River salinisation and alkalinisation have become one of the major environmental problems threatening the safety of global freshwater resources. With the accelerated climate change and aggravating anthropogenic influences, it is important to identify the trends and causes of river salinisation and alkalinisation so that better mitigation measures could be taken. This study has focused on the UK rivers because there has been insufficient investigation on this topic. To understand the salinisation and alkalinisation trends and causes of rivers in the UK over the past 20 years from a vertical (analysis of each river) and horizontal (comparison of all rivers) perspective, this study uses the Theil-Sen regression and Mann-Kendall test to deal with the trends of conductivity (proxy on salinisation) and pH (proxy on alkalinisation), obtains outliers of conductivity and pH by boxplot, and calculates the Pearson’s and the Kendall’s Tau correlation coefficients (α = 0.05) between the water quality data and the potential factors (potential road salting, normalized difference vegetation index (NDVI), river discharge, agricultural and urban lands). The results show that the UK rivers are becoming more alkaline with a median pH increase of 0.05 to 0.40, but less salty with a median conductivity decrease of 0.06 to 0.11 mS/cm. And the changes in conductivity and pH have seasonality and regionality, which shows that there are usually greater changes in trends and medians of them in winter or through reaches with more anthropogenic disturbance. Furthermore, from a vertical perspective, the conductivity of more than 50% of rivers in this study is negatively correlated with NDVI and river discharge, and positively correlated with potential road salting, and the pH of that is positively correlated with agricultural lands. While from a horizontal perspective, NDVI and agricultural lands are positively correlated with pH, and potential road salting and urban lands are positively correlated with conductivity. Therefore, road salting, urbanisation, agricultural lands, river discharge and vegetation cover can be considered to affect river salinisation and alkalinisation in the UK.

High-resolution air temperature data is indispensable for analysing heatwave-related non-accidental mortality. However, the limited number of weather stations in urban areas makes obtaining such data challenging. Multi-source data fusion has been proposed as a countermeasure to tackle such challenges. Satellite products often offered high spatial resolution but suffered from being temporally discontinuous due to weather conditions. The characteristics of the data from reanalysis models were the opposite. However, few studies have explored the fusion of these datasets. This study is the first attempt to integrate satellite and reanalysis datasets by developing a two-step downscaling model to generate hourly air temperature data during heatwaves in London at 1 km resolution. Specifically, MODIS land surface temperature (LST) and other satellite-based local variables, including normalised difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalised difference water index (MNDWI), elevation, surface emissivity, and ERA5-Land hourly air temperature were used. The model employed genetic programming (GP) algorithm to fuse multi-source data and generate statistical models and evaluated using ground measurements from six weather stations. The results showed that our model achieved promising performance with the RMSE of 0.335 °C, R-squared of 0.949, MAE of 1.115 °C, and NSE of 0.924. Elevation was indicated to be the most effective explanatory variable. The developed model provided continuous, hourly 1 km estimations and accurately described the temporal and spatial patterns of air temperature in London. Furthermore, it effectively captured the temporal variation of air temperature in urban areas during heatwaves, providing valuable insights for assessing the impact on human health.

With a shifting climate pattern and enhancement of human activities, coastal areas are exposed to threats of groundwater environmental issues. This work takes the eastern coast of Laizhou Bay as a research area to study the response of a coastal groundwater system to natural and human impacts with a combination of statistical, hydrogeochemical, and fuzzy classification methods. First, the groundwater level dynamics from 1980 to 2017 were analyzed. The average annual groundwater level dropped 13.16 m with a descent rate of 0.379 m/a. The main external environmental factors that affected the groundwater level were extracted, including natural factors (rainfall and temperature), as well as human activities (irrigated area, water-saving irrigated area, sown area of high-water-consumption crops, etc.). Back-propagation artificial neural network was used to model the response of groundwater level to the above driving factors, and sensitivity analysis was conducted to measure the extent of impact of these factors on groundwater level. The results verified that human factors including irrigated area and water-saving irrigated area were the most important influencing factors on groundwater level dynamics, followed by annual precipitation. Further, groundwater samples were collected over the study area to analyze the groundwater hydrogeochemical signatures. With the hydrochemical diagrams and ion ratios, the formation of groundwater, the sources of groundwater components, and the main hydrogeochemical processes controlling the groundwater evolution were discussed to understand the natural background of groundwater environment. The fuzzy C-means clustering method was adopted to classify the groundwater samples into four clusters based on their hydrochemical characteristics to reveal the spatial variation of groundwater quality in the research area. Each cluster was spatially continuous, and there were great differences in groundwater hydrochemical and pollution characteristics between different clusters. The natural and human factors resulted in this difference were discussed based on the natural background of the groundwater environment, and the types and intensity of human activity.

Environmental planning, hazard monitoring, and coastal management depend critically on accurate shoreline definition. This work utilizes high-resolution unmanned aerial vehicle (UAV) data to develop a deep learning framework based on a Residual U-Net architecture for shoreline semantic segmentation. The proposed model integrates residual learning blocks into the conventional U-Net architecture to enhance gradient flow, improve feature extraction, and preserve fine boundary details in challenging coastal settings. Under a supervised learning framework, the model has been trained and validated using a dataset including UAV-acquired photos and manually annotated shoreline masks. The preprocessed input data has been reinforced by geometric adjustments and contrast normalizing to improve resilience and generalization. The Adam optimizer and binary cross-entropy loss helped the model be trained across 150 epochs. F1-score and Intersection over Union (IoU) measures have been used in quantitative performance evaluation. With a peak validation F1-score of 0.9483 and an IoU of 0.9018, the findings demonstrate that the Residual U-Net achieves great segmentation accuracy, showing robust spatial alignment with ground truth annotations. Visual analysis of the expected masks confirmed the approach's applicability to realworld situations by revealing consistent coastline localization throughout diverse environmental circumstances. This work presents a scalable and accurate method for operational shoreline monitoring, demonstrating the potential of deep residual structures for coastal boundary mapping using UAV platforms. Large-scale geospatial analytics and real-time coastal change detection can both benefit from the framework's extension to multi-temporal and multi-sensor data.

For successful disaster management, risk assessment, and long-term growth in coastal areas that are at risk, it is essential to have accurate and up-to-date flood maps. We examine sophisticated deep learning models for distinguishing the meanings of flood-affected areas in China's most flood-prone coastal regions, including the Yangtze River Delta, Pearl River Delta, Eastern Coastal China, and the Bohai Sea region. We utilize a high-resolution aerial imagery dataset with manually annotated flood masks to evaluate the effectiveness of an Attention U-Net architecture designed explicitly for binary flood-water delineation. We utilize essential segmentation measures, including Intersection over Union (IoU), F1-score, accuracy, and recall, to evaluate the model's performance. The findings of the experiments suggest that the Attention U-Net is effective in identifying the difference between flooded and non-flooded areas, and it performs well across a wide range of geographic and land cover settings. The system shown here holds considerable promise for working with multi-sensor data fusion methodologies, making flood monitoring in complex coastal areas easier and faster. This research provides valuable insights for enhancing flood mapping using remote sensing, which will enable China's coastal megadeltas to respond more effectively to disasters and mitigate risks.

Rivers have historically been the primary water source for all life forms; however, their fluctuating flow patterns pose significant risks, particularly in coastal and flood-prone regions. This study evaluates the applicability of the Hydrologic Engineering Centers River Analysis System (HEC-RAS) for simulating water surface profiles and quantifying flood magnitudes across multiple flood events with varying return periods. The study employs the Kolmogorov–Smirnov test to identify the most suitable probability distribution for flood frequency analysis at the gauging station, where the Log Pearson Type III (LP3) distribution was the best fit. The estimated flood discharges for 5-, 10-, 50-, 100-, and 150-year return periods were computed using the Log-Normal (LN), Gumbel, and LP3 distributions. The discharge values (in m³/s) for LN were 4887, 5467, 6521, 6907, and 7179, for Gumbel 4968, 5417, 6405, 6822, and 7066, and LP3 4906, 5480, 6344, 6755, and 7155, respectively. The analysis revealed that a 50-year return period flood with a discharge of 5299 m³/s at the study area would result in an inundation area 500% larger than that caused by natural surges. This study integrates HEC-RAS simulations with flood frequency analysis to provide a detailed assessment of floodplain extents, water velocity dynamics, and high-risk zones. The results offer critical insights into flood management, enabling policymakers, engineers, and disaster management authorities to enhance flood preparedness and mitigation strategies. These findings can support the development of early warning systems, land-use planning, and infrastructure resilience in flood-prone regions. Overall, this study highlights the efficiency of HEC-RAS modeling in predicting flood hazards and provides a framework for floodplain mapping and risk assessment in riverine environments. Future research should incorporate climate change projections and real-time hydrological data to improve flood forecasting accuracy and resilience planning.