Reference_Manual_hymett.md
In HyMETT: Hydrologic Model Evaluation and Time-Series Tools

HyMETT Function Reference

benchmark_KGE_DOY
calc_annual_flow_stats
calc_annual_stat_trend
calc_logistic_regression
calc_qlpearsonIII
calc_qpearsonIII
censor_values
example_annual
example_mod
example_mod_zf
example_obs
example_obs_zf
example_preproc
GOF_correlation_tests
GOF_kling_gupta_efficiency
GOF_mean_absolute_error
GOF_mean_error
GOF_nash_sutcliffe_efficiency
GOF_percent_bias
GOF_rmse
GOF_summary
GOF_volumetric_efficiency
HyMETT-package
POR_apply_annual_hiflow_stats
POR_apply_annual_lowflow_stats
POR_calc_amp_and_phase
POR_calc_AR1
POR_calc_lp3_quantile
POR_deseasonalize
POR_distribution_metrics
preproc_audit_data
preproc_fill_daily
preproc_main
preproc_precondition_data
preproc_validate_daily

DESCRIPTION

Package: HyMETT
Type: Package
Title: Hydrologic Model Evaluation and Time-Series Tools
Version: 1.1.0
Date: 2023-10-03
Authors@R: c(
    person(family = "Penn",
           given = "Colin",
           role = c("aut","cre"),
           email = "cpenn@usgs.gov",
           comment = c(ORCID = "0000-0002-5195-2744")),
    person(family = "Simeone",
           given = "Caelan",
           role = c("aut"),
           email = "csimeone@usgs.gov",
           comment = c(ORCID = "0000-0003-3263-6452")),
    person(family = "Levin",
           given = "Sara",
           role = c("aut"),
           email = "slevin@usgs.gov",
           comment = c(ORCID = "0000-0002-2448-3129")),
    person(family = "Saxe",
           given = "Samuel",
           role = c("aut"),
           email = "ssaxe@usgs.gov",
           comment = c(ORCID = "0000-0003-1151-8908")),
    person(family = "Foks",
           given = "Sydney",
           role = c("aut"),
           email = "sfoks@usgs.gov",
           comment = c(ORCID = "0000-0002-7668-9735")),
    person(family = "Dudley",
           given = "Robert",
           role = c("dtc"),
           email = "rwdudley@usgs.gov",
           comment = c(ORCID = "0000-0002-0934-0568")),
    person(family = "Hodgkins",
           given = "Glenn",
           role = c("dtc"),
           email = "gahodgki@usgs.gov",
           comment = c(ORCID = "0000-0002-4916-5565")),
    person(family = "Hodson",
           given = "Timothy",
           role = c("aut"),
           email = "thodson@usgs.gov",
           comment = c(ORCID = "0000-0003-0962-5130")),
    person(family = "Over",
           given = "Thomas",
           role = c("dtc"),
           email = "@usgs.gov",
           comment = c(ORCID = "0000-0001-8280-4368")),
    person(family = "Russell",
           given = "Amy",
           role = c("dtc"),
           email = "arussell@usgs.gov",
           comment = c(ORCID = "0000-0003-0582-0094")))
Description: Facilitates the analysis and evaluation of hydrologic model output and 
    time-series data with functions focused on comparison of modeled (simulated) and observed data, 
    period-of-record statistics, and trends.  
URL: https://code.usgs.gov/hymett/hymett, https://hymett.code-pages.usgs.gov/hymett/
BugReports: https://code.usgs.gov/hymett/hymett/-/issues
Depends: R (>= 3.6.0)
Imports:
    checkmate,
    dplyr,
    EnvStats,
    lmomco,
    lubridate,
    plyr,
    rlang,
    stats,
    tibble,
    zoo
Suggests:
    knitr,
    rmarkdown,
    roxygen2,
    testthat
License: CC0
LazyLoad: yes
LazyData: yes
VignetteBuilder: knitr
BuildVignettes: true
Copyright: This software is in the public domain because it contains materials
    that originally came from the U.S. Geological Survey, an agency of
    the U.S. Department of Interior. For more information, see the
    official USGS copyright policy at
    http://www.usgs.gov/visual-id/credit_usgs.html#copyright
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
NeedsCompilation: no

`benchmark_KGE_DOY`

Calculate benchmark Kling–Gupta efficiency (KGE) values from daily observed time-series data

benchmark_KGE_DOY(obs_preproc)

obs_preproc 'data.frame' of daily observational data, preprocessed as output from preproc_precondition_data or preproc_main "daily".

This function calculates a "benchmark" KGE value (see Knoben and others, 2020) from a daily observed data time-series. First, the interannual mean and median is calculated for each day of the calendar year. Next, the interannual mean and median values are joined to each corresponding day in the observation time series. Finally, a KGE value (GOF_kling_gupta_efficiency) is calculated comparing the mean or median value repeated time series to the daily observational time series. These benchmark KGE values can be used as comparisons for modeled (simulated) calibration results.

A data.frame with columns "KGE_DOY_mean" and "KGE_DOY_median".

Knoben, W.J.M, Freer, J.E., Peel, M.C., Fowler, K.J.A, Woods, R.A., 2020. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments: Water Resources Research, v. 56. [Also available at https://doi.org/10.1029/2019WR025975.]

benchmark_KGE_DOY(obs_preproc = example_preproc)

`calc_annual_flow_stats`

Calculate annual flow statistics from daily data

calc_annual_flow_stats(
  data = NULL,
  Date,
  year_group,
  Q,
  Q3 = NA_real_,
  Q7 = NA_real_,
  Q30 = NA_real_,
  jd = NA_integer_,
  calc_high = FALSE,
  calc_low = FALSE,
  calc_percentiles = FALSE,
  calc_monthly = FALSE,
  calc_WSCVD = FALSE,
  longitude = NA,
  calc_ICVD = FALSE,
  zero_threshold = 33,
  quantile_type = 8,
  na.action = c("na.omit", "na.pass")
)

data 'data.frame'. Optional data.frame input, with columns containing Date, year_group, Q, and ⁠Q3, Q7, Q3 0, jd⁠ (if required). Column names are specified as strings in the corresponding parameter. Default is NULL. Date 'Date' or 'character' vector when data = NULL, or character' string identifying Date column name when data is specified. Date associated with each value in Q parameter. year_group 'numeric' vector when data = NULL, or 'character' string identifying grouping column name when data is specified. Year grouping for each daily value in Q parameter. Must be same length as Q parameter. Often year_group is water year or climate year. Q 'numeric' vector when data = NULL, or 'character' string identifying streamflow values column name when data is specified. Daily streamflow data. Must be same length as year_group. Q3 'numeric' vector when data = NULL, or 'character' string identifying Q3 column name when data is specified. 3-day moving average of daily streamflow data Q parameter, often returned from preproc_precondition_data. Default is NA_real_, required if calc_high or calc_low = TRUE. If specified, must be same length as Q parameter. Q7 'numeric' vector when data = NULL, or 'character' string identifying Q7 column name when data is specified. 7-day moving average of daily streamflow data Q parameter, often returned from preproc_precondition_data. Default is NA_real_, required if calc_high or calc_low = TRUE. If specified, must be same length as Q parameter. Q30 'numeric' vector when data = NULL, or 'character' string identifying Q30 column name when data is specified. 30-day average of daily streamflow data Q parameter, often returned from preproc_precondition_data. Default is NA_real_, required if calc_high or calc_low = TRUE. If specified, must be same length as Q parameter. jd 'numeric' vector when data = NULL, or 'character' string identifying jd column name when data is specified. Calendar Julian day of daily streamflow data Q parameter, often returned from preproc_precondition_data. Default is NA_integer_, required if calc_high, calc_low, calc_WSCVD or calc_ICVD = TRUE. If specified, must be same length as Q parameter. calc_high 'boolean' value. Calculate high flow statistics for years in year_group. Default is FALSE. See Details for more information. calc_low 'boolean' value. Calculate low flow statistics for years in year_group. Default is FALSE. See Details for more information. calc_percentiles 'boolean' value. Calculate percentiles for years in year_group. Default is FALSE. See Details for more information. calc_monthly 'boolean' value. Calculate monthly statistics for years in year_group. Default is FALSE. See Details for more information. calc_WSCVD 'boolean' value. Calculate winter-spring center volume date for years in year_group. Default is FALSE. See Details for more information. longitude 'numeric' value. Site longitude in North American Datum of 1983 (NAD83), required in WSCVD calculation. Default is NA. See Details for more information. calc_ICVD 'boolean' value. Calculate inverse center volume date for years in year_group. Default is FALSE. See Details for more information. zero_threshold 'numeric' value as percentage. The percentage of years of a statistic that need to be zero in order for it to be deemed a zero flow site for that statistic. For use in trend calculation. See Details on attributes. Default is 33 (33 percent) of the annual statistic values. quantile_type 'numeric' value. The distribution type used in the stats::quantile function. Default is 8 (median-unbiased regardless of distribution). Other types common in hydrology are 6 (Weibull) or 9 (unbiased for normal distributions). na.action 'character' string indicating na.action passed to stats::aggregate na.action parameter. Default is "na.omit", which removes NA values before aggregating statistics, or "na.pass", which will pass NA values and return NA in the grouped calculation if any NA values are present.

year_group is commonly water year, climate year, or calendar year.

Default annual statistics returned:

annual_mean annual mean in year_group

annual_sd annual standard deviation in year_group

annual_sum annual sum in year_group

If calc_high/low are selected, annual statistics returned: 1-, 3-, 7-, and 30-day high/low and Julian date (jd) of n-day high/low.

high_qn where n = 1, 3, 7, and 30

high_qn⁠_jd⁠ where n = 1, 3, 7, and 30

low_qn where n = 1, 3, 7, and 30

low_qn⁠_jd⁠ where n = 1, 3, 7, and 30

If calc_percentiles is selected, annual statistics returned: 1, 5, 10, 25, 50, 75, 90, 95, 99 percentile based on daily streamflow.

annual_n⁠_percentile⁠ where n = 1, 5, 10, 25, 50, 75, 90, 95, and 99

If calc_monthly is selected, annual statistics returned: Monthly mean, standard deviation, max, min, percent of annual for each month in year_group.

month⁠_mean⁠ monthly mean, where month = month.abb

month⁠_sd⁠ monthly standard deviation, where month = month.abb

month⁠_max⁠ monthly maximum, where month = month.abb

month⁠_min⁠ monthly minimum, where month = month.abb

month⁠_percent_annual⁠ monthly percent of annual, where month = month.abb

If calc_WSCVD is selected, Julian date of annual winter-spring center volume date is returned. Longitude (in NAD83 datum) is used to determine the ending month of spring. July for longitudes West of -95 degrees, May for longitudes east of -95 degrees. See References Dudley and others, 2017. Commonly calculated when year_group is water year.

WSCVD Julian date of winter-spring center volume

If calc_ICVD is selected, Julian date of annual inverse center volume date is returned. Commonly calculated when year_group is climate year.

ICVD Julian date of inverse center volume date

Attribute: zero_flow_years A data.frame with each annual statistic calculated, the percentage of years where the statistic = 0, a flag indicating if the percentage is over the zero_threshold parameter, and the number of years with a zero value. Columns in zero_flow_years:

annual_stat annual statistic

percent_zeros percentage of years with 0 statistic value

over_threshold boolean if percentage is over threshold

number_years number of years with 0 value statistic

The zero_flow_years attribute can be useful in trend calculation, where a trend may not be appropriate to calculate with many zero flow years.

A tibble (see tibble::tibble) with annual statistics depending on options selected. See Details.

Dudley, R.W., Hodgkins, G.A, McHale, M.R., Kolian, M.J., Renard, B., 2017, Trends in snowmelt-related streamflow timing in the conterminous United States: Journal of Hydrology, v. 547, p. 208-221. [Also available at https://doi.org/10.1016/j.jhydrol.2017.01.051.]

preproc_precondition_data

calc_annual_flow_stats(data = example_preproc, Date = "Date", year_group = "WY", Q = "value")

`calc_annual_stat_trend`

Calculate trend in annual statistics

calc_annual_stat_trend(data = NULL, year, value, ...)

data 'data.frame'. Optional data.frame input, with columns containing year and value. Column names are specified as strings in the corresponding parameter. Default is NULL. year 'numeric' vector when data = NULL, or 'character' string identifying year column name when data is specified. Year of each value in value parameter. value 'numeric' vector when data = NULL, or 'character' string identifying value column name when data is specified. Values to calculate trend on. ... further arguments to be passed to or from EnvStats::kendallTrendTest.

This function is a wrapper for EnvStats::kendallTrendTest with the passed equation value ~ year. The returned values include Mann-Kendall test statistic and p-value, Theil-Sen slope and intercept values, and trend details (Millard, 2013; Helsel and others, 2020).

z_stat Mann-Kendall test statistic, returned directly from EnvStats::kendallTrendTest

p_value z_stat p-value, returned directly from EnvStats::kendallTrendTest

sen_slope Sen slope in units value per year, returned directly from EnvStats::kendallTrendTest

intercept Sen slope intercept, returned directly from EnvStats::kendallTrendTest

trend_mag Trend magnitude over entire period, in units of value, calculated as ⁠sen_slope * (max(year)⁠ - ⁠min(year))⁠

val_beg/end Calculated value at beginning or end of period, calculated as sen_slope * year + intercept

val_perc_change Percentage change over period, calculated as (val_end - val_beg) / val_beg * 100

A tibble (see tibble::tibble) with test statistic, p-value, trend coefficients, and trend calculations. See Details.

Millard, S.P., 2013, EnvStats: An R Package for Environmental Statistics: New York, New York, Springer, 291 p. [Also available at https://doi.org/10.1007/978-1-4614-8456-1.]

Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3, 458 p. [Also available at https://doi.org/10.3133/tm4a3.]

kendallTrendTest

calc_annual_stat_trend(data = example_annual, year = "WY", value = "annual_mean")

`calc_logistic_regression`

Calculate logistic regression (Everitt and Hothorn, 2009) in annual statistics with zero values. A model fit to compute the probability of a zero flow annual statistic.

calc_logistic_regression(data = NULL, year, value, ...)

data 'data.frame'. Optional data.frame input, with columns containing year and value. Column names are specified as strings in the corresponding parameter. Default is NULL. year 'numeric' vector when data = NULL, or 'character' string identifying year column name when data is specified. Year of each value in value parameter. value 'numeric' vector when data = NULL, or 'character' string identifying value column name when data is specified. Values to calculate logistic regression on. ... further arguments to be passed to or from stats::glm.

This function is a wrapper for ⁠stats::glm(y ~ year, family = stats::binomial(link="logit")⁠ with y = 1 when value = 0 (for example a zero flow annual statistic) and y = 0 otherwise. The returned values include

p_value Probability value of the explanatory (year) variable in the logistic model

stdErr_slope Standard error of the regression slope (log odds per year)

odds_ratio Exponential of the explanatory coefficient (year coefficient)

prob_beg/end Logistic regression predicted (fitted) values at the beginning and ending year.

prob_change Change in probability from beginning to end.

Example, an odds ratio of 1.05 represents the odds of a zero-flow year (versus non-zero) increase by a factor of 1.05 (or 5 percent).

A tibble (see tibble::tibble) with logistic regression p-value, standard error of slope, odds ratio, beginning and ending probability, and probability change. See Details.

Everitt, B. S. and Hothorn T., 2009, A Handbook of Statistical Analyses Using R, 2nd Ed. Boca Raton, Florida, Chapman and Hall/CRC, 376p.

glm

calc_logistic_regression(data = example_annual, year = "WY", value = "annual_mean")

`calc_qlpearsonIII`

Quantile of Pearson Type III distribution for log-transformed data

calc_qlpearsonIII(p, meanlog = 0, sdlog = 1, skew = 0)

p Vector of non-exceedance probabilities, between 0 and 1, to calculate quantiles. meanlog Vector of mean of the distribution of the log-transformed data. sdlog Vector of standard deviation of the distribution of the log-transformed data. skew Vector of skewness of the distribution of the log-transformed data.

calc_qpearsonIII and calc_qlpearsonIII are functions to fit a log-Pearson type III distribution from a given mean, standard deviation, and skew. This source code is replicated, unchanged, from the swmrBase package in order to reduce the dependency on that package.

Quantiles for the described distribution

Asquith, W.H., Kiang, J.E., and Cohn, T.A., 2017, Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities: U.S. Geological Survey Scientific Investigation Report 2017–5038, 93 p. [Also available at https://doi.org/10.3133/sir20175038.]

Lorenz, D.L., 2015, smwrBase—An R package for managing hydrologic data, version 1.1.1: U.S. Geological Survey Open-File Report 2015–1202, 7 p. [Also available at https://doi.org/10.3133/ofr20151202.]

calc_qpearsonIII

calc_qlpearsonIII(0.1)

`calc_qpearsonIII`

Quantile of Pearson Type III distribution

calc_qpearsonIII(p, mean = 0, sd = 1, skew = 0)

p Vector of non-exceedance probabilities, between 0 and 1, to calculate quantiles. mean Vector of means of the distribution of the data. sd Vector of standard deviation of the distribution of the data. skew Vector of skewness of the distribution of the data.

Quantiles for the described distribution

calc_qpearsonIII(0.1)

`censor_values`

Replaces values in a vector with NA when above or below a censor level. Censoring is ⁠values censor_symbol censor_threshold⁠ are censored, for example with the defaults (values lte 0 set to NA) all values <= 0 are replaced with NA.

censor_values(
  value,
  censor_threshold = 0,
  censor_symbol = c("lte", "lt", "gt", "gte")
)

value 'numeric' vector. Values to censor. censor_threshold 'numeric' value. Threshold to censor values on. Default is 0. censor_symbol 'character' string. Inequality symbol to censor values based on censor_threshold. Accepted values are "gt" (greater than), "gte" (greater than or equal to), "lt" (less than), or "lte" (less than or equal to). Default is "lte".

'numeric' vector with censored values replaced with NA

censor_values(value = seq.int(1, 10, 1), censor_threshold = 5)

`example_annual`

An example dataset with daily observed streamflow processed to annual water year values.

example_annual

A data.frame with the following variables:

WY water year

annual_mean annual mean

annual_sd annual standard deviation

annual_sum annual sum

high_q1 annual maximum of daily mean

high_q3 annual maximum of 3-day mean

high_q7 annual maximum of 7-day mean

high_q30 annual maximum of 30-day mean

high_q1_jd Julian day of annual maximum of daily mean

high_q3_jd Julian day of annual maximum of 3-day mean

high_q7_jd Julian day of annual maximum of 7-day mean

high_q30_jd Julian day of annual maximum of 30-day mean

low_q7 annual minimum of 7-day mean

low_q30 annual minimum of 30-day mean

low_q3 annual minimum of 3-day mean

low_q1 annual minimum of daily mean

low_q7_jd Julian day of annual minimum of 7-day mean

low_q30_jd Julian day of annual minimum of 30-day mean

low_q3_jd Julian day of annual minimum of 3-day mean

low_q1_jd Julian day of annual minimum of daily mean

annual_1_percentile annual first percentile

annual_5_percentile annual 5th percentile

annual_10_percentile annual 10th percentile

annual_25_percentile annual 25th percentile

annual_50_percentile annual 50th percentile

annual_75_percentile annual 75th percentile

annual_90_percentile annual 90th percentile

annual_95_percentile annual 95th percentile

annual_99_percentile annual 99th percentile

Jan_mean annual January mean

Jan_sd annual January standard deviation

Jan_max annual January maximum

Jan_min annual January minimum

Jan_percent_annual annual January percentage of annual sum

Feb_mean annual February mean

Feb_sd annual February standard deviation

Feb_max annual February maximum

Feb_min annual February minimum

Feb_percent_annual annual February percentage of annual sum

Mar_mean annual March mean

Mar_sd annual March standard deviation

Mar_max annual March maximum

Mar_min annual March minimum

Mar_percent_annual annual March percentage of annual sum

Apr_mean annual April mean

Apr_sd annual April standard deviation

Apr_max annual April maximum

Apr_min annual April minimum

Apr_percent_annual annual April percentage of annual sum

May_mean annual May mean

May_sd annual May standard deviation

May_max annual May maximum

May_min annual May minimum

May_percent_annual annual May percentage of annual sum

Jun_mean annual June mean

Jun_sd annual June standard deviation

Jun_max annual June maximum

Jun_min annual June minimum

Jun_percent_annual annual June percentage of annual sum

Jul_mean annual July mean

Jul_sd annual July standard deviation

Jul_max annual July maximum

Jul_min annual July minimum

Jul_percent_annual annual July percentage of annual sum

Aug_mean annual August mean

Aug_sd annual August standard deviation

Aug_max annual August maximum

Aug_min annual August minimum

Aug_percent_annual annual August percentage of annual sum

Sep_mean annual September mean

Sep_sd annual September standard deviation

Sep_max annual September maximum

Sep_min annual September minimum

Sep_percent_annual annual September percentage of annual sum

Oct_mean annual October mean

Oct_sd annual October standard deviation

Oct_max annual October maximum

Oct_min annual October minimum

Oct_percent_annual annual October percentage of annual sum

Nov_mean annual November mean

Nov_sd annual November standard deviation

Nov_max annual November maximum

Nov_min annual November minimum

Nov_percent_annual annual November percentage of annual sum

Dec_mean annual December mean

Dec_sd annual December standard deviation

Dec_max annual December maximum

Dec_min annual December minimum

Dec_percent_annual annual December percentage of annual sum

WSV winter-spring volume

wscvd Julian date of winter-spring center volume

Generated with example_obs from

HyMETT::preproc_main(data = example_obs, 
                     Date = "Date", value = "streamflow_cfs", longitude = -68)$annual

example_obs, preproc_main

str(example_annual)

`example_mod`

An example dataset with daily modeled (simulated) streamflow.

example_mod

A data.frame with the following variables:

date date as 'character' column class.

streamflow_cfs modeled streamflow in units of feet^3/second.

Date date as 'Date' column class.

Generated from example data available at system.file("extdata", "01013500_MOD.csv", package = "HyMETT")

Johnson, M., D. Blodgett, 2020, NOAA National Water Model Reanalysis Data at RENCI, HydroShare, accessed September 17, 2020 at https://doi.org/10.4211/hs.89b0952512dd4b378dc5be8d2093310f

Johnson, M., 2021, nwmHistoric: National Water Model Historic Data. R package version 0.0.0.9000, accessed September 17, 2020 at https://github.com/mikejohnson51/nwmHistoric

str(example_mod)

`example_mod_zf`

An example dataset with daily modeled (simulated) streamflow that includes zero flows.

example_mod_zf

A data.frame with the following variables:

date date as 'character' column class.

streamflow_cfs modeled streamflow in units of feet^3/second.

Date date as 'Date' column class.

Generated from example data available at system.file("extdata", "08202700_MOD.csv", package = "HyMETT")

Johnson, M., D. Blodgett, 2020, NOAA National Water Model Reanalysis Data at RENCI, HydroShare, accessed September 17, 2020 at https://doi.org/10.4211/hs.89b0952512dd4b378dc5be8d2093310f

Johnson, M., 2021, nwmHistoric: National Water Model Historic Data. R package version 0.0.0.9000, accessed September 17, 2020 at https://github.com/mikejohnson51/nwmHistoric

str(example_mod_zf)

`example_obs`

An example dataset with daily observed streamflow.

example_obs

A data.frame with the following variables:

date date as 'character' column class.

streamflow_cfs observed streamflow in units of feet^3/second.

quality_cd qualifier for value in streamflow_cfs (U.S. Geological Survey, 2020b)

Date date as 'Date' column class.

Generated from example data available at system.file("extdata", "01013500_OBS.csv", package = "HyMETT")

De Cicco, L.A., Hirsch, R.M., Lorenz, D., and Watkins, W.D., 2021, dataRetrieval: R packages for discovering and retrieving water data available from Federal hydrologic web services, accessed September 16, 2020 at https://doi.org/10.5066/P9X4L3GE.

U.S. Geological Survey, 2020a, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed September 16, 2020, at https://doi.org/10.5066/F7P55KJN.

U.S. Geological Survey, 2020b, Instantaneous and Daily Data-Value Qualification Codes, in USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed September 16, 2020, at https://doi.org/10.5066/F7P55KJN. [information directly accessible at https://help.waterdata.usgs.gov/codes-and-parameters/instantaneous-value-qualification-code-uv_rmk_cd.]

str(example_obs)

`example_obs_zf`

An example dataset with daily observed streamflow that includes zero flows.

example_obs_zf

A data.frame with the following variables:

date date as 'character' column class.

streamflow_cfs observed streamflow in units of feet^3/second.

quality_cd qualifier for value in streamflow_cfs (U.S. Geological Survey, 2020b)

Date date as 'Date' column class.

Generated from example data available at system.file("extdata", "08202700_OBS.csv", package = "HyMETT")

U.S. Geological Survey, 2020a, USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed September 16, 2020, at https://doi.org/10.5066/F7P55KJN.

str(example_obs_zf)

`example_preproc`

An example dataset with daily observed streamflow preprocessed to include additional timing and n-day moving averages.

example_preproc

A data.frame with the following variables:

Date

value

year

month

day

decimal_date

WY Water Year: October 1 - September 30

CY Climate Year: April 1 - March 30

Q3 3-Day Moving Average: computed at end of moving interval

Q7 7-Day Moving Average: computed at end of moving interval

Q30 30-Day Moving Average: computed at end of moving interval

jd Julian date

Generated with example_obs from

HyMETT::preproc_main(data = example_obs, 
                     Date = "Date", value = "streamflow_cfs", longitude = -68)$daily`

example_obs, preproc_main

str(example_preproc)

`GOF_correlation_tests`

Calculates Kendall's Tau, Spearman's Rho, Pearson Correlation, and p-values as a wrapper to the stats::cor.test function. Output is tidy-style data.frame.

GOF_correlation_tests(mod, obs, na.rm = TRUE, ...)

mod 'numeric' vector. Modeled or simulated values. Must be same length as obs. obs 'numeric' vector. Observed or comparison values. Must be same length as mod. na.rm 'boolean' TRUE or FALSE. Should NA values be removed before computing. If any NA values are present in mod or obs, the ith position from each will be removed before calculating. If NA values are present and na.rm = FALSE, then function will return NA. Default is TRUE ... Further arguments to be passed to or from stats::cor.test.

See stats::cor.test for more details and further arguments to be passed to or from methods. Defaults are used.

A tibble (tibble::tibble) with test statistic values and p-values.

cor.test

GOF_correlation_tests(mod = example_mod$streamflow_cfs, obs = example_obs$streamflow_cfs)

`GOF_kling_gupta_efficiency`

Calculate Kling–Gupta Efficiency (KGE) (or modified KGE ('KGE)) between modeled (simulated) and observed values.

GOF_kling_gupta_efficiency(mod, obs, modified = FALSE, na.rm = TRUE)

mod 'numeric' vector. Modeled or simulated values. Must be same length as obs. obs 'numeric' vector. Observed or comparison values. Must be same length as mod. modified 'boolean' TRUE or FALSE. Should the KGE calculation use the original variability ratio in the standard deviations (see Gupta and others, 2009) (modified = FALSE) or the modified variability ratio in the coefficient of variations (see Kling and others, 2012) (modified = TRUE). Default is FALSE. na.rm 'boolean' TRUE or FALSE. Should NA values be removed before computing. If any NA values are present in mod or obs, the ith position from each will be removed before calculating. If NA values are present and na.rm = FALSE, then function will return NA. Default is TRUE.

Value of computed KGE or 'KGE.

Kling, H., Fuchs, M. and Paulin, M., 2012. Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios: Journal of Hydrology, v. 424-425, p. 264-277. [Also available at https://doi.org/10.1016/j.jhydrol.2012.01.011.]

Gupta, H.V., Kling, H., Yilmaz, K.K., and Martinez, G.G., 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling: Journal of Hydrology, v. 377, no.1-2, p. 80-91. [Also available at https://doi.org/10.1016/j.jhydrol.2009.08.003.]

GOF_kling_gupta_efficiency(
  mod = example_mod$streamflow_cfs, obs = example_obs$streamflow_cfs
)

`GOF_mean_absolute_error`

Calculates mean absolute error (MAE) between modeled (simulated) and observed values. Error is defined as modeled minus observed.

GOF_mean_absolute_error(mod, obs, na.rm = TRUE)

The absolute value of each modeled-observed pair error is calculated, then the mean of those values taken. Values returned are in units of input data.

Value of calculated mean absolute error (MAE).

GOF_mean_absolute_error(mod = example_mod$streamflow_cfs, obs = example_obs$streamflow_cfs)

`GOF_mean_error`

Calculates mean error between modeled (simulated) and observed values. Error is defined as modeled minus observed.

GOF_mean_error(mod, obs, na.rm = TRUE)

Values returned are in units of input data.

Value of calculated mean error.

GOF_mean_error(mod = example_mod$streamflow_cfs, obs = example_obs$streamflow_cfs)

`GOF_nash_sutcliffe_efficiency`

Calculate Nash–Sutcliffe Efficiency (NSE) (with options for modified NSE) between modeled (simulated) and observed values.

GOF_nash_sutcliffe_efficiency(mod, obs, j = 2, na.rm = TRUE)

mod 'numeric' vector. Modeled or simulated values. Must be same length as obs. obs 'numeric' vector. Observed or comparison values. Must be same length as mod. j 'numeric' value. Exponent value for modified NSE (mNSE) equation. Default value is j = 2, which is traditional NSE equation. na.rm 'boolean' TRUE or FALSE. Should NA values be removed before computing. If any NA values are present in mod or obs, the ith position from each will be removed before calculating. If NA values are present and na.rm = FALSE, then function will return NA. Default is TRUE.

Value of computed NSE or mNSE.

Krause, P., Boyle, D.P., and Base, F., 2005. Comparison of different efficiency criteria for hydrological model assessment: Advances in Geosciences, v. 5, p. 89-97. [Also available at https://doi.org/10.5194/adgeo-5-89-2005.]

Legates D.R and McCabe G.J., 1999, Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation: Water Resources Research. v. 35, no. 1, p. 233-241. [Also available at https://doi.org/10.1029/1998WR900018.]

Nash, J.E. and Sutcliffe, J.V., 1970, River flow forecasting through conceptual models part I: A discussion of principles: Journal of Hydrology, v. 10, no. 3, p. 282-290. [Also available at https://doi.org/10.1016/0022-1694(70)90255-6.]

GOF_nash_sutcliffe_efficiency(
  mod = example_mod$streamflow_cfs, obs = example_obs$streamflow_cfs
)

`GOF_percent_bias`

Calculates percent bias between modeled (simulated) and observed values.

GOF_percent_bias(mod, obs, na.rm = TRUE)

Values returned are in percent.

Value of calculated percent bias as percent.

GOF_percent_bias(mod = example_mod$streamflow_cfs, obs = example_obs$streamflow_cfs)

`GOF_rmse`

Calculate root-mean-square error (RMSE) between modeled (simulated) and observed values. Error is defined as modeled minus observed.

GOF_rmse(
  mod,
  obs,
  normalize = c("none", "mean", "range", "stdev", "iqr", "iqr-1", "iqr-2", "iqr-3",
    "iqr-4", "iqr-5", "iqr-6", "iqr-7", "iqr-8", "iqr-9", NULL),
  na.rm = TRUE
)

mod 'numeric' vector. Modeled or simulated values. Must be same length as obs. obs 'numeric' vector. Observed or comparison values. Must be same length as mod. normalize 'character' value. Option to normalize the root-mean-square error (NRMSE) by several normalizing options. Default is 'none'(no normalizing). RMSE is returned. 'mean'. RMSE is normalized by the mean of obs. 'range'. RMSE is normalized by the range (max - min) of obs. 'stdev'. RMSE is normalized by the standard deviation of obs. 'iqr-#'. RMSE is normalized by the inter-quartile range of obs, with distribution type (see stats::quantile function) indicated by integer number (for example "iqr-8"). If no type specified, default type is iqr-7, the quantile function default. na.rm 'boolean' TRUE or FALSE. Should NA values be removed before computing. If any NA values are present in mod or obs, the ith position from each will be removed before calculating. If NA values are present and na.rm = FALSE, then function will return NA. Default is TRUE.

'numeric' value of computed root-mean-square error (RMSE) or normalized root-mean-square error (NRMSE)

# RMSE
GOF_rmse(mod = example_mod$streamflow_cfs, obs = example_obs$streamflow_cfs)
# NRMSE
GOF_rmse(
  mod = example_mod$streamflow_cfs, obs = example_obs$streamflow_cfs, normalize = 'stdev'
)

`GOF_summary`

Calculate Goodness-of-fit (GOF) metrics for correlation, Kling–Gupta efficiency, mean absolute error, mean error, Nash–Sutcliffe efficiency, percent bias, root-mean-square error, normalized root-mean-square error, and volumetric efficiency, and output into a table.

GOF_summary(
  mod,
  obs,
  metrics = c("cor", "kge", "mae", "me", "nse", "pb", "rmse", "nrmse", "ve"),
  censor_threshold = NULL,
  censor_symbol = NULL,
  na.rm = TRUE,
  kge_modified = FALSE,
  nse_j = 2,
  rmse_normalize = c("mean", "range", "stdev", "iqr", "iqr-1", "iqr-2", "iqr-3", "iqr-4",
    "iqr-5", "iqr-6", "iqr-7", "iqr-8", "iqr-9", NULL),
  ...
)

mod 'numeric' vector. Modeled or simulated values. Must be same length as obs. obs 'numeric' vector. Observed or comparison values. Must be same length as mod. metrics 'character' vector. Which GOF metrics should be computed and output. Default is c ("cor", "kge", "mae", "me", "nse" , "pb", "rmse", "nrmse", "ve"). "cor". Correlation tests computed from GOF_correlation_tests. "kge". Kling–Gupta efficiency computed from GOF_kling_gupta_efficiency. "mae". Mean absolute error computed from GOF_mean_absolute_error. "me". Mean error computed from GOF_mean_error. "nse". Nash–Sutcliffe efficiency computed from GOF_nash_sutcliffe_efficiency with option for modified NSE specified by parameter nse_j. "pb". Percent bias computed from GOF_percent_bias. "rmse". Root-mean-square error computed from GOF_rmse. "nrmse". Normalized root-mean-square error computed from GOF_rmse and "normalize" option specified in parameter rmse_normalize. "ve". Volumetric efficiency computed from GOF_volumetric_efficiency. censor_threshold 'numeric' value. Threshold to censor values on utilizing censor_values function. Default is NULL, no censoring. If level specified, must also specify censor_symbol. censor_symbol 'character' string. Inequality symbol to censor values based on censor_threshold utilizing censor_values function. Accepted values are "gt" (greater than), "gte" (greater than or equal to), "lt" (less than), or "lte" (less than or equal to). Default is NULL, no censoring. If symbol specified, must also specify censor_value. na.rm 'boolean' TRUE or FALSE. Should NA values be removed before computing. If any NA values are present in mod or obs, the ith position from each will be removed before calculating. If NA values are present and na.rm = FALSE, then function will return NA. Default is TRUE. kge_modified 'boolean' TRUE or FALSE. Should the KGE calculation use the original variability ratio in the standard deviations (kge_modified = FALSE) or the modified variability ratio in the coefficient of variations (kge_modified = TRUE). Default is FALSE. nse_j 'numeric' value. Exponent value for modified NSE (mNSE) equation, utilized if "nse" option is in parameter metrics. Default value is nse_j = 2, which is traditional NSE equation. rmse_normalize 'character' value. Normalize option for NRMSE, utilized if "nrmse" option is in paramter metrics. Default is "mean". Options are 'mean'. RMSE is normalized by the mean of obs. 'range'. RMSE is normalized by the range (max - min) of obs. 'stdev'. RMSE is normalized by the standard deviation of obs. 'iqr-#'. RMSE is normalized by the inter-quartile range of obs, with distribution type (see stats::quantile function) indicated by integer number (for example "iqr-8"). If no type specified, default type is iqr-7, the quantile function default. ... Further arguments to be passed to or from stats::cor.test if "cor" is in metrics.

See GOF_correlation_tests, GOF_kling_gupta_efficiency, GOF_mean_absolute_error, GOF_mean_error, GOF_nash_sutcliffe_efficiency, GOF_percent_bias, GOF_rmse, and GOF_volumetric_efficiency.

A tibble (see tibble::tibble) with GOF metrics

censor_values, GOF_correlation_tests, GOF_kling_gupta_efficiency, GOF_mean_absolute_error, GOF_mean_error, GOF_nash_sutcliffe_efficiency, GOF_percent_bias, GOF_rmse, GOF_volumetric_efficiency

GOF_summary(mod = example_mod$streamflow_cfs, obs = example_obs$streamflow_cfs)

`GOF_volumetric_efficiency`

Calculate Volumetric efficiency (VE) between modeled (simulated) and observed values. VE is defined as the fraction of water delivered at the proper time (Criss and Winston, 2008).

GOF_volumetric_efficiency(mod, obs, na.rm = TRUE)

Volumetric efficiency was proposed in order to circumvent some problems associated to the Nash–Sutcliffe efficiency. It ranges from 0 to 1 and represents the fraction of water delivered at the proper time; its compliment represents the fractional volumetric mismatch (Criss and Winston, 2008).

Value of computed Volumetric efficiency.

Criss, R.E. and Winston, W.E., 2008, Do Nash values have value? Discussion and alternate proposals: Hydrological Processes, v. 22, p. 2723-2725. [Also available at https://doi.org/10.1002/hyp.7072.]

Zambrano-Bigiarini, M., 2020, hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series R package version 0.4-0. accessed September 16, 2020, at https://github.com/hzambran/hydroGOF. [Also available at https://doi.org/10.5281/zenodo.839854.]

GOF_volumetric_efficiency(
  mod = example_mod$streamflow_cfs, obs = example_obs$streamflow_cfs
)

`HyMETT-package`

This package facilitates the analysis and evaluation of hydrologic model output and time-series data with functions focused on comparison of modeled (simulated) and observed data, period-of-record statistics, and trends.

Please see \Sexpr[results=rd]{tools:::Rd_expr_doi("10.5066/P9FNXEWI")} for more details.

`POR_apply_annual_hiflow_stats`

This function computes the 50th and 90th percentiles of a streamflow time series from annual n-day high flow values and returns a data.frame in the format of other period-of-record (POR) metrics.

POR_apply_annual_hiflow_stats(annual_max, quantile_type = 8)

annual_max 'numeric' vector or data.frame. Vector or data.frame with columns of annual n-day maximum streamflows. quantile_type 'numeric' value. The distribution type used in the stats::quantile function. Default is 8 (median-unbiased regardless of distribution). Other types common in hydrology are 6 (Weibull) or 9 (unbiased for normal distributions).

annual maximum of n-day moving averages can be computed during pre-processing step using preproc_precondition_data and calc_annual_flow_stats, or preproc_main for both observed and modeled data.

Data.frame of 0.5 and 0.9 non-exceedance probabilities (50th and 90th percentiles), with metric names if annual_max is a data.frame with columns named by metric.

quantile, preproc_precondition_data, calc_annual_flow_stats, preproc_main

POR_apply_annual_hiflow_stats(annual_max = example_annual[ , c("high_q1", "high_q30")])

`POR_apply_annual_lowflow_stats`

Calculates 10-year and 2-year return periods of a streamflow time series from annual n-day low streamflow values and returns a data.frame in the format of other period-of-record (POR) metrics.

POR_apply_annual_lowflow_stats(annual_min)

annual_min 'numeric' vector or data.frame. Vector or data.frame with columns of annual n-day minimum streamflows.

POR_apply_POR_lowflow_metrics is a helper function that applies the POR_calc_lp3_quantile function to the data.frame of n-day moving averages, which can be computed during pre-processing step using preproc_precondition_data and calc_annual_flow_stats, or preproc_main for both observed and modeled data. This function returns a data.frame with the 10-year and 2-year return period streamflows for each n-day low streamflow in the input data.frame.

data.frame with 10-year and 2-year return period of n-day streamflows.

POR_calc_lp3_quantile, preproc_precondition_data, calc_annual_flow_stats, preproc_main

POR_apply_annual_lowflow_stats(annual_min = example_annual[ , c("low_q1", "low_q30")])

`POR_calc_amp_and_phase`

Calculates the seasonal amplitude and phase of a daily time series.

POR_calc_amp_and_phase(
  data = NULL,
  Date,
  value,
  time_step = c("daily", "monthly")
)

A data.frame with calculated seasonal amplitude and phase

Farmer, W.H., Archfield, S.A., Over, T.M., Hay, L.E., LaFontaine, J.H., and Kiang, J.E., 2014, A comparison of methods to predict historical daily streamflow time series in the southeastern United States: U.S. Geological Survey Scientific Investigations Report 2014–5231, 34 p. [Also available at https://doi.org/10.3133/sir20145231.]

POR_calc_amp_and_phase(data = example_obs, Date = "Date", value = "streamflow_cfs")

`POR_calc_AR1`

calculates lag-one autocorrelation (AR1) coefficient for a time series

POR_calc_AR1(data = NULL, Date, value, time_step = c("daily", "monthly"))

The function calculates lag-one autocorrelation (AR1) coefficient for a time series using the stats::ar function. When applied to an observed or modeled time series of streamflow, the POR_deseasonalize function can be applied to the raw data prior to running the POR_calc_AR1 function.

A data.frame with calculated seasonal amplitude and phase.

POR_deseasonalize, ar

POR_calc_AR1(data = example_obs, Date = "Date", value = "streamflow_cfs")

`POR_calc_lp3_quantile`

Calculate the specified flow quantile from a fitted log-Pearson type III distribution from a time series of n-day low flows.

POR_calc_lp3_quantile(annual_min, p)

annual_min 'numeric' vector. Vector of minimum annual n-day mean flows. p 'numeric' value of exceedance probabilities. Quantile of fitted distribution that is returned (p=0.1 for 10-year return period, p=0.5 for 2-year return period)

POR_calc_lp3_quantile fits an log-Pearson type III distribution to a series of annual n-day flows and returns the quantile of a user-specified probability using calc_qlpearsonIII. This represents a theoretical return period for than n-day flow.

Specified quantile from the fitted log-Pearson type 3 distribution.

calc_qlpearsonIII

POR_calc_lp3_quantile(annual_min = example_annual$low_q1, p = 0.1)

`POR_deseasonalize`

Removes seasonal trends from a daily or monthly time series. Daily data are deseasonalized by subtracting monthly mean values. Monthly data are deseasonalized by subtracting mean monthly values.

POR_deseasonalize(data = NULL, Date, value, time_step = c("daily", "monthly"))

The deseasonalize function removes seasonal trends from a daily or monthly time series and returns a deseasonalized time series, which can be used in the POR_calc_AR1 function.

Deseasonalized values.

POR_calc_AR1

POR_deseasonalize(data = example_obs, Date = "Date", value = "streamflow_cfs")

`POR_distribution_metrics`

Calculates various metrics that describe the distribution of a time series of streamflow, which can be of any time step.

POR_distribution_metrics(value, quantile_type = 8, na.rm = TRUE)

value 'numeric' vector of values (assumed to be streamflow) at any time step. quantile_type 'numeric' value. The distribution type used in the stats::quantile function. Default is 8 (median-unbiased regardless of distribution). Other types common in hydrology are 6 (Weibull) or 9 (unbiased for normal distributions). na.rm 'boolean' TRUE or FALSE. Should NA values be removed before computing. If NA values are present and na.rm = FALSE, then function will return NAs. Default is TRUE.

Metrics computed include:

p_n Flow-duration curve (FDC) percentile where n = 1, 5, 10, 25, 50, 75, 90, 95, and 99

POR_mean Period of record mean

POR_sd Period of record standard deviation

POR_cv Period of record coefficient of variation

POR_min Period of record minimum

POR_max Period of record maximum

LCV L-moment coefficient of variation

Lskew L-moment skewness

Lkurtosis L-moment kurtosis

A data.frame with FDC quantiles, and distribution metrics. See Details. This function calculates various metrics that describe the distribution of a time series of streamflow, which can be of any time step.

Asquith, W.H., 2021, lmomco—L-moments, censored L-moments, trimmed L-moments, L-comoments, and many distributions. R package version 2.3.7, Texas Tech University, Lubbock, Texas.

lmoms, quantile

POR_distribution_metrics(value = example_obs$streamflow_cfs)

`preproc_audit_data`

Audit daily data for total days in year. An audit is performed to inventory and flag missing days in daily data and help determine if further analyses are appropriate.

preproc_audit_data(
  data = NULL,
  Date,
  value,
  year_group,
  use_specific_years = FALSE,
  begin_year = NULL,
  end_year = NULL,
  days_cutoff = 360,
  date_format = "%Y-%m-%d"
)

Year grouping is commonly water year, climate year, or calendar year.

A data.frame with year_group, count (n, excluding NA values) of days in each year_group, and a complete years 'boolean' flag.

preproc_fill_daily, preproc_precondition_data

preproc_audit_data(
  data = example_preproc, Date = "Date", value = "value", year_group = "WY"
)

`preproc_fill_daily`

Fills daily data with missing dates as `NA` values

Fills daily data with missing dates as NA values. Days that are absent from the daily time series are inserted with a corresponding value of NA.

preproc_fill_daily(
  data = NULL,
  Date,
  value,
  POR_start = NA,
  POR_end = NA,
  date_format = "%Y-%m-%d"
)

Can be used prior to preproc_precondition_data to fill daily data before computation of n-day moving averages, or prior to preproc_audit_data.

A data.frame with Date and value, sequenced from POR_start to POR_end by 1 day.

preproc_audit_data, preproc_precondition_data

Dates = c(seq.Date(as.Date("2020-01-01"), as.Date("2020-01-10"), by = "1 day"),
          seq.Date(as.Date("2020-01-20"), as.Date("2020-01-31"), by = "1 day"))
values = c(seq.int(1, 22, 1))
preproc_fill_daily(Date = Dates, value = values)

`preproc_main`

A wrapper function for preproc_precondition_data, preproc_audit_data, and calc_annual_flow_stats

preproc_main(
  data = NULL,
  Date,
  value,
  date_format = "%Y-%m-%d",
  year_group = c("WY", "CY", "year"),
  use_specific_years = FALSE,
  begin_year = NULL,
  end_year = NULL,
  days_cutoff = 360,
  calc_high = TRUE,
  calc_low = TRUE,
  calc_percentiles = TRUE,
  calc_monthly = TRUE,
  calc_WSCVD = TRUE,
  longitude = NA,
  calc_ICVD = FALSE,
  zero_threshold = 33,
  quantile_type = 8,
  na.action = c("na.omit", "na.pass")
)

data 'data.frame'. Optional data.frame input, with columns containing Date and value. Column names are specified as strings in the corresponding parameter. Default is NULL. Date 'Date' or 'character' vector when data = NULL, or 'character' string identifying Date column name when data is specified. Dates associated with each value in value parameter. value 'numeric' vector when data = NULL, or 'character' string identifying year column name when data is specified. Values to precondition and calculate n-day moving averages from. N-day moving averages only calculated for daily data. date_format 'character' string. Format of Date. Default is "%Y-%m-%d". year_group 'character' value. Specify either "year" for calendar year, "WY" for water year, or "CY" for climate year. Used to select data after preconditioning for audit and annual statistics. Default is "WY". use_specific_years 'boolean' value. Flag to clip data to a certain set of years in year_group. Default is FALSE. begin_year 'numeric' value. If use_specific_years = TRUE, beginning year to clip value. Default is NULL. end_year 'numeric' value. If use_specific_years = TRUE, ending year to clip value. Default is NULL. days_cutoff 'numeric' value. Designating the number of days required for a year to be counted as full. Default is 360. calc_high 'boolean' value. Calculate high streamflow statistics for years in year_group. Default is TRUE. See Details for more information. calc_low 'boolean' value. Calculate low streamflow statistics for years in year_group. Default is TRUE. See Details for more information. calc_percentiles 'boolean' value. Calculate percentiles for years in year_group. Default is TRUE. See Details for more information. calc_monthly 'boolean' value. Calculate monthly statistics for years in year_group. Default is TRUE. See Details for more information. calc_WSCVD 'boolean' value. Calculate winter-spring center volume date for years in year_group. Default is TRUE. See Details for more information. longitude 'numeric' value. Site longitude in NAD83, required in WSCVD calculation. Default is NA. See Details for more information. calc_ICVD 'boolean' value. Calculate inverse center volume date for years in year_group. Default is FALSE. See Details for more information. zero_threshold 'numeric' value as percentage. The percentage of years of a statistic that need to be zero in order for it to be deemed a zero streamflow site for that statistic. For use in trend calculation. See Details on attributes. Default is 33 (33 percent) of the annual statistic values. quantile_type 'numeric' value. The distribution type used in the stats::quantile function. Default is 8 (median-unbiased regardless of distribution). Other types common in hydrology are 6 (Weibull) or 9 (unbiased for normal distributions). na.action 'character' string indicating na.action passed to stats::aggregate na.action parameter. Default is "na.omit", which removes NA values before aggregating statistics, or "na.pass", which will pass NA values and return NA in the grouped calculation if any NA values are present.

This is a wrapper function of preproc_precondition_data, preproc_audit_data, and calc_annual_flow_stats. Data are first passed to the precondition function, then audited, then annual statistics are computed. It also checks the timestep of the data to make sure that it is daily timestep. Other time steps are currently not supported and will return the data.frame without moving averages computed.

A list of three data.frames: 1 of preconditioned data, 1 data audit, and 1 annual statistics.

preproc_audit_data, preproc_precondition_data, calc_annual_flow_stats

preproc_main(data = example_obs, Date = "Date", value = "streamflow_cfs", longitude = -68)

`preproc_precondition_data`

Pre-conditions data with time information and n-day moving averages, with options to fill missing days with NA values.

preproc_precondition_data(
  data = NULL,
  Date,
  value,
  date_format = "%Y-%m-%d",
  fill_daily = TRUE
)

These columns are added to the data:

year

month

day

decimal_date

WY Water Year: October 1 to September 30

CY Climate Year: April 1 to March 30

Q3 3-Day Moving Average: computed at end of moving interval

Q7 7-Day Moving Average: computed at end of moving interval

Q30 30-Day Moving Average: computed at end of moving interval

jd Julian date

This function also checks the time step of the data to make sure that it is daily time step. Daily values with gaps are important to fill with NA to ensure proper calculation of n-day moving averages. Use fill_daily = TRUE or preproc_fill_daily. Other time steps are currently not supported and will return the data.frame without moving averages computed.

A data.frame with Date, value, and additional columns with time and n-day moving average information.

preproc_fill_daily, rollmean

preproc_precondition_data(data = example_obs, Date = "Date", value = "streamflow_cfs")

`preproc_validate_daily`

Validates that daily data do not contain gaps

preproc_validate_daily(
  data = NULL,
  Date = "Date",
  value = "value",
  date_format = "%Y-%m-%d"
)

Used to validate there are no gaps in the daily record before computing n-day moving averages in preproc_precondition_data or lag-1 autocorrelation in POR_calc_AR1. If gaps are present, preproc_fill_daily can be used to fill them with NA values.

An error message with missing dates, otherwise nothing.

preproc_validate_daily(data = example_obs, Date = "Date", value = "streamflow_cfs")

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HyMETT documentation built on Nov. 23, 2023, 1:08 a.m.

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HyMETT Hydrologic Model Evaluation and Time-Series Tools

inst/docs/Reference_Manual_hymett.md In HyMETT: Hydrologic Model Evaluation and Time-Series Tools

HyMETT Function Reference

DESCRIPTION

benchmark_KGE_DOY

Calculate benchmark Kling–Gupta efficiency (KGE) values from day-of-year (DOY) observations

Description

Usage

Arguments

Details

Value

References

Examples

calc_annual_flow_stats

Calculate annual flow statistics from daily data

Description

Usage

Arguments

Details

Value

References

See Also

Examples

calc_annual_stat_trend

Calculate trend in annual statistics

Description

Usage

Arguments

Details

Value

References

See Also

Examples

calc_logistic_regression

Calculate logistic regression in annual statistics with zero values

Description

Usage

Arguments

Details

Value

References

See Also

Examples

calc_qlpearsonIII

Quantile of Pearson Type III distribution for log-transformed data

Description

Usage

Arguments

Details

Value

References

See Also

Examples

calc_qpearsonIII

Quantile of Pearson Type III distribution

Description

Usage

Arguments

Details

Value

References

Examples

censor_values

Censor values above or below a threshold

Description

Usage

Arguments

Value

Examples

example_annual

Example Annual Observations

Description

Usage

Format

Details

See Also

Examples

example_mod

Example Model Output

Description

HyMETT
Hydrologic Model Evaluation and Time-Series Tools

inst/docs/Reference_Manual_hymett.md
In HyMETT: Hydrologic Model Evaluation and Time-Series Tools

`benchmark_KGE_DOY`

`calc_annual_flow_stats`

`calc_annual_stat_trend`

`calc_logistic_regression`

`calc_qlpearsonIII`

`calc_qpearsonIII`

`censor_values`

`example_annual`

`example_mod`

`example_mod_zf`

`example_obs`

`example_obs_zf`

`example_preproc`

`GOF_correlation_tests`

`GOF_kling_gupta_efficiency`

`GOF_mean_absolute_error`

`GOF_mean_error`