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1.
Patterns (N Y) ; 2(1): 100156, 2021 Jan 08.
Article in English | MEDLINE | ID: mdl-33511362

ABSTRACT

Digital technology is having a major impact on many areas of society, and there is equal opportunity for impact on science. This is particularly true in the environmental sciences as we seek to understand the complexities of the natural environment under climate change. This perspective presents the outcomes of a summit in this area, a unique cross-disciplinary gathering bringing together environmental scientists, data scientists, computer scientists, social scientists, and representatives of the creative arts. The key output of this workshop is an agreed vision in the form of a framework and associated roadmap, captured in the Windermere Accord. This accord envisions a new kind of environmental science underpinned by unprecedented amounts of data, with technological advances leading to breakthroughs in taming uncertainty and complexity, and also supporting openness, transparency, and reproducibility in science. The perspective also includes a call to build an international community working in this important area.

2.
Data Brief ; 33: 106338, 2020 Dec.
Article in English | MEDLINE | ID: mdl-33088871

ABSTRACT

We present a new water level dataset extracted from images taken by four Farson Digital Ltd river cameras for a Tewkesbury, UK flood event (21st November - 5th December 2012). This data article presents the new water level data together with a description of metadata, data acquisition, and extraction methods. The water level information was extracted from the images using measured points in the field-of-view of each camera using Leica GNSS and Total Station instruments with high spatial accuracy of order of 1 cm. We use river gauge data to verify the new dataset. The new dataset has a short duration but includes the rising limb, peak discharge and falling limb of the flood event. It has potential for verifying future automatic water level extraction methods and for development of automatic flood alert methods and can provide valuable information in data assimilation systems used for improving inundation forecasts.

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