Assessing climate change impacts with downscaling techniques: A case study

The review focuses on the intersection of climate change, water resources, and downscaling techniques. It covers various studies that examine the impact of climate change on hydrological models, agricultural productivity, and socio-economic conditions, particularly in regions like Bangladesh and India. Key studies include the evaluation of different downscaling techniques, such as Statistical Down Scaling Model (SDSM) and Artificial Neural Networks (ANNs), to project climate variables and their effects on water resources. The review also highlights the importance of downscaling methods in providing high-resolution climate data necessary for impact assessments. Additionally, it discusses the statistical analysis of climate trends, including temperature and rainfall variability, and their implications for water management and agricultural adaptation strategies. Through a comprehensive examination of past research, the review underscores the critical need for accurate climate modeling and downscaling to mitigate adverse climate impacts and inform sustainable resource management practices. This synthesis of existing literature provides a foundation for future research and policy development aimed at enhancing climate resilience in vulnerable regions.