Package 'PRSim' reference manual

Title:	Stochastic Simulation of Streamflow Time Series using Phase Randomization
Description:	Provides a simulation framework to simulate streamflow time series with similar main characteristics as observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform or the phases of the wavelet transform. The function prsim() is applicable to single site simulation and uses the Fourier transform. The function prsim.wave() extends the approach to multiple sites and is based on the complex wavelet transform. The function prsim.weather() extends the approach to multiple variables for weather generation. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of the simulation approach for single sites and an application example can be found in Brunner et al. (2019) <doi:10.5194/hess-23-3175-2019>. A detailed description and evaluation of the wavelet-based multi-site approach can be found in Brunner and Gilleland (2020) <doi:10.5194/hess-24-3967-2020>. A detailed description and evaluation of the multi-variable and multi-site weather generator can be found in Brunner et al. (2021) <doi:10.5194/esd-12-621-2021>. A detailed description and evaluation of the non-stationary streamflow generator can be found in Brunner and Gilleland (2024) <doi:10.1029/2023EF004238>.
Authors:	Manuela Brunner [aut, cre] , Reinhard Furrer [aut] , R Core Teamn [ctb, cph] (ks_test.c)
Maintainer:	Manuela Brunner <[email protected]>
License:	GPL-3
Version:	1.5
Built:	2025-03-13 05:01:55 UTC
Source:	https://github.com/cran/PRSim

Stochastic Simulation of Streamflow Time Series using Phase Randomization

Description

Provides a simulation framework to simulate streamflow time series with similar main characteristics as observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform or the phases of the wavelet transform. The function prsim() is applicable to single site simulation and uses the Fourier transform. The function prsim.wave() extends the approach to multiple sites and is based on the complex wavelet transform. The function prsim.weather() extends the approach to multiple variables for weather generation. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of the simulation approach for single sites and an application example can be found in Brunner et al. (2019) <doi:10.5194/hess-23-3175-2019>. A detailed description and evaluation of the wavelet-based multi-site approach can be found in Brunner and Gilleland (2020) <doi:10.5194/hess-24-3967-2020>. A detailed description and evaluation of the multi-variable and multi-site weather generator can be found in Brunner et al. (2021) <doi:10.5194/esd-12-621-2021>. A detailed description and evaluation of the non-stationary streamflow generator can be found in Brunner and Gilleland (2024) <doi:10.1029/2023EF004238>.

Details

The DESCRIPTION file:

Package:	PRSim
Type:	Package
Title:	Stochastic Simulation of Streamflow Time Series using Phase Randomization
Version:	1.5
Date:	2024-03-14
Authors@R:	c(person("Manuela", "Brunner", role = c("aut", "cre"), email = "[email protected]", comment = c(ORCID = "0000-0001-8824-877X")), person("Reinhard", "Furrer", role = c("aut"), email = "[email protected]", comment = c(ORCID = "0000-0002-6319-2332")), person("R Core Teamn", role = c("ctb", "cph"), comment="ks_test.c"))
Author:	Manuela Brunner [aut, cre] (<https://orcid.org/0000-0001-8824-877X>), Reinhard Furrer [aut] (<https://orcid.org/0000-0002-6319-2332>), R Core Teamn [ctb, cph] (ks_test.c)
Maintainer:	Manuela Brunner <[email protected]>
Description:	Provides a simulation framework to simulate streamflow time series with similar main characteristics as observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform or the phases of the wavelet transform. The function prsim() is applicable to single site simulation and uses the Fourier transform. The function prsim.wave() extends the approach to multiple sites and is based on the complex wavelet transform. The function prsim.weather() extends the approach to multiple variables for weather generation. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of the simulation approach for single sites and an application example can be found in Brunner et al. (2019) <doi:10.5194/hess-23-3175-2019>. A detailed description and evaluation of the wavelet-based multi-site approach can be found in Brunner and Gilleland (2020) <doi:10.5194/hess-24-3967-2020>. A detailed description and evaluation of the multi-variable and multi-site weather generator can be found in Brunner et al. (2021) <doi:10.5194/esd-12-621-2021>. A detailed description and evaluation of the non-stationary streamflow generator can be found in Brunner and Gilleland (2024) <doi:10.1029/2023EF004238>.
URL:	https://git.math.uzh.ch/reinhard.furrer/PRSim-devel
BugReports:	https://git.math.uzh.ch/reinhard.furrer/PRSim-devel/-/issues
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
Depends:	R (>= 3.5.0)
Suggests:	lattice, ismev, evd, GB2, boot, MASS
Imports:	stats, methods, lmomco, mev, goftest, wavScalogram, splus2R
RoxygenNote:	7.2.3
NeedsCompilation:	yes
Packaged:	2024-04-08 06:20:41 UTC; brunnerm
Date/Publication:	2024-04-08 08:33:00 UTC
Repository:	https://manuelabrunner.r-universe.dev
RemoteUrl:	https://github.com/cran/PRSim
RemoteRef:	HEAD
RemoteSha:	d9a6e5806c983b339e4d420905a16b1e128d2fb6

Index of help topics:

PRSim-package           Stochastic Simulation of Streamflow Time Series
                        using Phase Randomization
PRsim                   Simulate for one station
PRsim.wave              Simulate for multiple stations
PRsim.wave.nonstat      Simulate for multiple stations under
                        non-stationary conditions
PRsim.weather           Weather simulation (temperature and
                        precipitation) for multiple stations
runoff                  Sample runoff of a catchment
runoff_multi_site_T     Sample runoff and temperature data of two
                        catchments with a similar discharge regime
runoff_multi_sites      Sample runoff of four catchments with a similar
                        discharge regime
simulations             Simulated runoff
simulations_multi_sites
                        Simulated runoff for four catchments
weather_multi_sites     Sample temperature and precipitation of four
                        catchments derived from the ERA5-Land gridded
                        dataset
weather_sim_multi_sites
                        Simulated temperature and precipitation for two
                        grid cells

Contains two functions for the stochastic simulation of continuous discharge time series: prsim and prsim.wave both using phase randomization. prsim is based on the Fourier transform while prsim.wave uses the wavelet transform.

prsim: Simulation in the frequency domain is based on the randomization of the phases of the Fourier transform. We here combine phase randomization simulation with the flexible, four-parameter kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternative distributions or the empirical distribution can be used instead. The simulation approach consists of eight steps: (1) fitting of theoretical Kappa distribution, (2) normalization and deseasonalization, (3) Fourier transformation, (4) Fourier phases computation, (5) random phase generation, (6) inverse Fourier transformation, (7) back transformation, and (8) simulation.

prsim.wave: Simulation for multiple sites in the frequency domain based on the randomization of the phases of the continuous wavelet transform. We combine phase randomization with the flexible, four-parameter kappa distribution. Alternative theoretical distributions or the empirical distribution can be used instead. The simulation procedure consists of five steps: (1) Derivation of random phases from a white noise time series, (2) Fitting of kappa distribution, (3) Wavelet transform, (4) Inverse wavelet transform, and (5) Transformation to the kappa distribution (or the distribution of choice).

prsim.weather: Simulation of two variables (temperature and precipitation) for multiple sites in the frequency domain based on the randomization of the phases of the continuous wavelet transform. We combine phase randomization with the flexible, skewed exponential power (sep) and extended generalized pareto distributions (egpd). Alternative theoretical distributions can be used instead. The simulation procedure consists of five steps: (1) Derivation of random phases from a randomly sampled time series, (2) Fitting of temperature and precipitation disstributions, (3) Wavelet transform, (4) Inverse wavelet transform, and (5) Transformation to the desired distributions.

Author(s)

Manuela Brunner [aut, cre] (<https://orcid.org/0000-0001-8824-877X>), Reinhard Furrer [aut] (<https://orcid.org/0000-0002-6319-2332>), R Core Teamn [ctb, cph] (ks_test.c)

Maintainer: Manuela Brunner <[email protected]>

References

Brunner, M. I., A. Bárdossy, and R. Furrer (2019). Technical note: Stochastic simulation of streamflow time series using phase randomization. Hydrology and Earth System Sciences, 23, 3175-3187, https://doi.org/10.5194/hess-23-3175-2019.

Brunner, M. I., and E. Gilleland (2020). Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-24-3967-2020.

Brunner, M. I., and E. Gilleland (2021). Spatial compound hot-dry events in the United States: assessment using a multi-site multi-variable weather generator, in preparation.

Examples


demo("PRSim")
demo("PRSim-validate")
demo("PRSim_wave")
demo("PRSim_wave-validate")
demo("PRSim_weather")
demo("PRSim_weather-validate")

demo("PRSim")
demo("PRSim-validate")
demo("PRSim_wave")
demo("PRSim_wave-validate")
demo("PRSim_weather")
demo("PRSim_weather-validate")

Simulate for one station

Description

Applies the algorithm to a single station

Usage

prsim(data, station_id="Qobs", number_sim=1, win_h_length=15,
      marginal=c("kappa","empirical"), n_par=4, marginalpar=TRUE, 
      GoFtest=NULL, verbose=TRUE, suppWarn=FALSE, ...)
prsim(data, station_id="Qobs", number_sim=1, win_h_length=15,
      marginal=c("kappa","empirical"), n_par=4, marginalpar=TRUE, 
      GoFtest=NULL, verbose=TRUE, suppWarn=FALSE, ...)

Arguments

`data`	data frame containing the time indications and runoff of at least one station. See ‘Details’.
`station_id`	identifies the station in case several runoffs are present in `data`. See ‘Details’.
`number_sim`	number of simulations to be carried out.
`win_h_length`	(half-)length of moving window size.
`marginal`	marginal distribution to be used for the backtransformation. Can be either `"kappa"`, `"empirical"`, or any type of CDF (see ‘Details’). `"kappa"` uses the four-parameter kappa distribution for backtransformation, `"empirical"` uses the empirical distribution. CDF allows for specifying any distribution ‘Examples’.
`n_par`	number of parameters of the marginal distribution used
`GoFtest`	If (non-null) a GoF test for daily data should be performed: `"KS"` performs a Kolmogorof-Smirnov test, and `"AD"` performs an Anderson-Darling test. see ‘Details’)
`verbose`	logical. Should progress be reported?
`marginalpar`	logical. Should the estimated parameters of the distribution used be returned?
`suppWarn`	logical. See ‘Details’.
`...`	any other argument passed to the sub-function specifying the cdf for fitting. See ‘Details’ and ‘Examples’.

Details

Time can be given with three columns named "YYYY", "MM", "DD", or as in POSIXct format YYYY-MM-DD. All leap days (Feb 29th) will be omitted from the analysis, but no missing observations are allowed.

Stations are identified by column name (default "Qobs"), or by column index.

The function homtest::par.kappa might issue quite a few warnings of type In fn(par, ...) : value out of range in 'gammafn'. The argument suppWarn allows to silence warnings for the specific function call via suppressWarnings(). Of course, a subsequent check via warnings() is recommended.

Alternative distributions can be specified by providing three functions: (1) a function fitting the parameters of a distributions and providing a vector of these parameters as output (CDF_fit), (2) a function simulating random numbers from this distribution (rCDF), and (3) a function specifying the distribution (pCDF). See ‘Examples’ for the generalized beta for the second kind and for the Generalized Extreme Values (GEV) distribution.

When using the kappa distribution, the AD test can for certain values of the parameter h not be performed.

Value

A list with elements

`simulation`	A data frame with time information, observations, deseaonalized observations and `number_sim` columns containing the simulated runoff.
`pars`	A matrix containing the estimated parameters of the marginal distribution (if `marginalpar`).
`p_val`	A vector containing the p-values of `ks.test` or `ad.test` applied to the daily detrended data (if `GoFtest` is not NULL)

Author(s)

Manuela Brunner

References

Examples

data(runoff)
out <- prsim( runoff[ runoff$YYYY<1980, ], "Qobs", 1, suppWarn=TRUE)
# warnings() # as a follow-up to `suppWarn=TRUE`


## Specifying particular CDFs:
## (1) example with the Generalized Extreme Value (GEV) distribution
require("evd")
require("ismev")
rGEV <- function(n, theta)   rgev(n, theta[1], theta[2], theta[3])
pGEV <- function(x, theta)   pgev(x, theta[1], theta[2], theta[3])
GEV_fit <- function( xdat, ...)   gev.fit( xdat, ...)$mle
  

## (2) example with generalized Beta distribution of the second kind

require( "GB2")
rGB2 <- function(n, theta)  rgb2(n, theta[1], theta[2], theta[3], theta[4])
pGB2 <- function(x, theta)  pgb2(x, theta[1], theta[2], theta[3], theta[4])
GB2_fit <- function( xdat, ...)   ml.gb2( xdat, ...)$opt1$par


data(runoff)
out <- prsim( runoff[ runoff$YYYY<1980, ], "Qobs", 1, suppWarn=TRUE)
# warnings() # as a follow-up to `suppWarn=TRUE`


## Specifying particular CDFs:
## (1) example with the Generalized Extreme Value (GEV) distribution
require("evd")
require("ismev")
rGEV <- function(n, theta)   rgev(n, theta[1], theta[2], theta[3])
pGEV <- function(x, theta)   pgev(x, theta[1], theta[2], theta[3])
GEV_fit <- function( xdat, ...)   gev.fit( xdat, ...)$mle
  

## (2) example with generalized Beta distribution of the second kind

require( "GB2")
rGB2 <- function(n, theta)  rgb2(n, theta[1], theta[2], theta[3], theta[4])
pGB2 <- function(x, theta)  pgb2(x, theta[1], theta[2], theta[3], theta[4])
GB2_fit <- function( xdat, ...)   ml.gb2( xdat, ...)$opt1$par

Simulate for multiple stations

Description

Applies the wavelet-based simulation algorithm to multiple sites (single site possible as well)

Usage

prsim.wave(data, station_id="Qobs", number_sim=1, win_h_length=15, 
                  marginal=c("kappa","empirical"), n_par=4, n_wave=100, marginalpar=TRUE, 
                  GoFtest=NULL, verbose=TRUE, suppWarn=FALSE, ...)
prsim.wave(data, station_id="Qobs", number_sim=1, win_h_length=15, 
                  marginal=c("kappa","empirical"), n_par=4, n_wave=100, marginalpar=TRUE, 
                  GoFtest=NULL, verbose=TRUE, suppWarn=FALSE, ...)

Arguments

`data`	list of data frames. One list entry, i.e. data frame, corresponds to one station. Each data frame contains the time indications and runoff of one station. See ‘Details’.
`station_id`	identifies the station in case several time series are present in `data`. See ‘Details’.
`number_sim`	number of simulations to be carried out.
`win_h_length`	(half-)length of moving window size.
`marginal`	marginal distribution to be used for the backtransformation. Can be either `"kappa"`, `"empirical"`, or any type of CDF (see ‘Details’). `"kappa"` uses the four-parameter kappa distribution for backtransformation, `"empirical"` uses the empirical distribution. CDF allows for specifying any distribution ‘Examples’.
`n_par`	number of parameters of the marginal distribution used
`GoFtest`	If (non-null) a GoF test for daily data should be performed: `"KS"` performs a Kolmogorof-Smirnov test, and `"AD"` performs an Anderson-Darling test. see ‘Details’)
`verbose`	logical. Should progress be reported?
`marginalpar`	logical. Should the estimated parameters of the distribution used be returned?
`n_wave`	number of scales to be considered in the continuous wavelet transform.
`suppWarn`	logical. See ‘Details’.
`...`	any other argument passed to the sub-function specifying the cdf for fitting. See ‘Details’ and ‘Examples’.

Details

Stations are identified by list index.

When using the kappa distribution, the AD test can for certain values of the parameter h not be performed.

Value

A list with elements

`simulation`	A data frame with time information, observations, and `number_sim` columns containing the simulated runoff.
`pars`	A matrix containing the estimated parameters of the marginal distribution (if `marginalpar`).
`p_val`	A vector containing the p-values of `ks.test` or `ad.test` applied to the daily detrended data (if `GoFtest` is not NULL)

Author(s)

Manuela Brunner

References

Examples

data(runoff_multi_sites)

## Specifying particular CDFs:
## (1) example with the Generalized Extreme Value (GEV) distribution

require("evd")
require("ismev")
rGEV <- function(n, theta)   rgev(n, theta[1], theta[2], theta[3])
pGEV <- function(x, theta)   pgev(x, theta[1], theta[2], theta[3])
GEV_fit <- function( xdat, ...)   gev.fit( xdat, ...)$mle


## (2) example with generalized Beta distribution of the second kind

require( "GB2")
rGB2 <- function(n, theta)  rgb2(n, theta[1], theta[2], theta[3], theta[4])
pGB2 <- function(x, theta)  pgb2(x, theta[1], theta[2], theta[3], theta[4])
GB2_fit <- function( xdat, ...)   ml.gb2( xdat, ...)$opt1$par


data(runoff_multi_sites)

## Specifying particular CDFs:
## (1) example with the Generalized Extreme Value (GEV) distribution

require("evd")
require("ismev")
rGEV <- function(n, theta)   rgev(n, theta[1], theta[2], theta[3])
pGEV <- function(x, theta)   pgev(x, theta[1], theta[2], theta[3])
GEV_fit <- function( xdat, ...)   gev.fit( xdat, ...)$mle


## (2) example with generalized Beta distribution of the second kind

require( "GB2")
rGB2 <- function(n, theta)  rgb2(n, theta[1], theta[2], theta[3], theta[4])
pGB2 <- function(x, theta)  pgb2(x, theta[1], theta[2], theta[3], theta[4])
GB2_fit <- function( xdat, ...)   ml.gb2( xdat, ...)$opt1$par

Simulate for multiple stations under non-stationary conditions

Description

Applies the wavelet-based and non-stationary simulation algorithm to multiple sites (single site possible as well)

Usage

prsim.wave.nonstat(data, station_id="Qobs", number_sim=1, win_h_length=15, 
                  marginal=c("kappa","empirical"), n_par=4, n_wave=100, cov_name='T', 
                  marginalpar=TRUE, GoFtest=NULL, verbose=TRUE, 
                  suppWarn=FALSE, warming_level, ...)
prsim.wave.nonstat(data, station_id="Qobs", number_sim=1, win_h_length=15, 
                  marginal=c("kappa","empirical"), n_par=4, n_wave=100, cov_name='T', 
                  marginalpar=TRUE, GoFtest=NULL, verbose=TRUE, 
                  suppWarn=FALSE, warming_level, ...)

Arguments

`data`	list of data frames. One list entry, i.e. data frame, corresponds to one station. Each data frame contains the time indications and runoff of one station. See ‘Details’.
`station_id`	identifies the station in case several time series are present in `data`. See ‘Details’.
`number_sim`	number of simulations to be carried out.
`win_h_length`	(half-)length of moving window size.
`marginal`	marginal distribution to be used for the backtransformation. Can be either `"kappa"`, `"empirical"`, or any type of CDF (see ‘Details’). `"kappa"` uses the four-parameter kappa distribution for backtransformation, `"empirical"` uses the empirical distribution. CDF allows for specifying any distribution ‘Examples’.
`n_par`	number of parameters of the marginal distribution used
`GoFtest`	If (non-null) a GoF test for daily data should be performed: `"KS"` performs a Kolmogorof-Smirnov test, and `"AD"` performs an Anderson-Darling test. see ‘Details’)
`verbose`	logical. Should progress be reported?
`cov_name`	character. 'T' for temperature. Has to correspond to covariate name used in data list.
`marginalpar`	logical. Should the estimated parameters of the distribution used be returned?
`n_wave`	number of scales to be considered in the continuous wavelet transform.
`suppWarn`	logical. See ‘Details’.
`warming_level`	a vector of station-specific warming levels. Each vector entry contains the warming level for the corresponding station part of the data list. For example, vector entry 1 represents the warming level for station 1 in the data list.
`...`	any other argument passed to the sub-function specifying the cdf for fitting. See ‘Details’ and ‘Examples’.

Details

Stations are identified by list index.

When using the kappa distribution, the AD test can for certain values of the parameter h not be performed.

Value

A list with elements

`simulation`	A data frame with time information, observations, and `number_sim` columns containing the simulated runoff.
`pars`	A matrix containing the estimated parameters of the marginal distribution (if `marginalpar`).
`p_val`	A vector containing the p-values of `ks.test` or `ad.test` applied to the daily detrended data (if `GoFtest` is not NULL)

Author(s)

Manuela Brunner

References

Brunner, M. I., and E. Gilleland (2024). Future changes in floods, droughts, and their extents in the Alps: a sensitivity analysis with a non-stationary stochastic streamflow generator, Earth's Future.

Weather simulation (temperature and precipitation) for multiple stations

Description

Applies the wavelet-based weather simulation algorithm to multiple sites (single site possible as well)

Usage

prsim.weather(data_p, data_t, station_id_p="Precip",
station_id_t="Temp", number_sim=1, win_h_length=15,
n_wave=100,verbose=TRUE,t_margin='sep',p_margin='egpd',...)
prsim.weather(data_p, data_t, station_id_p="Precip",
station_id_t="Temp", number_sim=1, win_h_length=15,
n_wave=100,verbose=TRUE,t_margin='sep',p_margin='egpd',...)

Arguments

`data_p`	list of precipitation data frames. One list entry, i.e. data frame, corresponds to one station/grid cell. Each data frame contains the time indications and precipitation of one station. See ‘Details’.
`data_t`	list of temperature data frames. One list entry, i.e. data frame, corresponds to one station/grid cell. Each data frame contains the time indications and temperature of one station. See ‘Details’.
`station_id_p`	identifies the precipitation variable name in case several time series are present in `data_p`. See ‘Details’.
`station_id_t`	identifies the temperature variable name in case several time series are present in `data_t`. See ‘Details’.
`number_sim`	number of simulations to be carried out.
`win_h_length`	(half-)length of moving window size.
`t_margin`	marginal distribution to be used for the backtransformation of temperature. Can be either `"sep"` or any type of CDF (see ‘Details’). `"sep"` uses the four-parameter skewed-exponential power for backtransformation. CDF allows for specifying any distribution ‘Examples’.
`p_margin`	marginal distribution to be used for the backtransformation of precipitation. Can be either `"egpd"` or any type of CDF (see ‘Details’). `"egpd"` uses the extended GPD for backtransformation. CDF allows for specifying any distribution ‘Examples’.
`verbose`	logical. Should progress be reported?
`n_wave`	number of scales to be considered in the continuous wavelet transform.
`...`	any other argument passed to the sub-function specifying the cdf for fitting. See ‘Details’ and ‘Examples’.

Details

Stations are identified by list index.

Value

A list with elements temperature and precipitation of

simulation

data frames with time information, observations, and number_sim columns containing the simulated data.

Author(s)

Manuela Brunner

References

Brunner, M. I., and E. Gilleland (2021). Spatial compound hot-dry events in the United States: assessment using a multi-site multi-variable weather generator, in preparation.

Sample runoff of a catchment

Description

Artifical runoff data based on actual and simulated observations.

Usage

data("runoff")data("runoff")

Format

A data frame with 15695 observations of the following 4 variables.

YYYY: a numeric vector, year
MM: a numeric vector, month
DD: a numeric vector, day
Qobs: a numeric vector, synthetic observed runoff

Details

The data mimiks the runoff of the river Plessur at the gauging station Chur, Switzerland. The the flow regime of the river is melt dominated. More information is given in the reference below.

Source

The provided data is a weighted average of the acutually observed values and a particular simulated runoff. The actual discharge data can be ordered from http://www.bafu.admin.ch/wasser/13462/13494/15076/index.

References

Examples

data(runoff)
str(runoff) 
runoff$timestamp <- paste(runoff$YYYY, runoff$MM, runoff$DD, sep=" ")
runoff$timestamp <- as.POSIXct(strptime(runoff$timestamp,
                                 format="%Y %m %d", tz="GMT"))
plot(runoff$timestamp[1:1000], runoff$Qobs[1:1000], type="l", 
  xlab="Time [d]", ylab=expression(paste("Discharge [m"^3,"/s]")))
data(runoff)
str(runoff) 
runoff$timestamp <- paste(runoff$YYYY, runoff$MM, runoff$DD, sep=" ")
runoff$timestamp <- as.POSIXct(strptime(runoff$timestamp,
                                 format="%Y %m %d", tz="GMT"))
plot(runoff$timestamp[1:1000], runoff$Qobs[1:1000], type="l", 
  xlab="Time [d]", ylab=expression(paste("Discharge [m"^3,"/s]")))

Sample runoff and temperature data of two catchments with a similar discharge regime

Description

Observed runoff data from two catchments in Switzerland.

Usage

data("runoff_multi_site_T")data("runoff_multi_site_T")

Format

A list of two data frames (one list per station) of the following 5 variables.

YYYY: a numeric vector, year
MM: a numeric vector, month
DD: a numeric vector, day
Qobs: a numeric vector, observed runoff
T: a numeric vector, average temperature

Details

The data contains runoff for two Swiss gages: (i) Thur Andelfingen (FOEN 2044) and (ii) Alpbach Erstfeld (FOEN 2299).

Source

The actual discharge data were ordered from https://www.bafu.admin.ch/bafu/en/home/topics/water/state/data/obtaining-monitoring-data-on-the-topic-of-water/hydrological-data-service-for-watercourses-and-lakes.html. Temperature averages were computed from E-OBS (https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview).

References

Brunner, M. I. and Eric Gilleland in preparation.

Examples

data(runoff_multi_site_T)
str(runoff_multi_site_T) 
runoff_multi_site_T[[1]]$timestamp <- paste(runoff_multi_site_T[[1]]$YYYY, 
runoff_multi_site_T[[1]]$MM, runoff_multi_site_T[[1]]$DD, sep=" ")
runoff_multi_site_T[[1]]$timestamp <- 
as.POSIXct(strptime(runoff_multi_site_T[[1]]$timestamp,format="%Y %m %d", tz="GMT"))
plot(runoff_multi_site_T[[1]]$timestamp[1:1000], runoff_multi_site_T[[1]]$Qobs[1:1000], type="l", 
  xlab="Time [d]", ylab=expression(paste("Discharge [m"^3,"/s]")))
data(runoff_multi_site_T)
str(runoff_multi_site_T) 
runoff_multi_site_T[[1]]$timestamp <- paste(runoff_multi_site_T[[1]]$YYYY, 
runoff_multi_site_T[[1]]$MM, runoff_multi_site_T[[1]]$DD, sep=" ")
runoff_multi_site_T[[1]]$timestamp <- 
as.POSIXct(strptime(runoff_multi_site_T[[1]]$timestamp,format="%Y %m %d", tz="GMT"))
plot(runoff_multi_site_T[[1]]$timestamp[1:1000], runoff_multi_site_T[[1]]$Qobs[1:1000], type="l", 
  xlab="Time [d]", ylab=expression(paste("Discharge [m"^3,"/s]")))

Sample runoff of four catchments with a similar discharge regime

Description

Observed runoff data from four USGS sites.

Usage

data("runoff_multi_sites")data("runoff_multi_sites")

Format

A list of four data frames (one list per station) of the following 4 variables.

YYYY: a numeric vector, year
MM: a numeric vector, month
DD: a numeric vector, day
Qobs: a numeric vector, observed runoff

Details

The data contains runoff for four USGS gages: (i) Calawah River near Forks, WA (USGS 12043000), (ii) NF Stillaguamish River near Arlington, WA (USGS 12167000), (iii) Nehalem River near Foss, OR (USGS 14301000), and (iv) Steamboat Creek near Glide, OR (USGS 14316700).

Source

The actual discharge data were downloaded from https://waterdata.usgs.gov/nwis.

References

Examples

data(runoff_multi_sites)
str(runoff_multi_sites) 
runoff_multi_sites[[1]]$timestamp <- paste(runoff_multi_sites[[1]]$YYYY, 
runoff_multi_sites[[1]]$MM, runoff_multi_sites[[1]]$DD, sep=" ")
runoff_multi_sites[[1]]$timestamp <- 
as.POSIXct(strptime(runoff_multi_sites[[1]]$timestamp,format="%Y %m %d", tz="GMT"))
plot(runoff_multi_sites[[1]]$timestamp[1:1000], runoff_multi_sites[[1]]$Qobs[1:1000], type="l", 
  xlab="Time [d]", ylab=expression(paste("Discharge [m"^3,"/s]")))
data(runoff_multi_sites)
str(runoff_multi_sites) 
runoff_multi_sites[[1]]$timestamp <- paste(runoff_multi_sites[[1]]$YYYY, 
runoff_multi_sites[[1]]$MM, runoff_multi_sites[[1]]$DD, sep=" ")
runoff_multi_sites[[1]]$timestamp <- 
as.POSIXct(strptime(runoff_multi_sites[[1]]$timestamp,format="%Y %m %d", tz="GMT"))
plot(runoff_multi_sites[[1]]$timestamp[1:1000], runoff_multi_sites[[1]]$Qobs[1:1000], type="l", 
  xlab="Time [d]", ylab=expression(paste("Discharge [m"^3,"/s]")))

Simulated runoff

Description

The dataset is generated with the package own routines and represent 5 series of 18 years of runoff

Usage

data("simulations")data("simulations")

Format

A list of three elements, containing (i) a data frame with 6570 observations of the following variables

YYYY: a numeric vector, year
MM: a numeric vector, month
DD: a numeric vector, day
timestamp: POSIXct vector of the daily runoff
deseasonalized: deseasonalized time series
Qobs: observed runoff
r1,...,r5: 5 simulated runoff series

(ii) a data frame with the daily fitted kappa parameters and (iii) p-values of the daily ks.test.

Details

The data is included to illustrate the validation and visualization routines in demo("PRSim-validate").

Source

The data has been generated with

set.seed(14); prsim( runoff[ runoff$YYYY>1999,], number_sim=5, KStest=TRUE)

(default values for all other arguments).

References

Examples

data(simulations)
names(simulations) 
sim <- simulations$simulation
dim(sim)
sim$day_id <- rep(seq(1:365), times=length(unique(sim$YYYY)))
mean_obs <- aggregate(sim$Qobs, by=list(sim$day_id), FUN=mean, simplify=FALSE)
plot(unlist(mean_obs[,2]),lty=1,lwd=1,col="black", ylab="Discharge [m3/s]",
     xlab="Time [d]", main="Mean hydrographs", ylim=c(0,22), type="l")

for(r in 7:(length(names(sim))-1)){
  mean_hydrograph <- aggregate(sim[,r], by=list(sim$day_id), FUN=mean, simplify=FALSE)
  lines(mean_hydrograph, lty=1, lwd=1, col="gray")
}
lines( mean_obs, lty=1, lwd=1, col="black")
data(simulations)
names(simulations) 
sim <- simulations$simulation
dim(sim)
sim$day_id <- rep(seq(1:365), times=length(unique(sim$YYYY)))
mean_obs <- aggregate(sim$Qobs, by=list(sim$day_id), FUN=mean, simplify=FALSE)
plot(unlist(mean_obs[,2]),lty=1,lwd=1,col="black", ylab="Discharge [m3/s]",
     xlab="Time [d]", main="Mean hydrographs", ylim=c(0,22), type="l")

for(r in 7:(length(names(sim))-1)){
  mean_hydrograph <- aggregate(sim[,r], by=list(sim$day_id), FUN=mean, simplify=FALSE)
  lines(mean_hydrograph, lty=1, lwd=1, col="gray")
}
lines( mean_obs, lty=1, lwd=1, col="black")

Simulated runoff for four catchments

Description

The dataset is generated with the package own routines and represent 5 series of 38 years of runoff for four catchments

Usage

data("simulations_multi_sites")data("simulations_multi_sites")

Format

A list of four elements (one per catchment), containing a data frame each holding information about the observed time series and the stochastic simulations

YYYY: a numeric vector, year
MM: a numeric vector, month
DD: a numeric vector, day
timestamp: POSIXct vector of the daily runoff
Qobs: observed runoff
r1,...,r5: 5 simulated runoff series

Details

The data is included to illustrate the validation and visualization routines in demo("PRSim_wave-validate").

Source

The data has been generated with

prsim.wave(data=runoff_multi_sites, number_sim=5, marginal="kappa", GoFtest = NULL,pars=NULL, p_val=NULL)

(default values for all other arguments).

References

Examples

oldpar <- par(mfrow = c(2, 1), mar = c(3, 3, 2, 1))
### greys
col_vect_obs <- c('#cccccc','#969696','#636363','#252525')
### oranges
col_vect_sim <- c('#fdbe85','#fd8d3c','#e6550d','#a63603')
data(simulations_multi_sites)
sim <- simulations_multi_sites
dim(sim[[1]])
### plot time series for multiple sites
par(mfrow=c(2,1),mar=c(3,3,2,1))
### determine ylim
ylim_max <- max(sim[[1]]$Qobs)*1.5
### observed
plot(sim[[1]]$Qobs[1:1000],
    ylab=expression(bold(
        paste("Specific discharge [mm/d]"))),
    xlab="Time [d]",type="l",col=col_vect_obs[1],
    ylim=c(0,ylim_max),main='Observations')
for(l in 2:4){
  lines(sim[[l]]$Qobs[1:1000],col=col_vect_obs[l])
}
legend('topleft',legend=c('Station 1','Station 2',
        'Station 3','Station 4'),
    lty=1,col=col_vect_obs[1:4])
### simulated (one run)
plot(sim[[1]]$r1[1:1000],
    ylab=expression(bold(paste("Specific discharge [mm/d]"))),
    xlab="Time [d]",type="l",col=col_vect_sim[1],
    ylim=c(0,ylim_max),
    main='Stochastic simulations')
for(l in 2:4){
  lines(sim[[l]]$r1[1:1000],col=col_vect_sim[l])
}
par(oldpar)
oldpar <- par(mfrow = c(2, 1), mar = c(3, 3, 2, 1))
### greys
col_vect_obs <- c('#cccccc','#969696','#636363','#252525')
### oranges
col_vect_sim <- c('#fdbe85','#fd8d3c','#e6550d','#a63603')
data(simulations_multi_sites)
sim <- simulations_multi_sites
dim(sim[[1]])
### plot time series for multiple sites
par(mfrow=c(2,1),mar=c(3,3,2,1))
### determine ylim
ylim_max <- max(sim[[1]]$Qobs)*1.5
### observed
plot(sim[[1]]$Qobs[1:1000],
    ylab=expression(bold(
        paste("Specific discharge [mm/d]"))),
    xlab="Time [d]",type="l",col=col_vect_obs[1],
    ylim=c(0,ylim_max),main='Observations')
for(l in 2:4){
  lines(sim[[l]]$Qobs[1:1000],col=col_vect_obs[l])
}
legend('topleft',legend=c('Station 1','Station 2',
        'Station 3','Station 4'),
    lty=1,col=col_vect_obs[1:4])
### simulated (one run)
plot(sim[[1]]$r1[1:1000],
    ylab=expression(bold(paste("Specific discharge [mm/d]"))),
    xlab="Time [d]",type="l",col=col_vect_sim[1],
    ylim=c(0,ylim_max),
    main='Stochastic simulations')
for(l in 2:4){
  lines(sim[[l]]$r1[1:1000],col=col_vect_sim[l])
}
par(oldpar)

Sample temperature and precipitation of four catchments derived from the ERA5-Land gridded dataset

Description

Reanalysis data of four grid cells from ERA5-Land.

Usage

data("weather_multi_sites")data("weather_multi_sites")

Format

Contains two lists data_p and data_t containing precipitation and temperature data, respectively. Each list consists of four data frames (one list per station/grid cell) of the following 4 variables.

YYYY: a numeric vector, year
MM: a numeric vector, month
DD: a numeric vector, day
Precip/Temp: a numeric vector, observed precipitation/temperature

Details

The data contains data for four grid cells in the Pacific Northwest.

Source

The preciptation data were downloaded from ERA5-Land https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview.

References

Brunner, M. I., and E. Gilleland (2021). Spatial compound hot-dry events in the United States: assessment using a multi-site multi-variable weather generator, in preparation.

Examples

data(weather_multi_sites)
weather_multi_sites[[1]][[1]]$timestamp <- paste(weather_multi_sites[[1]][[1]]$YYYY, 
weather_multi_sites[[1]][[1]]$MM, weather_multi_sites[[1]][[1]]$DD, sep=" ")
weather_multi_sites[[1]][[1]]$timestamp <- 
as.POSIXct(strptime(weather_multi_sites[[1]][[1]]$timestamp,
format="%Y %m %d", tz="GMT"))
plot(weather_multi_sites[[1]][[1]]$timestamp[1:1000],
weather_multi_sites[[1]][[1]]$Qobs[1:1000], type="l", 
xlab="Time [d]", ylab=expression(paste("Temperature [degrees]")))
data(weather_multi_sites)
weather_multi_sites[[1]][[1]]$timestamp <- paste(weather_multi_sites[[1]][[1]]$YYYY, 
weather_multi_sites[[1]][[1]]$MM, weather_multi_sites[[1]][[1]]$DD, sep=" ")
weather_multi_sites[[1]][[1]]$timestamp <- 
as.POSIXct(strptime(weather_multi_sites[[1]][[1]]$timestamp,
format="%Y %m %d", tz="GMT"))
plot(weather_multi_sites[[1]][[1]]$timestamp[1:1000],
weather_multi_sites[[1]][[1]]$Qobs[1:1000], type="l", 
xlab="Time [d]", ylab=expression(paste("Temperature [degrees]")))

Simulated temperature and precipitation for two grid cells

Description

The dataset is generated with the package own routines and represent 5 series of 38 years of meteorological data for two grid cells

Usage

data("weather_sim_multi_sites")data("weather_sim_multi_sites")

Format

Two lists (one per variable) of four elements (one per catchment), containing a data frame each holding information about the observed time series and the stochastic simulations

YYYY: a numeric vector, year
MM: a numeric vector, month
DD: a numeric vector, day
timestamp: POSIXct vector of the daily runoff
Prec/Temp: observed precipitation/temperature
r1,...,r5: 5 simulated data series

Details

The data is included to illustrate the validation and visualization routines in demo("PRSim_weather-validate").

Source

The data has been generated with

prsim.weather(data_p=data_p, data_t=data_t, number_sim=5, p_margin='egpd',t_margin='sep')

(default values for all other arguments).

References

Brunner, M. I., and E. Gilleland (2021). Spatial compound hot-dry events in the United States: assessment using a multi-site multi-variable weather generator, in preparation.

Examples

oldpar <- par(mfrow = c(2, 1), mar = c(3, 3, 2, 1))
data(weather_sim_multi_sites)
sim <- weather_sim_multi_sites
### define plotting colors
col_sim <- adjustcolor("#fd8d3c",alpha=0.8)
col_sim_tran <- adjustcolor("#fd8d3c",alpha=0.2)
col_obs <- adjustcolor( "black", alpha.f = 0.2)
### greys
col_vect_obs <- c('#cccccc','#969696','#636363','#252525')
### oranges
col_vect_sim <- c('#fdbe85','#fd8d3c','#e6550d','#a63603')

### plot time series for multiple sites

### Temperature (first list entry)
par(mfrow=c(2,1),mar=c(3,3,2,1))
### determine ylim
ylim_max <- max(sim[[1]][[1]]$Temp)*1.5
### observed
plot(sim[[1]][[1]]$Temp[1:1000],
ylab=expression(bold(paste("Temperature [degrees]"))),
xlab="Time [d]",type="l",col=col_vect_obs[1],
ylim=c(0,ylim_max),main='Observations')
for(l in 2){
  lines(sim[[l]][[1]]$Temp[1:1000],col=col_vect_obs[l])
}
# legend('topleft',legend=c('Station 1','Station 2'
# ),lty=1,col=col_vect_obs[1:2])
### simulated (one run)
plot(sim[[1]][[1]]$r1[1:1000],
ylab=expression(bold(paste("Temperature [degrees]"))),
xlab="Time [d]",type="l",col=col_vect_sim[1],
ylim=c(0,ylim_max),main='Stochastic simulations')
for(l in 2){
  lines(sim[[l]][[1]]$r1[1:1000],col=col_vect_sim[l])
}


### precipitation (second list entry)
ylim_max <- max(sim[[1]][[2]]$Prec)*1
### observed
plot(sim[[1]][[2]]$Prec[1:1000],
ylab=expression(bold(paste("Precipitation [mm/d]"))),
xlab="Time [d]",type="l",col=col_vect_obs[1],
ylim=c(0,ylim_max),main='Observations')
for(l in 2){
  lines(sim[[l]][[2]]$Prec[1:1000],col=col_vect_obs[l])
}
# legend('topleft',legend=c('Station 1','Station 2'
# ),lty=1,col=col_vect_obs[1:2])
### simulated (one run)
plot(sim[[1]][[2]]$r1[1:1000],
ylab=expression(bold(paste("Precipitation [mm/d]"))),
xlab="Time [d]",type="l",col=col_vect_sim[1],
ylim=c(0,ylim_max),main='Stochastic simulations')
for(l in 2){
  lines(sim[[l]][[2]]$r1[1:1000],col=col_vect_sim[l])
}
par(oldpar)
oldpar <- par(mfrow = c(2, 1), mar = c(3, 3, 2, 1))
data(weather_sim_multi_sites)
sim <- weather_sim_multi_sites
### define plotting colors
col_sim <- adjustcolor("#fd8d3c",alpha=0.8)
col_sim_tran <- adjustcolor("#fd8d3c",alpha=0.2)
col_obs <- adjustcolor( "black", alpha.f = 0.2)
### greys
col_vect_obs <- c('#cccccc','#969696','#636363','#252525')
### oranges
col_vect_sim <- c('#fdbe85','#fd8d3c','#e6550d','#a63603')

### plot time series for multiple sites

### Temperature (first list entry)
par(mfrow=c(2,1),mar=c(3,3,2,1))
### determine ylim
ylim_max <- max(sim[[1]][[1]]$Temp)*1.5
### observed
plot(sim[[1]][[1]]$Temp[1:1000],
ylab=expression(bold(paste("Temperature [degrees]"))),
xlab="Time [d]",type="l",col=col_vect_obs[1],
ylim=c(0,ylim_max),main='Observations')
for(l in 2){
  lines(sim[[l]][[1]]$Temp[1:1000],col=col_vect_obs[l])
}
# legend('topleft',legend=c('Station 1','Station 2'
# ),lty=1,col=col_vect_obs[1:2])
### simulated (one run)
plot(sim[[1]][[1]]$r1[1:1000],
ylab=expression(bold(paste("Temperature [degrees]"))),
xlab="Time [d]",type="l",col=col_vect_sim[1],
ylim=c(0,ylim_max),main='Stochastic simulations')
for(l in 2){
  lines(sim[[l]][[1]]$r1[1:1000],col=col_vect_sim[l])
}


### precipitation (second list entry)
ylim_max <- max(sim[[1]][[2]]$Prec)*1
### observed
plot(sim[[1]][[2]]$Prec[1:1000],
ylab=expression(bold(paste("Precipitation [mm/d]"))),
xlab="Time [d]",type="l",col=col_vect_obs[1],
ylim=c(0,ylim_max),main='Observations')
for(l in 2){
  lines(sim[[l]][[2]]$Prec[1:1000],col=col_vect_obs[l])
}
# legend('topleft',legend=c('Station 1','Station 2'
# ),lty=1,col=col_vect_obs[1:2])
### simulated (one run)
plot(sim[[1]][[2]]$r1[1:1000],
ylab=expression(bold(paste("Precipitation [mm/d]"))),
xlab="Time [d]",type="l",col=col_vect_sim[1],
ylim=c(0,ylim_max),main='Stochastic simulations')
for(l in 2){
  lines(sim[[l]][[2]]$r1[1:1000],col=col_vect_sim[l])
}
par(oldpar)

Package 'PRSim'

Help Index

Stochastic Simulation of Streamflow Time Series using Phase Randomization

Description

Details

Author(s)

References

Examples

Simulate for one station

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Simulate for multiple stations

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Simulate for multiple stations under non-stationary conditions

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Weather simulation (temperature and precipitation) for multiple stations

Description

Usage

Arguments

Details

Value

Author(s)

References

Sample runoff of a catchment

Description

Usage

Format

Details

Source

References

Examples

Sample runoff and temperature data of two catchments with a similar discharge regime

Description

Usage

Format

Details

Source

References

Examples

Sample runoff of four catchments with a similar discharge regime

Description

Usage

Format

Details

Source

References

Examples

Simulated runoff

Description

Usage

Format

Details

Source

References

Examples

Simulated runoff for four catchments

Description

Usage