Getting Started • StreamCatTools

StreamCat API

We can pull data into R from the StreamCat API by simply passing a URL to extract from json. We have to hard-wire parameters though and are limited in the number of records returned through a GET request.

res <- jsonlite::fromJSON("https://api.epa.gov/StreamCat/streams/metrics?name=fert&areaOfInterest=cat&comid=179")
res$items

List API parameters

List StreamCat parameters: Get a list of available StreamCat values for certain parameters using the sc_get_params function via the API

library(StreamCatTools)
region_params <- sc_get_params(param='aoi')

name_params <- sc_get_params(param='metric_names')

print(paste0('region parameters are: ', paste(region_params,collapse = ', ')))
#> [1] "region parameters are: cat, catrp100, other, ws, wsrp100"
print(paste0('A selection of available StreamCat metrics include: ',paste(name_params[1:10],collapse = ', ')))
#> [1] "A selection of available StreamCat metrics include: "

We can also see what metrics are available for what areas of interest and what years using the sc_get_params function (which returns a tibble of information about StreamCat metrics):

var_info <- sc_get_params(param='variable_info')
head(var_info)
#> # A tibble: 6 × 13
#>   category     metric aoi   year  short_description long_description units  dsid
#>   <chr>        <chr>  <chr> <chr> <chr>             <chr>            <chr> <dbl>
#> 1 Anthropogen… NABD_… Cat,… NA    NABD Dam Density  Density of geor… Coun…    33
#> 2 Anthropogen… NABD_… Cat,… NA    NABD NID Reservo… Volume all rese… Cubi…    33
#> 3 Anthropogen… NABD_… Cat,… NA    NABD Normal Rese… Volume all rese… Cubi…    33
#> 4 Natural      Preci… Cat,… NA    Surplus Precipit… This dataset re… Kilo…    75
#> 5 Soils        agkff… Cat,… NA    Ag Soil Erodibil… Mean of STATSGO… Unit…    28
#> 6 Natural      al2o3… Cat,… NA    Mean Lithologica… Mean % of litho… Perc…    13
#> # ℹ 5 more variables: dataset <chr>, source_name <chr>, source_URL <chr>,
#> #   ACTIVE <dbl>, DSNAME <chr>

We can look up the display name or names for a metric using the sc_fullname function via the API

metric='pcthbwet2011'
fullname <- sc_fullname(metric)
fullname
#> [1] "Herbaceous Wetland Percentage 2011"

metric='pctdecid2019,fert'
fullname <- sc_fullname(metric)
fullname
#> NULL

We can additionally get a data frame of state FIPS codes, abbreviations and names, and the same information for counties as well using sc_get_params:

states <- sc_get_params(param='state')
head(states)
#> NULL

counties <- sc_get_params(param='county')
head(counties)
#>      fips state    county_name
#> 752 01001    AL Autauga County
#> 713 01003    AL Baldwin County
#> 655 01005    AL Barbour County
#> 755 01007    AL    Bibb County
#> 748 01009    AL  Blount County
#> 656 01011    AL Bullock County

Filter metric information by criteria

We can also filter metric names and information by the metric year(s), the indicator categories for metrics, the metric data set names, or the Areas of Interest the metrics are available for.

metrics <- sc_get_metric_names(category = c('Deposition','Climate'),aoi=c('Cat','Ws'))
head(metrics)
#> # A tibble: 6 × 9
#>   Category Metric AOI   Year  Short_Name Metric_Description Units Source Dataset
#>   <chr>    <chr>  <chr> <chr> <chr>      <chr>              <chr> <chr>  <chr>  
#> 1 Climate  bfi[A… Cat,… NA    Base Flow… Base flow is the … Perc… USGS … Base F…
#> 2 Deposit… inorg… Cat,… 2008  Mean Annu… Annual gradient m… Kilo… NADP   NADP   
#> 3 Deposit… nh4[Y… Cat,… 2008  Mean annu… Annual gradient m… Kilo… NADP   NADP   
#> 4 Deposit… no3[Y… Cat,… 2008  Mean Annu… Annual gradient m… Kilo… NADP   NADP   
#> 5 Climate  preci… Cat,… NA    30-year M… PRISM climate dat… Mill… PRISM  PRISM  
#> 6 Climate  preci… Cat,… 1991… 30-year M… PRISM climate dat… Mill… PRISM  PRISM

Get data for COMIDs

In this example we access several variables, for several areas of interest, and for several COMIDs using the sc_get_data function. Loads data into a tibble we can view.

df <- sc_get_data(metric='pcturbmd2006,damdens,tridens', aoi='rp100cat,cat,ws', comid='179,1337,1337420')
knitr::kable(df)

comid	pcturbmd2006cat	pcturbmd2006ws	damdensws
179	0.00	0.00	0.0000
1337	0.07	0.07	0.0000
1337420	0.00	0.01	0.0329

Get data for county

In this example we access a couple variables at the watershed scale for the area of interest of a county (Benton County in this case) using the sc_get_data function.

df <- sc_get_data(metric='pctwdwet2006', aoi='ws', county='41003')
knitr::kable(head(df))

comid	pctwdwet2006ws
23762961	1.11
23762985	0.84
23762915	1.14
23762959	1.14
23762967	1.10
23762761	1.72

Get all metrics for COMIDs or an Area of Interest

We can also get all StreamCat metrics for a set of COMIDs or an area of interest. Please do not request metric=‘all’ and aoi=‘conus’ in order not to overload requests to the server. Requesting metric=‘all’ for a state or multiple states or hydroregions will also take a long time to process.

df <- sc_get_data(comid='179', aoi='cat', metric='all')
knitr::kable(head(df))

<<<<<<< HEAD ======= >>>>>>> master <<<<<<< HEAD ======= >>>>>>> master <<<<<<< HEAD ======= >>>>>>> master <<<<<<< HEAD ======= >>>>>>> master <<<<<<< HEAD ======= >>>>>>> master <<<<<<< HEAD ======= >>>>>>> master <<<<<<< HEAD ======= >>>>>>> master <<<<<<< HEAD ======= >>>>>>> master <<<<<<< HEAD ======= >>>>>>> master <<<<<<< HEAD ======= >>>>>>> master

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cat	pcthighsev2014cat	pctincvegresp2014cat	pctnonprocmask2014cat	pctmodsev2016cat	pcthighsev2016cat	pctincvegresp2016cat	pctnonprocmask2016cat	pctundsev2017cat	pctlowsev2017cat	pctmodsev2017cat	pcthighsev2017cat	pctincvegresp2017cat	pctnonprocmask2017cat	waterinputcat	wwtpmajordenscat	wwtpminordenscat	wwtpalldenscat	pctimpslphigh2001cat	pctimpslphigh2004cat	pctimpslphigh2006cat	pctimpslphigh2008cat	pctimpslphigh2011cat	pctimpslphigh2013cat	pctimpslphigh2016cat	pctimpslphigh2019cat	pctimpslpmid2001cat	pctimpslpmid2004cat	pctimpslpmid2006cat	pctimpslpmid2008cat	pctimpslpmid2011cat	pctimpslpmid2013cat	pctimpslpmid2016cat	pctimpslpmid2019cat	pctundsev2000cat	pctlowsev2000cat	pctmodsev2000cat	pcthighsev2000cat	pctincvegresp2000cat	pctnonprocmask2000cat	sio2cat	compstrgthcat	pctfire2001cat	agkffactcat	agkffactcat	pctwdwet2011cat	coalminedenscat	elevcat	pcturbop2006cat	pcturbhi2001cat	hydcat	no32008cat	pctalluvcoastcat	fe2o3cat	pcturblo2011cat	cbnfcat	pctconif2006cat	pctgrs2001cat	pctfire2008cat	pctsallakecat	pctcoastcrscat	ncat	pctfire2003cat	pctglactilloamcat	pctfire2010cat	omcat	pctwatercat	pctow2011cat	pctice2011cat	pctwdwet2001cat	tmean2008cat	pctfire2000cat	pctcrop2001cat	pctextruvolcat	pcturbmd2011cat	pctfire2006cat	pctow2001cat	pcthbwet2001cat	na2ocat	pctglactilcrscat	tmean2009cat	damdenscat	pctdecid2011cat	sedcat	chemcat	manurecat	pctow2006cat	pctbl2001cat	sn2008cat	pctnonagintrodmanagvegcat	pcthydriccat	pctfire2005cat	pctconif2011cat	pctcarbresidcat	tmean8110cat	caocat	pctbl2011cat	kffactcat	kffactcat	pctglaclakecrscat	pctmxfst2011cat	k2ocat	pctice2006cat	pcteolfinecat	pcturbhi2006cat	precip8110cat	pcthay2006cat	pctsiliciccat	pctwdwet2006cat	pctfire2007cat	rdcrscat	nabd_denscat	pctice2001cat	damnidstorcat	precip2009cat	pcturbmd2006cat	tridenscat	pctglactilclaycat	wtdepcat	pestic1997cat	pcteolcrscat	pctmxfst2001cat	permcat	tempcat	popden2010cat	pcturbhi2011cat	al2o3cat	pcturblo2001cat	bficat	pctmxfst2006cat	conncat	pcthay2011cat	tmax8110cat	inorgnwetdep2008cat	nabd_nrmstorcat	huden2010cat	scat	p2o5cat	pctshrb2006cat	damnrmstorcat	nh42008cat	mgocat	pctgrs2011cat	pctalkintruvolcat	pctglaclakefinecat	pcturblo2006cat	pctshrb2011cat	canaldenscat	pcturbmd2001cat	habtcat	nabd_nidstorcat	pctbl2006cat	fertcat	npdesdenscat	tmin8110cat	pcthay2001cat	minedenscat	pctconif2001cat	superfunddenscat	pcturbop2011cat	pctfire2002cat	pcthbwet2011cat	rdcrsslpwtdcat	pctdecid2006cat	pctshrb2001cat	precip2008cat	sandcat	pctfire2009cat	claycat	pcturbop2001cat	pctdecid2001cat	pctcrop2006cat	pctnoncarbresidcat	runoffcat	pctgrs2006cat	pctcolluvsedcat	rckdepcat	hydrlcondcat	pcthbwet2006cat	rddenscat	pctcrop2011cat	pctconif2016cat	pctfire2004cat	pctcrop2016cat	pctdecid2016cat	pctgrs2016cat	pcthay2016cat	pcthbwet2016cat	pctice2016cat	pctmxfst2016cat	pctow2016cat	pctshrb2016cat	pcturbhi2016cat	pcturblo2016cat	pcturbmd2016cat	pcturbop2016cat	pctwdwet2016cat	sw_fluxcat	pctundsev1985cat	pctlowsev1985cat	pctmodsev1985cat	pcthighsev1985cat	pctincvegresp1985cat	pctnonprocmask1985cat	pctundsev1992cat	pctlowsev1992cat	pctmodsev1992cat	pcthighsev1992cat	pctincvegresp1992cat	pctnonprocmask1992cat	pctundsev1988cat	pctlowsev1988cat	pctmodsev1988cat	pcthighsev1988cat	pctbl2013cat	pctconif2013cat	pctcrop2013cat	pctdecid2013cat	pctgrs2013cat	pcthay2013cat	pcthbwet2013cat	pctice2013cat	pctmxfst2013cat	pctow2013cat	pctshrb2013cat	pcturbhi2013cat	pcturblo2013cat	pcturbmd2013cat	pcturbop2013cat	pctwdwet2013cat	pctbl2016cat	pctincvegresp1988cat	pctnonprocmask1988cat	pctundsev1993cat	pctfrstloss2001cat	pctfrstloss2002cat	pctfrstloss2003cat	pctfrstloss2004cat	pctfrstloss2005cat	pctfrstloss2006cat	pctfrstloss2007cat	pctfrstloss2008cat	pctfrstloss2009cat	pctfrstloss2010cat	pctfrstloss2011cat	pctfrstloss2012cat	pctfrstloss2013cat	precip9120cat	tmax9120cat	tmean9120cat	tmin9120cat	pctagslpmid2013cat	pctagslphigh2013cat	pctagslpmid2016cat	pctagslphigh2016cat	pctagslpmid2019cat	pctagslphigh2019cat	p_dep_2002cat	p_dep_2003cat	p_dep_2004cat	p_dep_2005cat	p_dep_2006cat	p_dep_2007cat	p_dep_2008cat	p_dep_2009cat	p_dep_2010cat	p_dep_2011cat	p_dep_2012cat	p_dep_2013cat	n_usgsww_1978cat	n_usgsww_1980cat	n_usgsww_1982cat	n_usgsww_1984cat	n_usgsww_1986cat	n_usgsww_1988cat	n_usgsww_1990cat	n_usgsww_1992cat	n_usgsww_1994cat	n_usgsww_1996cat	n_usgsww_1998cat	n_usgsww_2000cat	n_usgsww_2002cat	n_usgsww_2004cat	n_usgsww_2006cat	n_usgsww_2008cat	n_usgsww_2010cat	n_usgsww_2012cat	n_leg_1987cat	n_leg_1988cat	n_leg_1989cat	n_leg_1990cat	n_leg_1991cat	n_leg_1992cat	n_leg_1993cat	n_leg_1994cat	n_leg_1995cat	n_leg_1996cat	n_leg_1997cat	n_leg_1998cat	n_leg_1999cat	n_leg_2000cat	n_leg_2001cat	n_leg_2002cat	n_leg_2003cat	n_leg_2004cat	n_leg_2005cat	n_leg_2006cat	n_leg_2007cat	n_leg_2008cat	n_leg_2009cat	n_leg_2011cat	n_leg_2012cat	n_leg_2013cat	n_leg_2014cat	n_leg_2015cat	n_leg_2016cat	n_leg_2017cat	p_leg_1987cat	p_leg_1988cat	p_leg_1989cat	p_leg_1990cat	p_leg_1991cat	p_leg_1992cat	p_leg_1993cat	p_leg_1994cat	p_leg_1995cat	p_leg_1996cat	p_leg_1997cat	p_leg_1998cat	p_leg_1999cat	p_leg_2000cat	p_leg_2001cat	p_leg_2002cat	p_leg_2003cat	p_leg_2004cat	p_leg_2005cat	p_leg_2006cat	p_leg_2007cat	p_leg_2008cat	p_leg_2009cat	p_leg_2011cat	p_leg_2012cat	p_leg_2013cat	p_leg_2014cat	p_leg_2015cat	p_leg_2016cat	p_leg_2017cat	n_leg_2010cat	p_leg_2010cat	n_tin_1987cat	n_tin_1988cat	n_tin_1989cat	n_tin_1990cat	n_tin_1991cat	n_tin_1992cat	n_tin_1993cat	n_tin_1994cat	n_tin_1995cat	n_tin_1996cat	n_tin_1997cat	n_tin_1998cat	n_tin_1999cat	n_tin_2000cat	n_tin_2001cat	n_tin_2002cat	n_tin_2003cat	n_tin_2004cat	n_tin_2005cat	n_tin_2006cat	n_tin_2007cat	n_tin_2008cat	n_tin_2009cat	n_tin_2011cat	n_tin_2012cat	n_tin_2013cat	n_tin_2014cat	n_tin_2015cat	n_tin_2016cat	n_tin_2017cat	p_tin_1987cat	p_tin_1988cat	p_tin_1989cat	p_tin_1990cat	p_tin_1991cat	p_tin_1992cat	p_tin_1993cat	p_tin_1994cat	p_tin_1995cat	p_tin_1996cat	p_tin_1997cat	p_tin_1998cat	p_tin_1999cat	p_tin_2000cat	p_tin_2001cat	p_tin_2002cat	p_tin_2003cat	p_tin_2004cat	p_tin_2005cat	p_tin_2006cat	p_tin_2007cat	p_tin_2008cat	p_tin_2009cat	p_tin_2011cat	p_tin_2012cat	p_tin_2013cat	p_tin_2014cat	p_tin_2015cat	p_tin_2016cat	p_tin_2017cat	p_dep_1997cat	p_dep_1998cat	p_dep_1999cat	p_dep_2000cat	p_dep_2001cat
179	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1.117	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	3.17	3.17	0	3.17	0	3.17	0	0	2102.561	1675.837	0	4.89	1.52	3.87	0.05	1.09	1.04	0	49.06	0.05	0.91	0	0	0	0	37.52	0	0	0	0	0	0	88.2995	0	0	0	0	0	0	0.4413	3.17	0	0	0	0	0	0	0	0	0	0	4.89	1.52	3.85	0.13	1.09	1.09	0	49.04	0.13	0.89	0	0	0	0	37.39	0	4.91	1.54	3.7	0.35	1.09	0.71	0	49.54	0	0.25	0	0	0	0	37.9	878.2494	46.1099	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1.34e-05	0	0	0	NA	NA	NA	NA	NA	NA	NA	NA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	64.3313	169.6366	0	0.0026	NA	37.57	0	196.1233	0	0	0.998	6.3799	0	5.5091	0	0.0506	4.89	0.23	0	0	0	8e-04	0	100	0	12.28701	0	0.1	0	36.89	5.032428	0	1.52	0	0	0	0	1.95	1.0772	0	4.852966	0	3.8	0.9958425	0.972	0	0.08	0	342.1452	0	0	0	4.89	0	4.885117	7.9453	0	0.2442	NA	0	49.16	1.0557	0	0	0	1059.078	1.09	0	37.29	0	0	0	0	0	1108.386	0	0	0	49.21784	40.52718	0	48.76	5.130848	0.9983122	8.874548	0	10.276	0	52	49.01	1	1.09	10.59572	2.4653	0	5.910425	0.0161	0.1545	1.01	0	1.3213	2.2883	0.13	0	0	0	0.81	0	0	0.997	0	0	1.438	0	-0.8263614	1.22	0	4.89	0	0	0	0.94	0	3.8	0.73	1254.539	22.33772	0	14.35371	0	3.82	1.52	0	646	0.08	0	134.9983	0.2044	1.24	0.5276	1.52	4.91	0	1.54	3.87	0.05	1.09	0.71	0	49.67	0	0.25	0	0	0	0	37.9	133875	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4.89	1.52	3.77	0.05	1.09	0.76	0	49.7	0	0.38	0	0	0	0	37.85	0	0	0	0	0.71	0	0	0.35	0.03	0	0	0	0	0	0	0.23	0	1152.678	10.56534	5.015274	-0.5362276	3.17	0	3.17	0	3.17	0	20.32574	18.50136	17.59088	18.2278	18.81752	18.56952	18.38229	17.69823	20.08625	18.56114	16.50694	17.57473	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	8016.374	8243.797	8542.023	8787.955	9007.697	9287.582	9438.607	9722.905	9996.046	10686.69	11330.88	12057.65	12981.15	13353.16	13811.05	14472.36	15211.29	16194.87	17547.07	19142.33	20611	22116.82	23411.37	24590.54	24772.69	25101.07	25575.9	26197.07	26964.96	27879.69	11011.91	11171.62	11383.85	11564.13	11738.34	11915.36	12000.95	12203.13	12392.89	12464.94	12537.05	12605.38	12721.52	12932.75	13172.6	13492.15	13836.86	14248.79	14793.05	15395.81	15956.26	16542.52	17068.58	17624.04	17758.03	17917.15	18101.46	18310.91	18545.61	18805.58	24530.75	17538.92	731.5523	696.5523	774.7137	2922.933	2476.309	2357.526	2377.684	2603.939	2371.844	2908.544	2667.832	3015.62	3115.135	2434.286	2419.809	3328.306	3014.946	2782.592	3300.603	3461.606	3283.332	3181.2	2763.434	1886.577	2083.627	1995.734	2137.391	2040.086	2140.836	2305.558	226.4224	238.475	292.1372	261.341	256.9639	262.13	169.9578	285.1908	271.4696	152.4241	151.6671	149.4479	198.2835	293.852	322.9758	403.1997	420.7817	480.4471	605.2297	656.1878	606.3166	638.3252	584.3335	155.8621	210.9557	228.3564	245.8559	263.2626	280.8009	298.3469	18.46541	21.6809	18.73153	19.38125	17.18284

Get NLCD data

In this example we access National Land Cover Dataset (NLCD) data for 2001, just at the catchment level for several COMIDs using the <<<<<<< HEAD sc_get_nlcd function. Loads data into a tibble we can view.

df <- sc_get_nlcd(year='2001', aoi='cat',
              comid='179,1337,1337420')
knitr::kable(df)

We can also pass a couple years for a different area of interest for another region like a county.

df <- sc_get_nlcd(year='2006, 2019', aoi='ws',
=======
sc_nlcd function. Loads data into a tibble we can view.
df <- sc_nlcd(year='2001', aoi='cat',
              comid='179,1337,1337420')
knitr::kable(df)





















comid
pcturbhi2001cat
pctgrs2001cat
pctwdwet2001cat
pctcrop2001cat
pctow2001cat
pcthbwet2001cat
pctbl2001cat
pctice2001cat
pctmxfst2001cat
pcturblo2001cat
pcturbmd2001cat
pcthay2001cat
pctconif2001cat
pctshrb2001cat
pcturbop2001cat
pctdecid2001cat



179
0
0.23
36.89
1.52
0.0
1.95
0.00
0
48.76
0.00
0
1.22
4.89
0.73
0.00
3.82


1337
0
1.60
20.75
0.00
0.1
0.56
0.28
0
51.55
0.24
0
2.83
5.65
0.66
3.98
11.79


1337420
0
0.87
0.95
0.00
0.0
1.64
0.00
0
3.77
0.40
0
0.11
4.06
3.38
1.77
83.06



We can also pass a couple years for a different area of interest for
another region like a county.
df <- sc_nlcd(year='2006, 2019', aoi='ws',
>>>>>>> master
              county='41003')
knitr::kable(head(df))





































comid
pctbl2019ws
pctconif2019ws
pctcrop2019ws
pctdecid2019ws
pctgrs2019ws
pcthay2019ws
pcthbwet2019ws
pctice2019ws
pctmxfst2019ws
pctow2019ws
pctshrb2019ws
pcturbhi2019ws
pcturblo2019ws
pcturbmd2019ws
pcturbop2019ws
pctwdwet2019ws
pctice2006ws
pctmxfst2006ws
pctcrop2006ws
pcthay2006ws
pcturbop2006ws
pctbl2006ws
pctconif2006ws
pctwdwet2006ws
pctgrs2006ws
pcturbhi2006ws
pcturbmd2006ws
pctshrb2006ws
pcthbwet2006ws
pctdecid2006ws
pcturblo2006ws
pctow2006ws



23762961
0.01
36.68
4.65
1.03
1.51
33.28
3.44
0
7.32
0.08
3.99
0.10
1.69
0.30
3.46
2.46
0
5.85
3.00
35.58
3.44
0.01
26.93
1.11
5.74
0.09
0.23
10.78
4.79
0.66
1.67
0.11


23762985
0.00
51.52
1.58
1.03
2.21
20.06
0.84
0
9.14
0.04
6.06
0.05
1.46
0.24
4.60
1.18
0
7.16
1.05
21.16
4.62
0.01
37.51
0.84
8.44
0.04
0.19
15.78
1.22
0.51
1.44
0.04


23762915
0.01
55.83
0.01
1.58
2.74
6.60
0.40
0
18.61
0.05
7.45
0.01
0.60
0.14
4.65
1.33
0
19.10
0.01
6.87
4.63
0.01
49.77
1.14
4.71
0.01
0.12
11.15
0.58
1.27
0.58
0.05


23762959
0.01
36.29
4.66
1.02
1.50
33.72
3.51
0
7.24
0.09
3.95
0.10
1.68
0.30
3.43
2.51
0
5.79
3.02
36.01
3.41
0.01
26.64
1.14
5.68
0.09
0.23
10.66
4.88
0.66
1.66
0.12


23762967
0.01
38.81
3.54
1.04
1.60
31.71
3.49
0
7.53
0.09
4.30
0.09
1.65
0.29
3.40
2.47
0
5.98
2.42
33.49
3.39
0.01
28.48
1.10
6.20
0.08
0.23
11.36
4.86
0.64
1.64
0.13


23762761
0.00
57.44
0.40
1.90
3.08
2.45
0.27
0
20.77
0.01
6.65
0.00
0.31
0.07
4.89
1.76
0
19.15
0.05
2.84
4.91
0.00
38.13
1.72
7.95
0.00
0.04
23.53
0.31
1.06
0.29
0.01

comid	pctgrs2001cat	pctwdwet2001cat	pctcrop2001cat	pctow2001cat	pcthbwet2001cat	pctbl2001cat	pctmxfst2001cat	pcturblo2001cat	pcthay2001cat	pctconif2001cat	pctshrb2001cat	pcturbop2001cat	pctdecid2001cat
179	0.23	36.89	1.52	0.0	1.95	0.00	48.76	0.00	1.22	4.89	0.73	0.00	3.82
1337	1.60	20.75	0.00	0.1	0.56	0.28	51.55	0.24	2.83	5.65	0.66	3.98	11.79
1337420	0.87	0.95	0.00	0.0	1.64	0.00	3.77	0.40	0.11	4.06	3.38	1.77	83.06



Get COMIDs

In this example we use the sc_get_comid function to find
COMIDs for USGS stream gages we load into R. We use a .csv file with
coordinate columns and a known coordinate reference system.
gages = readr::read_csv(system.file("extdata","Gages_flowdata.csv", package = "StreamCatTools"),show_col_types = FALSE)
# we'll just grab a few variables to keep things simple
gages <- gages[,c('SOURCE_FEA','STATION_NM','LON_SITE','LAT_SITE')]
gages_coms <- sc_get_comid(gages, xcoord='LON_SITE',                   ycoord='LAT_SITE', crsys=4269)

# Add the COMID we found back to gages data frame
gages$COMID <- strsplit(gages_coms, ",")[[1]]
df <- sc_get_data(metric='huden2010', aoi='ws', comid=gages_coms)
df$COMID <- as.character(df$comid)
gages <- dplyr::left_join(gages, df, by='COMID')
knitr::kable(head(gages))


Get data for a hydroregion

In this example we access a couple watershed-only metrics for a
particular NHDPlus hydroregion using the sc_get_data
function.
df <- sc_get_data(metric='pctwdwet2006', aoi='ws', region='Region17')
knitr::kable(head(df))


comid
pctwdwet2006ws



22988611
0.99


24114311
0.29


24114309
0.27


24116239
0.42


22988639
0.00


22988711
0.02





Get data for CONUS

In this example we access a metric for conus using the
sc_get_data function - this is shown for demonstration but
not run as it takes a bit of time
df <- sc_get_data(metric='om', aoi='ws', conus='true')
knitr::kable(head(df))

comid	pctbl2019ws	pctconif2019ws	pctcrop2019ws	pctdecid2019ws	pctgrs2019ws	pcthay2019ws	pcthbwet2019ws	pctmxfst2019ws	pctow2019ws	pctshrb2019ws	pcturbhi2019ws	pcturblo2019ws	pcturbmd2019ws	pcturbop2019ws	pctwdwet2019ws	pctmxfst2006ws	pctcrop2006ws	pcthay2006ws	pcturbop2006ws	pctbl2006ws	pctconif2006ws	pctwdwet2006ws	pctgrs2006ws	pcturbhi2006ws	pcturbmd2006ws	pctshrb2006ws	pcthbwet2006ws	pctdecid2006ws	pcturblo2006ws	pctow2006ws
23762961	0.01	36.68	4.65	1.03	1.51	33.28	3.44	7.32	0.08	3.99	0.10	1.69	0.30	3.46	2.46	5.85	3.00	35.58	3.44	0.01	26.93	1.11	5.74	0.09	0.23	10.78	4.79	0.66	1.67	0.11
23762985	0.00	51.52	1.58	1.03	2.21	20.06	0.84	9.14	0.04	6.06	0.05	1.46	0.24	4.60	1.18	7.16	1.05	21.16	4.62	0.01	37.51	0.84	8.44	0.04	0.19	15.78	1.22	0.51	1.44	0.04
23762915	0.01	55.83	0.01	1.58	2.74	6.60	0.40	18.61	0.05	7.45	0.01	0.60	0.14	4.65	1.33	19.10	0.01	6.87	4.63	0.01	49.77	1.14	4.71	0.01	0.12	11.15	0.58	1.27	0.58	0.05
23762959	0.01	36.29	4.66	1.02	1.50	33.72	3.51	7.24	0.09	3.95	0.10	1.68	0.30	3.43	2.51	5.79	3.02	36.01	3.41	0.01	26.64	1.14	5.68	0.09	0.23	10.66	4.88	0.66	1.66	0.12
23762967	0.01	38.81	3.54	1.04	1.60	31.71	3.49	7.53	0.09	4.30	0.09	1.65	0.29	3.40	2.47	5.98	2.42	33.49	3.39	0.01	28.48	1.10	6.20	0.08	0.23	11.36	4.86	0.64	1.64	0.13
23762761	0.00	57.44	0.40	1.90	3.08	2.45	0.27	20.77	0.01	6.65	0.00	0.31	0.07	4.89	1.76	19.15	0.05	2.84	4.91	0.00	38.13	1.72	7.95	0.00	0.04	23.53	0.31	1.06	0.29	0.01

comid	pctwdwet2006ws
22988611	0.99
24114311	0.29
24114309	0.27
24116239	0.42
22988639	0.00
22988711	0.02