ITS1.16/HS12.1 | Invisible Infrastructure: Bridging Data Gaps and Securing Sustainable Data Flows in Freshwater Monitoring for Science and Society
EDI PICO
Invisible Infrastructure: Bridging Data Gaps and Securing Sustainable Data Flows in Freshwater Monitoring for Science and Society
WMO and UNESCO
Convener: Tunde OlarinoyeECSECS | Co-conveners: Moritz Heinle, Claudia Ruz VargasECSECS, Zora Leoni SchirmeisterECSECS, Washington OtienoECSECS

The global community is vastly off track to achieve the UN Sustainable Development Goal 6 on “clean water and sanitation for all” and urgent action is needed to correct this course. However, informed decision making requires sufficient and reliable long-term data and yet, large in situ data gaps still exist on almost all aspects of the hydrological cycle. This was clearly evident in the WMO Status of the Global Water Resources Report for 2023 and is reinforced in this year’s report for 2024.
This session aims to highlight studies that help to close this data gap. This includes the initiation or development of long-term in-situ monitoring programmes, the enhancement of monitoring programmes with novel methodology, or quality improvement of existing data. This session supports a wide range of United Nations programmes, notably the UN Early Warning for All Initiative with its pillar 2 on Detection, Observations, Monitoring, Analysis and Forecasting, output 3.3 of the UNESCO IHP IX (2022 - 2029) which promotes the availability of validated open access water data for sustainable water management, and the WMO Unified Data Policy that aims to implement free and unrestricted data exchange between member states. We invite contributions on the following topics:
1. Developing long-term monitoring:
- Initiation of long-term monitoring programmes emphasizing benefits and challenges
- Extension of existing long-term monitoring programmes, e.g. by combining different components of the hydrological cycle
2. Innovative methods to support long-term monitoring programmes
- Enhancing in-situ monitoring using remote sensing and modelling – reinforcing current monitoring and filling data gaps in the past
- Using citizen science and/or indigenous data sources to strengthen long-term monitoring programmes
- Digitizing written monitoring records applying machine learning and/or crowdsourcing

Solicited authors:
Katie Facer-Childs
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