Abstract
The Social-Ecological Systems framework serves as a valuable framework to explore and understand social and ecological interactions, and pathways in water governance. However, it lacks a robust understanding of change. We argue an analytical and methodological approach to engaging global changes in SES is critical to strengthening the scope and relevance of the SES framework. Relying on SES and resilience thinking, we propose an institutional and cognitive model of change where institutions and natural resources systems co-evolve. Our model of change provides a dynamic understanding of SES that stands on three causal mechanisms: institutional complexity trap, rigidity trap, and learning processes. We illustrate how data cube technology could overcome current limitations and offer reliable avenues for testing hypotheses about the dynamics of Social-Ecological Systems and water security by offering to combine spatial and time data with no major technical requirements for users.
Highlights
Pressures on natural resources are increasing and a number of challenges need to be overcome to address growing population in a period of environmental variability
We use the concepts of rigidity trap and institutional complexity trap to explain the phases of saturation, and draw on the adaptive governance literature to explain the phase of reorganization
We propose a model of change for SES that offers a theoretical background to the two main phases of an adaptive cycle—saturation and reorganization
Summary
The cumulative effects of overexploitation, pollution, and challenges posed by climate change demand careful monitoring and assessing of trends and evolution of water resources [6] This is an essential pre-condition to ensure a sustainable use and access to water that can serve as a basis for water security. This technology is a new big data tool providing ready-for-analysis datasets that combine Earth observation data over years as well as space [13,14] It requires no major technical skills from users or researchers and offers reliable avenues to test hypothesis about the dynamics of social-ecological systems and water security by offering to combine spatial and time data with no major technical requirements for users. We conclude by identifying the steps and avenues
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