Publications
13 results found
Rasheed S, Warder SC, Plancherel Y, et al., 2024, Nearshore tsunami amplitudes across the Maldives archipelago due to worst-case seismic scenarios in the Indian Ocean, Natural Hazards and Earth System Sciences, Vol: 24, Pages: 737-755, ISSN: 1561-8633
The Maldives face the threat of tsunamis from a multitude of sources. However, the limited availability of critical data, such as bathymetry (a recurrent problem for many island nations), has meant that the impact of these threats has not been studied at an island scale. Conducting studies of tsunami propagation at the island scale but across multiple atolls is also a challenging task due to the large domain and high resolution required for modelling. Here we use a high-resolution bathymetry dataset of the Maldives archipelago, as well as corresponding high numerical model resolution, to carry out a scenario-based tsunami hazard assessment for the entire Maldives archipelago to investigate the potential impact of plausible far-field tsunamis across the Indian Ocean at the nearshore island scales across the atolls. The results indicate that the bathymetry of the atolls, which are characterized by very steep boundaries offshore, is extremely efficient in absorbing and redirecting incoming tsunami waves. Results also highlight the importance that local effects have in modulating tsunami amplitude nearshore, including the location of the atoll in question, the location of a given island within the atoll, and the distance of that island to the reef, as well as a variety of other factors. We also find that the refraction and diffraction of tsunami waves within individual atolls contribute to the maximum tsunami amplitude patterns observed across the islands in the atolls. The findings from this study contribute to a better understanding of tsunamis across complex atoll systems and will help decision and policy makers in the Maldives assess the potential impact of tsunamis across individual islands. An online tool is provided which presents users with a simple interface, allowing the wider community to browse the simulation results presented here and assess the potential impact of tsunamis at the local scale.
Clare MCA, Warder SC, Neal R, et al., 2024, An Unsupervised Learning Approach for Predicting Wind Farm Power and Downstream Wakes Using Weather Patterns, Journal of Advances in Modeling Earth Systems, Vol: 16
Wind energy resource assessment typically requires numerical modeling at fine resolutions, which is computationally expensive for multi-year timescales. Increasingly, unsupervised machine learning techniques are used to identify representative weather patterns that can help simulate long-term behavior. Here we develop a novel wind energy workflow that combines the weather patterns from unsupervised clustering with a numerical weather prediction model (WRF) to obtain efficient long-term predictions of wind farm power and downstream wakes, which provide a good approximation to full WRF simulations at vastly reduced computational cost. We use ERA5 reanalysis data and compare clustering on low altitude pressure and wind velocity, a more relevant variable for wind resource assessment. We also compare varying domain sizes for the clustering. A WRF simulation is run at each cluster center and the results aggregated into a long-term prediction using a novel post-processing technique. We consider two case study regions and show that our long-term predictions achieve good agreement with a year of WRF simulations in 2% of the computational time. Moreover, clustering over a Europe-wide domain produces good agreement for predicting wind farm power output, but clustering over smaller domains is required for downstream wake predictions which agree with the year of WRF simulations. Our approach facilitates multi-year predictions of power output and downstream farm wakes, by providing a fast, reliable, and flexible methodology applicable to any global region. Moreover, this constitutes the first tool to help mitigate effects of wind energy loss downstream of wind farms.
Warder SC, Piggott MD, 2022, Optimal experiment design for a bottom friction parameter estimation problem, GEM-INTERNATIONAL JOURNAL ON GEOMATHEMATICS, Vol: 13, ISSN: 1869-2672
- Author Web Link
- Cite
- Citations: 1
Warder SC, Angeloudis A, Piggott MD, 2022, Sedimentological data-driven bottom friction parameter estimation in modelling Bristol Channel tidal dynamics, OCEAN DYNAMICS, Vol: 72, Pages: 361-382, ISSN: 1616-7341
- Author Web Link
- Cite
- Citations: 2
Warder S, Angeloudis A, Piggott M, 2022, Sedimentological data-driven bottom friction parameter estimation in modelling Bristol Channel tidal dynamics
<jats:p>Accurately representing the bottom friction effect is a significant challenge in numerical tidal models. Bottom friction effects are commonly defined via parameter estimation techniques. However, the bottom friction coefficient (BFC) can be related to the roughness of the sea bed. Therefore, sedimentological data can be beneficial in estimating BFCs. Taking the Bristol Channel and Severn Estuary as a case study, we perform a number of BFC parameter estimation experiments, utilising sedimentological data in a variety of ways. Model performance is explored through the results of each parameter estimation experiment, including applications to tidal range and tidal stream resource assessment. We find that theoretically derived sediment-based BFCs are in most cases detrimental to model performance. However, good performance is obtained by retaining the spatial information provided by the sedimentological data in the formulation of the parameter estimation experiment; the spatially varying BFC can be represented as a piecewise-constant field following the spatial distribution of the observed sediment types. By solving the resulting low-dimensional parameter estimation problem, we obtain good model performance as measured against tide gauge data. This approach appears well suited to modelling tidal range energy resource, which is of particular interest in the case study region. However, the applicability of this approach for tidal stream resource assessment is limited, since modelled tidal currents exhibit a strong localised response to the BFC; the use of piecewise-constant (and therefore discontinuous) BFCs is found to be detrimental to model performance for tidal currents.</jats:p>
Warder S, Kramer S, Piggott M, 2021, Non-deterministic effects in modelling the tidal currents in a high-energy coastal site
Rasheed S, Warder SC, Plancherel Y, et al., 2021, An improved gridded bathymetric data set and tidal model for the Maldives Archipelago, Earth and Space Science, Vol: 8, Pages: 1-15, ISSN: 2333-5084
The Maldives faces a unique range of environmental challenges. While the country is almost entirely dependent upon oceanic resources with more than 99% of the area covered by ocean, the absence of a suitable bathymetric map of the seafloor of the Maldives severely limits the adoption and application of modern scientific methods for the prediction of both physical and biological oceanic processes across the country. Here, we present a new bathymetric data set for the country based upon accumulating data from various sources and demonstrate that the synthesis of these provides a far more accurate representation of the shallow water areas of the region than currently available products. We also show that the new bathymetric data set is of sufficiently high resolution to model tidal flows across the archipelago for the first time. The new bathymetric data set provides numerous opportunities to better understand oceanic flow, associated physical and biogeochemical processes, and their correlation to one another across the Maldives archipelago.
Warder S, Piggott M, 2021, Optimal experiment design for bottom friction parameter estimation
Warder SC, Horsburgh KJ, Piggott MD, 2021, Adjoint-based sensitivity analysis for a numerical storm surge model, Ocean Modelling, Vol: 160, Pages: 1-13, ISSN: 1463-5003
Numerical storm surge models are essential to forecasting coastal flood hazard and informing the design of coastal defences. However, such models rely on a variety of inputs, many of which carry uncertainty. An awareness and understanding of the sensitivity of model outputs with respect to those uncertain inputs is therefore essential when interpreting model results. Here, we use an unstructured-mesh numerical coastal ocean model, Thetis, and its adjoint, to perform a sensitivity analysis for a hindcast of the 5th/6th December 2013 North Sea surge event, with respect to the bottom friction coefficient, bathymetry and wind stress forcing. The results reveal spatial and temporal patterns of sensitivity, providing physical insight into the mechanisms of surge generation and propagation. For example, the sensitivity of the skew surge to the bathymetry reveals the protective effect of a sand bank off the UK east coast. The results can also be used to propagate uncertainties through the numerical model; based on estimates of model input uncertainties, we estimate that modelled skew surges carry uncertainties of around 5 cm and 15 cm due to bathymetry and bottom friction, respectively. While these uncertainties are small compared with the typical spread in an ensemble storm surge forecast due to uncertain meteorological inputs, the adjoint-derived model sensitivities can nevertheless be used to inform future model calibration and data acquisition efforts in order to reduce uncertainty. Our results demonstrate the power of adjoint methods to gain insight into a storm surge model, providing information complementary to traditional ensemble uncertainty quantification methods.
Rasheed S, Warder SC, Plancherel Y, et al., 2021, Response of tidal flow regime and sediment transport in North Male' Atoll, Maldives to coastal modification and sea level rise, Ocean Science, Vol: 17, Pages: 319-334, ISSN: 1812-0784
Changes to coastlines and bathymetry alter tidal dynamics and associated sediment transport processes, impacting upon a number of threats facing coastal regions, including flood risk and erosion. Especially vulnerable are coral atolls such as those that make up the Maldives archipelago, which has undergone significant land reclamation in recent years and decades and is also particularly exposed to sea level rise. Here we develop a tidal model of Malé Atoll, Maldives, the first atoll-scale and multi-atoll-scale high-resolution numerical model of the atolls of the Maldives and use it to assess potential changes to sediment grain size distributions in the deeper atoll basin, under sea level rise and coastline alteration scenarios. The results indicate that the impact of coastline modification over the last two decades at the island scale is not limited to the immediate vicinity of the modified island but can also significantly impact the sediment grain size distribution across the wider atoll basin. Additionally, the degree of change in sediment distribution which can be associated with sea level rise that is projected to occur over relatively long time periods is predicted to occur over far shorter time periods with coastline changes, highlighting the need to better understand, predict and mitigate the impact of land reclamation and other coastal modifications before conducting such activities.
Warder S, Horsburgh K, Piggott M, 2020, Adjoint-based sensitivity analysis for a numerical storm surge model
Warder S, Angeloudis A, Kramer S, et al., 2020, A comparison of Bayesian inference and gradient-based approaches for friction parameter estimation
Goss Z, Warder S, Angeloudis A, et al., 2019, Tidal modelling with Thetis: preliminary English Channel benchmarking, Tidal modelling with Thetis: preliminary English Channelbenchmarking
This report describes the application and benchmarking of the Thetis coastal ocean model fortidal modelling, and makes use of a test case based upon the English Channel. Comparisonsare made between model predictions and tide gauge data at a number of locations across theEnglish Channel. A preliminary investigation of the impact of mesh resolution and bathymetrydata is given. A demonstration is also provided of Thetis’s ability to use adjoint technologyto optimise model predictions through the assimilation of observational data. In the examplepresented here the bottom friction field is optimised to provide an improved match betweenthe model results and tide gauge data. This adjoint based optimisation capability may alsobe used to optimise the location, size and design of tidal power generation schemes.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.