Climate change is fundamentally transforming global water systems, affecting the availability, quality, and ecological dynamics of water resources. This review synthesizes current scientific understanding of climate change impacts on hydrological systems, with a focus on freshwater ecosystems, and regional water availability. Rising global temperatures are disrupting thermal regimes in rivers, lakes, and ponds; intensifying the frequency and severity of extreme weather events; and altering precipitation and snowmelt patterns. These changes place mounting stress on aquatic ecosystems, threaten water security, and challenge conventional water management practices. The paper also identifies key vulnerabilities across diverse geographic regions and evaluates adaptation strategies such as integrated water resource management (IWRM), the water, energy and food (WEF) nexus, ecosystem-based approaches (EbA), the role of advanced technology and infrastructure enhancements. By adopting these strategies, stakeholders can strengthen the resilience of water systems and safeguard critical resources for both ecosystems and human well-being.

Flood impacts are intensifying due to the increasing frequency and severity of factors such as severe weather events, climate change, and unplanned urbanization. This study focuses on Briar Creek in Charlotte, North Carolina, an area historically affected by flooding. Three machine learning algorithms —bagging (random forest), extreme gradient boosting (XGBoost), and logistic regression—were used to develop a flood susceptibility model that incorporates topographical, hydrological, and meteorological variables. Key predictors included slope, aspect, curvature, flow velocity, flow concentration, discharge, and 8 years of rainfall data. A flood inventory of 750 data points was compiled from historic flood records. The dataset was divided into training (70%) and testing (30%) subsets, and model performance was evaluated using accuracy metrics, confusion matrices, and classification reports. The results indicate that logistic regression outperformed both XGBoost and bagging in terms of predictive accuracy. According to the logistic regression model, the study area was classified into five flood risk zones: 5.55% as very high risk, 8.66% as high risk, 12.04% as moderate risk, 21.56% as low risk, and 52.20% as very low risk. The resulting flood susceptibility map constitutes a valuable tool for emergency preparedness and infrastructure planning in high-risk zones.

Sentinel-1 SAR and JRC water inputs feed a UNet++/EfficientNet-B4 model, producing a 3-class flood map (background, permanent water, flood) for a river bend, applied to disaster response; macro IoU 0.734, F1 0.829. Accurate and timely flood mapping is essential for disaster response, humanitarian coordination, and infrastructure protection. Optical satellite data are often limited during flood events because of cloud cover and heavy rainfall, making Synthetic Aperture Radar (SAR) valuable for all-weather, day-and-night inundation monitoring. This study introduces a deep learning framework for automated flood mapping using Sentinel-1 SAR imagery, based on UNet ++ with a pre-trained EfficientNet-B4 encoder. Flood mapping is formulated as a three-class pixel-wise segmentation problem, distinguishing background, permanent water, and floodwater. The model uses three input channels: VV backscatter, VH backscatter, and a co-registered JRC Global Surface Water permanent water mask. This hydrological prior helps the network separate permanent water from event-driven inundation without requiring additional SAR acquisitions, optical imagery, or terrain data. The model was trained and tested using the Sen1Floods11 benchmark, containing 445 hand-labelled Sentinel-1 tiles from multiple global flood events. It was also evaluated on a held-out Bolivia flood event to assess geographic generalization and as an additional real-world demonstration, the trained model was applied to a Sentinel-1 image acquired over the Koshi Madesh Region flood affected area in Nepal in September 2024. To address class imbalance, a combined Focal and Dice loss was used with AdamW optimisation. The proposed model achieved macro IoU = 0.7340, macro F1 = 0.8294 on Sen1Floods11, and generalised well to Bolivia with macro IoU = 0.6772 and macro F1 = 0.8026. Macro IoU and macro F1 are computed over the permanent water and flood classes, excluding the background class. In Nepal, the model captured the overall water distribution but showed limited strict agreement with UNOSAT, with much of the discrepancy associated with the distinction between permanent and flood water, particularly in the complex braided channels of the Koshi River.