8th World Water Forum
Session Code: OS-TP-84
21-Mar-2018 4:30 - 6:00
Open Source and big data for water use efficiency and sustainable management.
The main challenges identified in the session involve capacity building, education, and means of implementation. Big data may be free and open source but the level of required knowledge, broadband service, and computing power is not free. Developing nations that have limited access to broadband service or where it cost prohibitive to download big data sets may not be able to use the freely available data. Furthermore, data or software may be free and openly available but the information about the data or software is only in English, which precludes some talented scientists from learning how to use it to process data and conduct analysis. There are also a number of modelling products that are open license but not open source. The challenge with that type of software is that you have to use it as-is. There are also challenges related to data sharing fears. Some water agencies are afraid that “their” data will be used in a way they do not want. Other times dark data exists but is spread across agencies and difficult to find. It may not be digital or may be locked in an agencies internal database. There is also a challenge with hydrologic, meteorologic, and environmental agencies in developing nations being under funded and left with obsolete computer systems. Another challenge is related to donor organizations suggesting or requiring proprietary software systems be used to implement a project they are funding. Unfortunately, once the project is completed and the funding dries up, licensing fees may be difficult pay and the data is lost. A final challenge discussed relates to stability and longevity. Free open source software may be free to users but is not really free. Someone used resources to develop the software. Ongoing update and maintenance was discussed as a challenge.
During the session their were not recommendations made to address the knowledge gap. The recommendation to address the broadband service and computing power challenge involved developing cloud based tools to analyze big data so uploading and downloading is not necessary. The English only documentation challenge was not addressed during the session. The open license challenge was mentioned but a recommendation was not made suggesting changes. It was suggested that donor entities should require open data and software to address the closed data (both not shared and dark data) and chilling of proprietary software. The funding challenge of developing nations resource agencies was not addressed during the session. Developing a large user pool was the recommendation to deal with ongoing software development and updates. It was also recommended that data needs to be findable, accessible, interoperable, and reusable to foster innovation in water management.
The following practices, projects, information sources, and tools were discussed when talking about successful open data or open source software: osgeo.org, foss4g.org, github.com, python.org, tensorflow.org, eros.usgs.gov, majisys.itc.utwente.nl/majisys/#home, itc.nl/ilwis/, geonetcastamericas.noaa.gov, oss.deltares.nl, hubeau.eaufrance.fr, api.gouv.fr, water.europa.eu, en.unesco.org/ihp-wins, landsat.usgs.gov, modis.gsfc.nasa.gov, earlywarning.usgs.gov, grace.jpl.nasa.gov, and www.waterml2.org.
An experience was shared that broke down the fear and led to data sharing. The parties were convinced to use a common open source platform. Then the research convinced the parties that for data security they needed to have the data stored in multiple locations. The research then suggested opening a little data from one party to the other and vice-versa. Which ended up leading to open data. There are numerous open source software tools but no single one has become the standard for water data storage, analysis, visualisation, and distribution.
Gabriel Senay, Ph.D., P.E.
Research Physical Scientist; U.S. Geological Survey
Graduando em Engenharia Florestal