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Collective flood intelligence

Pilot study with the ‘Policy Mobility for Natural Flood Management’ program

This project adopts a Policy Mobility approach to explore localized solutions for community resilience, through Natural Flood Management (NFM), using the Mersey River Basin (MRB) in England and the Shilabati River Basin (SRB) in India as comparative case studies. NFM is recognized as a vital strategy for mitigating flood risks and strengthening community resilience, thereby contributing to global sustainability efforts. To address this, the study adopts a Policy Mobility framework, investigating the transfer and adaptation of NFM strategies from the MRB to the tropical river basin context of the SRB. This two-way knowledge exchange facilitated through a global-to-local perspective, connecting stakeholders from the UK, India, and Japan. Concomitantly, a significant focus is also placed on the role of Local and Indigenous Knowledge Systems (LINKS) and gender perspectives in enhancing NFM practices.”

 

Natural Flood Management India – Foresight-CHAI

(CHAI = ‘collective human artificial intelligence’) – Jan 5-9. This is part of the project ‘Policy Mobility for Local Solutions and Community Resilience through Natural Flood Management’ (British Council funded). Includes Jan 5th lecture on ‘Natural flood management in un-natural times: pathways for the ‘collective NFM intelligence’: Jan 6th introduction to methods & tools: Jan 9th interactive workshop, ‘NFM-Foresight’:  details www.manchester.ac.uk/synergistics/collective-flooding-intelligence/  This is part of the international Foresight-CHAI program in Manchester – Kolkata – Beijing.   Public lectures follow in Kolkata: 

  • 13th Jan 1130: University of Calcutta: ‘Cities in co-evolution: towards a geography of the collective urban intelligence’: (Calcutta – abstract)
  • 13th Jan 1500: Presidency University: ‘Cities in a new world order: transformative pathways and the collective urban intelligence’: (Presidency – abstract )
  • 14th Jan 1530: Jadavpur University: ‘Towards the ‘PURL’ (peri-urban-rural linkages): adaptive pathways for the ‘collective peri-urban intelligence’.  (Jadavpur – abstract )

 

The NFM challenge:

‘NFM is recognized as a vital strategy for mitigating flood risks and strengthening community resilience’ … But – there are many barriers, social, technological, economic, political etc.  In the face of rapid transitions in climatic conditions, urbanization, industrialization, social / demographic change etc, NFM is often bypassed by large-scale engineering solutions. This calls for an integrated, forward-looking approach – which is the aim of this ‘Foresight NFM’.

 

Foresight NFM outline

is a global method & toolkit which inter-connects different forms of knowledge:  explores future opportunities & threats: forms new visions & synergies: applies these to practical pathways & plans. We are now developing an enhanced ‘Foresight-CHAI’ (‘collective human artificial intelligence’), with a leading AI platform, in a UK-China-India collaboration (www.manchester.ac.uk/synergistics/co-futuring/    

This Foresight-NFM workshop program has the following aims:

  • Contribute to the Policy Mobility project, by integration of research-policy knowledge
  • Demonstrate the foresight approach & test the Foresight-CHAI toolkit
  • Enable other free-standing projects to follow on with the NFM transformation

 

Foresight NFM key question:

‘how can natural flood management build resilience to climate-related disasters, combined with urbanization / industrialization, in the Indian sub-continent over the next 25 years’

Following questions:  how to bring together the many different fields and knowledge types involved in NFM? how to combine these in a way that leads to action? how to look beyond the immediate disaster, for a more strategic transformation of water systems in wider context? 

NFM knowledge landscape:

Multiple fields and sectors are relevant, each of them inter-connected with others. This concept graphic illustrates the mind-scape (cognitive landscape) with NFM at the centre: