Central regions of the Kshipra and the Northern and Central regions of the Betwa are highly drought prone. The outcome of the present study provides vulnerable drought regions with prolonged duration and high severity and intensity regions across the basin. It will support policymakers in implementing timely and targeted interventions for water resource management, facilitating the development of effective drought action plans.