Details of Award
NERC Reference : NE/P000452/1
Citizen science for landslide risk reduction and disaster resilience building in mountain regions
Grant Award
- Principal Investigator:
- Professor W Buytaert, Imperial College London, Civil & Environmental Engineering
- Co-Investigator:
- Mr A Joshi, Practical Action (International), Practical Action (Nepal)
- Co-Investigator:
- Mr R Supper, Geological Survey of Austria, Geophysics
- Co-Investigator:
- Dr A Schiller, Geological Survey of Austria, UNLISTED
- Co-Investigator:
- Mr JK Bhusal, SOHAM-Nepal, SOHAM-Nepal
- Co-Investigator:
- Mr S Dugar, Practical Action (International), Practical Action (Nepal)
- Co-Investigator:
- Professor M Dhital, Tribhuvan University, Geology
- Co-Investigator:
- Dr PJ Smith, Waternumbers, UNLISTED
- Co-Investigator:
- Ms P Shakya, Practical Action (International), Practical Action (Nepal)
- Co-Investigator:
- Mr D Bhandari, Practical Action (International), Practical Action (Nepal)
- Co-Investigator:
- Mr J Chatterjee, Practical Action (International), Practical Action (India)
- Co-Investigator:
- Dr JL Nayava, SOHAM-Nepal, SOHAM-Nepal
- Co-Investigator:
- Dr J Bayer, Intl Inst Applied Systems Analysis IIASA, Risk & Vulnerability Prog
- Co-Investigator:
- Dr W Liu, Intl Inst Applied Systems Analysis IIASA, Risk & Vulnerability Prog
- Co-Investigator:
- Dr B Neupane, UNESCO, Paris
- Grant held at:
- Imperial College London, Civil & Environmental Engineering
- Science Area:
- Atmospheric
- Earth
- Freshwater
- Terrestrial
- Overall Classification:
- Unknown
- ENRIs:
- Environmental Risks and Hazards
- Science Topics:
- Earth & environmental
- Meteorology
- Regional & Extreme Weather
- Satellite observation
- Social Policy
- Earth Surface Processes
- Landslides
- Environmental Informatics
- Abstract:
- Mountains are hotspot of natural disasters, in particular those related to landslides. At the same time, scientific understanding about the natural processes that cause these disasters is lagging behind, because of the complexity of the physical environment and the difficulties facing data collection. The impact of these disasters on society is very high, especially because mountain regions often host less developed infrastructure and vulnerable populations. As a result, there is an urgent need to improve our understanding about how natural disasters in mountain regions occur, how they can be mitigated, and how people at risk can be made more resilient. This proposal will leverage recent technological and conceptual breakthroughs in environmental data collection, processing and communication to leapfrog resilience building in data-scarce and poor mountain communities in South Asia. In particular, we identify three convergent evolutions that hold great promise. First, technological developments in sensor networks and data management allow for participatory and grass-roots data collection and citizen science. Second, web- and cloud based ICT makes it possible to build more powerful analysis and prediction systems, assimilating heterogeneous data sources and tracking uncertainties. Lastly, this enables a more tailored and targeted flow of information for knowledge co-creation and decision-making. These evolutions are part of a trend towards more bottom-up and participatory approaches to the generation of scientific evidence that supports decision making on environmental processes, which is often referred to as "citizen science". We believe that a citizen science approach is particularly promising in remote mountain environments, because improving resilience and humanitarian response in these regions are inherently polycentric activities: a wide range of actors is involved in generating relevant information and scientific evidence, in decision-making and policy building, and in implementing actions both during a hazard and before and after. It is therefore paramount to strengthen the flow of information between these centres of activity, to make best use of existing knowledge, to identify the major knowledge gaps, and to allocate resources to eliminate these gaps. We will use the Karnali basin in Western Nepal as a pilot study. The Karnali basin is a remote and understudied basin that suffers from a complex interplay of natural hazards, including hydrologically-induced landslides and cascading hazards such as flooding. Over the last years, these hazards have caused serious damage to local infrastructure (e.g., roads, irrigation canals, houses, bridges) and affected livelihoods (e.g., 34760 families in the August 2014 floods). Using cost-effective sensor technologies, we will implement grass-roots monitoring of precipitation, river flow, soil moisture, and geomorphology. We will use those data to analyse meteorological extremes, and their impact on spatiotemporal patterns of landslide risk. By merging these data will other data sources such as satellite imagery, we aim to generate landslide risk maps at unprecedented resolution. At the same time, our participatory citizen science approach will enable us to design and implement a framework for bottom-up and polycentric community disaster resilience, based upon knowledge co-generation and sharing. Lastly, we will build upon the existing community-based flood early warning system implemented by our partner Practical Action Nepal, to create a comprehensive multi-hazard early warning system and knowledge exchange platform. For this, we will leverage recent developments in open-standards based, decentralized data processing and knowledge dissemination, such as mobile phones and web-interfaces.
- Period of Award:
- 1 Sep 2016 - 31 May 2022
- Value:
- £1,376,240 Lead Split Award
Authorised funds only
- NERC Reference:
- NE/P000452/1
- Grant Stage:
- Completed
- Scheme:
- Directed - International
- Grant Status:
- Closed
- Programme:
- SHEAR
This grant award has a total value of £1,376,240
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | Exception - Other Costs | DA - Investigators | DI - Staff | Exception - Staff | DA - Estate Costs | DI - T&S | DA - Other Directly Allocated | Exception - T&S |
|---|---|---|---|---|---|---|---|---|---|
| £95,511 | £141,808 | £268,554 | £15,036 | £197,908 | £485,429 | £57,616 | £48,006 | £3,922 | £62,449 |
If you need further help, please read the user guide.