Rationale
WS has emerged as a predominant framework in the assessment and understanding of the relationship between the environment and society. Past research has produced a variety of framing, definitions, assessment tools and indices. Despite the diversity, all proposed tools and concepts require information (obtained via data) to be used. The following needs were therefore identified:
The need for data - Deputy Secretary-General Eliasson referred to data as the “lifeblood of decision-making and the raw material for accountability” (UN Water 2016). Several barriers exist before reaching well-integrated, accessible and global data. Collecting data requires resources year (Espey et al. 2015). Data ownership, management and access add additional layer of complexity to the issue (Hering 2017). As a consequence, data gaps still exist (Schmidt-Traub et al. 2017; UN-Water 2019) and will affect decision-making (York and Bamberger 2020). Data and information are the foundations of any assessment of WS.
The need for data gathering strategies - When data exist, it may not necessarily translate into information. In several cases “data seems to be collected without a clear statement to be evaluated” (Rose and Smith 1992). To reduce this possibility, it is essential to elaborate an efficient and robust data gathering strategy through careful experimental design. This aspect is not always given the right amount of attention and sampling errors “are believed to dominate the errors of analytical measurement during the entire environmental data acquisition process” (Zhang and Zhang 2012) and will produce different results (Abbatangelo et al. 2019; Wang, Wilson, and VanBriesen 2015).
The need for place-based application – Once operationalized, WS definitions and framing adapt to specific contexts (Gerlak et al. 2018). It is therefore essential to incorporate community context. Therefore, rather than a rigid set of standards and rules a method to build a data gathering strategy is suggested. The user will need to understand what boundaries, threshold, methods, risks are more relevant to the study site.
The need for improve the linkage between data - information - stakeholder - change - Text to be added.
The need to optimize new data acquisition to available resources - add text
For these reasons, a data gathering strategy (DGS) becomes an essential activity to optimize resources, address leverage points, transform research into impact and reduce uncertainties. This document tries to support this important phase of a project.
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