Design Thinking and Socio-Hydrological Modelling
Abstract
Socio-hydrologists focus on gaining a holistic understanding of coupled human-water systems. Conceptual and computational quantitative models should reflect reality as accurately as possible, in order to optimise the actions that are taken based on model outcomes: decision making, policy making, and behavioural changes. However, due to its multidisciplinary nature, and the inherently complex interactions between humans and natural systems, socio-hydrological modelling is challenging. As technological advances and access to higher-quality data have led to a proliferation of new tools for socio-hydrological modellers, it is crucial for the modelling community to reflect critically on how and why models are developed. This involves unpacking assumptions and theories about data, tools and visualisations. Design disciplines, such as architecture, information visualisation and user experience, have a long history of analysing and creating solutions to complex challenges. While the principles of good design are well established, there has been limited integration of design thinking with environmental science. Using design thinking principles which draw on logic, imagination, intuition and systemic reasoning, we present a rigorous framework to improve the conceptualisation of socio-hydrological models. Using the framework, modellers are guided through multiple cycles of problem framing and system analysis by utilising design concepts, such as persona creation and stakeholder engagement. This process instils in the modeller a deep understanding of the system, leading to a more detailed perceptual model of the problem and, in turn, improvements to the conceptual socio-hydrological model.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.H11O1730T
- Keywords:
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- 1630 Impacts of global change;
- GLOBAL CHANGE;
- 1803 Anthropogenic effects;
- HYDROLOGY;
- 1880 Water management;
- HYDROLOGY;
- 4303 Hydrological;
- NATURAL HAZARDS