Groundwater exists in underground aquifers and is largely hidden and intangible to water users. As such, groundwater models are one of the main vehicles through which groundwater is made legible. They are critical for water supply planning purposes. However, models are imperfect representations of limited data and contain much uncertainty, posing challenges for the water supply planning process. In this paper, we draw on a case from the Greater Chicago area to examine efforts by the authors and the Illinois State Water Survey to engage with local water managers to develop future water supply scenarios. Much of this area has been dependent upon the Cambrian-Ordovician Aquifer System for over 150 years. Over this period, water levels have declined by over 300 m and aquifers are expected to be unviable by 2030. Here we advance the growing field of participatory groundwater modelling (PGM) to identify forms of uncertainty and their influence on understandings of water supply and risk perceptions of depletion. Conceptually, we draw on the idea of models as world builders, where uncertainties are elucidated through knowledge production in the act of model building, while model development is simultaneously influenced by expectations, beliefs, and ambiguity surrounding those using the models. Through planning meetings and focus group discussions between groundwater modelers and water supply stakeholders, we identify four forms of interconnected uncertainty that hinder planning efforts: 1) hydrogeologic uncertainty, 2) modelling uncertainty; 3) water demand uncertainty; and 4) urban planning uncertainty. We describe our PGM efforts to reduce uncertainty and find stakeholder perceptions are as important as model uncertainties in water management decisions. Participatory modelling is effective in reducing and clarifying these four forms of uncertainty, particularly applied to short-term management decisions in a rapidly changing system. We conclude that future participatory modelling efforts need to focus on reducing communication barriers between scientists and local users.
All Science Journal Classification (ASJC) codes
- Sociology and Political Science