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>Flood events are becoming increasingly common with the increase in the frequency of extreme weather driven by climate change. 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) measurements represent an indispensable data source for flood disaster planners and managers, given their ability to scan the Earth's surface nearly independently of weather conditions and the time of day. 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 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 historical floods. Additionally, historical Landsat-based surface water class probabilities are used to distinguish floods from permanent or seasonally occurring surface water. Using this algorithm, we can get a flood inundation map of the region of interest in less than 10 seconds. We tested the algorithm over Houston, Texas following the Hurricane Harvey in late August 2017 and the results showed an accuracy of 89.9%. 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>

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<p>The increasing frequency of heatwave events poses new threats to the health of urban residents. This effect can be exacerbated by the urban heat island (UHI) phenomenon. Air temperature is widely utilised in public health to quantify and analyse nonaccidental mortality attributable to heatwaves in urban areas throughout the world. Therefore, monitoring air temperature at the city level is important for identifying high-risk areas during heatwaves. However, measuring the spatial distribution patterns of air temperature in urban areas is challenging due to the lack of weather stations. The coarse spatial resolution of existing global and regional climate models is insufficient to detect the changes in microclimates, especially in complex-topography areas. In this study, a downscaling method for acquiring the 1-km hourly daytime air temperature data is proposed. It aims to produce a regression model by adopting Genetic Programming (GP) algorithm to estimate air temperature. Using multi-source datasets is considered to combine the advantages of spatial and temporal resolution from different datasets. This research used six weather stations from UK Met Office to assess the regression model obtained from seven satellite- and model-based products. The products consist of six satellite-based datasets retrieved from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Terra MODIS, Shuttle Radar Topography Mission (SRTM) and Landsat 8, and one model-based dataset from the newly released ERA5-Land produced by the European Centre for Medium Range Weather Forecasts (ECMWF). The study demonstrates the potential of the proposed model in retrieving high-resolution urban air temperature. The regression model validation showed good results with an R-squared value of 0.992, an RMSE of 0.001 °C, an MAE of 0.322 °C and an NSE of 0.989. The novelty of the study is threefold: (a) unlike previous studies that only estimated the spatial distribution patterns of maximum daily temperatures in urban areas, this study is the first to produce estimations at a one-hour time granularity; (b) it innovatively combines multi-source datasets with GP algorithm to explore possible downscaling models; and (c) it makes the model more reflective of the temperature distribution of extremely hot days than others considering that the regression model is obtained based on data during heatwaves. This study provides a general framework for obtaining hourly air temperature data in urban areas, which could provide theoretical support for heatwave-related decisions. Simultaneously, it can help public health scholars improve the estimation process of mortality caused by heatwave events.</p>

Since the demand for accelerated construction is increasing these years, much attention has been paid to accelerated bridge construction (ABC) methods. The self-propelled modular transporters (SPMTs) are widely utilised in the ABC method as a versatile transport carrier. However, since the limitation of the SPMTs method, several structural system conversions will happen during truss installation, and tensile stress will potentially appear at the upper chord of the truss. Moreover, it is worth noticing the dynamic effects caused by utilising SPMTs to lift the truss can enlarge the impact of tensile stress. As one type of prestressing, beams prestressed with external tendons can effectively reduce the tensile stress. In order to reduce the impact of cracks caused by tensile stress, the feasibility of adopting temporary external pre-stressing tendons is discussed combined with the simulation results of MIDAS in this research.