Alejandra Botero-Acosta , Amanda L Cox, Vasit Sagan, Ibrahim Demir, Marian Muste, Paul Boyd, Chandra Pathak

Sedimentation processes in reservoirs can jeopardize their functionality and compromise dam safety. Climate change and associated hydrologic uncertainty are introducing additional stressors to US reservoirs, and data-driven indicators of climate impacts on upstream soil erosion and reservoir’s sedimentation processes are crucial to evaluate their aggradation and life expectancy. The US Army Corps of Engineers developed the Enhancing Reservoir Sedimentation Information for Climate Preparedness and Resilience (RSI) system to consolidate historical information of elevation-capacity surveys. However, the multiple surveying technologies, protocols, and computational analysis methods used over the service life of reservoirs can impact the quality of reservoir survey data in the RSI system. The objective of this study was to develop a methodology to detect anomalous records and identify multivariate relationships between historical sedimentation data for 184 US reservoirs and associated watershed variables. For this purpose, unsupervised machine learning techniques including Principal Component Analysis (PCA), Autonomous Anomaly Detection, and Kolmogorov-Smirnov and Efron anomaly detection were assembled in an anomaly-detection protocol that led to the detection of 20 reservoirs with anomalous records. The variables contributing most to anomaly detection were related to elevation characteristics (watershed and channel slopes, and minimum elevation), precipitation (maximum and cumulative monthly precipitation), dam properties (time since dam completion and initial trap efficiency), and curve number (CN). PCA results indicated that reservoirs in the Mediterranean California ecoregion although experiencing substantial extreme precipitation events, had small basin areas and CN values that reflected in small capacity losses, contrasting with larger capacity losses found at reservoirs in the Great Plains and Eastern Temperate Forests ecoregions. The developed anomaly detection protocol represents a powerful tool for the analysis and monitoring of this large and heterogenous dataset with the potential of providing reliable information on the impacts of historical climate and watershed properties on erosion and sedimentation processes in US reservoirs.