Reconnecting Stochastic Methods With Hydrogeological Applications: A Utilitarian Uncertainty Analysis and Risk Assessment Approach for the Design of Optimal Monitoring Networks. Issue 3 (25th March 2018) - Bode, Felix
Abstract: Collaboration between academics and practitioners promotes knowledge transfer between research and industry, with both sides benefiting greatly. However, academic approaches are often not feasible given real‐world limits on time, cost and data availability, especially for risk and uncertainty analyses. Although the need for uncertainty quantification and risk assessment are clear, there are few published studies examining how scientific methods can be used in practice. In this work, we introduce possible strategies for transferring and communicating academic approaches to real‐world applications, countering the current disconnect between increasingly sophisticated academic methods and methods that work and are accepted in practice. We analyze a collaboration between academics and water suppliers in Germany who wanted to design optimal groundwater monitoring networks for drinking‐water well catchments. Our key conclusions are: to prefer multiobjective over single‐objective optimization; to replace Monte‐Carlo analyses by scenario methods; and to replace data‐hungry quantitative risk assessment by easy‐to‐communicate qualitative methods. For improved communication, it is critical to set up common glossaries of terms to avoid misunderstandings, use striking visualization to communicate key concepts, and jointly and continually revisit the project objectives. Ultimately, these approaches and recommendations are simple and utilitarian enough to be transferred directly to other practical water resource related problems. Key Points: We discuss the disconnection between academia and practice in stochastic hydrogeology and analyze project strategies to bridge that gap We recommend multiobjective over single‐objective optimization and scenario analysis over Monte‐Carlo simulation We recommend easy‐to‐communicate qualitative risk assessment methods instead of data‐hungry quantitative (probabilistic) assessment