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.

<p>Flood events are expected to become increasingly common with the global increases in weather extremes. The present state of the technologies for flood risk mapping is typically tested on small geographical regions due to limitation of flood inundation observations, which hinders the implementation of flood risk management activities. Synthetic aperture radar (SAR) sensors represent an indispensable data source for flood disaster planners and responders, given their ability to image the Earth's surface nearly independently of weather conditions and the time of day or night. The decision by the European Space Agency (ESA) Copernicus program to open data from its Sentinel-1 SAR satellites to the public marks the first time of global, operational SAR data freely available. Combined with the emergence of cloud computing platforms like the Google Earth Engine (GEE), this development presents a tremendous opportunity to the disaster response community, for whom rapid access to analysis-ready data is needed to inform effective flood disaster response interventions and management plans. Here, we present an algorithm that exploits all available Sentinel-1 SAR images in combination with historical Landsat and other auxiliary data sources hosted on the GEE to rapidly map surface inundation during flood events. Our algorithm relies on multi-temporal SAR statistics to identify unexpected floods in near real-time. Additionally, historical Landsat-based surface water class probabilities are used to distinguish unexpected floods from permanent or seasonally occurring surface water. The flexibility of our algorithm will allow for the rapid processing of future open-access SAR data, including data from future Sentinel-1 missions.</p>

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.

<p><strong>Abstract: </strong>With climate change, rainfall is expected to get more intense, leading to cities being increasingly at risk of urban flooding. Understanding local climate change over cities has therefore become a priority for the scientific community and city planners on building resilient cities and mitigating hydrometeorological disasters. Very high resolution (km-scale, ‘convection-permitting’) climate models are required to adequately represent cities and local rainfall extremes. Here we assess the Weather Research and Forecasting (WRF) model for simulating urban rainfall. Despite the wide application of WRF in rainfall simulations (including urban areas), there are limited investigations on the impact of the domain size and how to search for a suitable domain size over a particular city region.</p> <p>To fill this knowledge gap, Newcastle upon Tyne is selected as the study area to simulate a summer heavy rainfall event with ERA5 (a fifth-generation dataset of global reanalysis developed by the European Centre for Medium-Range Weather Forecasts) as the input data and a radar product from the UK Met Office for validation. Accordingly, different domain sizes with the convection-permitting resolutions from 1 km to 4.5 km (increment: 0.5 km) are explored, and the hourly model outputs are compared with the radar observation data.</p> <p>This study has proposed and tested a method to decide the most suitable domain size. By using eight assessment indexes (including pattern, cumulative time series, hourly time series, particular values (max/min/mean) as well as the seven statistical indicators of each data and overall data), there are two preliminary conclusions: 1) 200 km × 200 km is the best domain size for the single domain simulation; 2) For 200 km × 200 km or smaller domain sizes, higher resolution produces better results, but for 250 km × 250 km or large domain sizes, resolution sensitivity is opposite. Regarding next steps, the above procedure will be further investigated by applying it to more extreme rainfall case studies and to other cities in order to assess whether results here are generally applicable, and therefore the optimal domain configuration can be usefully applied to produce reliable urban rainfall simulations.</p>

<p>In the twenty-first century, extreme weather events leading to flooding and heat waves, have become one of the most severe challenges in urban areas, especially under the circumstances of local climate change and rapid urbanisation. In the future, cities are going to encounter more severe natural disaster risks and understanding how these could combine with modification of the urban environment (for example through adoption of green infrastructure) is critical for decisions relating to mitigation and adaptation to climate change. Green infrastructure is a subset of resilient infrastructure, which may mitigate the adverse effects caused by extreme weather and contribute to regulating urban climate. In addition, high-performing green spaces bring additional benefits for society in terms of health and wellbeing. The Weather Research and Forecasting (WRF) model is a numerical weather prediction system supporting both atmospheric research and operational forecasting. Within this modelling system, there is the possibility to modify parameters according to various urban areas within the WRF-Urban configuration. In this study, Newcastle upon Tyne (a UK city with the benefit of a lot of observational sensor data) is selected as an initial target city for identifying the optimal WRF configuration by varying the model resolution, domain size and nesting strategy. Future work will explore the influence of implementing green infrastructure in the context of climate change and urbanisation, then extending this analysis to London.</p>