Manufacturing surprise: How information content, modeling capabilities and decision making purpose influence optimal streamflow monitoring

Streamflow monitoring is a key input to water resource management, as it is an important source of information for understanding hydrological processes and prediction catchment behaviour and resulting flows. Both the monitored and the predicted flows support important decisions in areas such as infrastructure design, flood forecasting and resource allocation. It is therefore essential that the predictive information we have about our water resources serves these various needs.

Since observations are from the past and our decisions affect the future, models are needed to extrapolate measurements in time. Similarly, streamflow is not always measured at places where the information is needed, so interpolation or extrapolation is needed in space or across catchment properties and climates. Recent advances in publicly available large datasets of streamflow records and corresponding catchment characteristics have enabled succesful applications of machine learning to this prediction problem, leading to increased predictability in ungauged basins.

Since information content is related to surprise, we could see the objective of monitoring networks as manufacturing surprising data. This is formalized in approaches for monitoring network design based on information theory, where often the information content of the sources, i.e. the existing monitoring stations, has been investigated, including the effects of redundancy due to shared information between stations.

In this research, we argue that information content is related to unpredictability, but is inevitably filtered through several layers, which should be considered for monitoring network design. Examples of such filters are the models used for extrapolation to ungauged sites of interest, the target statistics of interest to be predicted, and the decision making purpose of those predictions. This means that the optimal monitoring strategy (where to measure, with how much precision and resolution, and for how long) depend on evolving modeling capabilities and representation of societal needs. Also, biases in the current neworks may exist as a function of how they are funded.

In this presentation, these theoretical aspects are investigated with examples from an ongoing project to investigate the streamflow monitoring network in British Columbia, Canada, which recently experienced record-breaking floods.