Infrastructure Engineering - Research Publications
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ItemA dataset of direct observations of sea ice drift and waves in ice(NATURE PORTFOLIO, 2023-05-03)Variability in sea ice conditions, combined with strong couplings to the atmosphere and the ocean, lead to a broad range of complex sea ice dynamics. More in-situ measurements are needed to better identify the phenomena and mechanisms that govern sea ice growth, drift, and breakup. To this end, we have gathered a dataset of in-situ observations of sea ice drift and waves in ice. A total of 15 deployments were performed over a period of 5 years in both the Arctic and Antarctic, involving 72 instruments. These provide both GPS drift tracks, and measurements of waves in ice. The data can, in turn, be used for tuning sea ice drift models, investigating waves damping by sea ice, and helping calibrate other sea ice measurement techniques, such as satellite based observations.
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ItemSurface Wave Mixing Modifies Projections of 21st Century Ocean Heat Uptake(MDPI, 2023-03)Climate models do not explicitly account for the smaller scale processes of ocean surface waves. However, many large-scale phenomena are essentially coupled with the waves. In particular, waves enhance mixing in the upper ocean and thereby accelerate the ocean response to atmospheric changes. Here, we introduced a representation of wave-induced turbulent mixing into the one-way coupled ACCESS-OM2-025 ocean model to study its effect on ocean heat content throughout the 21st century under the RCP4.5 scenario. We made two projections on ocean heat uptake for the end of the century: one which accounts for wave-induced mixing (the ‘modified’ projection) and the other which does not (the ‘standard’ projection). Both projections showed upper ocean heat content to increase by more than 2.2 × 1022 J. This projected ocean heat uptake was reduced by about 3% in the modified projection. Whilst the inclusion of wave-induced mixing reduces projected ocean heat uptake globally, some areas are expected to warm considerably faster, particularly the North Atlantic sub-tropics, the Tasman Sea, the Sea of Japan, and parts of the South Atlantic.
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ItemAnalysis of the 20-Year Variability of Ocean Wave Hazards in the Northwest Pacific(MDPI, 2023-05-26)In the Northwest Pacific (NWP), where a unique monsoon climate exists and where both typhoons and extratropical storms occur frequently, hazardous waves pose a significant risk to maritime safety. To analyze the 20-year variability of hazardous waves in this region, this study utilized hourly reanalysis data from the ECMWF ERA5 dataset covering the period from 2001–2020, alongside the wave risk assessment method. The ERA5 data exhibits better consistency, in both the temporal and spatial dimensions, than satellite data. Although hazardous wind seas occur more frequently than hazardous swells, swells make hazardous waves travel further. Notably, the extreme wave height (EWH) shows an increasing trend in high- and low-latitude areas of the NWP. The change in meridional wind speeds is the primary reason for the change in the total wind speed in the NWP. Notably, the maximum annual increase rate of 0.013 m/year for EWH exists in the region of the Japanese Archipelago. This study elucidated the distributions of wave height intensity and wave risk levels, noting that the EWHs of the 50-year and 100-year return periods can reach 20.92 m and 23.07 m, respectively.
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ItemThe impact of spume droplets induced by the bag-breakup mechanism on tropical cyclone modeling(Frontiers Media SA, 2023-01-01)Spume, large-radius seawater droplets that are ejected from the ocean into the atmosphere, can exchange moisture and heat fluxes with the surrounding air. Under severe weather conditions, spume can substantially mediate air-sea fluxes through thermal effects and thus needs to be physically parameterized. While the impact made by spume on air-sea interactions has been considered in bulk turbulent air-sea algorithms, various hypotheses in current models have resulted in uncertainties remaining regarding the effect of spume on air-sea coupling. In this study, we extended a classic bulk turbulent air-sea algorithm with a “bag-breakup” physical scheme of spume generation parameterizations to include spume effects in a complicated numerical model. To investigate the impact of spume on air-sea coupling, we conducted numerical experiments in a simulation of Tropical Cyclone Narelle. We observed a significant improvement in the ability to model minimum central pressure and maximum sustained surface wind speed when including the bag-breakup spume scheme. In particular, the impact of the bag breakup–generated spume is observed in the intensity, structure, and size of the tropical cyclone system through the modulation of local wind speed (U10), wave height (Hs), and sea surface temperature.
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ItemEstimating the elastic modulus of landfast ice from wave observations(Cambridge University Press (CUP), 2023-01-01)Abstract Progress in our understanding of wave–ice interactions is currently hindered by the lack of in situ observations and information of sea-ice properties, including the elastic modulus. Here, we estimate the effective elastic modulus of sea ice using observations of waves in ice through the deployment of three open-source geophone recorders on landfast sea ice. From observations of low-frequency dispersive waves, we obtain an estimate of the effective elastic modulus in the range of 0.4–0.7 GPa. This is lower than the purely elastic modulus of the ice estimated at 1 GPa as derived from in situ beam experiments. Importantly, our experimental observation is significantly lower than the default value currently in use in wave models. While our estimate is not representative for all sea ice, it does indicate that considerably more measurements are required to provide confidence in the development of parameterizations for this complex sea-ice property for wave models.
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ItemValidity of the wave stationarity assumption on estimates of wave attenuation in sea ice: toward a method for wave-ice attenuation observations at global scales(CAMBRIDGE UNIV PRESS, 2023-08)Abstract In situ observations of wave attenuation by sea ice are required to develop and validate wave–ice interaction parameterizations in coupled wave models. To estimate ice-induced wave attenuation in the field, the wave field is typically assumed to be stationary. In this study we investigate the validity of this assumption by creating a synthetic wave field in sea ice for different attenuation rates. We observe that errors in estimates of the wave attenuation rates are largest when attenuation rates are small or temporal averaging periods are short. Moreover, the adoption of the wave stationarity assumption can lead to negative estimates of the instantaneous wave attenuation rate. These apparent negative values should therefore not be attributed to wave growth or erroneous measurements a priori. Surprisingly, we observe that the validity of the wave stationarity assumption is irrelevant to the accuracy of estimates of wave attenuation rates as long as the temporal averaging period is taken sufficiently long. This may provide opportunities in using satellite-derived products to estimate wave attenuation rates in sea ice at global scales.
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ItemA collection of wet beam models for wave-ice interaction(COPERNICUS GESELLSCHAFT MBH, 2023-02-27)Abstract. Theoretical models for the prediction of decay rate and dispersion process of gravity waves traveling into an integrated ice cover expanded over a long way are introduced. The term “wet beam” is chosen to refer to these models as they are developed by incorporating water-based damping and added mass forces. Presented wet beam models differ from each other according to the rheological behavior considered for the ice cover. Two-parameter viscoelastic solid models accommodating Kelvin–Voigt (KV) and Maxwell mechanisms along with a one-parameter elastic solid model are used to describe the rheological behavior of the ice layer. Quantitative comparison between the landfast ice field data and model predictions suggests that wet beam models, adopted with both KV and Maxwell mechanisms, predict the decay rate more accurately compared to a dry beam model. Furthermore, the wet beam models, adopted with both KV and Maxwell mechanisms, are found to construct decay rates of disintegrated ice fields, though they are built for a continuous ice field. Finally, it is found that wet beam models can accurately construct decay rate curves of freshwater ice, though they cannot predict the dispersion process of waves accurately. To overcome this limitation, three-parameter solid models, termed standard linear solid (SLS) mechanisms, are suggested to be used to re-formulate the dispersion relationship of wet beam models, which were seen to construct decay rates and dispersion curves of freshwater ice with an acceptable level of accuracy. Overall, the two-parameter wet beam dispersion relationships presented in this research are observed to predict decay rates and dispersion process of waves traveling into actual ice covers, though three-parameter wet beam models were seen to reconstruct the those of freshwater ice formed in a wave flume. The wet beam models presented in this research can be implemented in spectral models on a large geophysical scale.
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ItemDevelopment and validation of a transient simulation model of a full-scale PCM embedded radiant chilled ceiling(TSINGHUA UNIV PRESS, 2023-06)Abstract The recent significant rise in space cooling energy demand due to the massive use of air-conditioning systems has adversely changed buildings’ energy use patterns globally. The updated energy technology perspectives highlight the need for innovative cooling systems to address this growing cooling demand. Phase change material embedded radiant chilled ceiling (PCM-RCC) has lately acquired popularity as they offer more efficient space cooling together with further demand-side flexibility. Recent advancements in PCM-RCC applications have increased the necessity for reliable simulation models to assist professionals in identifying improved designs and operating settings. In this study, a transient simulation model of PCM-RCC has been developed and validated using measured data in a full-scale test cabin equipped with newly developed PCM ceiling panels. This model, developed in the TRNSYS simulation studio, includes Type 399 that uses the Crank-Nicolson algorithm coupled with the enthalpy function to solve transient heat transfer in PCM ceiling panels. The developed model is validated in both free-running and active operation modes, and its quality is then evaluated using several validation metrics. The results obtained in multiple operating scenarios confirm that the model simulates the transient behaviour of the PCM-RCC system with an accuracy within ±10%. Aided by this validated model, which offers the user detailed flexibilities in the system design and its associated operating schemas, PCM-RCC’s potentials regarding peak load shifting, energy savings, and enhanced thermal comfort can be investigated more reliably.
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ItemNo Preview AvailableCharacterizing Topographic Influences of Bushfire Severity Using Machine Learning Models: A Case Study in a Hilly Terrain of Victoria, Australia(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023)Topography plays a significant role in determining bushfire severity over a hilly landscape. However, complex interrelationships between topographic variables and bushfire severity are difficult to quantify using traditional statistical methods. More recently, different machine learning (ML) models are becoming popular in characterizing complex relationships between different environmental variables. Yet, few studies have specifically evaluated the suitability of ML models in predictive bushfire severity analysis. Hence, the aim of this research is twofold. First, to determine suitable ML models by assessing their performances in bushfire severity predictions using remote sensing data analytics, and second to identify and investigate topographic variables influencing bushfire severity. The results showed that random forest (RF) and gradient boosting (GB) models had their distinct advantages in predictive modeling of bushfire severity. RF model showed higher precision (86% to 100%) than GB (59% to 72%) while predicting low, moderate, and high severity classes. Whereas GB model demonstrated better recall, i.e., completeness of positive predictions (56% to 75%) than RF (49% to 61%) for those classes. Closer investigations on topographic characteristics showed a varying relationship of severity patterns across different morphological landform classes. Landforms having lower slope curvatures or with unchanging slopes were more prone to severe burning than those landforms with higher slope curvatures. Our results provide insights into how topography influences potential bushfire severity risks and recommends purpose-specific choice of ML models.
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ItemNo Preview AvailableMulti-Scale Feature Map Aggregation and Supervised Domain Adaptation of Fully Convolutional Networks for Urban Building Footprint Extraction(MDPI, 2023-01)Automated building footprint extraction requires the Deep Learning (DL)-based semantic segmentation of high-resolution Earth observation images. Fully convolutional networks (FCNs) such as U-Net and ResUNET are widely used for such segmentation. The evolving FCNs suffer from the inadequate use of multi-scale feature maps in their backbone of convolutional neural networks (CNNs). Furthermore, the DL methods are not robust in cross-domain settings due to domain-shift problems. Two scale-robust novel networks, namely MSA-UNET and MSA-ResUNET, are developed in this study by aggregating the multi-scale feature maps in U-Net and ResUNET with partial concepts of the feature pyramid network (FPN). Furthermore, supervised domain adaptation is investigated to minimise the effects of domain-shift between the two datasets. The datasets include the benchmark WHU Building dataset and a developed dataset with 5× fewer samples, 4× lower spatial resolution and complex high-rise buildings and skyscrapers. The newly developed networks are compared to six state-of-the-art FCNs using five metrics: pixel accuracy, adjusted accuracy, F1 score, intersection over union (IoU), and the Matthews Correlation Coefficient (MCC). The proposed networks outperform the FCNs in the majority of the accuracy measures in both datasets. Compared to the larger dataset, the network trained on the smaller one shows significantly higher robustness in terms of adjusted accuracy (by 18%), F1 score (by 31%), IoU (by 27%), and MCC (by 29%) during the cross-domain validation of MSA-UNET. MSA-ResUNET shows similar improvements, concluding that the proposed networks when trained using domain adaptation increase the robustness and minimise the domain-shift between the datasets of different complexity.