hydrotoolbox.indices

hydrotoolbox.indices(*indice_codes, input_ts='-', water_year='YE-SEP', drainage_area=1, use_median=False, columns=None, source_units=None, start_date=None, end_date=None, dropna='no', clean=False, round_index=None, skiprows=None, index_type='datetime', names=None, target_units=None)

Calculate hydrologic indices.

Code	Description
MA1	Mean of the daily mean flow values for the entire flow record. cubic feet per second—temporal
MA2	Median of the daily mean flow values for the entire flow record. cubic feet per second—temporal
MA3	Mean (or median) of the coefficients of variation (standard deviation/mean) for each year. Compute the coefficient of variation for each year of daily flows. Compute the mean of the annual coefficients of variation. percent—temporal
MA4	Standard deviation of the percentiles of the logs of the entire flow record divided by the mean of percentiles of the logs. Compute the log10 of the daily flows for the entire record. Compute the 5th, 10th, 15th, 20th, 25th, 30th, 35th, 40th, 45th, 50th, 55th, 60th, 65th, 70th, 75th, 80th, 85th, 90th, and 95th percentiles for the logs of the entire flow record. Percentiles are computed by interpolating between the ordered (ascending) logs of the flow values. Compute the standard deviation and mean for the percentile values. Divide the standard deviation by the mean. percent–spatial
MA5	The skewness of the entire flow record is computed as the mean for the entire flow record (MA1) divided by the median (MA2) for the entire flow record. dimensionless—spatial
MA6	Range in daily flows is the ratio of the 10-percent to 90-percent exceedance values for the entire flow record. Compute the 5-percent to 95-percent exceedance values for the entire flow record. Exceedance is computed by interpolating between the ordered (descending) flow values. Divide the 10-percent exceedance value by the 90-percent value. dimensionless—spatial
MA7	Range in daily flows is computed like MA6, except using the 20 percent and 80 percent exceedance values. Divide the 20 percent exceedance value by the 80 percent value. dimensionless—spatial
MA8	Range in daily flows is computed like MA6, except using the 25-percent and 75-percent exceedance values. Divide the 25-percent exceedance value by the 75-percent value. dimensionless—spatial
MA9	Spread in daily flows is the ratio of the difference between the 90th and 10th percentile of the logs of the flow data to the log of the median of the entire flow record. Compute the log10 of the daily flows for the entire record. Compute the 5th, 10th, 15th, 20th, 25th, 30th, 35th, 40th, 45th, 50th, 55th, 60th, 65th, 70th, 75th, 80th, 85th, 90th, and 95th percentiles for the logs of the entire flow record. Percentiles are computed by interpolating between the ordered (ascending) logs of the flow values. Compute MA9 as (90th –10th) /log10(MA2). dimensionless—spatial
MA10	Spread in daily flows is computed like MA9, except using the 20th and 80th percentiles. dimensionless—spatial
MA11	Spread in daily flows is computed like MA9, except using the 25th and 75th percentiles. dimensionless—spatial
MA12 to MA23	Means (or medians) of monthly flow values. Compute the means for each. Means (or medians) of monthly flow values. Compute the means for each month over the entire flow record. For example, MA12 is the mean of all January flow values over the entire record (cubic feet per second— temporal).
MA24 to MA35	Variability (coefficient of variation) of monthly flow values. Compute the standard deviation for each. Variability (coefficient of month in each year over the entire flow record. Divide the standard deviation by the mean for each month. Average (or take median of) these values for each month across all years. percent—temporal
MA36	Variability across monthly flows. Compute the minimum, maximum, and mean flows for each month in the entire flow record. MA36 is the maximum monthly flow minus the minimum monthly flow divided by the median monthly flow. dimensionless-spatial
MA37	Variability across monthly flows. Compute the first (25th percentile) and the third (75th percentile) quartiles (every month in dimensionless— the flow record). MA37 is the third quartile minus the first quartile divided by the median of the monthly means.
MA38	Variability across monthly flows. Compute the 10th and 90th percentiles for the monthly means (every month in the flow record). MA38 is the 90th percentile minus the 10th percentile divided by the median of the monthly means. dimensionless—spatial
MA39	Variability across monthly flows. Compute the standard deviation for the monthly means. MA39 is the standard deviation times 100 divided by the mean of the monthly means. percent—spatial
MA40	Skewness in the monthly flows. MA40 is the mean of the monthly flow means minus the median of the monthly means divided by the median of the monthly means. dimensionles-sspatial
MA41	Annual runoff. Compute the annual mean daily flows. MA41 is the mean of the annual means divided by the drainage area. cubic feet per second/ square mile—temporal
MA42	Variability across annual flows. MA42 is the maximum annual flow minus the minimum annual flow divided by the median annual flow. dimensionless-spatial
MA43	Variability across annual flows. Compute the first (25th percentile) and third (75th percentile) quartiles and the 10th and 90th — percentiles for the annual means (every year in the flow record). MA43 is the third quartile minus the first quartile divided by the median of the annual means. dimensionless-spatial
MA44	Variability across annual flows. Compute the first (25th percentile) and third (75th percentile) quartiles and the 10th and 90th percentiles for the annual means (every year in the flow record). MA44 is the 90th percentile minus the 10th percentile divided by the median of the annual means. dimensionless-spatial
MA45	Skewness in the annual flows. MA45 is the mean of the annual flow means minus the median of the annual means divided by the median of the annual means. dimensionless-spatial
ML1 to ML12	Mean (or median) of minimum flows for each month across all years. Compute the minimums for each month over the entire flow record. For example, ML1 is the mean of the minimums of all January flow values over the entire record. cubic feet per second— temporal
ML13	Variability (coefficient of variation) across minimum monthly flow values. Compute the mean and standard deviation for the minimum monthly flows over the entire flow record. ML13 is the standard deviation times 100 divided by the mean minimum monthly flow for all years. percent—spatial
ML14	Compute the minimum annual flow for each year. ML14 is the mean of the ratios of minimum annual flows to the median flow for each year. dimensionless—temporal
ML15	Low-flow index. ML15 is the mean of the ratios of minimum annual flows to the mean flow for each year. dimensionless—temporal
ML16	Median of annual minimum flows. ML16 is the median of the ratios of minimum annual flows to the median flow for each year. dimensionless— temporal
ML17	Base flow. Compute the mean annual flows. Compute the minimum of a 7-day moving average flows for each year and divide them by the mean annual flow for that year. ML17 is the mean (or median—Use Preferenceset by using the Preference option) of those ratios. dimensionless—temporal
ML18	Variability in base flow. Compute the standard deviation for the ratios of 7-day moving average flows to mean annual flows for each year. ML18 is the standard deviation times 100 divided by the mean of the ratios. percent—spatial
ML19	Base flow. Compute the ratios of the minimum annual flow to mean annual flow for each year. ML19 is the mean (or median) of these ratios times 100. dimensionless—temporal
ML20	Base flow. Divide the daily flow record into 5-day blocks. Find the minimum flow for each block. Assign the minimum flow as a base flow for that block if 90 percent of that minimum flow is less than the minimum flows for the blocks on either side. Otherwise, set it to zero. Fill in the zero values using linear interpolation. Compute the total flow for the entire record and the total base flow for the entire record. ML20 is the ratio of total flow to total base flow. dimensionless—spatial
ML21	Variability across annual minimum flows. Compute the mean and standard deviation for the annual minimum flows. ML21 is the standard deviation times 100 divided by the mean. percent—spatial
ML22	Specific mean annual minimum flow. ML22 is the mean (or median) of the annual minimum flows divided by the drainage area. cubic feet per second/square mile—temporal
MH1 to MH12	Mean (or median) maximum flows for each month across all years. Compute the maximums for each month over the entire cubic feet per flow record. For example, MH1 is the mean of the maximums of all January flow values over the entire record. second—temporal
MH13	Variability (coefficient of variation) across maximum monthly flow values. Compute the mean and standard deviation for the maximum monthly flows over the entire flow record. MH13 is the standard deviation times 100 divided by the mean maximum monthly flow for all years. percent—spatial
MH14	Median of annual maximum flows. Compute the annual maximum flows from monthly maximum flows. Compute the ratio of annual maximum flow to median annual flow for each year. MH14 is the median of these ratios. dimensionless—temporal
MH15	High flow discharge index. Compute the 1-percent exceedance value for the entire data record. MH15 is the 1-percent exceedance value divided by the median flow for the entire record. dimensionless—spatial
MH16	High flow discharge index. Compute the 10-percent exceedance value for the entire data record. MH16 is the 10-percent exceedance value divided by the median flow for the entire record. dimensionless—spatial
MH17	High flow discharge index. Compute the 25-percent exceedance value for the entire data record. MH17 is the 25-percent exceedance value divided by the median flow for the entire record. dimensionless—spatial
MH18	Variability across annual maximum flows. Compute the logs (log10) of the maximum annual flows. Find the standard percent—spatial deviation and mean for these values. MH18 is the standard deviation times 100 divided by the mean.
MH19	Skewness in annual maximum flows. dimensionless—spatial
MH20	Specific mean annual maximum flow. MH20 is the mean (or median) of the annual maximum flows divided by the drainage area. cubic feet per second/square mile—temporal
MH21	High flow volume index. Compute the average volume for flow events above a threshold equal to the median flow for the entire record. MH21 is the average volume divided by the median flow for the entire record. days—temporal
MH22	High flow volume. Compute the average volume for flow events above a threshold equal to three times the median flow for the entire record. MH22 is the average volume divided by the median flow for the entire record. days—temporal
MH23	High flow volume. Compute the average volume for flow events above a threshold equal to seven times the median flow for the entire record. MH23 is the average volume divided by the median flow for the entire record. days—temporal
MH24	High peak flow. Compute the average peak flow value for flow events above a threshold equal to the median flow for the entire record. MH24 is the average peak flow divided by the median flow for the entire record. dimensionless—temporal
MH25	High peak flow. Compute the average peak flow value for flow events above a threshold equal to three times the median flow for the entire record. MH25 is the average peak flow divided by the median flow for the entire record. dimensionless—temporal
MH26	High peak flow. Compute the average peak flow value for flow events above a threshold equal to seven times the median flow for the entire record. MH26 is the average peak flow divided by the median flow for the entire record. dimensionless—temporal
MH27	High peak flow. Compute the average peak flow value for flow events above a threshold equal to 75th-percentile value for the entire flow record. MH27 is the average peak flow divided by the median flow for the entire record. dimensionless—temporal
FL1	Low flood pulse count. Compute the average number of flow events with flows below a threshold equal to the 25th-percentile value for the entire flow record. FL1 is the average (or median) number of events. number of events/year—temporal
FL2	Variability in low pulse count. Compute the standard deviation in the annual pulse counts for FL1. FL2 is 100 times the standard deviation divided by the mean pulse count. percent—spatial
FL3	Frequency of low pulse spells. Compute the average number of flow events with flows below a threshold equal to 5 percent of the mean flow value for the entire flow record. FL3 is the average (or median) number of events. number of events/year—temporal
FH1	High flood pulse count. Compute the average number of flow events with flows above a threshold equal to the 75th-percentile value for the entire flow record. FH1 is the average (or median) number of events. number of events/year—temporal
FH2	Variability in high pulse count. Compute the standard deviation in the annual pulse counts for FH1. FH2 is 100 times the standard deviation divided by the mean pulse count. number of events/year—spatial
FH3	High flood pulse count. Compute the average number of days per year that the flow is above a threshold equal to three times the median flow for the entire record. FH3 is the mean (or median) of the annual number of days for all years. number of days/year—temporal
FH4	High flood pulse count. Compute the average number of days per year that the flow is above a threshold equal to seven times the median flow for the entire record. FH4 is the mean (or median) of the annual number of days for all years. number of days/year—temporal
FH5	Flood frequency. Compute the average number of flow events with flows above a threshold equal to the median flow value for the entire flow record. FH5 is the average (or median) number of events. number of events/year—temporal
FH6	Flood frequency. Compute the average number of flow events with flows above a threshold equal to three times the median flow value for the entire flow record. FH6 is the average (or median) number of events. number of events/year—temporal
FH7	Flood frequency. Compute the average number of flow events with flows above a threshold equal to seven times the median flow value for the entire flow record. FH6 is the average (or median) number of events. number of events/year—temporal
FH8	Flood frequency. Compute the average number of flow events with flows above a threshold equal to 25-percent exceedance value for the entire flow record. FH8 is the average (or median) number of events. number of events/year—temporal
FH9	Flood frequency. Compute the average number of flow events with flows above a threshold equal to 75-percent exceedance value for the entire flow record. FH9 is the average (or median) number of events. number of events/year—temporal
FH10	Flood frequency. Compute the average number of flow events with flows above a threshold equal to median of the annual cubic feet/second minima for the entire flow record. FH10 is the average (or median) number of events. number of events/year—temporal
DL1	Annual minimum daily flow. Compute the minimum 1-day average flow for each year. DL1 is the mean (or median) of these cubic feet per values. second—temporal
DL2	Annual minimum of 3-day moving average flow. Compute the minimum of a 3-day moving average flow for each year. DL2 cubic feet per is the mean (or median) of these values. second—temporal
DL3	Annual minimum of 7-day moving average flow. Compute the minimum of a 7-day moving average flow for each year. DL3 cubic feet per is the mean (or median) of these values. second—temporal
DL4	Annual minimum of 30-day moving average flow. Compute the minimum of a 30-day moving average flow for each year. cubic feet per DL4 is the mean (or median) of these values. second—temporal

Parameters:

• indice_codes –

  A list of the hydrologic indice codes, stream classifications, and/or flow regime indices to be computed.

  The hydrologic indice codes are taken as is, but the collected stream classifications are intersected with the flow regime indices.

• water_year –

  [optional, default=”YE-SEP”]

  The water year to use for the calculation. This uses the one of the “A-…” Pandas offset codes. The “YE-SEP” code represents the very end of September (the start of October) as the end of the water year.

• use_median (bool) –

  [optional, default=False]

  If True, use the median instead of the mean for the calculations.

• drainage_area –

  [optional, default=1]

  The drainage area to use for the calculations. This is the drainage area in square miles.

• input_ts (str) –

  [optional though required if using within Python, default is ‘-’ (stdin)]

  Whether from a file or standard input, data requires a single line header of column names. The default header is the first line of the input, but this can be changed for CSV files using the ‘skiprows’ option.

  Most common date formats can be used, but the closer to ISO 8601 date/time standard the better.

  Comma-separated values (CSV) files or tab-separated values (TSV):

  File separators will be automatically detected.
  
  Columns can be selected by name or index, where the index for
  data columns starts at 1.
  

  Command line examples:

  Keyword Example	Description
  –input_ts=fn.csv	read all columns from ‘fn.csv’
  –input_ts=fn.csv,2,1	read data columns 2 and 1 from ‘fn.csv’
  –input_ts=fn.csv,2,skiprows=2	read data column 2 from ‘fn.csv’, skipping first 2 rows so header is read from third row
  –input_ts=fn.xlsx,2,Sheet21	read all data from 2nd sheet all data from “Sheet21” of ‘fn.xlsx’
  –input_ts=fn.hdf5,Table12,T2	read all data from table “Table12” then all data from table “T2” of ‘fn.hdf5’
  –input_ts=fn.wdm,210,110	read DSNs 210, then 110 from ‘fn.wdm’
  –input_ts=’-’	read all columns from standard input (stdin)
  –input_ts=’-’ –columns=4,1	read column 4 and 1 from standard input (stdin)

  If working with CSV or TSV files you can use redirection rather than use –input_ts=fname.csv. The following are identical:

  From a file:

  command subcmd –input_ts=fname.csv

  From standard input (since ‘–input_ts=-’ is the default:

  command subcmd < fname.csv

  Can also combine commands by piping:

  command subcmd < filein.csv | command subcmd1 > fileout.csv

  Python library examples:

  You must use the `input_ts=...` option where `input_ts` can be
  one of a [pandas DataFrame, pandas Series, dict, tuple, list,
  StringIO, or file name].
  

• columns –

  [optional, defaults to all columns, input filter]

  Columns to select out of input. Can use column names from the first line header or column numbers. If using numbers, column number 1 is the first data column. To pick multiple columns; separate by commas with no spaces. As used in toolbox_utils pick command.

  This solves a big problem so that you don’t have to create a data set with a certain column order, you can rearrange columns when data is read in.

• source_units (str) –

  [optional, default is None, transformation]

  If unit is specified for the column as the second field of a ‘:’ delimited column name, then the specified units and the ‘source_units’ must match exactly.

  Any unit string compatible with the ‘pint’ library can be used.

• start_date (str) –

  [optional, defaults to first date in time-series, input filter]

  The start_date of the series in ISOdatetime format, or ‘None’ for beginning.

• end_date (str) –

  [optional, defaults to last date in time-series, input filter]

  The end_date of the series in ISOdatetime format, or ‘None’ for end.

• dropna (str) –

  [optional, defauls it ‘no’, input filter]

  Set dropna to ‘any’ to have records dropped that have NA value in any column, or ‘all’ to have records dropped that have NA in all columns. Set to ‘no’ to not drop any records. The default is ‘no’.

• clean –

  [optional, default is False, input filter]

  The ‘clean’ command will repair a input index, removing duplicate index values and sorting.

• round_index –

  [optional, default is None which will do nothing to the index, output format]

  Round the index to the nearest time point. Can significantly improve the performance since can cut down on memory and processing requirements, however be cautious about rounding to a very course interval from a small one. This could lead to duplicate values in the index.

• skiprows (list-like or integer or callable) –

  [optional, default is None which will infer header from first line, input filter]

  Line numbers to skip (0-indexed) if a list or number of lines to skip at the start of the file if an integer.

  If used in Python can be a callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be

  lambda x: x in [0, 2].

• index_type (str) –

  [optional, default is ‘datetime’, output format]

  Can be either ‘number’ or ‘datetime’. Use ‘number’ with index values that are Julian dates, or other epoch reference.

• names (str) –

  [optional, default is None, transformation]

  If None, the column names are taken from the first row after ‘skiprows’ from the input dataset.

  MUST include a name for all columns in the input dataset, including the index column.

• target_units (str) –

  [optional, default is None, transformation]

  The purpose of this option is to specify target units for unit conversion. The source units are specified in the header line of the input or using the ‘source_units’ keyword.

  The units of the input time-series or values are specified as the second field of a ‘:’ delimited name in the header line of the input or in the ‘source_units’ keyword.

  Any unit string compatible with the ‘pint’ library can be used.

  This option will also add the ‘target_units’ string to the column names.

• print_input –

  [optional, default is False, output format]

  If set to ‘True’ will include the input columns in the output table.