Bacton Digital Beach Twin

Case Study Overview

The Bacton Digital Beach Twin (DBT) is a virtual mirror of the Bacton Sandscaping scheme. This £20M coastal protection scheme consists of a mega-nourishment that protects a nationally critical gas terminal and buys adaptation time for the surrounding villages.

What challenges does it solve?

The DBT helps ensure that future protection for the terminal, as well as allowing adaptation plans for the communities, is put in place in a timely way:

▪ not too late (which could involve catastrophic erosion and flooding impacts)
▪ not too early (which would lead to sub-optimal investment, and possibly even unnecessary investment)

The moment of intervention is determined by triggers for intervention. During the design stage, the lifetime of the scheme was determined by comparing the results of predictive coastal models to these triggers. Through recalibration of the models, based on regular beach surveys, the DBT reduces the uncertainty regarding the remaining level of protection as time goes by.

How have you integrated data & technology?

The DBT consists of a backend (used by expert users) combined with a frontend (including data storage) that end users can interact with. The backend is an offline algorithm written in Python that works in conjunction with the DHI MIKE suite of predictive computational models. This algorithm supports the coastal modeller with recalibrating the existing predictive model based on surveyed data and prepares the survey data and model output for inclusion into the frontend of the DBT. The frontend of the DBT was built using Esri ArcGIS JS APIs in combination with an Esri ArcGIS server. Bringing the backend algorithm online was not feasible due to budgetary constraints.

Do you use a user interface to share information?

The information in the DBT is presented to the end user via a (password-protected) online portal. The user sees a map with geospatial data (that represents the past and present state of the beach), as well as a graph with beach profiles (selectable on the map) showing a section through that geospatial information, as well as modelling results and the trigger for intervention. But most importantly, the user sees a graph that identifies when (and where along the coast) an intervention and thus reinvestment is expected to be needed. This interface was developed in close collaboration with the client group, and shows them exactly what they need to see.

What outcomes have you delivered?

The DBT helps to sustain the benefits created by the Bacton Sandscaping scheme, by improving the efficiency and effectiveness of the scheme’s whole life beach management. It automates a large part of this analysis, reducing human errors, and making the process much more time efficient

Have you delivered any unexpected benefits?

The algorithm that is part of the backend of the Digital Beach Twin has already been used to automate analysis that are part of other projects. It also provides an example of a Digital Twin that is different from a lot of the digital twins that are currently under development, stimulating wider thinking.

What lessons have you learn that apply to future work?

▪ Direct, intensive collaboration with the end users is very important and will be a main factor in the success or failure of a digital twin.
▪ The development of this digital twin highlighted the effectiveness of a synergy between a digital solution and expert knowledge. When combined correctly, this is a very powerful tool.
▪ Funding of the development of a DT solution is challenging. The business case developed for the DBT shows that in the long term the benefits outweigh the costs, and this will be a valuable tool in convincing future users.
▪ End users need to know what their ultimate management challenges are and what the processes supporting their decision-making look like. In case of the Bacton Sandscaping Scheme, this question was simple, and an assessment routine for the remaining life of the scheme was determined during the design of the scheme. But where this is not clear-cut, it is difficult to convince end users that a digital twin can benefit them, and mapping the most important management decisions that the twin will need to support is the first step.