Earth Institute Research Collection

Heatwaves exert disproportionately strong and sometimes irreversible impacts on forest ecosystems. These impacts remain poorly understood at the tree and species level and across large spatial scales. Here, we investigate the effects of the record-breaking 2018 European heatwave on tree growth and tree water status using a collection of high-temporal resolution dendrometer data from 21 species across 53 sites. Relative to the two preceding years, annual stem growth was not consistently reduced by the 2018 heatwave but stems experienced twice the temporary shrinkage due to depletion of water reserves. Conifer species were less capable of rehydrating overnight than broadleaves across gradients of soil and atmospheric drought, suggesting less resilience toward transient stress. In particular, Norway spruce and Scots pine experienced extensive stem dehydration. Our high-resolution dendrometer network was suitable to disentangle the effects of a severe heatwave on tree growth and desiccation at large-spatial scales in situ, and provided insights on which species may be more vulnerable to climate extremes.

A project to formalise and expand Academic Advising has been implemented at the UCD Civil Engineering School. The goals of this project were twofold: on the one hand, it aimed at training faculty members in Academic Advising roles and providing them with the necessary resources. On the other hand, the project sought to expand student interaction, in particular by engaging students informally in order to build a rapport between them and the academic advisors that we expect will bring long term benefits. The resulting model combines elements of both the prescriptive, e.g., formal training, informative talks on key topics, and developmental approaches, e.g., coffee mornings for students and faculty members. The evaluation of the project was carried out through questionnaires and focus groups. It highlighted very positive feedback from the students, who find these new lines of communication with the academic staff to be useful and productive.

The costs incurred in the design and implementation of planning policy instruments are not always considered sufficiently. In order to increase the efficacy of planning policy instruments, these transaction costs need to be taken into account. While such transaction costs are expected to vary according to their institutional design and arrangements, up to now there has been no systematic research concerned with how planners should consider transaction costs, and other institutional aspects, as evaluation criteria in planning policy analysis. This paper investigates how, and in which stages, these costs can be included in planning policy design and analysis. Using the literature of transaction costs and new institutional economics, this paper proposes a framework for integrating these costs into evaluating planning policy instruments. This framework consists of different factors that influence transaction costs in designing and implementing a planning policy instrument. Although some researchers have discussed the influence of factors concerning the characteristics of transactions and transactors, there has been limited consideration of the importance of factors related to the characteristics of a policy. This paper argues that policy characteristics, such as, simplicity, age of the policy, precision of the policy, policy approach, public involvement and participation, and policy credibility and consistency, can affect transaction costs in any policy. Therefore, the paper concludes that, in addition to transaction and transactor characteristics, a 'policy characteristics' category should be included to emphasise the importance of policy selection and design in transaction costs of a planning policy instrument.

This paper investigates the accuracy of the Hilbert-Huang transform (HHT) in capturing the time-varying frequencies of a short-span bridge traversed by a vehicle travelling at a constant speed. The bridge and vehicle are modelled as a simply supported beam and a quarter-car, respectively. The HHT uses empirical mode decomposition to divide the original signal into mono-component signals, called intrinsic mode functions (IMFs), where the Hilbert transform can extract instantaneous frequencies (IFs). Each IMF is associated with a dominant frequency band, although mode mixing is possible. In order to improve the predicted frequencies, several filters are applied before and after performing the HHT with a threefold purpose: (i) to remove the static component, (ii) to isolate the first mode of vibration, and (iii) to obtain meaningful and denoised IFs. The influences of a localized stiffness loss in the bridge, different vehicle speeds, and three signal-to-noise ratios on the results are discussed.

Moving Force Identification (MFI) theory can be used to create an algorithm for a Bridge Weigh-in-Motion (WIM) system that can produce complete force histories of the loads that have traversed a bridge structure. MFI is based on general inverse theory, however, and calibration of such a system requires a complete Finite Element (FE) model of the bridge to be available for implementation in the field. This is something that is often infeasible in practice as FE models created using theoretical values for material properties bear a poor relation to reality. The Cross Entropy optimisation method has been adapted here to address this calibration problem. The general system FE global mass and stiffness matrices of the bridge FE model are found by best fit optimisation to match field measurements. In this fashion a fully automated calibration procedure is developed for an MFI algorithm. This system is tested theoretically using three different FE plate models, coupled with a 3-dimensional vehicle model, allowing for Vehicle–Bridge Interaction (VBI).

Some infrastructure is experiencing a fast rate of deterioration as a result of poor design/ construction/ maintenance or more demanding loads than anticipated, and many buildings, transport, and energy infrastructure are getting close to the ends of their design lives. There is a need for assessing the safety of these structures, i.e., to check that the probability of the structural response falling beyond an established threshold for a given period of time is acceptable. This calculation involves defining a number of input variables that include loading as well as structural properties such as moduli of elasticity, material strength, geometry, boundary conditions, etc.

This paper provides guidance for the planning of urban heritage documentation using aerial laser scanning. The paper presents standard industrial considerations typically undertaken by commercial data providers and the additional factors that such be raised in the planning stage when urban heritage documentation is the goal.

This paper presents a workflow including a novel algorithm for road detection from dense LiDAR fused with high-resolution aerial imagery data. Using a supervised machine learning approach point clouds are firstly classified into one of three groups: building, ground, or unassigned. Ground points are further processed by a novel algorithm to extract a road network. The algorithm exploits the high variance of slope and height of the point data in the direction orthogonal to the road boundaries. Applying the proposed approach on a 40 million point dataset successfully extracted a complex road network with an F-measure of 76.9%.

Nitrous oxide (N2O) is a potent greenhouse gas associated with nitrogen fertiliser inputs to agricultural production systems. Minimising N2O emissions is important to improving the efficiency and sustainability of grassland agriculture. Multispecies grassland swards composed of plants from different functional groups (grasses, legumes, herbs) have been considered as a management strategy to achieve this goal.

Heavy traffic and aggressive environmental conditions can cause unexpected bridge deterioration. Traditional condition evaluation is expensive. An alternative is Terrestrial laser scanning (TLS) which is a non-contact approach that safe, fast, and applicable to a range of weather conditions. This paper reviews applications of TLS on bridge measurement involving geometric documentation, surface defect determination, and corrosion evaluation, and crack identification. Currently, most post-processing of TLS is manual or within third party software. This paper discusses potential approaches to automatic post-processing.

The translational restraints associated to pin and rocker bearings are typically idealized in the form of fixed and free conditions. However, elastomeric bearings need to be represented with springs to reasonably predict the time- and frequency-domain response of bridges under traffic-induced vibrations. Therefore, changes in the response of these bearings are common as a result of aging, deterioration, variation in loading levels and/or environmental changes. The latter makes difficult to discern if changes in the frequency content of the structural response to ambient vibration are due to changes in temperature, changes in normal operational loads or the occurrence of damage. In this paper, the bridge is idealized by a beam model supported on a hysteretic translational sprung support. The purpose is twofold: (a) to gather a better understanding of the variations of the bridge response with bearing performance; and (b) to be able to quickly identify an anomaly in the bearing. Empirical Mode Decomposition and the Hilbert-Huang Transform are employed to capture changes in the bearing stiffness from the bridge response.

The translational restraints associated to pin and rocker bearings are typically idealized in the form of fixed and free conditions. However, elastomeric bearings need to be represented with springs to reasonably predict the time- and frequency-domain response of bridges under traffic induced vibrations. Therefore, changes in the response of these bearings are common as a result of aging, deterioration, variation in loading levels and/or environmental changes. The latter makes difficult to discern if changes in the frequency content of the structural response to ambient vibration are due to changes in temperature, changes in normal operational loads or the occurrence of damage. In this paper, the bridge is idealized by a beam model supported on a hysteretic translational sprung support. The purpose is twofold: (a) to gather a better understanding of the variations of the bridge response with bearing performance; and (b) to be able to quickly identify an anomaly in the bearing. Empirical Mode Decomposition and the Hilbert-Huang Transform are employed to capture changes in the bearing stiffness from the bridge response.

We quantified seasonal nitrous oxide (N2O) emissions and the associated emission factors (EFs) from: (i) winter oilseed rape (WOSR) cultivated under conventional tillage (CT) and strip tillage (ST) at four fertilizer rates (0, 160, 240 and 320 kg N ha−1) in 2014/2015, and (ii) grassland plots receiving no fertilizer (0 kg N ha−1), or mineral nitrogen (67 kg N ha−1), and either cattle or pig slurry (50, 100 and 200 m3 ha−1). Greater fluxes were observed at higher soil temperatures and a higher water filled pore space, suggesting that denitrification was the main source of N2O-N from the applied fertilizer/slurry. For WOSR, the N2O EFs ranged from 0.03 to 1.20% with no effect of the cultivation practice on EFs for equal rates of nitrogen fertilizer. Lower EF values were linked to differences in plant growth at individual sites rather than a specific management effect. For the grassland, the N2O EFs were highly variable, ranging from −0.70 to 0.49%, but were generally the highest in treatments receiving the highest concentrations of slurry. The EF values for WOSR illustrates that the Tier 1 approach for calculating EFs may be inadequate and the identification of site-specific effects can aid in refining N2O EF inventories. For the grassland plots all the EFs were significantly lower than the IPCC default values. Although the reason(s) for the low EFs with slurry amendments on grassland is not known, ammonia volatilization could decrease the pool of inorganic N that is available to nitrifying bacteria thereby lowering N2O fluxes.

Rainwater is considered as a dependable potable and non-potable water source, used for domestic purposes as well as for human consumption in many cases. While it is usually believed that rainwater is safe for drinking purposes, many studies have explored the existence of trace metals in harvested rainwater, which can impose a serious health risk to human beings when present in relatively high concentrations. The concentration of trace elements in atmospheric precipitation including rainwater also provides a good indication of the environmental pollution caused by anthropogenic activities.

We present a Bayesian hierarchical model for reconstructing the continuous and dynamic evolution of relative sea-level (RSL) change with quantified uncertainty. The reconstruction is produced from biological (foraminifera) and geochemical (δ13C) sea-level indicators preserved in dated cores of salt-marsh sediment. Our model is comprised of three modules: (1) a new Bayesian transfer (B-TF) function for the calibration of biological indicators into tidal elevation, which is flexible enough to formally accommodate additional proxies; (2) an existing chronology developed using the Bchron age–depth model, and (3) an existing Errors-In-Variables integrated Gaussian process (EIV-IGP) model for estimating rates of sea-level change. Our approach is illustrated using a case study of Common Era sea-level variability from New Jersey, USA We develop a new B-TF using foraminifera, with and without the additional (δ13C) proxy and compare our results to those from a widely used weighted-averaging transfer function (WA-TF). The formal incorporation of a second proxy into the B-TF model results in smaller vertical uncertainties and improved accuracy for reconstructed RSL. The vertical uncertainty from the multi-proxy B-TF is  ∼  28 % smaller on average compared to the WA-TF. When evaluated against historic tide-gauge measurements, the multi-proxy B-TF most accurately reconstructs the RSL changes observed in the instrumental record (mean square error  =  0.003 m2). The Bayesian hierarchical model provides a single, unifying framework for reconstructing and analyzing sea-level change through time. This approach is suitable for reconstructing other paleoenvironmental variables (e.g., temperature) using biological proxies.

A challenge in marine and offshore engineering is structural integrity management (SIM) of assets such as ships, offshore structures, mooring systems, etc. Due to harsh marine environments, fatigue cracking and corrosion present persistent threats to structural integrity. SIM for such assets is complicated because of a very large number of rewelded plates and joints, for which condition inspections and maintenance are difficult and expensive tasks. Marine SIM needs to take into account uncertainty in material properties, loading characteristics, fatigue models, detection capacities of inspection methods, etc. Optimising inspection and maintenance strategies under uncertainty is therefore vital for effective SIM and cost reductions. This paper proposes a value of information (VoI) computation and Bayesian decision optimisation (BDO) approach to optimal maintenance planning of typical fatigue-prone structural systems under uncertainty. It is shown that the approach can yield optimal maintenance strategies reliably in various maintenance decision making problems or contexts, which are characterized by different cost ratios. It is also shown that there are decision making contexts where inspection information doesn’t add value, and condition based maintenance (CBM) is not cost-effective. The CBM strategy is optimal only in the decision making contexts where VoI > 0. The proposed approach overcomes the limitation of CBM strategy and highlights the importance of VoI computation (to confirm VoI > 0) before adopting inspections and CBM.

Agriculture faces the challenge of achieving sustainable, profitable production while maintaining environmental quality. Conventional agricultural production is highly dependent on nutrient inputs of P and N in fertilizer and feed and poor use efficiency of these resources is associated with losses to the environment and impacts on water quality, GHG emissions, air quality, acidification and biodiversity. The AgriBenchmark project explored the possibilities for benchmarking of nutrient management performance on Irish farms.

This paper proposes a Big Data approach to automatically identify and extract buildings from a digital surface model created from aerial laser scanning data. The approach consists of two steps. The first step is a MapReduce process where neighboring points in a digital surface model are mapped into cubes. The second step uses a non-MapReduce algorithm first to remove trees and other obstructions and then to extract adjacent cubes. According to this approach, all adjacent cubes belong to the same object and an object is a set of adjacent cubes that belong to one or more adjacent buildings. Finally, an evaluation study is presented for a section of Dublin, Ireland to demonstrate the applicability of the approach resulting in a 92% quality level for the extraction of 106 buildings over 1 km2 including buildings that had more than 10 adjacent components of different heights and complicated roof geometries. The proposed approach is notable not only for its Big Data context but its usage of vector data.

Invasive species are recognized as one of the major environmental problems worldwide and responsible for a myriad of impacts on ecosystems and ecosystem processes. Although many invasive species exert a range of detrimental effects a more nuanced approach is now emerging, which acknowledges that they can make a positive or beneficial contribution (Schlaepfer et al., 2011; Vimercati et al., 2020, 2022; Mantoani et al., 2022). Clearly, a positive impact may not always be beneficial (Vimercati et al., 2022) and nutrient enrichment due the introduction of an alien nitrogen-fixing plant species, for instance, could result in the loss of important resident species with low nutrient requirements.

Catchment characterisation integrates an understanding of the physical characteristics, sources, pathways and pressures in a catchment, and provides a scientific basis for evaluation of mitigation measures required by the EU Water Framework Directive. In Ireland, the Catchment Characterisation Tool (CCT) has been developed to assess the potential risk posed by nitrate and phosphate from diffuse agricultural sources to surface and groundwater receptors, and to delineate critical source areas in Irish sub-catchments (typically from 10 – 200 km2 in size) as a means of facilitating the targeting of mitigation measures. The CCT for nitrate, which is presented in this paper, is a steady-state model based on annual average nitrate loadings transported from their land sources along near surface and subsurface pathways to each receptor. The GIS-based model links spatial datasets, such as land-use, soil and geological properties with transport and delivery factors derived from field and literature data. The model can distinguish between the contaminant loads transported through each of the major hydrological pathways. The CCT calculates the nitrate loading to surface waters following the source-pathway-receptor methodology, and results are displayed in pollution impact potential maps. A key issue with such export models is how they can be validated. This paper describes the validation methodology which compared a national dataset of measured nitrate concentrations in Irish water bodies with values predicted by the CCT. More detailed comparisonswith local test catchments that are more intensively monitored showed satisfactory correlation between the CCT predictions and measured concentrations. This paper thus shows both the potential of the CCT approach, the likely range of the uncertainty to be expected, and the issues that arise from its validation.