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• Journal article
  De Nardi A, Marini G, Dorigatti I, Rosà R, Tamba M, Gelmini L, Prosperi A, Menegale F, Poletti P, Calzolari M, Pugliese Aet al., 2025,

  Quantifying West Nile virus circulation in the avian host population in Northern Italy

  , Infectious Disease Modelling, Vol: 10, Pages: 375-386, ISSN: 2468-2152

  West Nile virus (WNV) is one of the most threatening mosquito-borne pathogens in Italy where hundreds of human cases were recorded during the last decade. Here, we estimated the WNV incidence in the avian population in the Emilia-Romagna region through a modelling framework which enabled us to eventually assess the fraction of birds that present anti-WNV antibodies at the end of each epidemiological season. We fitted an SIR model to ornithological data, consisting of 18,989 specimens belonging to Corvidae species collected between 2013 and 2022: every year from May to November birds are captured or shot and tested for WNV genome presence. We found that the incidence peaks between mid-July and late August, infected corvids seem on average 17% more likely to be captured with respect to susceptible ones and seroprevalence was estimated to be larger than other years at the end of 2018, consistent with the anomalous number of recorded human infections. Thanks to our modelling study we quantified WNV infection dynamics in the corvid community, which is still poorly investigated despite its importance for the virus circulation. To the best of our knowledge, this is among the first studies providing quantitative information on infection and immunity in the bird population, yielding new important insights on WNV transmission dynamics.

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• Journal article
  do Prado AH, Mair D, Garefalakis P, Silveira BC, Whittaker AC, Schlunegger Fet al., 2025,

  The influence of grain size sorting on the roughness parametrization of gravel riverbeds

  , Geomorphology, Vol: 471, ISSN: 0169-555X

  Grain size and surface roughness play crucial roles in modelling flow resistance and boundary shear stress in fluvial systems. However, the impact of grain size sorting on surface roughness, particularly for gravel-bed rivers composed of poorly-sorted sediments, has yet to be elucidated. Here we utilize a stochastic model to simulate generic riverbed surfaces, investigating the influence of sediment sorting on roughness. Through comparison with field-acquired data, we explore the relationships between grain size, sorting, presence of textural patches, and local roughness. Our analysis reveals significant spatial roughness variations on surfaces with poorer sorting conditions, driven by stochastic grain arrangements. Notably, surfaces with poorly sorted grains exhibit meter-scale patches, each with distinct roughness attributes. Consequently, upon characterizing the roughness of riverbeds made up of m-scale gravel bars, the sorting of the grains needs to be considered to account for the complexity of the relationships between water flow and riverbed.

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• Journal article
  Warder SC, Piggott MD, 2025,

  The future of offshore wind power production: Wake and climate impacts

  , APPLIED ENERGY, Vol: 380, ISSN: 0306-2619
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• Journal article
  Mohammadpour A, Paluszny A, Zimmerman RW, 2025,

  A robust 3D finite element framework for monolithically coupled thermo-hydro-mechanical analysis of fracture growth with frictional contact in porous media

  , COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 434, ISSN: 0045-7825
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• Journal article
  Pang B, Cheng S, Huang Y, Jin Y, Guo Y, Prentice IC, Harrison SP, Arcucci Ret al., 2025,

  Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data

  , Computers and Geosciences, Vol: 195, ISSN: 0098-3004

  Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behavior. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIDN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIDN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIDN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations.

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• Journal article
  Curran-Sebastian J, Andersen FM, Bhatt S, 2025,

  Modelling the stochastic importation dynamics and establishment of novel pathogenic strains using a general branching processes framework

  , MATHEMATICAL BIOSCIENCES, Vol: 380, ISSN: 0025-5564
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• Journal article
  Ningthoujam R, Bloomfield KJ, Crawley MJ, Estrada C, Prentice ICet al., 2025,

  Hyperspectral sensing of abovegroundbiomass and species diversity in a longrunninggrassland experiment

  , Ecological Informatics, ISSN: 1574-9541
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• Journal article
  Callaghan A, Bidlot J-R, deLeeuw G, O'Dowd Cet al., 2025,

  Comparing Estimates of Whitecap Coverage from a Spectral Wave Model with Oceanic Observations

  , Geophysical Research Letters, ISSN: 0094-8276
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• Journal article
  Zhang J, Chen Y-S, Gryspeerdt E, Yamaguchi T, Feingold Get al., 2025,

  Radiative forcing from the 2020 shipping fuel regulation is large but hard to detect

  , Communications Earth & Environment, Vol: 6

  <jats:title>Abstract</jats:title> <jats:p>Reduction in aerosol cooling unmasks greenhouse gas warming, exacerbating the rate of future warming. The strict sulfur regulation on shipping fuel implemented in 2020 (IMO2020) presents an opportunity to assess the potential impacts of such emission regulations and the detectability of deliberate aerosol perturbations for climate intervention. Here we employ machine learning to capture cloud natural variability and estimate a radiative forcing of +0.074 ±0.005 W m<jats:sup>−2</jats:sup> related to IMO2020 associated with changes in shortwave cloud radiative effect over three low-cloud regions where shipping routes prevail. We find low detectability of the cloud radiative effect of this event, attributed to strong natural variability in cloud albedo and cloud cover. Regionally, detectability is higher for the southeastern Atlantic stratocumulus deck. These results raise concerns that future reductions in aerosol emissions will accelerate warming and that proposed deliberate aerosol perturbations such as marine cloud brightening will need to be substantial in order to overcome the low detectability.</jats:p>

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• Journal article
  Assareh N, Beddows A, Stewart G, Holland M, Fecht D, Walton H, Evangelopoulos D, Wood D, Vu T, Dajnak D, Brand C, Beevers SDet al., 2025,

  What Impact Does Net Zero Action on Road Transport and Building Heating Have on Exposure to UK Air Pollution?

  , Environ Sci Technol

  This study explores the cobenefits of reduced nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM), through net zero (NZ) climate policy in the UK. Two alternative NZ scenarios, the balanced net zero (BNZP) and widespread innovation (WI) pathways, from the UK Climate Change Committee's Sixth Carbon Budget, were examined using a chemical transport model (CTM). Under the UK existing policy, Business as Usual (BAU), reductions in NO2 and PM were predicted by 2030 due to new vehicle technologies but plateau by 2040. The BNZP and WI scenarios show further reductions particularly by 2040, driven by accelerated electric vehicle (EV) uptake and low-carbon heating in buildings, with the building contribution to PM reduction being 2-3 times greater than road transport. The results demonstrate that the NZ transition to EVs (cars and vans) reduces both exhaust and nonexhaust emissions, as well as reducing traffic volumes. O3 trends are complex with a small overall increase by 2030 and a decrease by 2040. Although uncertain, 2050 predictions of BNZP showed important additional air pollution benefits. Our findings highlight the efficacy of NZ strategies, providing insights for UK and international policymakers interested in the air pollution cobenefits of climate policy.

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