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Climate change is likely to impact precipitation as well as snow accumulation and melt in the Northeastern and Upper Midwest United States, ultimately affecting the quantity and seasonal distribution of streamflow. The objective of this study is to analyze seasonality of long-term daily annual maximum streamflow (AMF) records and its changes for 158 sites in Northeastern and Upper Midwest Unites States. A comprehensive circular statistical approach comprising a kernel density method was used to assess the seasonality of AMF. Temporal changes were analyzed by separating the AMF records into two 30-year sub-periods (1951–1980 and 1981–2010). Results for temporal change in seasonality showed mixed pattern/trend across the stations. While for majority of stations, the distribution of AMF timing is strongly unimodal (concentrated around spring season) for the period 1951–1980, the seasonal modes have weakened during the period 1981–2010 for several stations along the coastal region with simultaneous emergence of multiple modes indicating changes of seasonality therein. The fresh statistical approach based on non-parametric circular density estimates reduces some of the limitations of previous studies to detect and model event timing distributions with multiple seasons and addresses issues of non-stationarity in the data records of extreme events.




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Assessment of Hydrological and Hydro-Meterological Extreme Events




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Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.