Water needs assessment: Learning to deal with scale, subjectivity and high stakes

Kim S. Alexander, Magnus Mogliab, Craig Miller

    Research output: Contribution to journalArticlepeer-review

    27 Citations (Scopus)


    Water issues are inherently local and inter-dependent and almost fully reliant on the interaction between humans and their socio-technical environments. Hence, the development of water management policies requires evidence-based information that deals with complex, contextual and multi-faceted issues. Composite indicator methodologies have been found to be useful tools supporting policy development and decision making in several fields. Targeted water management decisions can benefit from indicator methodologies that combine statistical and qualitative information to assess water issues and rank the differing 'needs' of locations. However, due to limitations, we argue that indicator methodologies are best supported by a broader deliberative process of engagement, involving in-depth exploration of issues using inclusive and collective learning processes.In this paper, an integrative and deliberative process is described and applied to understand and evaluate watershed resources and management in the Tigum-Aganan watershed, Philippines. Collectively, local knowledge from stakeholders and quantitative information have been used in developing the water needs index, providing data and informing the selection of data sources for use in the index methodology. In subscribing to the exchange of descriptive narratives we achieve a greater degree of context dependence, transparency and stakeholder participation in the assessment process. Whilst our assessment of the usefulness of the composite methodology is subjective, we believe that the methodology shows great promise; in particular in supporting the emergence of collective action.
    Original languageEnglish
    Pages (from-to)251-257
    Number of pages7
    JournalJournal of Hydrology
    Issue number3-4
    Publication statusPublished - Jul 2010


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