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Journal articleZhao J, Paschalis A, Gentine P, et al., 2026,
Limited capability of current satellite solar-induced chlorophyll fluorescence reconstructions to capture stomatal responses to environmental stresses
, Communications Earth and Environment, Vol: 7Quantification of the impact of environmental stress on terrestrial vegetation photosynthesis is crucial for our understanding of the global carbon cycle, particularly under a changing climate. Vegetation responses to environmental stress manifest first as plant physiological changes, and at later stages through changes in canopy structure. Here we leverage CO<inf>2</inf> and water flux data from 103 eddy covariance towers and satellite thermal images to assess whether current satellite reconstructions of solar-induced chlorophyll fluorescence capture these plant mechanisms. After removing seasonality using standardized anomalies (z-scores), we found that the relationship between tower-observed gross primary productivity and fluorescence reconstructions considerably weakened across a wide range of biomes. This loss of correlation results from a decoupling between stomatal responses and the physiological emission yield (Φ<inf>F</inf>) of fluorescence reconstructions during soil and atmospheric dry periods. The consequence is that productivity derived from fluorescence reconstructions will be progressively overestimated as dry conditions persist.
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Journal articleZhou Z, Chandresh R, Whittaker A, et al., 2026,
Sediment supply controls on channel morphological adjustments to tectonics and lithology
, Earth and Planetary Science Letters, Vol: 685, ISSN: 0012-821XIn nature, rivers not only incise bedrock but also transport sediment supplied to them; however, incision is often assumed to dominate over sediment transport in shaping channel geometry so as to exploit the simplicity of the stream power law. This likely flawed assumption raises fundamental concerns about inverting channel morphology to map external forcing, such as active deformation. Central to this issue is how relative sediment flux (i.e. sediment supply relative to transport capacity, Qs/Qt) modulates the efficacy of excess shear stress in incising bedrock, known as the relative sediment flux function. Two competing functions have been proposed, but resolving them in the field has proven challenging to date. Here, we address this issue by contrasting two rivers in the Gulf of Corinth, Greece that traverse comparable gradients in tectonics and lithology with distinct relative sediment fluxes. We show that the sediment-rich Phoenix river, with an estimated Qs/Qt value ∼0.8, has a much lower sensitivity of excess shear stress to tributary sediment input than the sediment-poor Sithas river. To our knowledge, this represents the first field evidence supporting the function with a markedly decreased sensitivity to Qs/Qt at high Qs/Qt values. This allows us to clarify the importance and mechanisms of channel slope versus width adjustment in reaching the excess shear stress required for equilibrium. As a result, we outline the conditions under which the widely used stream power law and its associated metrics, such as steepness and knickpoints, can (or cannot) be sensibly employed.
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Journal articleWarder SC, Piggott MD, 2026,
Wind farm wake losses under future build-out scenarios
, Wind Energy and Engineering Research, Vol: 5, Pages: 100025-100025, ISSN: 2950-3604 -
Journal articleShu S, Yang X, Ming Z, et al., 2026,
A carbon reduction incentive model for crowdsourced urban freight: Facilitating freight pooling and electric truck adoption
, Transportation Research Part E Logistics and Transportation Review, Vol: 209, ISSN: 1366-5545Urban freight transport faces significant decarbonization pressure, yet existing strategies such as freight pooling and electric truck adoption often struggle with limited uptake due to operational complexities, costs, and infrastructure challenges. Critically, current research lacks an integrated, operational incentive framework specifically designed for multi-stakeholder participation in urban crowdsourced logistics, where task-level operational decisions across multiple stakeholders play a central role in system-level carbon reduction. This study introduces a Carbon Reduction Incentive Model (CRIM) that addresses this gap. The CRIM incentivizes individual shippers and independent carriers within a crowdsourced logistics system by assigning task-level rewards for freight pooling and electric truck usage. Rewards are quantified by tonne-kilometer savings relative to conventional individual diesel deliveries, further adjusted by a time-based factor to encourage off-peak operations. The CRIM is embedded within an enhanced pick-up and delivery model that explicitly accounts for stakeholder cost components, vehicle heterogeneity, charging requirements, and time-sensitive feasibility (PDPTW-HEC). To optimize the system’s complex trade-off between costs and carbon emissions, a customized heuristic algorithm is developed. Scenario-based case studies using real-world data and international carbon accounting standards validate the proposed incentive model’s performance. Results demonstrate that CRIM can achieve 9.5–38.1% higher electric truck adoption and an 8.4–28.7% reduction in total carbon emissions. This framework offers a practical and scalable approach for designing and evaluating task-level carbon reduction incentives in urban freight operations.
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Journal articleAl Khalili U, Christou M, Karmpadakis I, 2026,
Numerical prediction of wave particle velocities in the coastal zone
, Applied Ocean Research, Vol: 170, ISSN: 0141-1187Modelling wave particle kinematics in coastal regions remains challenging due to the complex andhighly nonlinear physical processes involved. This study quantifies how sea-state steepness and bedslope affect phase-resolved horizontal velocities in coastal waters and evaluates the ability of existingkinematic theories to predict these velocities. Velocity profiles beneath extreme wave crests areobtained through extensive numerical simulations of long-crested irregular waves. The results revealthat steeper slopes accelerate shoaling, whereas milder slopes experience stronger breaking-inducedreductions. These lead to variations of up to 48% in shallow-water velocities across bathymetries. Theperformance of commonly used wave theories is assessed to provide practical modelling guidance.Regular wave theories (Stokes and Stream Function) yield accurate estimates in intermediate depthsbut fail in shallow water where nonlinear interactions dominate. In such cases, the implementationof irregular wave theories is found to be increasingly important. In particular, the methods of Molinand Donelan perform very well due to their ability to capture spectral superposition. In intermediatewater depths they produce an error of the order of 5%. However, underestimations of up to 40% areevident in shallower water. These findings highlight both the capabilities and the limitations of currentkinematic models and underscore the need for improved formulations under breaking conditions.
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Journal articleAlmalki YR, Karmpadakis I, 2026,
Effects of OWC geometry on total energy efficiency and turbulent dissipation
, Applied Ocean Research, Vol: 170, ISSN: 0141-1187This study investigates the impact of front and back wall geometries on the performance of oscillating water column (OWC) devices embedded in a fixed caisson breakwater. Combining experimental and numerical approaches, we analyse vortex formation and energy efficiency in relation to draft designs. Experiments, conducted at the Hydrodynamics Laboratory, Imperial College London, explores widest variety of draft shapes in the literature, including sharp and rounded profiles. Large eddy simulations (LES) were conducted using OpenFOAM® at laboratory and field scales to assess scale effects. The simulations accurately reproduce experimental data and reveal how draft geometry influences vortex dynamics, turbulence, and energy efficiency. It is shown that the design of the front and back drafts of an OWC can have a profound impact on its energy efficiency. In quantifying the generation of turbulence across different geometries, guidance is provided towards the most efficient geometries as well as the effects of physical model scale. Physical insight in this study provide clear recommendations for practical considerations in the design of OWC.
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Journal articleCao R, Padilla E, Chen X, et al., 2026,
A representative underlying scale for spectrally-resolved energy dissipation in surface breaking waves
, Journal of Geophysical Research (JGR): Oceans, ISSN: 2169-9275In the Duncan-Phillips framework for breaking wave energy dissipation, the underlying scale at which dissipation occurs is commonly inferred from a measure of the breaking-wave phase speed, most often taken as the spectrally-informed phase speed(Cs), the local phasespeed at incipient breaking (Cb), and the white cap advancing speed(Cw). However, energy loss occurs across a finite spectral band, and the link between these single-speed measures and the representative dissipation scale requires further investigation. Using unidirectional laboratory wavegroups, we identify the spectral range over which energy dissipation occurs in breaking waves (≈0.95fp-1.8fp, with fp the peak frequency). From this, we define an energy-dissipation-weighted frequency fΥ and a corresponding phase speed CΥ(via the linear dispersion relation), which characterise the effective scale at which energy is lost from the wave group. We show that for the waves studied here this dissipative scale is systematically smaller than local and spectral speed measures, with CΥ≃O(0.95Cb)≃ 27O(0.87Cs). When inferred from whitecap-based measures (most commonly implemented in the field from optical remote sensing),CΥcorresponds to approximately O(0.91-0.96) of the time-averaged white cap advancing speed, depending on how the whitecap speed is defined. Taken together, these correlations indicate that the commonly used measures Cs,Cb, and white cap-based speeds are broadly connected, with our quantitative analysis further demonstrating that their relationships are modulated by the strength of breaking. Overall, our study provides a clearer physical basis for identifying dissipation scales in broadband breaking waves and helps reconcile existing laboratory, numerical, and field-based approaches.
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Journal articleDu T, Taylor S, Salah P, et al., 2026,
Accelerating tropical cyclone wave height estimation via machine learning and deep latent surrogates
, Ocean Engineering, Vol: 352, ISSN: 0029-8018Tropical cyclones (TCs) are a major driver of coastal damage and require reliable risk assessment–particularly for extreme coastal waves. Classical partial differential equation (PDE)- based wave models such as SWAN, WAVEWATCH III and MIKE21 have long been used for such estimations, but remain computationally expensive, with practitioners increasingly requiring faster, lightweight tools. This study presents machine learning (ML) and deep learning (DL) surrogates that emulate commercial-grade wind-to-wave models. Our modelling framework aims to estimate Significant Wave Height (H<inf>s</inf>) during TCs, and we target its common underestimation in ML models. The data pre-processing pipeline explicitly targets the under-estimation of the maximum values of H<inf>s</inf>. It combines oversampling of rare extremes, loss functions weighted toward high-impact cases, and dimensionality reduction via principal component analysis (PCA) to rebalance inputs in a latent space. We evaluate both point-trained tree ensembles for nearshore estimation (Random Forest, XGBoost), and architectures that model space-time structure–convolutional neural networks (CNNs), temporal convolutional networks (TCNs), and long short-term memory (LSTM) networks–in order to capture the complex space-time dependencies in wave dynamics that simpler models fail to represent. We find that a PCA-TCN-LSTM surrogate results in the best peak H<inf>s</inf> estimation. Across models, runtime drops from around 40 hours on CPU clusters to seconds on a personal computer while maintaining high accuracy for H<inf>s</inf> (MSE (Formula presented), R(Formula presented) ). These surrogates provide practical tools for scientists, engineers, and first responders to conduct low-cost, real-time coastal-hazard assessment and strengthen climate resilience.
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Journal articleMillar O, Ma L, Karmpadakis I, 2026,
Experimental assessment and prediction of wave loading around abrupt depth transitions
, Coastal Engineering, Vol: 206, ISSN: 0378-3839Abrupt depth transitions cause significant changes in the characteristics of the wave field, increasing the non-linearity of the wave train and the likelihood of extreme events. The free surface elevation and wave kinematics exhibit different spatial behaviour depending on the local bathymetry. As a result, the critical location for wave loading cannot be identified from the free field properties alone. This study presents the results of a comprehensive experimental analysis of wave loading on a vertical cylinder around a shoal bathymetry. Extreme crest heights are most prevalent immediately downstream of the crest of the shoal, while extreme loads are found to be most frequent above the crest. However, this is influenced by the presence of wave breaking, which generates enhanced loading events of increased magnitude. The prediction of wave loading using Morison’s equation is investigated, with wave kinematics estimated using linear random wave theory and a numerical model (SWASH). The findings demonstrate the importance of the empirical inertia coefficient, which must reflect both the loading regime and the choice of kinematics model.
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Journal articleLi H-Y, Lawrence JA, Mason PJ, et al., 2026,
A framework for accurate annual regional crop yield prediction
, Remote Sensing, ISSN: 2072-4292Food insecurity occurs due to the impact of climate change and intense global conditions. Thus, understanding crop farming plans and monitoring crop yields have become major tasks for decision makers. Previous work has applied remote sensing techniques and empirical methods to predict the yields and analyse the relationships between spectral indices and historical crop yield data. However, a limitation of these studies is that they do not extract the values of spectral indices by crop types when the testing area isregional with multiple farmlands and requires a crop classification process. This can cause inaccurate results when investigating the correlations between the yield and the spectral indices. This research develops a yield prediction framework with historical crop mapsby means of unsupervised classification with zero ground truth using Sentinel-2 imagery to retrieve the values of spectral indices of winter barley. The extracted spectral indices and the meteorological and historical yield data in North Norfolk, UK, are implemented in 1D Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and CNN–LSTM for winter barley yield predictions. LSTM has outstanding performance overall and the best result approaches a Root Mean Square Error (RMSE)of0.406kg/hectare, a Mean Square Error (MSE) of 0.165 kg/hectare and a Mean Absolute Error (MAE) of10.495 kg/hectare. The EVI in April, May and June is the most important feature in the LSTM model and shows strong positive correlation with the yield of winter barley. The developed framework with unsupervised crop classification and LSTM can be applied to multiple crop types and in different regions using opensource datasets, historical yields, spectral indices and meteorological data. Correlations between these datasets indicate that higher EVI and maximum and minimum temperature and sun hours at the germination and seedling growth stages increase the yields of winter barley, but excess Water Content (WC)
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