Imports: sp, dplyr, httr, tools, rappdirs, sf, RSQLite, units
Packaged: 2017-10-30 18:52:31 UTC; lawinslow

Imports: dplyr, dbplyr, httr, tools, rappdirs, sf, RSQLite, units,
Packaged: 2018-02-08 19:33:43 UTC; lawinslow

Packaged: 2018-03-26 15:18:11 UTC; lawinslow

Title: Detect and Characterize Sub-Daily Flow Fluctuations
Authors@R: c(person("Bettina", "Grün", role = c("cre", "ctb"),
person("Franz", "Greimel", role = "ctb",
Description: An important environmental impact on running water ecosystems
is caused by hydropeaking - the discontinuous release of turbine water
because of peaks of energy demand. An event-based algorithm is implemented
to detect flow fluctuations referring to increase events (IC) and decrease
events (DC). For each event, a set of parameters related to the fluctuation
intensity is calculated: maximum flow fluctuation rate mafr(), mean flow
fluctuation rate mefr(), amplitude amp(), flow ratio fr(), and duration dur().
The framework is introduced in Greimel et al. (2016) "A method to detect
and characterize sub-daily flow fluctuations" <doi:10.1002/hyp.10773> and
can be used to identify different fluctuation types according to the potential
source: e.g., sub-daily flow fluctuations caused by hydropeaking, rainfall,
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), data.table
Author: Bettina Grün [cre, ctb] (<https://orcid.org/0000-0001-7265-4773>),

intensity is calculated. The framework is introduced in Greimel et al. (2016)
"A method to detect and characterize sub-daily flow fluctuations"
<doi:10.1002/hyp.10773> and can be used to identify different fluctuation
types according to the potential source: e.g., sub-daily flow fluctuations
caused by hydropeaking, rainfall, or snow and glacier melt.
This is a companion to the package 'hydroroute', which is used to detect and
follow hydropower plant-specific hydropeaking waves at the sub-catchment
scale and to describe how hydropeaking flow parameters change along the
longitudinal flow path as proposed and validated in Greimel et al. (2022).