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Weather surveillance radar (WSR) provide distributed quantitative precipitation estimates (QPEs) of great value to the modelling, understanding and management of many hydro‐meteorological processes. To obtain these observations over regional or larger scale domains it is necessary to composite data from multiple WSRs. These composites are often produced operationally by national or international meteorological agencies yet valuable data from ad‐hoc sources such as research groups and local‐level WSR operators are not included in these products. This study presents a methodology for incorporating data from a research radar deployment (the National Centre for Atmospheric Science mobile X‐band weather radar, NXPol‐1) into a national scale composite (the UK Met Office British Isles gridded composite) using a quality‐index. Firstly a quality‐index is developed for NXPol‐1 using an intuitive, multi‐factor approach. The quality‐index is then cross‐referenced with the existing quality‐index for the national composite, to allow production of a dynamically merged two source WSR QPE. The method developed is then evaluated using surface precipitation measurements from an extensive rain gauge network. Merging QPE from the two sources using a quality‐index improves the accuracy of WSR QPE when compared to either individual data source, showing it is possible to combine ad‐hoc WSR data with national products dynamically such that precipitation estimation is improved. Improving local QPE using additional radar deployments will benefit flood forecasting accuracy and local incident response, particularly when that data is used to enhance existing coverage. This research demonstrates the derivation of a quality index for Quantitative Precipitation Estimates (QPE) from an X‐band research radar and its subsequent use in combining the research QPE with national weather surveillance radar data. Using an extensive rain gauge network over a two‐year period this research shows that merging third party research QPE into a preexisting national radar composite improves local QPE, successfully accounting for the differences between the two QPE products.

Every year in the UK, many flood risk assessments are carried out on small catchments, typically draining areas of less than 25 km2. Standard hydrological practice in all UK catchments is to apply the methods presented in the Flood Estimation Handbook (FEH) and its subsequent updates. FEH methods are practical, relatively easy to apply and based on extensive statistical analyses. However, uncertainties can be large, especially in atypical catchments, and small catchments can present unique challenges in terms of heavy urbanisation and rapid flood responses. Compared to larger catchments, small catchment flood data are limited. In this study, we use a dataset of annual maxima and digital catchment descriptors at 205 small catchments to benchmark the QMED and Q100 estimation performance of current UK flood estimation methods: the FEH statistical method, ReFH2 and MacDonald and Fraser’s method, in rural and urbanised catchments separately. All methods perform similarly in rural catchments overall, although MacDonald and Fraser’s method underestimates QMED in urbanised catchments. The methods show a larger factorial standard error against this small catchment dataset than they do against typical datasets of mixed-size catchments. Further work will evaluate the performance of ReFH2 in combination with the latest FEH13 rainfall model.

Technology is transforming societies worldwide. A major innovation is the emergence of robotics and autonomous systems (RAS), which have the potential to revolutionize cities for both people and nature. Nonetheless, the opportunities and challenges associated with RAS for urban ecosystems have yet to be considered systematically. Here, we report the findings of an online horizon scan involving 170 expert participants from 35 countries. We conclude that RAS are likely to transform land use, transport systems and human–nature interactions. The prioritized opportunities were primarily centred on the deployment of RAS for the monitoring and management of biodiversity and ecosystems. Fewer challenges were prioritized. Those that were emphasized concerns surrounding waste from unrecovered RAS, and the quality and interpretation of RAS-collected data. Although the future impacts of RAS for urban ecosystems are difficult to predict, examining potentially important developments early is essential if we are to avoid detrimental consequences but fully realize the benefits. The future challenges and potential opportunities of robotics and autonomous systems in urban ecosystems, and how they may impact biodiversity, are explored and prioritized via a global horizon scan of 170 experts.

Recognizing individual variability is essential for developing targeted, personalized medical interventions. Vocal fatigue is a prevalent symptom and complaint among occupational voice users, but its identification has yielded mixed results. Vocal fatigue is a complex issue with heterogeneous biophysiological responses to vocal demands among individuals. This research aims to classify individuals as vocal demand responders to measure changes in vocal performance consistent with state vocal fatigue. A total of 37 participants (19F, 18M) completed a 30-minute vocal loading task (VLT) which consisted of loud speaking with background noise. Participants provided speech samples pre- and post-VLT and rated their vocal effort levels before, every 5 minutes during, and after the VLT. Perceived effort ratings and measured vocal performance from the speech samples were used to classify participants into distinct subgroups of vocal demand responders. Prior to classification there were few detectable changes associated with the VLT. However, the subgroup with both vocal effort and voice production demand responses displayed significant changes consistent with vocal fatigue while the other subgroups did not. These findings support the need for an individual-based approach to subtyping and measuring vocal fatigue and highlight its heterogeneous nature.

Recognizing individual variability is essential for developing targeted, personalized medical interventions. Vocal fatigue is a prevalent symptom and complaint among occupational voice users, but its identification has yielded mixed results. Vocal fatigue is a complex issue with heterogeneous biophysiological responses to vocal demands among individuals. This research aims to classify individuals as vocal demand responders to measure changes in vocal performance consistent with state vocal fatigue. A total of 37 participants (19F, 18M) completed a 30-minute vocal loading task (VLT) which consisted of loud speaking with background noise. Participants provided speech samples pre- and post-VLT and rated their vocal effort levels before, every 5 minutes during, and after the VLT. Perceived effort ratings and measured vocal performance from the speech samples were used to classify participants into distinct subgroups of vocal demand responders. Prior to classification there were few detectable changes associated with the VLT. However, the subgroup with both vocal effort and voice production demand responses displayed significant changes consistent with vocal fatigue while the other subgroups did not. These findings support the need for an individual-based approach to subtyping and measuring vocal fatigue and highlight its heterogeneous nature.