Keywords: Ecology, Hydrology, Framework, Time Series
Webpages:
https://github.com/mundl/smires,
http://www.smires.eu/ Many hydrological and ecological metrics are constructed in a similar way. A common family of metrics is calculated from a univariate time series (e.g. daily streamflow observations) aggregated for given periods of time. More complex ones involve the detection of events (e.g. no-flow periods or flood events) or several levels of aggregation (e.g. mean annual minimum flow).
Although some
R packages (
hydrostats,
IHA,
hydroTSM, …) providing hydrological metrics exist, they usually strictly require daily time series and do not allow for a free choice of the aggregation period. By contrast the package
smires tries to generalize the calculation and visualization of hydrological metrics for univariate time series providing a generic framework which is developed around
dplyrs (Wickham and Francois 2016) split-apply-combine strategy. It takes into account the peculiarities of hydrological data e.g., the strong seasonal component or the handling of missing data.
The general approach comprises four steps. (1) First the time series can be
preprocessed, e.g. by interpolating missing values or by applying a moving average. If necessary, an optional step (2) involves the identification of
distinct events such as low flow periods. For each event a set of new variables (e.g. event duration or event onset) is derived. In a third step (3)
summary statistics are calculated for arbitrary periods (e.g. months, seasons, calendar years, hydrological years, decades). This step can be repeated until the original time series is aggregated to a single value.
The user keeps full control over the frequency of the time series (daily, weekly, monthly), the choice of preprocessing functions, the aggregation periods, the aggregation functions as well as the handling of events spanning multiple periods. Thus,
smires enables the user to obtain a wide range of metrics whilst minimizing programming effort and error-proneness.
References Bond, Nick. 2016.
Hydrostats: Hydrologic Indices for Daily Time Series Data.
https://CRAN.R-project.org/package=hydrostats.
The Nature Conservancy. 2009.
Indicators of Hydrologic Alteration.
https://www.conservationgateway.org/ConservationPractices/Freshwater/EnvironmentalFlows/MethodsandTools/IndicatorsofHydrologicAlteration/Pages/indicators-hydrologic-alt.aspx.
Wickham, Hadley, and Romain Francois. 2016.
Dplyr: A Grammar of Data Manipulation.
https://CRAN.R-project.org/package=dplyr.
Zambrano-Bigiarini, Mauricio. 2014.
HydroTSM: Time Series Management, Analysis and Interpolation for Hydrological Modelling.
https://CRAN.R-project.org/package=hydroTSM.
