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Installing and loading StreamCatTools

To install, currently you need to install from GitHub using devtools

library(devtools)
install_github('USEPA/StreamCatTools')

After installing load the library

Background

The StreamCatTools package was designed to simplify the use of StreamCat data in R, leveraging the new API for StreamCat.

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
#>   fertws fertcat comid
#> 1  1.438   1.438   179

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

region_params <- sc_get_params(param='areaOfInterest')

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: agkffact, al2o3, bankfulldepth, bankfullwidth, bfi, canaldens, cao, cbnf, chem, clay"

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 Natural      Rckde… Cat,… NA    Mean Bedrock Dep… Mean depth (cm)… Cent…    56
#> 6 Soils        agkff… Cat,… NA    Ag Soil Erodibil… Mean of STATSGO… Unit…    28
#> # 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
#> character(0)

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)
#>   st_fips st_abbr     st_name
#> 1      01      AL     Alabama
#> 2      04      AZ     Arizona
#> 3      05      AR    Arkansas
#> 4      06      CA  California
#> 5      08      CO    Colorado
#> 6      09      CT Connecticut
counties <- sc_get_params(param='county')
head(counties)
#>    fips state    county_name
#> 1 01001    AL Autauga County
#> 2 01003    AL Baldwin County
#> 3 01005    AL Barbour County
#> 4 01007    AL    Bibb County
#> 5 01009    AL  Blount County
#> 6 01011    AL Bullock County

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 damdenscat damdensws tridensws tridenscat pcturbmd2006cat pcturbmd2006ws
179 0 0.0000 0 0 0.00 0.00
1337 0 0.0000 0 0 0.07 0.07
1337420 0 0.0329 0 0 0.00 0.01

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))
comid pctimpslphigh2001cat pctimpslphigh2004cat pctimpslphigh2006cat pctimpslphigh2008cat pctimpslphigh2011cat pctimpslphigh2013cat pctimpslphigh2016cat pctimpslphigh2019cat sw_fluxcat pctconif2016cat pctcrop2016cat pctdecid2016cat pctgrs2016cat pcthay2016cat pcthbwet2016cat pctice2016cat pctmxfst2016cat pctow2016cat pctshrb2016cat pcturbhi2016cat pcturblo2016cat pcturbmd2016cat pcturbop2016cat pctwdwet2016cat pctbl2016cat pctundsev2003cat pctlowsev2003cat pctmodsev2003cat pcthighsev2003cat pctincvegresp2003cat pctnonprocmask2003cat pctmodsev1993cat pcthighsev1993cat pctincvegresp1993cat pctnonprocmask1993cat pctlowsev1993cat pctundsev1993cat pctundsev2005cat pctlowsev2005cat pctmodsev2005cat pcthighsev2005cat pctincvegresp2005cat pctnonprocmask2005cat pctundsev2014cat pctlowsev2014cat pctmodsev2014cat pcthighsev2014cat pctincvegresp2014cat pctnonprocmask2014cat pctagdrainagecat pctagslpmid2004cat pctagslphigh2004cat pctagslpmid2008cat pctagslphigh2008cat pctundsev1984cat pctlowsev1984cat pctmodsev1984cat pcthighsev1984cat pctincvegresp1984cat pctnonprocmask1984cat rockncat pctundsev1986cat pctlowsev1986cat pctmodsev1986cat pcthighsev1986cat pctincvegresp1986cat pctnonprocmask1986cat pctundsev1989cat pctlowsev1989cat pctmodsev1989cat pcthighsev1989cat pctincvegresp1989cat pctnonprocmask1989cat pctundsev1994cat pctlowsev1994cat pctmodsev1994cat pcthighsev1994cat pctincvegresp1994cat pctnonprocmask1994cat pctundsev1997cat pctlowsev1997cat pctmodsev1997cat pcthighsev1997cat pctincvegresp1997cat pctnonprocmask1997cat pctundsev2000cat pctlowsev2000cat pctmodsev2000cat pcthighsev2000cat pctincvegresp2000cat pctnonprocmask2000cat pctundsev2001cat pctlowsev2001cat pctmodsev2001cat pcthighsev2001cat pctincvegresp2001cat pctnonprocmask2001cat pctundsev2009cat pctlowsev2009cat pctmodsev2009cat pcthighsev2009cat pctincvegresp2009cat pctnonprocmask2009cat pctundsev2012cat pctlowsev2012cat pctmodsev2012cat pcthighsev2012cat pctincvegresp2012cat pctnonprocmask2012cat pctundsev2015cat pctlowsev2015cat pctmodsev2015cat pcthighsev2015cat pctincvegresp2015cat pctnonprocmask2015cat pctagslpmid2011cat pctagslphigh2011cat nsurpcat nanicat septiccat pctbl2013cat pctconif2013cat pctcrop2013cat pctdecid2013cat pctgrs2013cat pcthay2013cat pcthbwet2013cat pctice2013cat pctmxfst2013cat pctow2013cat pctshrb2013cat pcturbhi2013cat pcturblo2013cat pcturbmd2013cat pcturbop2013cat pctwdwet2013cat pctundsev1985cat pctlowsev1985cat pctmodsev1985cat pcthighsev1985cat pctincvegresp1985cat pctnonprocmask1985cat pctundsev1987cat pctlowsev1987cat pctmodsev1987cat pcthighsev1987cat pctincvegresp1987cat pctnonprocmask1987cat pctundsev1988cat pctlowsev1988cat pctmodsev1988cat pcthighsev1988cat pctincvegresp1988cat pctnonprocmask1988cat pctundsev1990cat pctlowsev1990cat pctmodsev1990cat pcthighsev1990cat pctincvegresp1990cat pctnonprocmask1990cat pctundsev1995cat pctlowsev1995cat pctmodsev1995cat pcthighsev1995cat pctincvegresp1995cat pctnonprocmask1995cat pctundsev1999cat pctlowsev1999cat pctmodsev1999cat pcthighsev1999cat pctincvegresp1999cat pctnonprocmask1999cat pctundsev2002cat pctlowsev2002cat pctmodsev2002cat pcthighsev2002cat pctincvegresp2002cat pctnonprocmask2002cat pctundsev2007cat pctlowsev2007cat pctmodsev2007cat pcthighsev2007cat pctincvegresp2007cat pctnonprocmask2007cat pctundsev2008cat pctlowsev2008cat pctmodsev2008cat pcthighsev2008cat pctincvegresp2008cat pctnonprocmask2008cat pctundsev2010cat pctlowsev2010cat pctmodsev2010cat pcthighsev2010cat pctincvegresp2010cat pctnonprocmask2010cat pctundsev2011cat pctlowsev2011cat pctmodsev2011cat pcthighsev2011cat pctincvegresp2011cat pctnonprocmask2011cat wwtpmajordenscat wwtpminordenscat wwtpalldenscat waterinputcat cbnfcat manurecat fertcat bficat canaldenscat coalminedenscat damdenscat damnidstorcat damnrmstorcat elevcat tridenscat npdesdenscat superfunddenscat pctfire2001cat pctfire2008cat pctfire2003cat pctfire2010cat pctfire2000cat pctfire2006cat pctfire2005cat pctfire2007cat pctfire2002cat pctfire2009cat pctfire2004cat sio2cat fe2o3cat na2ocat caocat k2ocat al2o3cat scat p2o5cat mgocat ncat hydrlcondcat compstrgthcat sedcat tempcat chemcat hydcat conncat habtcat pctimp2001cat pctimp2004cat pctimp2006cat pctimp2008cat pctimp2011cat pctimp2013cat pctimp2016cat pctimp2019cat pctbl2008cat pctconif2008cat pctcrop2008cat pctdecid2008cat pctgrs2008cat pcthay2008cat pcthbwet2008cat pctice2008cat pctmxfst2008cat pctow2008cat pctshrb2008cat pcturbhi2008cat pcturblo2008cat pcturbmd2008cat pcturbop2008cat pctwdwet2008cat pctbl2019cat pctconif2019cat pctcrop2019cat pctdecid2019cat pctgrs2019cat pcthay2019cat pcthbwet2019cat pctice2019cat pctmxfst2019cat pctow2019cat pctshrb2019cat pcturbhi2019cat pcturblo2019cat pcturbmd2019cat pcturbop2019cat pctwdwet2019cat wdrw_ldcat wetindexcat pctundsev1996cat pctlowsev1996cat pctmodsev1996cat pcthighsev1996cat pctincvegresp1996cat pctnonprocmask1996cat pctundsev2013cat pctlowsev2013cat pctmodsev2013cat pcthighsev2013cat pctincvegresp2013cat pctnonprocmask2013cat agkffactcat kffactcat pctalluvcoastcat pctsallakecat pctcoastcrscat pctglactilloamcat pctwatercat pctextruvolcat pctglactilcrscat pcthydriccat pctcarbresidcat pctglaclakecrscat pcteolfinecat pctsiliciccat pctglactilclaycat pcteolcrscat pctalkintruvolcat pctglaclakefinecat pctnoncarbresidcat pctcolluvsedcat minedenscat nabd_denscat nabd_nrmstorcat nabd_nidstorcat no32008cat sn2008cat inorgnwetdep2008cat nh42008cat pcturbhi2001cat pctgrs2001cat pctwdwet2001cat pctcrop2001cat pctow2001cat pcthbwet2001cat pctbl2001cat pctice2001cat pctmxfst2001cat pcturblo2001cat pcturbmd2001cat pcthay2001cat pctconif2001cat pctshrb2001cat pcturbop2001cat pctdecid2001cat pcturbop2006cat pctconif2006cat pctow2006cat pctice2006cat pcturbhi2006cat pcthay2006cat pctwdwet2006cat pcturbmd2006cat pctmxfst2006cat pctshrb2006cat pcturblo2006cat pctbl2006cat pctdecid2006cat pctcrop2006cat pctgrs2006cat pcthbwet2006cat pctwdwet2011cat pcturblo2011cat pctow2011cat pctice2011cat pcturbmd2011cat pctdecid2011cat pctconif2011cat pctbl2011cat pctmxfst2011cat pcturbhi2011cat pcthay2011cat pctgrs2011cat pctshrb2011cat pcturbop2011cat pcthbwet2011cat pctcrop2011cat pctnonagintrodmanagvegcat pestic1997cat tmean2008cat tmean2009cat precip2009cat precip2008cat tmean8110cat precip8110cat tmax8110cat tmin8110cat rddenscat rdcrscat rdcrsslpwtdcat runoffcat sandcat claycat omcat wtdepcat permcat rckdepcat popden2010cat huden2010cat pctimpslpmid2001cat pctimpslpmid2004cat pctimpslpmid2006cat pctimpslpmid2008cat pctimpslpmid2011cat pctimpslpmid2013cat pctimpslpmid2016cat pctimpslpmid2019cat pctbl2004cat pctconif2004cat pctcrop2004cat pctdecid2004cat pctgrs2004cat pcthay2004cat pcthbwet2004cat pctice2004cat pctmxfst2004cat pctow2004cat pctshrb2004cat pcturbhi2004cat pcturblo2004cat pcturbmd2004cat pcturbop2004cat pctwdwet2004cat pctburnarea1984cat pctburnarea1985cat pctburnarea1986cat pctburnarea1987cat pctburnarea1988cat pctburnarea1989cat pctburnarea1990cat pctburnarea1991cat pctburnarea1992cat pctburnarea1993cat pctburnarea1994cat pctburnarea1995cat pctburnarea1996cat pctburnarea1997cat pctburnarea1998cat pctburnarea1999cat pctburnarea2000cat pctburnarea2001cat pctburnarea2002cat pctburnarea2003cat pctburnarea2004cat pctburnarea2005cat pctburnarea2006cat pctburnarea2007cat pctburnarea2008cat pctburnarea2009cat pctburnarea2010cat pctburnarea2011cat pctburnarea2012cat pctburnarea2013cat pctburnarea2014cat pctburnarea2015cat pctburnarea2016cat pctburnarea2017cat pctburnarea2018cat pctundsev1991cat pctlowsev1991cat pctmodsev1991cat pcthighsev1991cat pctincvegresp1991cat pctnonprocmask1991cat pctundsev1992cat pctlowsev1992cat pctmodsev1992cat pcthighsev1992cat pctincvegresp1992cat pctnonprocmask1992cat pctundsev1998cat pctlowsev1998cat pctmodsev1998cat pcthighsev1998cat pctincvegresp1998cat pctnonprocmask1998cat pctundsev2004cat pctlowsev2004cat pctmodsev2004cat pcthighsev2004cat pctincvegresp2004cat pctnonprocmask2004cat pctundsev2006cat pctlowsev2006cat pctmodsev2006cat pcthighsev2006cat pctincvegresp2006cat pctnonprocmask2006cat pctundsev2016cat pctlowsev2016cat pctmodsev2016cat pcthighsev2016cat pctincvegresp2016cat pctnonprocmask2016cat pctundsev2017cat pctlowsev2017cat pctmodsev2017cat pcthighsev2017cat pctincvegresp2017cat pctnonprocmask2017cat pctundsev2018cat pctlowsev2018cat pctmodsev2018cat pcthighsev2018cat pctincvegresp2018cat pctnonprocmask2018cat pctagslpmid2001cat pctagslphigh2001cat pctagslphigh2006cat pctagslpmid2006cat precip9120cat tmax9120cat tmean9120cat tmin9120cat pctagslpmid2013cat pctagslphigh2013cat pctagslpmid2016cat pctagslphigh2016cat pctagslpmid2019cat pctagslphigh2019cat pctfrstloss2001cat pctfrstloss2002cat pctfrstloss2003cat pctfrstloss2004cat pctfrstloss2005cat pctfrstloss2006cat pctfrstloss2007cat pctfrstloss2008cat pctfrstloss2009cat pctfrstloss2010cat pctfrstloss2011cat pctfrstloss2012cat pctfrstloss2013cat
179 NA NA NA NA NA NA NA NA 133875 4.91 1.54 3.87 0.05 1.09 0.71 0 49.67 0 0.25 0 0 0 0 37.9 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 0 3.17 0 0 0 0 0 0 0 88.2995 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 3.17 0 2102.561 1675.837 1.117 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 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 0.0506 0 1.438 52 0 0 0 0 0 196.1233 0 0 0 0 0 0 0 0 0 0 0 0 0 0 64.3313 5.5091 1.0772 7.9453 1.0557 10.276 0.0161 0.1545 2.2883 8e-04 0.2044 169.6366 0.9958425 0.9983122 0.7883752 0.9983122 1 0.997275 0 0 0 0 0 0 0 0 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 4.91 1.54 3.7 0.35 1.09 0.71 0 49.54 0 0.25 0 0 0 0 37.9 0.4413 878.2494 0 0 0 0 0 0 0 0 0 0 0 0 NA NA 0 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6.3799 342.1452 2.4653 1.3213 0 0.23 36.89 1.52 0 1.95 0 0 48.76 0 0 1.22 4.89 0.73 0 3.82 0 4.89 0.08 0 0 1.09 37.29 0 49.01 1.01 0 0 3.8 1.52 0.08 1.24 37.57 0 0.1 0 0 3.8 4.89 0 49.16 0 1.09 0.13 0.81 0 0.94 1.52 0 40.52718 5.032428 4.852966 1108.386 1254.539 4.885117 1059.078 10.59572 -0.8263614 0.5276 0 0 646 22.33772 14.35371 12.28701 49.21784 5.130848 134.9983 8.874548 5.910425 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 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 3.17 0 0 3.17 1152.678 10.56534 5.015274 -0.5362276 3.17 0 3.17 0 3.17 0 0.71 0 0 0.35 0.03 0 0 0 0 0 0 0.23 0

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 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',
              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

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))