Requesting Statistics from the USGS Statistics Service¶

The USGS calculates various types of statistics for its data and provides these values through a web service. You can access this service through the stats function. Learn more about the USGS Statistics Service.

There are three types of report that you can request using the StatReportType parameter.

‘annual’: This summarizes all of the official daily data for each year using max, min, mean, and the 5, 10, 20, 25, 50, 75, 80, 90, and 95th percentiles.
‘monthly’: This calculates the mean of the 28 to 31 daily values that occur for each of the months in each of the years of record.
‘daily’: This summarizes all of the data for this month and day, using max, min, mean, and the 5, 10, 20, 25, 50, 75, 80, 90, and 95th percentiles.

Request multiple sites¶

You can request multiple sites by separating them with commas, like this: '01541200,01542500'

Providing additional arguments¶

The USGS Statistics Service allows you to specify a wide array of additional parameters in your request. You can provide these parameters as keyword arguments, like in this example:

hf.stats('01452500', parameterCD='00060')

This will only request statistics for discharge, which is specified with the ‘00060’ parameter code.

Limiting requests to only certain parameters¶

The default behavior for the USGS Statistics Service is to provide statistics for every parameter that is collected at a site. This can make for a long table that you will have to filter by the parameter that you want, like this:

my_stat_dataframe.loc(my_stat_dataframe['parameter_cd']='00060')

Alternatively, you can just request the parameter that you are interested in, rather than all of the parameters. To limit your request, provide the parameterCD keyword argument, like this:

hf.stats('01452500', parameterCD='00060')

You can request more than one parameter by listing every parameter code that you are interested in, separated by a comma: parameterCD='00060,00065'

Calculating annual statistics using water years¶

The default behavior for the USGS Statistics Service is to calculate annual statistics using calendar years. Unfortunately, for many places in the US, this will split the wet season in half. Since discharge data tends to be autocorrelated, you are more likely to get a large flood in January 2020 if you had a large flood in December 2019. To fix this, hydrologists often use ‘Water Years’, which split the year during the more or less dry season, on October 1st. To calculate annual statistics using the water year, provide the statYearType='water' argument, like this:

hf.stats('01452500','annual', statYearType='water')

Missing data¶

The default behavior for the USGS Statistics Service is to not calculate statistics for months or years if there are -ANY- missing values. In other words, in an annual report, every year reported will be based on 365 or 366 (leap year) values. You can override this behavior by providing the missingData='on' parameter. This will calculate the statistics as long as there are at least one measurement. You can decide whether or not to use the statistic by looking at the count_nu column to see how many values were used to generate the statistic.

Viewing the metadata header or the data¶

The USGS accompanies every dataset with a header that explains the data. Hydrofunctions will automatically display this header along with the data. To access just one item, use either the .header or .df attribute.

test = stats('01542500')

test        # Print the header & dataframe
test.header # print just the header
test.df     # print just the dataframe.

Examples¶

The first step as always is to import hydrofunctions.

[1]:

import hydrofunctions as hf
print(hf.__version__)

0.2.0

To get started, let’s request some data from Karthus, PA to see what typically gets collected there.

[2]:

may_2019 = hf.NWIS('01542500', 'dv', '2019-05-01', '2019-06-01')
may_2019

Requested data from https://waterservices.usgs.gov/nwis/dv/?format=json%2C1.1&sites=01542500&startDT=2019-05-01&endDT=2019-06-01

[2]:

USGS:01542500: WB Susquehanna River at Karthaus, PA
    00010: <Day>  Temperature, water, degrees Celsius
    00060: <Day>  Discharge, cubic feet per second
    00095: <Day>  Specific conductance, water, unfiltered, microsiemens per centimeter at 25 degrees Celsius
    00300: <Day>  Dissolved oxygen, water, unfiltered, milligrams per liter
    00400: <Day>  pH, water, unfiltered, field, standard units
Start: 2019-05-01 00:00:00+00:00
End:   2019-06-01 00:00:00+00:00

Requesting annual statistics¶

This site has collected discharge data since 1960, but other parameters, such as water temperature (‘00010’), have only been collected since 2010. Unfortunately, in 2010, only 41 days of water temperature measurements were collected. By setting the missingData argument to on, we can ask the USGS to report averages for incomplete years. Now it is up to you to decide if 41 values is an adequate number!

[3]:

annual_stats = hf.stats('01542500', 'annual', missingData='on')
# Use annual_stats.header to access just the header, or .df for just the dataframe.
# If you don't specify, both will be provided.
annual_stats

Retrieving annual statistics for site #01542500 from https://waterservices.usgs.gov/nwis/stat/?statReportType=annual&statType=all&sites=01542500&format=rdb&missingData=on

[3]:

#
#
# US Geological Survey, Water Resources Data
# retrieved: 2020-03-04 21:24:34 -05:00 (natwebcaas01)
#
# This file contains USGS Annual Statistics
#
# Note:The statistics generated are based on approved daily-mean data and may not match those published by the USGS in official publications.
# The user is responsible for assessment and use of statistics from this site.
# For more details on why the statistics may not match, visit http://help.waterdata.usgs.gov/faq/about-statistics.
#
# Data heading explanations.
# agency_cd -- agency code
# site_no -- Site identification number
# parameter_cd -- Parameter code
# station_nm -- Site name
# loc_web_ds -- Additional measurement description
#
# Data for the following 1 site(s) are contained in this file
# agency_cd site_no parameter_cd station_nm (loc_web_ds)
# USGS 01542500 00010 WB Susquehanna River at Karthaus, PA
# USGS 01542500 00060 WB Susquehanna River at Karthaus, PA
# USGS 01542500 00095 WB Susquehanna River at Karthaus, PA
# USGS 01542500 00300 WB Susquehanna River at Karthaus, PA
#
# Explanation of Parameter Codes
# parameter_cd Parameter Name
# 00010 Temperature, water, degrees Celsius
# 00060 Discharge, cubic feet per second
# 00095 Specific conductance, water, unfiltered, microsiemens per centimeter at 25 degrees Celsius
# 00300 Dissolved oxygen, water, unfiltered, milligrams per liter
#
# Data heading explanations.
# year_nu ... Calendar year for value.
# mean_va ... Mean of daily mean values for this month.
# count_nu ... Number of values used in the calculation.
#

	agency_cd	site_no	parameter_cd	ts_id	loc_web_ds	year_nu	mean_va	count_nu
0	USGS	01542500	00010	118870	NaN	2010	4.70	41
1	USGS	01542500	00010	118870	NaN	2011	12.92	354
2	USGS	01542500	00010	118870	NaN	2012	13.98	360
3	USGS	01542500	00010	118870	NaN	2013	12.76	365
4	USGS	01542500	00010	118870	NaN	2014	12.43	362
5	USGS	01542500	00010	118870	NaN	2015	12.46	365
6	USGS	01542500	00010	118870	NaN	2016	12.64	358
7	USGS	01542500	00010	118870	NaN	2017	12.37	358
8	USGS	01542500	00010	118870	NaN	2018	11.64	353
9	USGS	01542500	00010	118870	NaN	2019	11.80	362
10	USGS	01542500	00010	118870	NaN	2020	2.46	14
11	USGS	01542500	00060	118867	NaN	1960	325.50	92
12	USGS	01542500	00060	118867	NaN	1961	2418.00	365
13	USGS	01542500	00060	118867	NaN	1962	2048.00	365
14	USGS	01542500	00060	118867	NaN	1963	1682.00	365
15	USGS	01542500	00060	118867	NaN	1964	2247.00	366
16	USGS	01542500	00060	118867	NaN	1965	1820.00	365
17	USGS	01542500	00060	118867	NaN	1966	2019.00	365
18	USGS	01542500	00060	118867	NaN	1967	2693.00	365
19	USGS	01542500	00060	118867	NaN	1968	2045.00	366
20	USGS	01542500	00060	118867	NaN	1969	1624.00	365
21	USGS	01542500	00060	118867	NaN	1970	3063.00	365
22	USGS	01542500	00060	118867	NaN	1971	2522.00	365
23	USGS	01542500	00060	118867	NaN	1972	3923.00	366
24	USGS	01542500	00060	118867	NaN	1973	2796.00	365
25	USGS	01542500	00060	118867	NaN	1974	2713.00	365
26	USGS	01542500	00060	118867	NaN	1975	3232.00	365
27	USGS	01542500	00060	118867	NaN	1976	2319.00	366
28	USGS	01542500	00060	118867	NaN	1977	3114.00	365
29	USGS	01542500	00060	118867	NaN	1978	2670.00	365
...	...	...	...	...	...	...	...	...
50	USGS	01542500	00060	118867	NaN	2010	2222.00	365
51	USGS	01542500	00060	118867	NaN	2011	3398.00	365
52	USGS	01542500	00060	118867	NaN	2012	1793.00	366
53	USGS	01542500	00060	118867	NaN	2013	1845.00	365
54	USGS	01542500	00060	118867	NaN	2014	2017.00	365
55	USGS	01542500	00060	118867	NaN	2015	2462.00	365
56	USGS	01542500	00060	118867	NaN	2016	1860.00	366
57	USGS	01542500	00060	118867	NaN	2017	2494.00	365
58	USGS	01542500	00060	118867	NaN	2018	4482.00	365
59	USGS	01542500	00060	118867	NaN	2019	2781.00	346
60	USGS	01542500	00095	118873	NaN	2010	363.40	41
61	USGS	01542500	00095	118873	NaN	2011	391.70	354
62	USGS	01542500	00095	118873	NaN	2012	413.90	358
63	USGS	01542500	00095	118873	NaN	2013	401.70	365
64	USGS	01542500	00095	118873	NaN	2014	376.40	362
65	USGS	01542500	00095	118873	NaN	2015	387.10	365
66	USGS	01542500	00095	118873	NaN	2016	404.50	358
67	USGS	01542500	00095	118873	NaN	2017	385.20	358
68	USGS	01542500	00095	118873	NaN	2018	296.70	353
69	USGS	01542500	00095	118873	NaN	2019	346.40	362
70	USGS	01542500	00095	118873	NaN	2020	260.60	14
71	USGS	01542500	00300	118879	NaN	2011	9.56	227
72	USGS	01542500	00300	118879	NaN	2012	9.42	243
73	USGS	01542500	00300	118879	NaN	2013	9.38	246
74	USGS	01542500	00300	118879	NaN	2014	9.45	224
75	USGS	01542500	00300	118879	NaN	2015	9.91	258
76	USGS	01542500	00300	118879	NaN	2016	9.30	208
77	USGS	01542500	00300	118879	NaN	2017	9.07	205
78	USGS	01542500	00300	118879	NaN	2018	9.71	227
79	USGS	01542500	00300	118879	NaN	2019	9.33	164

80 rows × 8 columns

Requesting monthly statistics¶

The monthly report provides the mean value for each parameter for every month since 1960, when data collection began at this site.

Since this site collects lots of parameters, we can limit our display of the dataframe by filtering everything out except the discharge parameter (‘00060’).

[4]:

monthly_stats = hf.stats('01542500', 'monthly')
monthly_stats.df.loc[monthly_stats.df['parameter_cd']=='00060']

Retrieving monthly statistics for site #01542500 from https://waterservices.usgs.gov/nwis/stat/?statReportType=monthly&statType=all&sites=01542500&format=rdb

[4]:

	agency_cd	site_no	parameter_cd	ts_id	loc_web_ds	year_nu	month_nu	mean_va	count_nu
93	USGS	01542500	00060	118867	NaN	1960	10	258.8	31
94	USGS	01542500	00060	118867	NaN	1960	11	441.1	30
95	USGS	01542500	00060	118867	NaN	1960	12	280.5	31
96	USGS	01542500	00060	118867	NaN	1961	1	474.2	31
97	USGS	01542500	00060	118867	NaN	1961	2	5155.0	28
98	USGS	01542500	00060	118867	NaN	1961	3	6108.0	31
99	USGS	01542500	00060	118867	NaN	1961	4	6145.0	30
100	USGS	01542500	00060	118867	NaN	1961	5	3320.0	31
101	USGS	01542500	00060	118867	NaN	1961	6	2109.0	30
102	USGS	01542500	00060	118867	NaN	1961	7	1004.0	31
103	USGS	01542500	00060	118867	NaN	1961	8	1107.0	31
104	USGS	01542500	00060	118867	NaN	1961	9	564.7	30
105	USGS	01542500	00060	118867	NaN	1961	10	286.6	31
106	USGS	01542500	00060	118867	NaN	1961	11	1576.0	30
107	USGS	01542500	00060	118867	NaN	1961	12	1457.0	31
108	USGS	01542500	00060	118867	NaN	1962	1	2391.0	31
109	USGS	01542500	00060	118867	NaN	1962	2	2387.0	28
110	USGS	01542500	00060	118867	NaN	1962	3	6124.0	31
111	USGS	01542500	00060	118867	NaN	1962	4	7340.0	30
112	USGS	01542500	00060	118867	NaN	1962	5	1699.0	31
113	USGS	01542500	00060	118867	NaN	1962	6	707.5	30
114	USGS	01542500	00060	118867	NaN	1962	7	296.1	31
115	USGS	01542500	00060	118867	NaN	1962	8	271.0	31
116	USGS	01542500	00060	118867	NaN	1962	9	586.3	30
117	USGS	01542500	00060	118867	NaN	1962	10	891.4	31
118	USGS	01542500	00060	118867	NaN	1962	11	1267.0	30
119	USGS	01542500	00060	118867	NaN	1962	12	704.3	31
120	USGS	01542500	00060	118867	NaN	1963	1	1666.0	31
121	USGS	01542500	00060	118867	NaN	1963	2	706.1	28
122	USGS	01542500	00060	118867	NaN	1963	3	7965.0	31
...	...	...	...	...	...	...	...	...	...
616	USGS	01542500	00060	118867	NaN	2017	6	3073.0	30
617	USGS	01542500	00060	118867	NaN	2017	7	1582.0	31
618	USGS	01542500	00060	118867	NaN	2017	8	810.5	31
619	USGS	01542500	00060	118867	NaN	2017	9	450.4	30
620	USGS	01542500	00060	118867	NaN	2017	10	724.0	31
621	USGS	01542500	00060	118867	NaN	2017	11	2341.0	30
622	USGS	01542500	00060	118867	NaN	2017	12	1265.0	31
623	USGS	01542500	00060	118867	NaN	2018	1	2708.0	31
624	USGS	01542500	00060	118867	NaN	2018	2	7874.0	28
625	USGS	01542500	00060	118867	NaN	2018	3	3952.0	31
626	USGS	01542500	00060	118867	NaN	2018	4	4711.0	30
627	USGS	01542500	00060	118867	NaN	2018	5	4117.0	31
628	USGS	01542500	00060	118867	NaN	2018	6	3448.0	30
629	USGS	01542500	00060	118867	NaN	2018	7	2900.0	31
630	USGS	01542500	00060	118867	NaN	2018	8	2458.0	31
631	USGS	01542500	00060	118867	NaN	2018	9	7562.0	30
632	USGS	01542500	00060	118867	NaN	2018	10	4185.0	31
633	USGS	01542500	00060	118867	NaN	2018	11	5450.0	30
634	USGS	01542500	00060	118867	NaN	2018	12	4855.0	31
635	USGS	01542500	00060	118867	NaN	2019	1	3982.0	31
636	USGS	01542500	00060	118867	NaN	2019	2	5452.0	28
637	USGS	01542500	00060	118867	NaN	2019	3	3927.0	31
638	USGS	01542500	00060	118867	NaN	2019	4	4020.0	30
639	USGS	01542500	00060	118867	NaN	2019	5	4316.0	31
640	USGS	01542500	00060	118867	NaN	2019	6	3618.0	30
641	USGS	01542500	00060	118867	NaN	2019	7	1594.0	31
642	USGS	01542500	00060	118867	NaN	2019	8	855.8	31
643	USGS	01542500	00060	118867	NaN	2019	9	810.0	30
644	USGS	01542500	00060	118867	NaN	2019	10	795.1	31
645	USGS	01542500	00060	118867	NaN	2019	11	1357.0	30

553 rows × 9 columns

Requesting daily reports¶

The daily statistics report is different from the monthly and annual reports in that it aggregates multiple years together from across the entire period of record. So in the following example, in line 0, the report provides statistics for January 1st by calculating the mean of every January 1st from 1961 (‘begin_yr’) to 2019 (‘end_yr’).

Note that there are 366 rows, or 365 days each year plus Febrary 29th on leap years.

[5]:

daily_stats = hf.stats('01542500', 'daily', parameterCd='00060')
daily_stats.df

Retrieving daily statistics for site #01542500 from https://waterservices.usgs.gov/nwis/stat/?statReportType=daily&statType=all&sites=01542500&format=rdb&parameterCd=00060

[5]:

	agency_cd	site_no	parameter_cd	ts_id	loc_web_ds	month_nu	day_nu	begin_yr	end_yr	count_nu	...	mean_va	p05_va	p10_va	p20_va	p25_va	p50_va	p75_va	p80_va	p90_va	p95_va
0	USGS	01542500	00060	118867	NaN	1	1	1961	2019	46	...	2850	520.0	807	970	1080	2180	3790	4340	6800	7600.0
1	USGS	01542500	00060	118867	NaN	1	2	1961	2019	46	...	2950	487.0	800	1040	1100	2080	3730	3960	6400	9460.0
2	USGS	01542500	00060	118867	NaN	1	3	1961	2019	46	...	2880	523.0	793	1110	1290	2110	3400	4590	6450	8390.0
3	USGS	01542500	00060	118867	NaN	1	4	1961	2019	46	...	2720	541.0	753	1120	1280	1890	3280	4190	6070	7510.0
4	USGS	01542500	00060	118867	NaN	1	5	1961	2019	46	...	2710	551.0	716	1080	1180	1850	3880	4480	5760	8090.0
5	USGS	01542500	00060	118867	NaN	1	6	1961	2019	46	...	2850	556.0	802	1040	1120	2140	3820	4420	6160	7460.0
6	USGS	01542500	00060	118867	NaN	1	7	1961	2019	46	...	2780	576.0	819	1020	1100	1900	3650	4060	5500	6930.0
7	USGS	01542500	00060	118867	NaN	1	8	1961	2019	46	...	2630	603.0	792	976	1100	1770	3700	4050	4870	6090.0
8	USGS	01542500	00060	118867	NaN	1	9	1961	2019	46	...	2710	621.0	788	929	1130	1920	3620	3720	5280	8450.0
9	USGS	01542500	00060	118867	NaN	1	10	1961	2019	46	...	2560	696.0	847	961	1070	1720	3320	3700	5370	7960.0
10	USGS	01542500	00060	118867	NaN	1	11	1961	2019	46	...	2430	738.0	829	979	1100	1660	3340	3780	5170	6370.0
11	USGS	01542500	00060	118867	NaN	1	12	1961	2019	46	...	2450	649.0	773	1070	1240	1750	3150	3480	4620	6550.0
12	USGS	01542500	00060	118867	NaN	1	13	1961	2019	46	...	2750	621.0	771	1000	1170	2020	3690	4130	6160	7620.0
13	USGS	01542500	00060	118867	NaN	1	14	1961	2019	46	...	2760	607.0	778	1000	1190	1900	3780	3940	6600	7040.0
14	USGS	01542500	00060	118867	NaN	1	15	1961	2019	46	...	2640	614.0	740	974	1080	1900	3520	4050	5850	7560.0
15	USGS	01542500	00060	118867	NaN	1	16	1961	2019	46	...	2480	601.0	724	938	1040	1790	3250	3340	4630	7750.0
16	USGS	01542500	00060	118867	NaN	1	17	1961	2019	46	...	2420	587.0	716	860	948	2040	2910	3440	5160	6920.0
17	USGS	01542500	00060	118867	NaN	1	18	1961	2019	46	...	2380	554.0	715	893	988	1780	2950	3630	6000	7630.0
18	USGS	01542500	00060	118867	NaN	1	19	1961	2019	46	...	2390	564.0	687	964	1120	1610	2760	3540	5490	7260.0
19	USGS	01542500	00060	118867	NaN	1	20	1961	2019	46	...	2340	570.0	678	962	1090	1740	3160	3470	4700	6780.0
20	USGS	01542500	00060	118867	NaN	1	21	1961	2019	46	...	2340	544.0	655	894	1070	1690	2960	3270	5060	6480.0
21	USGS	01542500	00060	118867	NaN	1	22	1961	2019	46	...	2330	581.0	662	952	1100	1470	2830	3080	4750	7770.0
22	USGS	01542500	00060	118867	NaN	1	23	1961	2019	46	...	2250	572.0	662	991	1070	1640	2870	3260	3980	6860.0
23	USGS	01542500	00060	118867	NaN	1	24	1961	2019	46	...	2340	544.0	667	1000	1000	1510	3150	3550	4870	5960.0
24	USGS	01542500	00060	118867	NaN	1	25	1961	2019	46	...	2780	521.0	716	989	1040	1620	3860	4550	6270	8750.0
25	USGS	01542500	00060	118867	NaN	1	26	1961	2019	46	...	2950	492.0	772	993	1100	1740	3830	4130	6700	8240.0
26	USGS	01542500	00060	118867	NaN	1	27	1961	2019	46	...	3040	496.0	757	1070	1090	1860	3900	5470	7430	7980.0
27	USGS	01542500	00060	118867	NaN	1	28	1961	2019	46	...	2960	510.0	766	999	1050	2050	4180	4730	6530	9460.0
28	USGS	01542500	00060	118867	NaN	1	29	1961	2019	46	...	2750	497.0	791	928	956	2210	3880	4370	5970	8140.0
29	USGS	01542500	00060	118867	NaN	1	30	1961	2019	46	...	2880	487.0	800	888	943	1930	3780	4430	6130	7100.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
336	USGS	01542500	00060	118867	NaN	12	2	1961	2020	47	...	3010	375.0	784	1130	1180	1880	3710	4400	6840	9630.0
337	USGS	01542500	00060	118867	NaN	12	3	1961	2020	47	...	2810	415.0	782	1090	1170	2030	3750	4040	6500	7330.0
338	USGS	01542500	00060	118867	NaN	12	4	1961	2020	47	...	2720	401.0	745	994	1070	1930	3720	4370	5820	6970.0
339	USGS	01542500	00060	118867	NaN	12	5	1961	2020	47	...	2670	517.0	757	967	1070	1830	3850	4020	5980	6920.0
340	USGS	01542500	00060	118867	NaN	12	6	1961	2020	47	...	2650	708.0	871	994	1310	1800	3570	4180	5590	7450.0
341	USGS	01542500	00060	118867	NaN	12	7	1961	2020	47	...	2810	713.0	812	1070	1270	2140	3770	4210	5330	7230.0
342	USGS	01542500	00060	118867	NaN	12	8	1961	2020	47	...	2790	535.0	730	1050	1310	2220	3900	4150	4650	9390.0
343	USGS	01542500	00060	118867	NaN	12	9	1961	2020	47	...	2800	483.0	690	1130	1370	2140	3480	3890	5640	9520.0
344	USGS	01542500	00060	118867	NaN	12	10	1961	2020	47	...	2850	407.0	718	1120	1320	2170	3720	4320	5630	8720.0
345	USGS	01542500	00060	118867	NaN	12	11	1961	2020	47	...	2980	444.0	673	1320	1650	2440	3680	4130	6360	8870.0
346	USGS	01542500	00060	118867	NaN	12	12	1961	2020	47	...	2890	624.0	935	1330	1400	2430	4000	4360	6440	7060.0
347	USGS	01542500	00060	118867	NaN	12	13	1961	2019	46	...	2880	637.0	906	1190	1380	2440	4350	4580	5490	6460.0
348	USGS	01542500	00060	118867	NaN	12	14	1961	2019	46	...	2750	695.0	893	1200	1340	2430	3750	4240	4880	7310.0
349	USGS	01542500	00060	118867	NaN	12	15	1961	2019	46	...	2670	684.0	770	1100	1270	2120	3760	4100	5020	7450.0
350	USGS	01542500	00060	118867	NaN	12	16	1961	2019	46	...	2610	531.0	766	1030	1270	2110	3710	3950	4830	6140.0
351	USGS	01542500	00060	118867	NaN	12	17	1961	2019	46	...	2600	530.0	707	1020	1190	2230	3510	4100	4740	6160.0
352	USGS	01542500	00060	118867	NaN	12	18	1961	2019	46	...	2510	580.0	669	1030	1180	2250	3630	3870	4540	5150.0
353	USGS	01542500	00060	118867	NaN	12	19	1961	2019	46	...	2670	611.0	660	1020	1210	2120	3260	3440	4390	10400.0
354	USGS	01542500	00060	118867	NaN	12	20	1961	2019	46	...	2680	576.0	662	1050	1200	2000	3160	3510	5740	10000.0
355	USGS	01542500	00060	118867	NaN	12	21	1961	2019	46	...	2690	535.0	683	1000	1200	1900	3820	4480	5980	8600.0
356	USGS	01542500	00060	118867	NaN	12	22	1961	2019	46	...	3020	587.0	682	959	1230	1890	5000	5810	7070	8830.0
357	USGS	01542500	00060	118867	NaN	12	23	1961	2019	46	...	2990	607.0	718	1110	1450	1920	4160	4540	7530	8910.0
358	USGS	01542500	00060	118867	NaN	12	24	1961	2019	46	...	3060	607.0	660	1250	1310	1950	3830	4820	7250	8070.0
359	USGS	01542500	00060	118867	NaN	12	25	1961	2019	46	...	3160	601.0	718	1140	1340	2530	4370	4800	6590	8800.0
360	USGS	01542500	00060	118867	NaN	12	26	1961	2019	46	...	3040	544.0	770	1130	1380	2660	4280	4730	5840	8060.0
361	USGS	01542500	00060	118867	NaN	12	27	1961	2019	46	...	3030	499.0	720	1040	1280	2600	4100	4610	5810	8770.0
362	USGS	01542500	00060	118867	NaN	12	28	1961	2019	46	...	3060	525.0	658	1050	1200	2530	4500	4570	5800	8950.0
363	USGS	01542500	00060	118867	NaN	12	29	1961	2019	46	...	2980	528.0	727	1030	1100	2560	4130	4480	6180	7660.0
364	USGS	01542500	00060	118867	NaN	12	30	1961	2019	46	...	2830	514.0	680	961	1080	2440	3820	4390	6130	7520.0
365	USGS	01542500	00060	118867	NaN	12	31	1961	2019	46	...	2890	533.0	706	924	1110	2270	4130	4550	6050	7770.0

366 rows × 24 columns