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WP 2 Developing shared environmental intelligence
WP 2 Developing shared environmental intelligence
To build on and integrate existing tools and approaches towards an environmental intelligence protocol applied in transboundary river contexts, incorporating ground-based, satellite and citizen data sources alongside existing large-scale models and decision-support tools. Some example results are shown below.
Natural flood storage
WaterWorld calculates the capacity of landscapes to retain flood water as natural flood storage. The map shows the greatest storage in each pixel. Yellow is canopy stoarge, red floodplain storage, green is snow and ice, blue is wetland storage and orange is soil storage. Canopy and snow and ice are the dominant stores in the Beki basin.
Greatest hydrological conservation priority
Co$tingNature/local calculates the greatest hydrological conservation priority as the highest priority of three water ecosystem services (pollution dilution services, natural flood/drought storage and water resource provision), current human pressure and future threat. The lower Beki basin is shown. In the blue areas pollution dilution is highest, in the brown areas current human pressure, in the red areas future threat. The green areas show where biodversity is the greatest hydrological conservation priority.
Flood risk trajectory under climate change
WaterWorld calculates flood risk trajectory as the difference in flood risk between baseline (2020) and a climate or land use change scenario. Here the trajectory is driven by a climate change scenario (mean of 21 GCMs, RCP60, 2050s). Red shows where areas of current high flood risk increase, green where high risk decreases, yellow where low risk decreases and blue where current low risk increases. These changes reflect the combined impact of changes in rainfall, evapo-transpiration and snow-melt under climate change.
WP 3 Advancing decision-support tools: co-design
WP 3 Advancing decision-support tools: co-design
To apply elements of the environmental intelligence protocol to two empirical cases (Brahmaputra and Maas/Meuse river basins) by combining citizen science, local knowledge and spatial decision-support modelling to assess potential flood/drought risk mitigation and adaptation actions.
WP 4 - 6 Implementing social learning and open innovation: Policy labs (BRB) & learning alliance (MRB)
WP 4 - 6 Implementing social learning and open innovation: Policy labs (BRB) & learning alliance (MRB)
To empirically test living policy labs for participation and assessment of flooding risk between state and non-state actors and to appraise resilience of water diplomacy (WP4). To empirically identify riparian cultural knowledge and normative frameworks that inform flood and low-flow management practices for a co-developed integrated toolkit for transboundary water policy (WP5).
Fisherman using traditional fishnet in Majuli (river island in India) on the Brahmaputra River.
Sunrise in the Brahmaputra River; to the people in the Brahmaputra valley, the river brings sorrow and joy.
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