SY047-15
Approaching uncertain water futures in the Middle Rio Grande through the integration of data and model-based reasoning

Monday, 14 December 2020: 07:43
Virtual
Katalina Salas and Deana D Pennington, University of Texas at El Paso, El Paso, TX, United States
Abstract:
Scientific data and models are and will continue to be used and developed by the scientific community. Scientists are aware that providing these data and models to the community is essential in securing a better and more sustainable future. In regions such as the Southwestern US with high water stress, scientists must learn to effectively communicate these data and models, enabling forward-looking strategies and communication to achieve water sustainability informed by evidence.
Due to the complexity of the issues involved in addressing water stress and their nature to cross environmental, political, and economic variables, interactive simulations are often used to promote communication and translation between experts and decision-makers.
My research focuses on how people understand, communicate, and reason about future scenarios of food and water availability using scientific data and models. We want to know to what extent scientific data and models can facilitate understanding, decision making, and impact user knowledge and competencies about food and water sustainability. Our study questions focus on data and model reasoning competencies, including understanding, knowledge, systems thinking, strategic thinking, futures thinking, collaboration skills, data skills, and values thinking. We will be investigating the mechanisms for presenting scientific data and models, including static graphs, canned models, interactive models, and conceptual models. This will be done through a series of workshops where participants will be exposed to more complex data and models each time. They will also be using a variety of online tools.
These findings will prove useful in improving our understanding of how non-scientists engage with scientific models and data. We will also assess the effectiveness of the various mechanisms used to present scientific data and models to these non-scientific users.