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Detection of long-term changes in hydro-climatological data combining quantile sampling with moving average trend statistics and empirical mode decomposition.

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Detection of long-term changes in hydro-climatological data combining quantile sampling with moving average trend statistics and empirical mode decomposition

This repository stores R-code used to investigate long-term changes in central European river runoff. Main results were published in:

Rottler, E., Francke, T., Bürger, G., and Bronstert, A.: Long-term changes in central European river discharge for 1869–2016: impact of changing snow covers, reservoir constructions and an intensified hydrological cycle, Hydrol. Earth Syst. Sci., 24, 1721–1740, https://doi.org/10.5194/hess-24-1721-2020, 2020.

Summary

Recent climatic changes have the potential to severely alter river runoff, particularly in snow-dominated river basins. Effects of changing snow covers superimpose with changes in precipitation and anthropogenic modifications of the watershed and river network. In the attempt to identify and disentangle long-term effects of different mechanisms, we employ a set of analytical tools to extract long-term changes in river runoff at high resolution. We combine quantile sampling with moving average trend statistics and empirical mode decomposition and apply these tools to discharge data recorded along rivers with nival, pluvial and mixed flow regimes as well as temperature and precipitation data. With a focus on central Europe, we analyse the long-term impact of snow cover and precipitation changes along with their interaction with reservoir constructions.

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Should you have any questions, suggestion or want more information, please do not hesitate to write an email to: rottler(a)uni-potsdam.de

Funding

This research was funded by Deutsche Forschungsgemeinschaft (DFG) within the graduate research training group NatRiskChange (GRK 2043/1-P2) at the University of Potsdam: https://www.uni-potsdam.de/en/natriskchange

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Detection of long-term changes in hydro-climatological data combining quantile sampling with moving average trend statistics and empirical mode decomposition.

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