Data and codes related to the article: Horner et al. Streamflow uncertainty due to the limited sensitivity of controls at hydrometric stations
DOI10.5281/zenodo.5886744Zenodo5886744MaRDI QIDQ6697070
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 21 January 2022
Copyright license: No records found.
The data files and R code files are related to the article Horner et al. Streamflow uncertainty due to the limited sensitivity of controls at hydrometric stations published in Hydrological Processes. Data: 1/ Q_Craponne.txt Original streamflow time series of Craponne hydrometric station which was used to generate the synthetic stage time series using the theoretical equations of the 5 fictive hydrometric stations. Column separator: semi-colon (;) Column 1 == Time (%Y-%m-%d %H:%M) Column 2 == Streamflow (in m3/s) 2/ h_Mercier.txt Original stage time series for Mercier hydrometric station Column separator: semi-colon (;) Column 1 == Time (%Y-%m-%d %H:%M) Column 2 == Stage(in mm) Missing value code: NA 3/ Gaugings_Mercier.txt Gaugings (date, stage, streamflow and associated uncertainty) of the Mercier hydrometric station before and after hydraulic control change which occured in November 2013. Column separator: semi-colon (;) Column 1 == Date (%Y-%m-%d) Column 2 == Stage_m (in m) Column 3 == Streamflow_m3_per_s (in m3/s) Column 3 == Uncertainty (unitless) R codes: 0/ other ressources: All the ressources related to the bayesian estimation of the rating curve can be found on github: https://github.com/BaM-tools Time aggregation of time series was done using the tAgg R package available on github: https://github.com/IvanHeriver/tAgg 1/ functions.R This files contains several functions. Comments within the file explains each of the function: hydraulicEquations(): returns a list of the theoretical equations for the rating curves of the five fictive cases hydraulicEquationsInverter(): inverts of a theoretical rating curve (given a function Q=f(h), it returns a function h=f(Q)) computeAM30(): computes the AM30 generate_nonsyst_errors(): generates a matrix of non systematic errors generate_syst_errors(): generates a matrix of systematic errors get_resampling_indices_from_periodicity(): returns the indices of the resampling time steps used to generate systematic errors given a time vector and a periodicity Some of the code require the following packages: dplyr and RcppRoll 2/ examples.R This file contains some code illustrating the usage of the functions in the functions.R file.
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