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Environmental Data Analytics for Climate Change Assessment

Author(s) Dr. Sofia M. Alvarez
Country Spain
Abstract Environmental data analytics has emerged as an important approach for understanding climate change, environmental variability, and associated ecological and socioeconomic impacts. The increasing availability of satellite observations, remote sensing products, meteorological measurements, oceanographic datasets, sensor networks, geographic information systems, and climate-model outputs has created substantial opportunities for data-driven climate assessment. This study examines the role of environmental data analytics in climate change assessment, with particular emphasis on data integration, climate trend analysis, environmental monitoring, predictive modelling, extreme-event assessment, and decision support. A qualitative descriptive and conceptual research design was adopted using secondary academic literature.
The analysis examines how statistical methods, machine learning, geospatial analytics, time-series analysis, and visualization techniques can transform complex environmental datasets into meaningful information for climate assessment. Environmental data analytics can support the identification of temperature trends, precipitation changes, drought conditions, sea-level variations, vegetation changes, and other climate-related indicators. Integration of satellite imagery and ground-based observations can improve spatial coverage and facilitate monitoring of environmental changes across large geographical areas. However, effective climate assessment requires careful attention to data quality, missing observations, spatial and temporal inconsistencies, uncertainty, model validation, computational requirements, and methodological transparency.
The study emphasizes integrated analytical frameworks that combine environmental observations, statistical techniques, intelligent technologies, and geospatial information. Such frameworks can strengthen evidence-based assessment of climate impacts and support adaptation, environmental management, resource planning, and sustainable development. Future research should focus on interoperable environmental datasets, explainable analytical models, real-time monitoring, uncertainty quantification, high-resolution spatial analysis, and interdisciplinary data integration to improve the reliability and practical usefulness of climate change assessments.
Keywords Environmental Data Analytics, Climate Change, Climate Assessment, Environmental Monitoring, Remote Sensing, Geospatial Analytics, Machine Learning, Climate Data, Predictive Analytics, Sustainability
Field Engineering
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-31
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