From Forecast To Foresight: A Decision-Support Framework For Visioning Channel Evolution In River Management

Understanding the evolutionary trajectory of river morphology is essential for those involved in strategic planning for river management, whether related to land use, flood risk alleviation, channel stability, or river restoration. Improvements in simulating and communicating anticipated channel morphological evolutionary responses to changes in environmental forcing over management timeframes would foreshadow the development of foresight competency in river management, allowing resource managers to envisage several plausible futures in channel evolution and to plan towards the most preferred. Foresight competency is a six-fold undertaking (framing-scanning-forecasting-visioning-planning-acting) in which the forecasting and visioning components are the least well developed. Development of an intermediate complexity forecasting tool, FRAME, simulating likely modes of channel morphological evolution over decadal to centennial timeframes over long distances, is the subject of a related presentation (Soar, et al.). Here we focus on the weakest link in providing channel evolutionary foresight, visioning. Visioning involves translating scientific forecasts into a format suitable for use by resource managers via a user-friendly and interactive decision support tool that supports transparent decision making. Critically, the approach requires converting model outputs into metrics of channel evolution that alert managers to the likelihood of various progressive or threshold-based transitions either within or between channel morphology states. Bound by the twin constraints of the ‘dimensions’ of channel morphology change supported by the numerical forecast model (here, FRAME), and management requirements related to land-use planning, hazard diminution/asset maximisation, and river conservation, seven process-based state-transition metrics are proposed. The metrics, covering channel planform, morphological stability, corridor belt width, floodplain connectivity, bank erosion rate, bedform habitat diversity, and ecohydraulic diversity, are derived primarily from regime theory. The computed metrics are subsequently converted from their analytical formulation into graphical indicators that are intuitive for management use and assembled into several prototype dashboard-style graphical user interfaces designed to facilitate interactivity, one of the hallmarks of functional decision support. We illustrate several proof-of-concept applications of this decision support tool (provisionally, RUBRIC, ‘RUles-Based morphological Response in River Channels’) in linking forecasts with visioning to provide the core technical qualities for foresight competency of channel evolution in river management. Near-term priorities in developing RUBRIC towards a fully-operational decision support tool include integrating interactive functionality into the dashboard displays, refining metrics to incorporate new forecast outputs from FRAME, developing map-based indicators, displaying computational uncertainties, and testing on user groups. Progress with these priorities is reported. Developing foresight competency for channel evolution has the potential to greatly improve decision-making in river management, enabling resource managers to steer rivers towards preferred futures that include resilient functional and sustainable ecosystem attributes. However, beyond improvements in strategic forecasting and visioning capabilities such as reported here, foresight competency is highly demanding of the underlying database of empirical and theoretical knowledge in fluvial geomorphology.