Collaborative Research: Novel Integration of Direct Measurements with Numerical Models for Real-time Estimation and Forecasting of Streamflow Response to Cyclical Processes

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Continuous monitoring of natural rivers supports socio-economic needs related to water resources management and forecasting and provides benchmark data for scientific investigations. The current protocols for continuously monitoring streamflows are based on approaches that fail to accurately capture the short-time effects of flood wave propagation and the seasonal impacts of changes to stream bank vegetation. While decades of substantial advancements in instrumentation technologies have dramatically transformed our in-situ measurement capabilities, this progress has not been mirrored by advancements in streamflow monitoring protocols. The outcomes of the proposed research will fill this gap by offering critical support for water science and management and by adding reliability to predictive models used to issue flood warnings required to protect communities and critical infrastructures. Increasing the accuracy of streamflow measurements in real time and improving the reliability of forecast models will have direct positive impacts on the wellbeing and safety of the public at a time when flooding continues to be a major threat to communities. The research outcomes will include rapid tools derived with artificial intelligence techniques that supplement existing forecasting capabilities. The proposed research combines inferences from experimental, data-driven (i.e., machine learning) and physically-based numerical investigations to enable adoption of a reach-scale monitoring method (rather than cross-sectional) and real time tracking of changes to all flow variables induced by unsteady flows and riparian vegetation growth. A heterogeneous routing approach complemented by extensive data analysis will allow the extraction of interdependencies among flow variables produced by subtle features of the hysteretic behavior associated with the above-mentioned cyclical processes. Generalization of the inferences for a wide range of flow and site conditions will cost-effectively improve the protocols for predictive streamflow relationships using only in-situ acquired data, without making recourse to modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.