Geospatial analysis and modelling of riverine systems: realising the potential for satellite supplied data assimilation

Lead Research Organisation: University of Nottingham
Department Name: Faculty of Engineering


This project aims to analyse the potential and practicalities of creating a Digital Twin of a riverine environment for use by water managers and engineers. The high rate of spatial and temporal change in rivers make them difficult to monitor in-situ especially over an extended period of time. As a result, satellite remote sensing has been identified as a key tool to improve system management. Many riverine characteristics have been measured from space but for the most part, these are limited to proof-of-concept studies and a lack of research into conversion into actionable and applicable products.
Thus, there is currently a research niche to be found in producing a Digital Twin virtual environment powered by Earth Observation to solve real world engineering management problems. First this would involve examining which river characteristics are most important for different types of river manager or engineer. These will be organised into those that can currently be measured through earth observation, those that theoretically can be measured but currently lack algorithms or methodology to be implemented and those that would require in-situ sensors or higher resolution imagery. The former category will be developed into a novel dashboard, analogous to a low-level Digital Twin, which will collate these existing methods and provide a state of the art for managers to rely on for decision making. Cloud computing software, such as Google Earth Engine, will be relied upon to provide the framework to compose the big data required for this assimilation and the processing power to make it feasible to use.
Using this dashboard as a foundation there are three workflows to be undertaken. Firstly, similar systems and APIs are very difficult for a non-expert to use and when compared, to the vast visualisation leaps taken by computer games, these can be viewed as clunky and unappealing. Google has created some conceptual flood maps which use gaming engines to give a real idea of what predicted flood water would look like in a 3D valley and this work package will aim to incorporate some of these improvements into the dashboard.
Secondly, it is proposed that a set of studies will be undertaken to compare different resolutions of satellite data, aerial and drone based remote sensing in the areas already identified. Direct use cases will be assessed, in order to outline an understanding of what the business cases are for companies to pay for proprietary satellite data or field programmes to enhance their Digital Twins. This would also include an appreciation of the required twinning rate (how often the virtual environment would need to be updated) during the example scenarios in order to better understand the system.
Finally, a work package will be put together to investigate the possible ways in which characteristics, not currently measurable through satellite remote sensing, can be measured through aerial remote sensing or sensor systems. It is expected that some of the key variables for managers will not be available and therefore the use of a Digital Twin will be reliant on alternative methods fitting seamlessly into the model. Therefore, some of these methods will be tested in field programmes with sensors or smaller scale remote sensing methods assessing their possible contribution and interactions. The final product will be a comprehensive understanding of the ability for satellite Earth Observation to live up to its potential to revolutionise river management and engineering. This will be facilitated through the construction and improvements made to a Digital Twin collating all these available data and algorithms. Along with work packages assessing the possible improvements and viability of different aspects of this virtual environment including its ability to visually communicate, the need for higher quality data in specific cases and the ability to add external datasets to compensate where satellite data cannot carry out the task


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023577/1 31/03/2019 29/09/2027
2422800 Studentship EP/S023577/1 30/09/2020 29/09/2024 Samuel Valman