Future Marine Data Collection and Sharing - the Digital Ocean

Transcription

1 David Mills & Graham Worley Marine Centre Wales Bangor University Menai Bridge Anglesey UK Future Marine Data Collection and Sharing - the Digital Ocean

2 Introduction The Digital Ocean a journey Data collection o Importance of the European marine regions o Why do we need data? o Current and future approaches Data sharing o Good practice o The Bangor experience Overview

3 Frequency monthly My digital journey From data poor to data rich... Chlorophyll concentration in the North Sea Ship based observation - low frequency spation RV Prince Madog Recording fluorometer

4 chlorophyll (mg m-3) TOXN ( M) Cefas SmartBuoy Programme Data rich to data richer over-winter max NAS TOXN WMS TOXN CTD toxn WMS SILICA CTD Si Oct-00 Jun-01 Feb-02 Oct-02 Jun-03 Feb-04 Oct-04 Jun In situ nitrate analyser spring bloom 40 Multichannel logger - CTD, OBS, O2 Flurometers Water sampler Oct-00 Jun-01 Feb-02 Oct-02 Jun-03 Feb-04 Oct-04 Jun Beginnings of the data deluge Frequency (1 hz 1 day)

5 Digital Ocean - Numerical Modelling Linux cluster Tides, wind, river input 3-d hydro-dynamical model GETM For every column Irradiance, air, temperature, humidity 1-d hydro dynamical model GOTM Information poor to information rich.. irradiation CO 2,O 2, nutrients For every layer 0.5D ERSEM model Pelagic model Benthic model Modelling horizontal transport Modelling vertical transport ( stratification ) Benthic nutrient regeneration model

6 Simulated vs observed bottom oxygen concentrations in the North Sea Oxygen concentration (mmol 02 m-3) Modelled Ship SmartBuoy

7 Moorings Spatial offshore survey Intertidal Data imardis - Integrated Marine Data and Information System Data transmission Data storage Information Application Decision Delayed mode NRW Bathymetry Policy Compliance Delayed mode Real-time Data upload interface BGS BODC imardis Database EMODnet Copernicus Analytics and visualisation Particle dynamics Seabed habitat User Query Licensing Environmental Impact Assessment Resource assessment MRE Site selection Planning Decision taker ACTION Real-time Model results Currents Operational management

8 Integrated Marine Data & Informtion System

9 imardis Digital Technologies

10 Future data collection LR Wave Glider MOST AutoNaut ASV C-Enduro

11 The importance and challenge of observing European seas Maritime area > total land area of the EU. EU coastline of km >3 times the US. About 50% EU's population lives < 50 km from the sea; 14 % of entire EU population live within 500 m of the coast. 63% European holidaymakers holiday at seaside. An estimated 8 million to 10 million anglers fish for sport or pleasure worth 8-10 billion per year. Assets within 500 m of sea worth billion. Coastline protection (erosion and flooding) to reach 5.4 billion per year for the 1990 to 2020 period.

12 Overview of observational needs & purpose Design: expected operating conditions and climate (oil & gas, pipelines, cables, mineral extraction, renewables, ship design), costal developments, defence, fisheries, aquaculture Forecasts:- (early warning) for weather, safe operations, fishing, surveys, defence, coastal defence, HABs, response to spills, search and rescue, navigation route planning, recreation: Ecosystem health:- discharge consents, eutrophication, hypoxia, algal toxins, pathogens, acidification, litter, noise Habitats:- living marine resources Sustainable use of resources Detecting effects of climate change Years Hrs-days Tsunami mins-hrs Months - years Years > decadel Years > decadel Years > decadel

13 Times have changed - & observations UCP (Credit, Oscar Schofield)

14 Platform-centric Sensing Systems Digital Technologies: transforming environmental information & evidence provision Sensor driven, Net-centric, Highly Distributed Sensing Systems A transformation in the way we collect, manage and use data The age of observation and simulation Sea of data

15 Submarine gliders Advantages Operate in adverse conditions High spatial and temporal resolution Cost efficiency (?) Issues Sensor calibration and validation Requires additional data/information from other sources (ships, moorings) Qualitative vs quantitative observations Assessing the potential of autonomous submarine gliders for ecosystem monitoring across multiple trophic levels (plankton to cetaceans) and pollutants in shallow shelf seas. Suberg et al.,

16 How can we reduce the cost of making observations? UK-IMON International Workshop on New Monitoring Technologies: Themes Autonomous systems Conventional systems Sensors Other data acquisition systems VoOp

17 UK-IMON -Autonomous platforms ranking Platform name Score TRL AUV Class #1 (small / coastal / shore based) 15 5 AUV Class #2 (medium / shelf) AUV / Hybrid / longrange /3 AUV Class #3 (large / deep) Bio-mimetic fish 1 Crawlers / rovers / benthic Buoyancy Gliders deep Buoyancy Gliders shallow RPAs / UAVs / UAS 17 5 Mammals (OR IN OTHERS?) 17 5 USV #1 (Short duration) 16 4/5 USV #2 (long endurance) /5 Buoyancy floats 20 5 Surface Drifters 19 5 Research Operational Key TRL Technology Readiness Level AUV Autonmous Underwater Vehicle RPV Remotely Piloted Vehicle UAV Unmanned Ariel Vehicle UAS Unmanned Ariel Systems USV Unmanned Surface Vehicle

18 Lab on chip Sensor/Platform nitrate 3 phosphate 3 silicate 3 ammonium 2 iron/manganese 2 conductivity, temperature, oxygen 3-4 nucleic acid sensor 1-2 synthetic immuno sensor - proteins, small organics, small molecules 1 cytometry 1-2 ph 3 Total Alkalinity / DIC 2 Optode CO2 3 mass spectrometry 4 "Sniffer" methan, PAH/hydrocarbon, carbon dioxide 5 RAMAN 2 ESP/nucleic acid and protein sensor 4-5 Biotaguard (mussel valve opening and sponge) 4-5 acoustic recorders (hydrophone, passive, wide range) 5 automated flow cytometry (full size, in-line, data automation, different types) 5 TRL Sensors Lab on a chip

19 Data sharing

20 Data sharing Data sharing benefits the data collector, funders, data repositories, the scientific community, and civil society. It encourages more connection and collaboration between scientists, with society and better science and communication leads to better decision making. Good practice guide covers key issues Industry what industry and doing what exactly? Definition - releasing data in a form that can be used by other individuals

21 Licensing data sharing Multi-license model License/tagging Non- Commercial ACADEMIC Data License/tagging Commercial Non-academic PUBLIC License/tagging Single User BUSINESS License/tagging SEACAMS License Specific Users Depositor specified

22 Technological Administrative Challenges in Sharing Data Challenges Possible Solutions Comment Restrictive Licencing Slow production of data products No machine-tomachine access mechanisms Slow performance Poor harmonisation in access mechanisms Encourage clearer licence terms. Easy cost-model for commercial exploitation. Better resourcing of data collection and subsequent processing. Utilise REST based APIs to allow extensible access to systems. Use scalable cloud-computing model for infrastructure. Develop federated systems and shared standards for access. Currently a time consuming process required to vary the terms of a licence to allow additional uses. Open data doesn t mean free data. Financial resource required the task. Existing sites often powered by old web technology. Recover proportionate operating costs from the end-user to fund required performance. EMODnet facilitates harmonisation of DACs?

23 Overview Digital technologies disrupt marine data collection o Microprocessors, microcontrollers o HPC o Robotic and autonomous systems Transformation in data collection requires transformation in data management and data use o Cloud storage scaleable solutions o Web technologies data to decisions Analytics, visualisation Solution in search of problem.

24 Thanks for listening...