WARREDOC International Winter School on Data Rich Hydrology 2019

The "Data Rich Hydrology" Winter School 2019 took place in beautiful Colombella, Perugia (Italy). It was jointly organised by the Water Resources Research and Documentation Center (WARREDOC) and UNESCO World Water Assessment Program (WWAP). The WARREDOC was established at the Università per Stranieri di Perugia (UNISTRAPG) since 1985 - developing research, advanced training and scientific communication in the field of water, environment and disaster risk management.

http://warredoc.unistrapg.it/en/events/2019-winter-school/

The days were organised into a serious of lectures and lab sessions and food and accommodation were provided all at the location of the Villa Colombella, that was an extraordinary experience. We were completely immersed in this place students and lecturers altogether.

The program encompassed mainly lectures of absolute high scientific standard and well presented by the experienced and well-known lecturers.

- The Era of Data Rich Hydrology, 1st keynote lecture, by Prof. Rafael L. Bras

Prof. Bras is one of the forefathers of hydrology (Google Scholar). He gave us a history lesson of conceptual, numerical and later computational hydrology and modelling of catchments. He concluded with the outlook of what we as young hydrologists should keep striving towards to improve understanding and modelling of the hydrological cycle.

- The WWDR and SDG 6 Synthesis Report, 2nd keynote lecture, by Prof. Stefan Uhlenbrook, also head of UNESCO WWAP

- Remote sensing and data assimilation in hydrology by Prof. Fabio Castelli

- Hydrologic modelling in a data rich world by Prof. Prof Riccardo Rigon

- Citizen science and big data in hydrology by Prof. Fernando Nardi

- Beyond traditional extreme value theory: lessons learned from rainfall and hurricane intensity by Prof. Marco Marani

More topics got covered by further renowned professors, researchers and practitioners in hydrological and hydraulic modelling:

- Groundwater hydrology and hydrological process mechanics
- The water-food-energy nexus
- Modelling scaling properties of precipitation fields
- Hydrologic measurements and novel observation technologies
- Drones in Hydrology (lecture & hands on)
- Hydrological risk assessment: Return period and probability of failure
- Advances in the space-time analysis of rainfall extremes
- Data poor vs. data rich cases for flood hazard (lecture & hands on)
- Distributed Data quality and urban flood modelling uncertainty
- Stream flow measurements: ground and satellite observations
- Remote sensing data and tools to foster inland water monitoring and flood modeling

I also had the pleasure to get interviewed by research fellow and PhD student Francisco Pena, who does a radio show on Disaster Risk Reduction. We had a great chat about our ideas and views on the topics and lectures during this Winter School on Hydrology and did some brainstorming:

http://www.radiophonica.com/podcast/13941 (link to the radio show)

If you like to check out Francisco's pages: https://www.linkedin.com/in/franciscope%C3%B1a/ (LinkedIn) and and https://twitter.com/FebronioPena (Twitter)

Interoperable exchange of groundwater data with OGC GroundWaterML2

WaterML2 has become a well-known synonym for internationally standardised hydrological data exchange, in particular for government agencies and research institutes across North America, Europe, Australia and New Zealand. Technically, WaterML2 is becoming a suite of standards actively promoted and endorsed by the World Meteorological Organisation (WMO), more details: http://www.whycos.org/wordpress/?page_id=929)

- WaterML 2.0: Part 1 - Time series of Observations

- WaterML 2.0: Part 2 - Ratings, Gaugings and Sections

- WaterML 2.0: Part 3 - Surface Hydrology Features (aka HY_Features)

- WaterML 2.0: Part 4 - aka GroundWaterML 2 (GWML2) Data Exchange for Groundwater Features (including wells, springs, borelogs and well constructions)

Now there is a scientific publication that explains the GWML2 standard, its development and application in hydrogeology in detail:

"GWML2 is an international standard for the online exchange of groundwater data that addresses the problem of data heterogeneity. This problem makes groundwater data hard to find and use because the data are diversely structured and fragmented into numerous data silos. Overcoming data heterogeneity requires a common data format; however, until the development of GWML2, an appropriate international standard has been lacking. GWML2 represents key hydrogeological entities such as aquifers and water wells, as well as related measurements and groundwater flows. It is developed and tested by an international consortium of groundwater data providers from North America, Europe, and Australasia, and facilitates many forms of data exchange, information representation, and the development of online web portals and tools."

Brodaric, B., Boisvert, E., Chery, L. et al. (2018) Enabling global exchange of groundwater data: GroundWaterML2 (GWML2). Hydrogeology Journal. https://doi.org/10.1007/s10040-018-1747-9

Related links and information:

https://link.springer.com/article/10.1007%2Fs10040-018-1747-9

https://www.researchgate.net/publication/323914313_Enabling_global_exchange_of_groundwater_data_GroundWaterML2_GWML2


WaterML2 Part 4: GroundWaterML2 (GWML2) http://www.opengeospatial.org/standards/gwml2

Groundwater SWG http://www.opengeospatial.org/projects/groups/groundwaterswg

OGC GroundWaterML 2 – GW2IE FINAL REPORT https://portal.opengeospatial.org/files/?artifact_id=64688

A call to science and technology to work on standards for environmental data sharing

Which recent Geoscience related journal article has most influenced your work?

For me it was Laniak et al. (2013) "Integrated Environmental Modeling: A Vision and Roadmap for the Future". With a BSc in Computer Science I had worked in the IT industry before I started in academia. When I read Laniak et al. (2013) my Geography Master’s I knew that that was exactly how I would want to apply my computational background. Laniak et al. presented a vison for the future of integrated environmental modelling. They called to science and technology to work on standards for data sharing, and envisioned web-based platforms for transdisciplinary community interactions. I knew that science is not only about observations and theory. But it was then when I deeply understood how the capabilities of modern computers support research, make it reproducible and, thus, can accelerate research. The potential of linking people and knowledge from different disciplines in order to jointly understand natural processes and to make decisions together overwhelmed me. This landmark paper has since influenced me throughout my PhD and beyond.

Reference:

Laniak, Gerard F, Gabriel Olchin, Jonathan Goodall, Alexey Voinov, Mary Hill, Pierre Glynn, Gene Whelan, et al. 2013. “Integrated Environmental Modeling: A Vision and Roadmap for the Future.” Environmental Modelling & Software 39 (0):3–23. https://dx.doi.org/10.1016/j.envsoft.2012.09.006

Hydrological Society Conference - Web-based real-time processing of environmental measurements

This case study was presented as poster abstract.

Kmoch, A., White, P. A., & Klug, H. (2015). Sensor Observation Service and web-based real-time Processing of environmental Measurements in the Upper Rangitaiki Catchment (Poster). In The NZ Hydrological Society Conference 2015, 26th November, in Hamilton, New Zealand

Abstract:

Environmental assessments naturally depend on field observations and technological advancements. , such as tTelemetry, allow the automated collection, transmission and processing of these measurements. However, modelling of natural processes is typically a complex challenge and involves applying expertise of scientists as well as a host of data preparation steps (White, 2006, White et al., 2003).
In addition, automation of model execution with the most recent observation data is dependent on the integration of the data collection, storage and processing elements (Klug and Kmoch, 2014). This paper demonstrates a system that integrates a Sensor Observation Service (SOS) that includinges field observations and internet-based environmental data with a rainfall recharge model that allows near-real time calculation of rainfall recharge in the Upper Rangitaiki catchment, Bay of Plenty region.
The SOS specification is an Open Geospatial Consortium (OGC) standard for the open and standardised integration of environmental sensors into an internet-based environmental data infrastructure (Klug and Kmoch, 2015, Klug, Kmoch, Reichel, 2015).

Figure 1. Process of data flow from field site sensors, to SOS data service to a simulation model process

Results:

• We showed that it is possible to link the collected data directly to a simple rainfall recharge model (Figure 1)
• The low cost sensor and circuit board instrumentation collects data and forwards them to the field computer in 10-minute intervals via robust, low power, ZigBee wireless protocol
• The field computer running a standard Linux operating system, transfers observation data in 10 minute intervals via a 3G mobile data connection to an online SOS server.
• From the service the observations are available in a standardised open format.
• A website can access the raw data from the SOS server and plotted data points within 5-10 minutes of field measurement
• A rainfall recharge model runs with the latest data points from the online SOS server.

References:

Klug, H., & Kmoch, A. (2014). A SMART groundwater portal: An OGC web services orchestration framework for hydrology to improve data access and visualisation in New Zealand. Computers & Geosciences, 69(0), 78–86. http://dx.doi.org/10.1016/j.cageo.2014.04.016

Klug, H., Kmoch, A. (2015). Operationalizing environmental indicators for real time multi-purpose decision making and action support. Ecological Modelling, 295, 66-74. http://dx.doi.org/10.1016/j.ecolmodel.2014.04.009.

White, P. A. (2006). Some Future Directions in Hydrology. Journal of Hydrology (NZ), 45(2), 63–68.

White, P. A., Hong, Y.-S., Murray, D. L., Scott, D. M., & Thorpe, H. R. (2003). Evaluation of regional models of rainfall recharge to groundwater by comparison with lysimeter measurements, Canterbury, New Zealand. Journal of Hydrology (NZ), 42(1), 39–64.

Klug, H., Kmoch, A., & Reichel, S. (2015). Adjusting the Frequency of Automated Phosphorus Measurements to Environmental Conditions. GI_Forum 2015 - Journal for Geographic Information Science - Geospatial Minds for Society, 1, 590–599. http://doi.org/10.1553/giscience2015s590

Using Liquid Democracy for Water Resources Management: A Review

... and how to link governmental policy processes and geosciences via an interactive decision making platform for water resources co-management.

Initial Context - Case Study New Zealand

The responsibility of management of natural resources, in particular water, is typically delegated to the Regional Councils by the Resource Management Act. Decisions regarding water management are regulated through the National Policy Statement for Freshwater Management. However, those decisions need to be backed by thorough science and in consultation with all stakeholders, be it Iwi, domestic, agricultural or industrial water users, or the general public in regards to recreational services that water resources provide.

Current policy and management decision processes follow a rigid procedure, 1) the science to understand the resources, 2) a consultation process (if at all) about the plan how to manage the resource and 3) the development of a long term strategy and policy decision.

The main concern that I’d like to address is the agility of that process. These steps follow the traditional waterfall project management model, which probably is due to the limitations of current tools. Scientific models that aim to characterise the availability and of natural resources and dynamics of environmental process are developed, possible impact assessed and then described in reports for further use. The data and assumptions utilised in the research process are limited snapshots in time, dependent on the quality of data collection and curation processes, and scope and depth vary with available budget. Based on the information from these reports resource managers discuss strategies how to manage the water resources in reconciliation with demands. The consultation with users is again a cost- intensive process, because it is time-consuming. Furthermore, assumptions e.g. limits or local/regional differences in water allocation for the modelling process cannot be changed flexibly.

The consultation process therefore seems limited to very few pre-decided scenarios, which might not have considered all important stakeholders (who are all important stakeholders anyway?). Thus, a final management and policy decision is often not satisfactory.

I would like to propose more research into an agile, aka “liquid resource management system”. Recent advances in computer and web technologies for example allow dynamic exposure of datasets and scientific software. So it might be time to link data with models and a “delegated voting” mechanism into an online resource management feedback system. Such an online platform would provide the capabilities to run different scenarios transparently within a democratic discussion forum. Through delegates stakeholders can have their interests represented in a co-management approach which allows a close link of the resource managers with the affected communities while having the current science at hand.

Background Liquid Democracy

Probably one of the first explicit thoughts on Liquid Democracy originate from Bryan Ford's draft named Delegative Democracy from 2002. Back then it was uncertain what scientific venue(s) it would be suitable for. Bryan unfortunately didn’t manage to get back to exploring and developing it in any rigorous scientific fashion.

• Delegative Democracy: http://www.brynosaurus.com/deleg/deleg.pdf

However, he published a well cited informal blog post revisiting the idea and pointing to some of the interesting developments since 2002:

• Delegative Democracy Revisited: http://bford.github.io/2014/11/16/deleg.html

The closest academic work Bryan referred to as a reasonably serious, rigorous exploration of the topic of any kind (and the only peer-reviewed and published work directly on the topic that by then was James Green-Armytage’s political-economic analysis:

• Direct Voting and Proxy Voting: http://jamesgreenarmytage.com/proxy.pdf

• Green-Armytage, J. (2015). Direct Voting and Proxy Voting. Constitutional Political Economy, 26(2), 190–220. http://doi.org/10.1007/s10602-014-9176-9

This paper took a first step at theoretically defining and analyzing the idea from a cost/benefit perspective, but it’s only a first step and leaves a lot of unanswered questions and issues, and of does not contributes to the empirical space.

Helene Landemore, a colleague of Bryan in political science at Yale, has been interested in this and related “collective intelligence/decision-making” topics for a while. She is working with Rob Reich and Lucy Bernholz at Stanford on some events in the near future exploring this and other related “digital democracy” topics. One might try reaching out to any or all of them as additional points of contacts with more experience in the political and social sciences space.

Further academic or applied works around Liquid Democracy can be found occasionally, for example:

• Blum, C. & Zuber, C.I., 2015. Liquid Democracy: Potentials, Problems, and Perspectives. The Journal of Political Philosophy, 24(August 2014), pp.6–9. Available at: http://doi.wiley.com/10.1111/jopp.12065

• Rodriguez, M. et al., 2007. Smartocracy: Social Networks for Collective Decision Making. In 2007 40th Annual Hawaii International Conference on System Sciences (HICSS’07). IEEE, pp. 90–90. Available at: http://public.lanl.gov/jhw/Jen/Publications_files/Smarto-Share.pdf

• Paulin, A., 2014. Through Liquid Democracy to Sustainable Non-Bureaucratic Government. CeDEM, 6(2), pp.216–230. Available at: http://research.apaulin.com/research/2014/cedem14-ld-snbg

• Boldi, P. et al., 2011. Viscous democracy for social networks. Communications of the ACM, 54(6), p.129. Available at: http://portal.acm.org/citation.cfm?id=1953154

• Zwattendorfer, B., Hillebold, C. & Teufl, P., 2013. Secure and Privacy-Preserving Proxy Voting System. 2013 IEEE 10th International Conference on e-Business Engineering, 0, pp.472–477

• Link, J.Á.B., Tchorbadjiiski, A. & Wehrle, K., 2014. Opportunistic Vote Delegation for E-Voting Based on Liquid Democracy. In Proceedings of the 6th Extreme Conference on Communication. Galapagos Islands: ACM. Available at: https://www.comsys.rwth-aachen.de/fileadmin/papers/2014/2014-bitsch-extremecom-liquid-democracy.pdf

• Behrens, J. et al., 2014. The Liquid Democracy Journal - Issue 1, Berlin. Available at: http://www.liquid-democracy-journal.org/.

• Behrens, J. et al., 2014. The Principles of LiquidFeedback. Interaktive Demokratie e. V. Berlin. ISBN: 978–3–00–044795–2, p.240

Liquid Democracy as an Integrating Technology Platform

Possibly the most prominent of the application of Liquid Democracy is the German Pirate Party [1], for which the Software Liquid Feedback was developed [2]. A Medium article describes the distinctive features [3]:

Liquid Democracy is a new form for collective decision making that gives voters full decisional control. Voters can either vote directly on issues, or they can delegate their voting power to delegates (i.e. representatives) who vote on their behalf. Delegation can be domain specific, which means that voters can delegate their voting power to different experts in different domains. This is in contrast with direct democracy, where participants are required to personally vote on all issues; and in contrast with representative democracy, where participants vote for representatives once in a certain election cycle and then never worry about voting anymore.

Coming back to the water sciences and data issues around the water resources management process. In the last decade so called geoportals evolved to integrated systems of systems, not only providing data, but also processing routines and visualisations of the processed geospatial data to support science and education as well as policy and decision making for particular environmental domains. From a scientific and data-centric point of view, it becomes an obvious choice to link the democratic processes with the data in an online platform, e.g. as suggested by Craglia & Shanley (2015), or in an elaborate study "When Water Becomes the New Oil" (Kwiatkowski & Höchli, 2016) from the Swiss Gottlieb Duttweiler Institute (GDI), an independent think tank based in Rüschlikon near Zurich, which frames this as a participatory process.

• Craglia, M. & Shanley, L., 2015. Data democracy - increased supply of geospatial information and expanded participatory processes in the production of data. International Journal of Digital Earth, 8(9), pp.679–693. Available at: http://www.tandfonline.com/doi/full/10.1080/17538947.2015.1008214

• Kwiatkowski, M. & Höchli, B., 2016. Wenn Wasser zum neuen Öl Wird Wie die Schweiz die Konflikte der Zukunft meistert, Zurich. Available at: http://www.gdi.ch/de/Think-Tank/Studien/Wenn-Wasser-zum-neuen-Oel-wird/727

Links

1. http://liquidfeedback.org/
2. http://techpresident.com/news/wegov/22154/how-german-pirate-partys-liquid-democracy-works
3. https://medium.com/organizer-sandbox/liquid-democracy-true-democracy-for-the-21st-century-7c66f5e53b6f
4. https://en.wikipedia.org/wiki/LiquidFeedback

Geoscience Data Mining and Visualisation Brainstorming Weekend

Early 2016 New Zealand Ministry for Business, Innovation and Employment (MBIE) have put out calls for interested parties for a business to govt (B2G) data innovation challenge, one "opportunity" in particular is about "Geoscience Data management" - thought that I have some strengths and competency to contribute and was keen to have a say.

The Challenge: 

This so called R9 Accelerator brings the public and private sectors together to make it easier for business to interact with New Zealand government.

http://www.r9accelerator.co.nz/opportunities/opportunity14/

A prototype model could be applied to a range of other large databases managed by government, businesses and science institutions across the country. Data management issues are common internationally, so the model could have applications overseas.

On 29th Jan to 31st Jan 2016 was a full weekend information workshop in Miramar, Wellington. Subsequently, if interested, one would have to apply for the 3 months accelerator programme (either as team member or as mentor/domain expert).

http://www.r9accelerator.co.nz/apply/

http://www.r9accelerator.co.nz/timeline/

If teams would get elected to the full programme they would take part in the three month Accelerator starting 1st of March, then pitch to investors. This could be an opportunity to learn how private sector could better interact with govt data.

Alternatively, one could to consider to be involved on higher level into the process, which would be sort of part-time mentorship govt navigator or domain expert type participation.

http://www.r9accelerator.co.nz/take-part/support-a-team/

http://www.r9accelerator.co.nz/take-part/invest-in-a-team/

The plan was to show up and try to form a team and develop an idea and basic plan over the weekend, which will then be pitched on Sunday in a two minute presentation. This apparently would have the most impact on a team's chance of being accepted into the 3 months intense programme, where a real prototype is supposed to be developed by the team. The official application via an online form is then only a formal act to be completed subsequently for an already consistent and focussed team from the weekend).

The Geoscience Data Management opportunity was only one out of 14 or 15, and it's not obvious how many teams tackle each opportunity and how many applications are thought to go forward.

The Team Brainstorming Weekend:

There was a wild crowd of young and old, but only the team around the geodata challenge seemed to be high profile.

Katalyst / KDM Spectrum Data from Australia, Schlumberger, and the NZ agencies MBIE, LINZ, NIWA, GNS (Guy Maslen / Globe Claritas) had representatives there. So we were locked away over the weekend to brainstorm ideas to address MBIE's and NZPM immediate problem of nicer representation/delivery/visualisation of prospectivity data for possible investors in oil&gas and minerals.

From Dave Darby pitching the challenge...

WELLINGTON, NEW ZEALAND - January 29: R9 Accelerator Day 1: January 29, 2016 in Wellington, New Zealand. (Photo by Mark Tantrum/ http://mark tantrum.com, COPYRIGHT:2016 Mark Tantrum)

over group discussions...

WELLINGTON, NEW ZEALAND - January 30: R9 Accelerator Day 2. January 30, 2016 in Wellington, New Zealand. (Photo by Elias Rodriguez/ eliasrodriguez.co.nz) COPYRIGHT:2015 Elias Rodriguez

WELLINGTON, NEW ZEALAND - January 30: R9 Accelerator Day 2. January 30, 2016 in Wellington, New Zealand. (Photo by Elias Rodriguez/ eliasrodriguez.co.nz) COPYRIGHT:2015 Elias Rodriguez

.. toward the final pitch of what a team could possibly achieve if funded (respectively participate in this accelerator program):

WELLINGTON, NEW ZEALAND - January 31: R9 Accelerator Day 3. January 31, 2016 in Wellington, New Zealand. (Photo by Elias Rodriguez/ eliasrodriguez.co.nz) COPYRIGHT:2015 Elias Rodriguez

The (preliminary) Summary:

It could have been a great set-up for creating specific start-up type business solutions for MBIE across their departments.

We came up with designated/suggested team members , e.g. Guy Holmes and Tony Duffy(KDM Spectrum Data), Marielle Lange, a developer, Gavin Chapman, geodata management team at MBIE and I. We also suggested an advisory group, as far as I get it together: Dave Darby (MBIE), James Johnson (MBIE), Richard Garlick (MBIE), Jochen Schmidt (NIWA), Guy Maslen (GNS / Globe Claritas), Grep Byrom (LINZ).

If the proposal would have been accepted then the team would have had to develop a prototype with a little funding type stipend, and present that prototype to MBIE and other possible investors by June. Based on that further commercialization/contracting may arise. However, the professional team members were mainly supposed to support themselves (presumably KDM as big business, MBIE seconding their participant), and few of us would have to go full in and see if we'd be eligible for a part of the team stipend to basically live the start-up work life for the coming three months.

However, while the team, the idea, and the pitch were great, my situation would of course complicate my personal setting my PhD and within SMART programme. Eventually, I had to make a decision and withdrew to wrap up my PhD first. After all, it was a great opportunity to meet fascinating people and talk about possibly disruptive ways of re-shaping geoscience data management, visualization at governmental and even global scale.

Environment Southland Information Management Conference

Regional councils and government agencies are increasingly under pressure to resolve data questions and discover how best to acquire, manage, collate, analyse, report and disseminate data, while managing  the associated costs. Steering organisations through these complex issues requires a solid understanding of what technologies are available and the information demands of the future *(source).

I had the great opportunity to speak at the Environment Southland Information Management Conference in Invercargill. It was a great event, well organised and very informative. I believe I could contribute my part to the line up and fill a few more gaps in the whole picture.
This was not a business as usual conference, it was obvious that the speakers took it serious to cater their presentations to the needs of the stakeholders. And with 70 attendees from regional and central government, as well as visitors from research and industry.

It was great to see the emerging patterns around NZ and similar approaches to a holistic, comprehensive and modern data strategy. If you are interested, this is a link to programme, and please see below for my slides. Watch the talk on Youtube.

Geospatial web-enablement for environmental data in New Zealand

This blog post can be seen as a sequel to a former blog post on the introduction on geospatial data sharing and spatial data infrastructures (SDI), where I explained the basics of OGC standards and web services. Quite some research organisations and governmental agencies already employ OGC standards to make data available online, often even free of charge for the public. I would like to present some really good examples of interoperable data sharing in New Zealand.

Through the standardised and web-based access to so many data sources, not only traditional geographical processing and analysis (GIS) based research is made easier, but also complete new technical and methodological research possibilities arise.

LINZ - Land Information New Zealand

I would like to start with Land Information New Zealand (LINZ). LINZ, as a governmental body, has issued and maintains New Zealand’s geospatial strategy. LINZ runs the LINZ Data Service, which provide tons of NZ-related data sets, topography, maps, place names and much more, almost all of it is available under a NZ Creative Commons license. You can register for free, get an API key and use data directly through web, basically as long as you tell that it is LINZ data. LINZ provides standard OGC CSW, WMS and WFS web service interfaces.

http://data.linz.govt.nz

More news about the NZ geospatial strategy can be found on here.

DOC – Department of Conservation

Also the New Zealand Department of Conservation is going towards geospatial web services. It looks like they use ESRI software, which supports OGC standards to certain bit already, although ESRI (producer of the ArcGIS software) as a commercial closed-source software provider has been known to notoriously neglect open standards. However, the Shapefile format is open and besides ESRI REST services, the DOC Geoportal also allows for OGC-based access (CSW/ISO 19139 metadata for search and discovery and WMS/WFS for map/feature data access)

http://geoportal.doc.govt.nz

GNS Science

The Institute of Geological and Nuclear Sciences is one of the 9 New Zealand Crown Research Institutes (CRI), which conduct about one half publicly/governmentally funded and the other half commercial research projects and, together with the universities of course, can be seen as New Zealand’s main science and research providers, each claiming a particular scientific domains. GNS Science is New Zealand’s leading provider of Earth, geoscience and isotope research and the geological survey of New Zealand. GNS’s research topics also include volcanoes, earthquakes, geothermal features and groundwater.

GNS has published the 1:250 000 Geological Map of New Zealand (QMAP. It is also digitally accessible – GNS exports the QMAP as OGC WMS and WFS in the OGC format GeoSciML. An easy way and very interesting example for interoperability is to explore New Zealand’s geology is through the OneGeology project, which sources and displays such services from geological surveys from all over the world.

The GNS-EU collaborative SMART Acquifer Characterisation programme (SAC) also aims to connect OGC based data sources. Within the research aim “Data Synthesis and Visualisation” the SMART Data Portal aims to develop an integrated OGC framework for discovery, access, processing and visualisation of hydrogeological data.

http://data.gns.cri.nz/metadata/

NIWA – National Institute of Water and Atmospheric Research

NIWA NIWA, another CRI, has a strong reputation in climate, marine and marine ecosystem and biodiversity sciences. Whereas a lot of organisations and agencies make data available first and then (if at all) add more sophisticated search technology, NIWA started the other way round. They established a discovery portal - the Environmental Information Browser, which is basically a catalogue, where one can search by keywords, places, data and time. All the data NIWA has, will eventually be listed and can be queried and also harvested through the OGC CSW interface. Furthermore NIWA is also moving towards providing OGC web services to their data sets. One particular example has been a “Summer of eResearch” project and its progress documented on the eResearch website.

http://ei.niwa.co.nz

Landcare Research

Landcare Research is also a New Zealand Crown Research Institute and focuses on the management of terrestrial biodiversity and land resources in order to both protect and enhance the terrestrial environment. I have come across several Landcare projects on soils and land use data, where Landcare not only uses OGC standards, but also participates in the development and maturing of some of those standards. Like the former parties, Landcare runs a data or geoportal (LRIS), too, which can be accessed and queried through CSW, WMS and WFS web interfaces,

https://lris.scinfo.org.nz/

Landcare also hosts a dedicated soil map portal (S-map), which sources the digital soil information layers based on WMS. Furthermore they drive the development of a global soil map portal (http://www.globalsoilmap.net/), which under the hood, of course, uses OGC standards again. To enable international, comparable, interoperable soil data exchange Landcare participates in the development of a soil information standard.

Outlook

Regional councils are on the way, too. Many regional councils already make data accessible on their web sites. A quick investigation shows for example Environment Waikato, Horizons, HBRC, BOP or Environment Canterbury. However most of these data sources need to be accessed manually and/or do not provide a standardised interface. Not to speak of a generalised way to actually find them. In conjunction with the open data initiatives (Open and Transparent Government , Open New Zealand) and catalogues available ( government datasets online, Open Data Catalogue), there is massive potential to link diverse datasets, relate and analyse seemingly unrelated datasets and gain new insights, find and (re-use) data by type, time and location or just enable ubiquitous mobile access to the data you need. However, we might end up needing a catalogue for the catalogues, and of course a lot of existing data needs to be geo-located/geo-referenced, so that they could be found by location. There is still a way to go and definitely some more research necessary in that space.

Originally posted on the New Zealand eResearch website - 16/04/2013