http://theses.ncl.ac.uk/jspui/handle/10443/73 2026-05-06T18:18:23Z 2026-05-06T18:18:23Z Shotton, Ronald Keith http://theses.ncl.ac.uk/jspui/handle/10443/6759 2026-05-06T15:19:36Z 2025-01-01T00:00:00Z

Title: Improving estimation of spatial precipitation in a mountain region Authors: Shotton, Ronald Keith Abstract: Due to its importance for water resources, as well as flood and drought planning, an improved understanding of spatial precipitation patterns in mountain regions is needed. Precipitation gauge networks are sparse and traditional methods of interpolation yield inadequate precipitation fields for sparsely gauged mountain catchments. This work builds on a new method, Random Mixing, to generate multiple random spatial daily precipitation fields, conditioned on gauge observations. The Random Mixing algorithm has so far been tested on larger, densely gauged catchments. This project adapts the approach for a sparsely gauged, small 9.4 km2 mountain catchment, Marmot Creek Research Basin (MCRB) in Alberta, Canada. Three modifications have been made to the Random Mixing method in developing the new technique, which is referred to as RM-mountain: (1) improving spatial covariance, (2) introducing elevation dependence and (3) evaluating seasonal effects. Addition of each modification in turn increases the spatial variance of precipitation values across simulated fields. Leave-one-out cross-validation was used, and results compared with outputs from four deterministic spatial interpolation techniques. The best fit precipitation time series simulated by the RM-mountain generated ensemble members demonstrated improved precipitation estimates compared to the four deterministic techniques. Precipitation totals across the MCRB catchment generated by RM-mountain are higher than those from the other methods tested. Due to its random nature, RM-mountain enables generation of precipitation within the catchment on days when the gauges are dry. In contrast, deterministic spatial interpolation methods yield zero precipitation across the entire catchment on days with zero observed precipitation. Inclusion of modifications 1-3 in RM-mountain noticeably increased the likelihood of simulating more realistic precipitation values within the generated ensemble. To optimise selection of the most plausible fields, ensemble hydrological simulations were run, using a modified spatially-distributed version of the HBV conceptual model, and the physically-based Cold Regions Hydrological Model (CRHM), with a 200-member ensemble of time series spatial precipitation fields generated on a 50 m x 50 m regular model grid. Optimisation involved the use of Nash-Sutcliffe Efficiency (NSE) and bias metrics, to identify a best constructed time series that most closely simulates the observed streamflows. The improvement in streamflow bias with HBV was from -20.94 to 0.14; with CRHM, bias was 2 improved slightly from 2.04 to 1.88. Increases in NSE values were from 0.76 to 0.96 with HBV and from 0.54 to 0.74 with CRHM. Some noticeable differences between catchment responses with HBV and CRHM were observed, relating to the complexity of the models, i.e., the relative simplicity of the conceptual HBV model in contrast to the more complex physically-based CRHM. Notable examples of these differences were snowmelt earlier in the year and much less variation in the streamflow ensemble with HBV. A much greater variety of streamflow hydrographs in the CRHM-generated ensemble were due to CRHM’s much higher sensitivity to differences in observed meteorological input data, particularly wind speed. This work demonstrates that modifying a random method, by adapting how it randomly samples from observed precipitation at a small number of gauges, includes elevation gradients and seasonal variation, improves estimation of spatiotemporal precipitation patterns for a small mountain catchment and improves hydrological simulations. The new method has the potential to be used to enhance precipitation datasets to improve water resource and flood modelling in other sparsely gauged mountain regions. Description: PhD Thesis

2025-01-01T00:00:00Z Khalil, Mohamad http://theses.ncl.ac.uk/jspui/handle/10443/6758 2026-05-06T11:34:01Z 2025-01-01T00:00:00Z

Title: Machine learning applications for building energy analytics Authors: Khalil, Mohamad Abstract: As smart meters in buildings continue to be implemented worldwide, an unprecedented volume of time series data sets related to building energy consumption and performance have been collected. However, traditional analytics models, and physics based approaches used in the building sector face challenges in effectively handling and managing time series data from smart meters. This is mainly because of its inherent variation, rapid velocity, and the need for advanced data preprocessing methods to handle it. This thesis proposes a range of data-driven frameworks and models to address several key challenges in the domain of building energy consumption and performance. The primary focus revolves around three key components: 1) Predicting the presence of occupants in buildings through the application of pre trained data-driven models, this thesis proposes a novel transfer learning framework. This framework leverages past knowledge from similar domains, enabling efficient adaptation to new occupancy prediction tasks and boosting accuracy, especially in scenarios with scarce training data. 2) Examining the effectiveness of employing global forecasting models under the context of building energy consumption, rather than relying on a singular forecasting model. Unlike single forecasting methods, the proposed global forecasting models can simultaneously learn from multiple time series associated with building energy consumption. This helps uncover hidden connections in smart metering data, improving transfer performance and knowledge across various forecasting tasks. 3) Unravelling the underlying properties of energy consumption through the analysis of time series features, this thesis presents a forecastability framework tailored to meet this specific need. The framework relies on a feature matrix extracted through interpretable time series feature extraction techniques. Through a supervised learning, the framework is trained to establish a mapping between the extracted features and the target label of interest. This enables the prediction of how forecastable a given time series is within the context of energy consumption. Keywords: Machine Learning, Forecasting Building Energy Consumption, Smart metering. Description: Ph. D. Thesis.

2025-01-01T00:00:00Z Clarke, Jessica L. http://theses.ncl.ac.uk/jspui/handle/10443/6756 2026-05-06T08:18:30Z 2025-01-01T00:00:00Z

Title: Marine Antifouling Coatings Determine Biofilm Community Composition and Microbiome Development in Settling Invertebrates Authors: Clarke, Jessica L. Abstract: This study investigated the impact of biocidal and non-biocidal marine anti-fouling coatings on the microbial composition and functionality of associated biofilms, marine invertebrates and their larvae using 16S rRNA gene sequencing. This is complemented by exploratory shotgun sequencing which was used to identify resistance genes present within the anti-fouling treatments. Differences between biocidal and non-biocidal biofilm treatments were identified, with unique ASVs found in each treatment. Although there was a high level of community overlap between treatments, there were diversity distinctions between treatments. Predicted functional analysis also demonstrated distinct differences in their antimicrobial resistance potential between biocidal and non-biocidal treatments. Shot gun sequencing analysis identified polymyxin and multi-drug as the dominant resistance gene types. In the barnacle Semibalanus balanoides, the adult microbiome acquisition upon settlement was heavily influenced by the settling substrate, forming beta-diversity rated distinct communities, where the biggest distinction in the microbiome was between the planktonic cyprid and calcification. In established adult-stage Ciona intestinalis and Bugula neritina, the coating they were attached to impacted their associated microbial communities. C. intestinalis on biocidal coatings with booster biocides demonstrated a significant reduction in dominant taxa compared to the other treatments. Similarly, in B. neritina the dominant taxa composition was significantly different between those with booster biocides and the other treatments. The microbiome AMR potential was characterised using shotgun sequencing and lab culture. Ampicillin and ciprofloxacin resistance were reduced in biocidal coatings in bryozoan samples, whereas kanamycin resistance was more prevalent in biocidal treatments. Larval microbiomes were distinct from their parents; however, they demonstrated both trans-generational transfer and uptake from the surrounding environment. The findings from this study not only contribute to the understanding of ecological consequences of antifouling strategies, but also offer insights into the potential implications of marine microbiome change. Description: PhD Thesis

2025-01-01T00:00:00Z Catherall, Harrison John Norman http://theses.ncl.ac.uk/jspui/handle/10443/6755 2026-05-06T08:11:36Z 2025-01-01T00:00:00Z

Title: The ecological structure of Laminaria hyperborea kelp forests along the North Sea coast of the United Kingdom Authors: Catherall, Harrison John Norman Abstract: While kelp forests play a crucial role in coastal ecosystems, providing habitat, supporting biodiversity, and contributing to carbon sequestration, their response to environmental perturbations, such as pollution, is not well understood. This thesis investigated the structure and ecological function of Laminaria hyperborea kelp forests along the understudied UK North Sea coastline, with a focus on the impacts of historic coal mine waste on kelp growth, productivity, and associated biodiversity (including microbiome). Field surveys were conducted to assess the structural characteristics and carbon standing stock of kelp forests in northeast England and southeast Scotland. The results indicate significant variation in kelp forest structure across depth gradients and small spatial scales. Kelp density, biomass, and length decreased with depth, while carbon standing stock varied across sites, highlighting the influence of local environmental factors. This work provides a baseline for kelp forests in understudied regions of the UK’s North Sea and gives evidence to suggest they are structured and function similarly to L hyperborea forests at similar latitudes. To investigate the effects of coal mine waste on kelp forests ecosystems, comparative studies were conducted between polluted and non-polluted sites. The results show that kelp forests affected by historic coal mine waste have largely recovered, with growth patterns and carbon contributions similar to unpolluted sites. However, holdfast-associated fauna exhibited reduced abundance and diversity in polluted areas. Whilst this was predominantly an effect of habitat volume, it suggests that there could be lingering ecological impacts that may be affecting broader ecosystem dynamics. Additionally, examination of the effects of historic pollution on the kelp microbiome showed that while bacterial taxa adapted to polluted sites were more abundant, the overall diversity, structure, and abundance of surface microbiomes were similar between polluted and non-polluted kelp forests. This research advances understanding of both natural and pollution-driven variability in L. hyperborea forests, demonstrating that while the structural recovery of kelp forests impacted by mining activities has been successful, biodiversity in some areas remains compromised. These findings underscore the resilience of kelp ecosystems but also highlight the ongoing need for conservation and management to protect these valuable habitats from historic and future environmental stressors. Description: PhD Thesis

2025-01-01T00:00:00Z