United States
Environmental Protection
Agency
EPA/600/R/13/067
May 2013
User's Guide and Metadata for WestuRe:
U.S. Pacific Coast Estuary/Watershed
Data and R Tools
Office of
Research & Development
National Health and
Environmental Effects
Research Laboratory
-------
User's Guide and Metadata for WestuRe: U.S. Pacific
Coast Estuary/Watershed Data and R Tools
M.R. Frazier1, D.A. Reusser2, H. Lee II1, L.M. McCoy3, C. Brown1 and W. Nelson1
May 2013 (vl)
^.S. Environmental Protection Agency, Office of Research and Development, NHEERL,
Western Ecology Division, Pacific Coastal Ecology Branch, Newport, OR, 97365, USA
2U.S. Geological Survey, Western Fisheries Research Center, Newport, OR 97365, USA
34298 N Beaver Creek Rd, Seal Rock OR 97376
Disclaimer This document has been reviewed in accordance with U.S. Environmental Protec-
tion Agency policy and approved for publication. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
Acknowledgments We would like to thank Jim Kaldy, Ted Dewitt, and Chris Janousek for
helpful discussions that helped shape this project. The reviews from John Bauer and
Laura Mattison greatly improved the final product. And, a special thanks to Jim Kaldy for
beta-testing the tools.
Preferred citation for data/tools M. R. Frazier, D. A. Reusser, H. Lee II, L. M. McCoy, C. Brown
and W. Nelson. 2013. WestuRe: U.S. Pacific Coast estuary/watershed data and R tools.
U.S. EPA, Office of Research and Development, National Health and Environmental Effects
Research Laboratory, Western Ecology Division. EPA/600/R/13/067.
Cover credits The watercolor map is from Stamen Design (www.stamen.com) and was ob-
tained using the R package "ggmap" (Kahle and Wickham 2013).
-------
CONTENTS U.S. EPA and USGS
Contents
1 Overview 3
2 The Data 5
2.1 Estuary data 5
2.2 Watershed data 10
2.3 NOAA salinity zones 12
2.4 Climate data 13
2.5 Area normalized freshwater flow 14
3 The Tools 17
3.1 Program requirements 17
3.2 Starting R 17
3.3 Input data 18
3.4 R tools 20
3.5 R terminology 31
3.6 Error messages 32
4 Metadata 33
-------
1 OVERVIEW
U.S. EPA and USGS
1 Overview
There are about 350 estuaries along the
U.S. Pacific Coast (U.S. Fish and Wildlife 2011).
Basic descriptive data for these estuaries, such
as their size and watershed area, are impor-
tant for coastal-scale research and conserva-
tion planning. However, this information is
spread among many sources, making it diffi-
cult to find and standardize. The goal of the
WestuRe Project is to provide a framework to:
(1) make general descriptive data for estuaries
and their watersheds more accessible, and (2)
provide tools to make analyzing and visualiz-
ing these data easier.
The WestuRe download includes data
describing U.S. Pacific Coast estuaries and
their corresponding watersheds from north-
ern Washington (including the region lo-
cated along the Strait of Juan de Fuca that
goes from Port Townsend to Cape Flattery,
48.383°N) to southern California (Tijuana Estu-
ary, 32.557°N), excluding Puget Sound proper
and coastal islands (Fig. 1). The WestuRe data
currently include shapefiles of estuary and wa-
tershed polygons as well as CSV files summa-
rizing geomorphological and climate data (Fig.
2, Section 2). The WestuRe tools help users
extract and view relevant data using the sta-
tistical program R and Google Earth (Fig. 3,
Section 3).
Potential applications of the data include:
• Describing and comparing estuaries and
watersheds at the landscape scale
• Identifying relationships between estu-
ary/watershed variables
• Incorporating estuary/watershed at-
tributes in models to predict species and
habitat distributions
• Classifying estuaries according to mor-
phology, climate, and habitat (Lee and
Brown 2009)
Figure 1: Data includes estuaries along the
U.S. Pacific Coast (yellow region).
-------
1 OVERVIEW
U.S. EPA and USGS
Figure 2: Overview of data (details provided in Section 2).
Estuary Watershed Climate
SHP& CSV files
SHP& CSV files
CSV files
We modified the geospatial NWI data
(U.S. Fish a Wildlife 2011) to make it
more conducive to estuary research
by:(1) including only estuarine
wetland habitats, and (2) assigning
each habitat polygon an estuary ID so
data for individual estuaries can be
identified and compiled.
In addition to the SHPfiles, a CSVfile
includes estuarine: name, state,
latitude, longitude,
total/intertidal/subtidal area.
The U.S. EPA (Lee and Brown 2009)
delineated watersheds to capture
the entire drainage area of each
estuary (i.e., Estuarine Drainage
Area, EDA). Coastal Drainage Areas
(CDAs) that do not feed into an
estuary were also identified.
In addition to the SHP file, a CSVfile
includes watershed: type and area.
Monthly climate data are available
for: (1) sea surface temperature
outside the mouth of the estuary
(Payne et al. 2011); (2) air
temperature at the mouth of the
estuary (Thornton 2009): and, (3)
precipitation and air temperature
data averaged over the watershed
(PRISM Climate Group 2011).
Screenshot of NWI Wetlands
Mapper for Yaquina Estuary, OR.
The WestuRe estuary data is
derived from the NWI.
-------
2 THE DATA
U.S. EPA and USGS
Figure 3: Overview of WestuRe tools for obtaining and visualizing estuary/watershed data
using the statistical program R and Google Earth (details provided in Section 3).
Viewing the files generated by the WestuRe
visualization tools in Google Earth. Output
includes sample points, estuarine habitats,
and watershed boundaries.
STEP1
WestuRe tools require a user
provided CSV file with
latitude/longitude coordinates.
Example CSV data file:
1 -12 01
1 -12 12
2 -12 08
3 -123 93
4 -12 01
1 -124.01
1_ -124.01
5 -124.04
1 -124.03
^m^^m
46.96
47.02
46.89
46.90
44.62
44.60
44.57
44.43
44.43
„«
3
5
2
4
15
2
5
12
STEP 2
In R, run a couple lines
of EASY code
Behind the scenes
R overlays the coordinate data
on the WestuRe data to obtain
sample specific habitat data
and estuary/watershed data
that can be viewed in Excel
and Google Earth.
1 .Samples: Original data with appended
site specific data (NWI habitat,
estuary, etc.)
xl-l -124.I
xl-l -124.01 46.96 1 P280X E2AB/USN E2
X3-1 -114.12 47.02 3 P280X E2AB/USN E2
X3-2 -124.08 46.89 5 P280X E1UBL El
2.Estuary: Estuarine and watershed
data associated with sample data
P200X Estuary OR
Vaquina
Grays
Harbor
x Estuary WA
3.ClimateMonthlv: Monthly climate data
for watersheds and estuaries
P2(]0x
PZlOx
pzao«
Feb
Mar
332.ai
234.74
245.67
1.39
2.37
2.89
2 The Data
WestuRe provides data and tools for ana-
lyzing and visualizing estuary and watershed
data for the U.S. Pacific Coast (Table 1). In
this guide, we provide a brief description of
the WestuRe data files and maps, and how they
were derived. Most of these data are updated
from the U.S. EPA report, Classification of Re-
gional Patterns of Environmental Drivers and
Benthic Habitats in Pacific Northwest Estuaries
(Lee and Brown 2009). This report invento-
ried the estuaries of the Pacific Northwest to
develop a classification scheme to better under-
stand the vulnerability of estuaries to anthro-
pogenic nutrient loading. The Classification
Report provides a more comprehensive descrip-
tion of the data and how it may be used to help
assess estuarine vulnerability.
2.1 Estuary data
The estuary geospatial files and corre-
sponding estuary geomorphology data in the
WestuRe output are based on a modified ver-
sion of the U.S. Fish and Wildlife Service's
National Wetlands Inventory data (NWI, U.S.
Fish and Wildlife Service 2011). The estu-
arine habitat data from the NWI is an excel-
lent resource for analyses requiring regional
scale data because of its extensive and con-
-------
2 THE DATA
U.S. EPA and USGS
Table 1: Files included in the WestuRe download.
Data (folder)
Contents
Size
EstMouths (folder) Geospatial SHP and KML flies of estuary mouthpoints N=349 estuaries +
based on NWP estuarine habitats 2 Humboldt and 2 SF lobes'
NWI_WA (folder)
Geospatial SHP file of estuarine habitats in Washington N=1516 polygon features
NWI_OR (folder)
Geospatial SHP file of estuarine habitats in Oregon
N=6698 polygon features
NWI_CA (folder)
Geospatial SHP file of estuarine habitats in California N= 10058 polygon features
Watershed (folder) Geospatial SHP and KML files of U.S. Pacific Coast
watershed boundaries
N=506 polygon features
NOAAsalinity (folder)
Geospatial SHP and KML files of U.S. Pacific Coast
estuary salinity zones
N=36 estuaries (plus some
bays)
EstuaryData (CSV)
Estuary data
N=349 estuaries +
2 Humboldt and 2 SF lobes'
WatershedData (CSV) Watershed data
N=506 watersheds +
Humboldt and SF combined
lobes
ClimateMonthly (CSV) Monthly climate data for watersheds and estuaries
N=508 x 12 months = 6108
RFunctions (folder)
Functions (R)
Code for points2est and points2kml tools that extend the
utility of the estuary/watershed data
MapColors (CSV) Color data for visualizing estuary habitat data (internal
file)
Other Files
ExampleData (CSV)
Example data used in SampleScript.R
SampleScript (R)
Demonstration code for R tools
SampleOutput (CSV)
Example output files generated by the points2est and
points2kml tools using "ExampleData.csv" data
Users Guide (pdf)
PDF describing the data and R tools
*NWI refers to the U.S. Fish and Wildlife Services' National Wetlands Inventory (U.S. Fish and Wildlife Service 2011).
'Humboldt and San Francisco have additional data corresponding to their two estuary lobes.
-------
2 THE DATA
U.S. EPA and USGS
sistent geographic coverage. The NWI pro-
vides regional-scale maps of wetlands and
deepwater habitats which are classified us-
ing a hierarchical system organized as Sys-
tem/Subsystem/Class/Subclass plus some ad-
ditional modifiers (Cowardin et al. 1979, Ap-
pendix A). The habitat classes identified at the
system-level include marine, estuarine, river-
ine, palustrine, and lacustrine (Fig. 4). These
habitats are identified by analyzing high alti-
tude imagery in conjunction with other data
sources and field work. We modified the NWI
geospatial data for estuarine research by in-
cluding only habitat polygons classified as ma-
rine, estuarine, and tidal riverine. Further-
more, we assigned a unique Watershed ID to
each estuary habitat polygon, that identifies its
watershed (Section 2.2).
Figure 4: Screenshot of NWI Wetlands
Mapper for Yaquina Estuary, OR.
Following Lee and Brown (2009), estuar-
ies were defined as waterbodies containing an
NWI estuarine polygon and directly discharg-
ing into the ocean. Semi-enclosed harbors or
bays with only marine polygons and coastal
streams with only tidal riverine polygons were
not classified as estuaries. The NWI was re-
vised in 2011, after the release of the Lee and
Brown report, and several new estuaries were
identified, particularly in California where 78
estuaries were added. One result of this revi-
sion is that there is no longer a strict one-to-
one correspondence between all estuaries and
watersheds, and some coastal drainage areas
(CDA's) now contain one or more estuaries. For
most users, this will be of little consequence
because the newly added estuaries tend to be
very small with an average area of 0.03 km2.
To prevent confusion, WestuRe only includes
the estuarine area data for the 267 estuaries
with unique watersheds.
Based on our analysis of the NWI data,
there were 349 estuaries along the U.S. Pa-
cific Coast, excluding Puget Sound, WA (Table
2). Descriptive statistics for each estuary were
obtained by summing the NWI estuarine habi-
tat polygons sharing the same watershed ID,
for: intertidal, subtidal, tidal riverine, and in
a few cases (based on best professional judg-
ment), marine habitats. There is just over 3600
km2 total estuarine habitat on the U.S. Pacific
Coast (including the estuaries along the Strait
of Juan de Fuca, but otherwise excluding Puget
Sound). Extensive intertidal habitat is one of
7
-------
2 THE DATA
U.S. EPA and USGS
the defining characteristics of U.S. West Coast
estuaries, with about 1380 km2, or 38%, of the
total estuarine habitat identified as intertidal.
Intertidal habitat provides many valuable ser-
vices including aquaculture and bird viewing
opportunities (Lamberson et al. 2011). About
65% of the inventoried estuaries were smaller
than 0.5 km2, and the 4 largest estuaries (San
Francisco, Columbia River, Willapa Bay, and
Grays Harbor) account for about 83% of the
total estuarine area (Fig. 5). Despite their
diminutive size, small estuaries are included in
these data because they provide critical habitat
for salmon (Lackey 2004, Lackey et al. 2006a,
2006b, Lawson et al. 2004), and have ecologi-
cal, economic, and cultural significance.
Table 2: Number of estuaries by state. Only
estuaries with unique watersheds have
estuary area data.
State
Washington2
Oregon
California
Columbia River3
Total
Estuary count
Unique
All watersheds1
40
64
244
1
349
38
62
166
1
267
Estuarine area
> 0.5 km2 > 1 km2
14
20
56
1
91
8
16
35
1
60
Estuaries added in 2011 revision of NWI typically do not have a unique watershed
2 Excludes Puget Sound
3Washington and Oregon
WestuRe provides the raw estuary data as
CSV and SHP files located in the "Data" folder.
The EstuaryData.csv file can be opened in Ex-
cel and contains descriptive spatial statistics
for each estuary (Table 8). The geomorpholog-
ical NWI habitat data are available as SHP files
(NWI_WA, NWI_OR, NWI_CA) which can be
visualized in Arc CIS, or other spatial software.
The estuary mouthpoints are available as ei-
ther a SHP or KML file (EstMouths). The KML
version can be viewed in Google Earth. If the
points2est tool is run in R, these data will be
used to generate some of the data variables in
the "Samples.csv" (Table 4) and "Estuary.csv"
(Table 5) files that are returned. To generate
the estuary data in "Samples.csv", R overlays
the user provided sample points onto the NWI
data to obtain the estuary names and habitats
where the point falls. For "Estuary.csv", the
descriptive data for estuaries that have sample
points are returned. If the points2kml tool is
run, maps of the estuarine habitat boundaries,
watershed boundaries, and sample points are
created for viewing in Google Earth.
Despite the overall high quality of the
NWI data, there are some limitations (Lee and
Brown 2009). Typically the NWI habitats are
not as detailed or as accurate as those from
habitat maps specifically developed for partic-
ular estuaries. The NWI habitat designations
for some estuaries are based on limited field
validation, and consequently, habitats may be
misclassified. In particular, the designations
between estuarine and tidal riverine polygons
should be viewed cautiously. The demarcation
between estuarine and tidal riverine polygons
was defined by salinity, with the transition oc-
8
-------
2 THE DATA
U.S. EPA and USGS
Figure 5: Distribution of estuary areas along the U.S. Pacific Coast (N=267). Most estuaries
are smaller than 1 km2. The four largest estuaries are San Francisco, CA; Columbia River,
OR/WA; Willapa Bay, WA; and Grays Harbor, WA (from largest to smaller).
Estuaries < 15 km2
N = 254
L
a close-up
0 50
Smaller estuaries
100
I
150
-14
-12
-10
- 8
- 6
- 4
- 2
200 250
Larger estuaries
01
o
o
o
o
o m
ffl
-3
Ol
O
O
Estuary size ranking
curring where salinity exceeds 0.5 during the
period of annual average low flow. This bound-
ary can be difficult to accurately identify even
with ample field data, which is not available
for many of the estuaries. Furthermore, some
of the NWI data were generated in the late-
1970s and early-1980s, and thus are historical
snapshots of estuarine conditions. Estuaries
are dynamic systems and the locations of their
mouths and habitats may have changed over
time. The habitat classification codes used by
the NWI may not always be consistent among
estuaries: in some cases, the codes may be
obsolete; in other cases, the use of the more
detailed modifiers is not consistent among es-
tuaries. For certain types of analyses, it may be
better to disregard the more detailed classifiers
or to consolidate the codes into broader habi-
tat classes as recommended by Lee and Brown
(2009, Table 2-1). The U.S. Fish and Wildlife
Service has an ongoing effort to update and
improve the NWI data so some of these issues
-------
2 THE DATA
U.S. EPA and USGS
will improve over time. tend to be very small (average size of 0.03
km2), and consequently, the accurate delin-
2.2 Watershed data . r , , , ,.,.,.. . ,
eauon or watersheds may be difficult because
The U.S. EPA (Lee and Brown 2009) delin- the watershed areas tend to be overestimated
eated watersheds to create a one-to-one rela- for estuaries smaller than <0.1 km2 (Lee and
tionship between each estuary and watershed Brown 2009).
(Fig. 6). These data were derived from a wa- About 506 EDAs and CDAs were identified,
tershed geospatial layer originally created for with a total drainage area of 850,000 km2(and
NOAA's Coastal Assessment Framework (NOAA 308,000 km2 when the Columbia River water-
1999). Because the NOAA watershed bound- shed is excluded) along the U.S. Pacific Coast
aries were not sufficiently detailed for estuary- (Table 3).
scale analyses they were further delineated us-
ing additional data (Lee and Brown 2009). The
drainage area for each estuary was delineated
(Estuarine Drainage Area, EDA1) to capture the
entire landscape contributing to the nutrient
loading of the estuary. Coastal Drainage Areas
(CDA) that drain into the ocean but do not con-
Table 3: Summary of U.S. Pacific coast
watersheds.
Watershed
Type
Estuary
Drainage
Area (EDA)
Coastal
Drainage
Area (CDA)
Definition Count
Watershed draining into a 256
single estuary
Watershed that does not drain 253
into an estuary, but due to a
recent update of the NWI, 39
CDAs contain one or more
small estuaries
Total Area
(km2)
437,625
7,890
tain an estuarine polygon were also identified
(Fig.6).
The delineation of watershed boundaries
was based on estuaries identified in an earlier
version of the NWI data. Due to updates of the
NWI there are now a few mismatches between In general, larger estuaries have larger wa-
the estuarine and watershed geospatial data, tersheds, with a general scaling relationship
Consequently, some estuaries are now located of2:
within CDAs, which by definition should not watershed ~ estuary°'rr
drain into an estuary. The WestuRe watershed On average, an estuary that is twice as
data may be updated in the future to delineate large as another will have a watershed that
watersheds for the new estuaries. However, is about 70% larger (Fig. 7). Estuaries with
the estuaries without a dedicated watershed relatively large watersheds for their size tend
lrThis is equivalent to merging NOAAs Estuarine Drainage Area (EDA, portion of watershed that empties directly into the estuary
and is affected by tides) and Fluvial Dranage Area (FDA, portion of an estuary's watershed upstream of the EDA boundary) for an
estuary.
2Reduced major axis regression model to allow for error in the independent variable (95% CI of 0.71-0.84, R2 = 0.51)
10
-------
2 THE DATA
U.S. EPA and USGS
Figure 6: Watersheds of the U.S. Pacific Coast. Example of coastal drainage area (CDA) and
the estuary drainage area (EDA) of Yaquina Estuary, OR.
to be more river-like; whereas, estuaries with
relatively small watersheds tend to be more
ocean-dominated. This is likely to have signif-
icant implications for an estuary's biota and
susceptibility to certain types of stressors. This
metric can be further refined by incorporat-
ing estimates of rainfall on the watershed (see
Section 2.5).
WestuRe provides the raw watershed data
as CSV and SHP files located in the "Data"
folder. Watershed boundaries are available
as either a SHP or KML file. The Watershed-
Data.csv file can be opened in Excel and con-
tains descriptive spatial statistics for each EDA
and CDA (Table 9). If the points2est tool is run
in R, these data are used to generate the water-
shed variables in the "Estuary.csv" file (Table
5) that is returned.
Some complexities of the watershed data
are worth noting. The Humboldt and San Fran-
cisco estuaries are both comprised of two es-
tuarine lobes with discrete watersheds. De-
pending on the analysis, it may be appropri-
ate to treat these lobes as separate systems
and analyze them independently. To allow
the most flexibility, we provide data for the
individual lobes as well as for the combined
estuaries. Humboldt's northern "Arcata Bay"
is supplied by watershed PI30x02 and south-
ern "Humboldt Bay" is supplied by watershed
PISOxOl. San Francisco's northern bay is sup-
plied by P090a and the southern bay by P090w.
The Columbia River watershed extends into
Canada, affecting the availability of some data
11
-------
2 THE DATA
U.S. EPA and USGS
Figure 7: Relationship between estuary and watershed area. Estuaries above the line (e.g.
Klamath and Rogue Rivers) tend to be more river dominated. Those below the line tend
to be more ocean dominated (e.g. Humboldt and Netarts Bays).
Estuary area, km
parameters. Specifically, the watershed climate
data for the Columbia River does not include
the Canadian region.
2.3 NOAA salinity zones
We provide the salinity zones from NOAAs
Coastal Assessment Framework (NOAA 1999),
describing the average annual and depth aver-
aged salinity concentrations for 36 U.S. Pacific
Coast estuaries (plus some bays). These zones
drive the distribution of biological communi-
ties and contribute to the understanding of es-
tuarine circulation. The salinity concentrations
used to demarcate the zones were: 0.0-0.5
ppt = tidal fresh zone; 0.5 to 25 ppt = mix-
ing zone; >25 ppt = seawater zone. The zone
boundaries were based upon salinity data from
published and unpublished sources as well as
discussions with experts. The "best guess" of
informed experts was often necessary because
the data required to estimate the location of
these features were incomplete or nonexistent
(National Estuarine Inventory 1985). Salinity
zones are spatially and temporally variable due
12
-------
2 THE DATA _ U.S. EPA and USGS
to factors that affect salt water intrusion and Project (PFSST V50 and V51). From these
fresh-water inflow such as tides, rainfall, and data, the USGS has created a data product
wind. with monthly SST data for a >28 year period
The NOAA salinity zone maps are pro- at a 4 km grid cell resolution for coastal re-
vided as SHP and KML files (Salinity Zones- gions within 20 km of the shoreline (Payne
NOAA). If the points2est tool is run, R overlays et al. 2011). From the USGS product, we
the coordinates of the user provided sample extracted the SST data within 10 km of each
points onto the NOAA salinity zone map to ob- estuary mouth and averaged the monthly val-
tain the salinity zone of each sample. These ues for a 28 year period from 1982 to 2009.
data are included in the "Samples.csv" file that Estuary ciimate data
is returned (Table 4).
The monthly air temperature data (aver-
2.4 Climate data ^ mmimum) ancj maximum) at the estu-
Monthly climate data is summarized for: ary mouth were estimated using Daymet data
sea surface temperature outside the mouth (http://daymet.ornl.gov/gridded, Thorn-
of the estuary; air temperature at the mouth ton 2009). Daymet is a model that generates
of the estuary; and air temperature and pre- daily surface weather data using daily observa-
cipitation averaged over the watershed (Fig. tions of minimum and maximum temperatures
8). The raw monthly climate data for all and precipitation from ground-based meteo-
watersheds/estuaries are provided in "Cli- rological stations which are modeled over the
mateMontly.csv" in the "Data" folder. If the conterminous United States. Daymet provides
points2est tool is run in R, these climate data daily climate data for the years 1980-2003 at
will be summarized for the estuaries contain- a 1 km x 1 km resolution. For each estuary, the
ing sample points and used to generate some daily climate data nearest the mouthpoint co-
of the data variables in the "Estuary.csv" (Table ordinates were extracted. The daily data were
5) file that is returned. then averaged across all years for each month
Sea surface temperature (SST) to calculate monthly means'
The average monthly surface tempera- Watershed climate data
tures of the near-coastal waters of each estu- The average monthly air temperature
ary mouth were estimated using satellite based (average, minimum, and maximum) and
remote sensing observations from Advanced cumulative monthly rainfall was estimated
Very High Resolution Radiometer (AVHRR). for each watershed using PRISM NOR-
These data were compiled into monthly means MALS climate model data (http: //prism.
as part of the Pathfinder versions 5.0 and 5.1 oregonstate.edu, Daly et al. 2007, PRISM
13
-------
2 THE DATA U.S. EPA and USGS
Climate Group 2011). PRISM (Parameter- such as evaporation and percolation (Lee and
elevation Regressions on Independent Slopes Brown 2009). Once in the estuary, the extent
Model) uses point measurements of climate that the freshwater mixes with ocean water is
data, elevation, and other data to produce con- dependent partially on the volume of the es-
tinuous digital grid estimates of monthly cli- tuary. Ideally, cumulative annual precipitation
matic data. Hourly weather station data are would be normalized by estuarine volume. In
used to determine the daily maximum and min- the absence of these data, estuary area is a
imum temperatures for a 24-h local period, reasonable proxy for estuary volume (Lee and
These daily observations are then aggregated Brown 2009). This index is calculated by the
to calculate the monthly average maximum points2est tool for each estuary that contains
and minimum temperatures. The PRISM data a sample point and is included in the "Estu-
provides monthly data averaged over 1971- ary.csv" (Table 5) file that is returned.
2000 at a resolution of 30-arcsec (800 m). To We used the cumuiative volume of water-
average the climate data across the watershed, shed rainfall to obtain an index for area nor.
we took the average of the raster cells within malized freshwater flow because these data
each watershed polygon, weighting the values are readily available for all estuaries - includ-
by the percentage of each raster cell included ing small) ungaged ones. Moreover, despite
within the watershed polygon. The average me coarseness of this index, it appears to suc-
air temperature was calculated by taking the cessfully capture key aspects of tide- versus
mean of the minimum and maximum air tern- river-domination as measured by salinity for
peratures (see FAQ of PRISM website). estuaries in the Pacific Northwest (Lee and
Brown 2009). Estuaries with low volumes
2.5 Area normalized freshwater flow
of watershed rainfall relative to estuary area,
One important variable that can be esti- have less seasonal variation in salinity as well
mated using the WestuRe data is an index for as less variation along the main axis of the
classifying tide- and river-dominated estuar- estuary. Furthermore, a positive relationship
ies which is calculated by dividing the annual was demonstrated between this index and the
cumulative volume of rainfall over the entire variation in salinity over a tidal cycle at the
watershed (m3, PRISM climate data, Daly et al. mouth of seven target Pacific Northwest es-
2007) by the area of the estuary (m2). Using tuaries. Despite the advantages of this met-
the annual cumulative volume of rainfall on a ric, estimates of freshwater flow derived using
watershed results in a correlated index of fresh- gaged stations and/or models will be prefer-
water flow into the estuary because not all the able in many instances because they provide a
water flows into the estuary due to processes direct estimate of freshwater flow (rather than
14
-------
2 THE DATA
U.S. EPA and USGS
Figure 8: Monthly climate data for representative U.S. Pacific coast estuaries.
Waatch River Estuary
Yaquina Estuary
Rogue River Estuary
Humboldt Bay Estuary
o
Big River Estuary
o
San Francisco Estuary
°
California
Morro Bay Estuary o
Canada Del Cojo Estuary •
Los Angeles River Estuary *
San Diego Bay Estuary *
Watershed Precipitation
o
o
I*
O)
?>
O
o
Jan Feb Mat Apr May Jun Jul Aug Sep Get Nov Dec
Month
20 -i
U
o
0)
O) -
03
0)
Estuary Air Temperature
10 J
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
22 -
20 -
18 -
0) 16 -
D)
to
t 14 H
12 -
10 -
Sea Surface Temperature
Jan Feb Mai Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Month
15
-------
2 THE DATA
U.S. EPA and USGS
a correlated index) and comparisons among
estuaries may be more accurate. However, this
method of estimating freshwater flow is not
entirely free of assumptions given that flow is
typically measured in a single or limited num-
ber of an estuary's tributaries.
Comparisons among estuaries will be
most appropriate when they are located within
the same ecoregion because the percentage
of water that evaporates or percolates into
the soil will be similar for these watersheds,
such that a similar fraction of the rainfall will
flow into each estuary. For example, most es-
tuaries in the Pacific Northwest are probably
comparable, with the possible exception of the
Columbia River due to the large size of its wa-
tershed. Caution should be applied when com-
paring estuaries from different ecoregions such
as Southern California and Pacific Northwest.
Despite this complication, these comparisons
may still be meaningful because large differ-
ences in rainfall between these two regions
likely drive most of the variation in this metric.
Lee and Brown (2009) used the follow-
ing preliminary thresholds to classify Pacific
Northwest estuaries:
• Tidal-dominated: < 175 m3m~2 year"1
• Moderately river-dominated: 175-400
m3 m~2 year^1
3 m-2
• Highly river-dominated: > 400 m3 m
year"1
16
-------
3 THE TOOLS U.S. EPA and USGS
3 The Tools
We have developed some tools to use within the statistical program R (R Development
Core Team 2011) to help researchers:
1. associate their sample data to estuary and watershed data (points2est, Fig. 13)
2. visualize these data in Google Earth (points2kml, Fig. 14).
The WestuRe data is available without these tools (located in the "Data" folder), however, the
tools provide a method of streamlining data acquisition and providing non-GIS experts with a
way to map their data and view it in the context of other datasets.
These tools require the user to provide a dataset with latitude and longitude coordinates
for sample points located within one or more estuaries on the U.S. Pacific Coast. The points2est
tool aligns the sample coordinates with the geospatial watershed, NWI estuarine habitat, and
NOAA salinity zone data, in order to return data specific to the sample points and the estuaries
and watersheds where they are located (Tables 7, 9, 10). The points2kml tool takes an object
created by points2est and outputs KML files (i.e., Google Earth) of the original sample points,
NWI estuarine habitats, and watershed boundaries.
The WestuRe download includes some additional files that demonstrate how these tools
are used. The SampleScript.R file is an R file that includes all the code needed to use the
points2est and points2kml tools. This file will need to be modified for the user's computer/file
configuration, the details of which are provided below. Also included is a sample dataset
(ExampleData.csv) and the corresponding output files that are returned by the points2est and
points2kml tools (File "SampleOutput").
3.1 Program requirements
The freeware statistical program R is needed to use the WestuRe tools. R can be down-
loaded from: http://cran.r-project.org/ (See: Appendix B for installation instructions;
Section 3.5 for R terminology). The KML map files are viewed in Google Earth, which can be
downloaded from: http://www.google.com/earth/index.html.
3.2 Starting R
When you open R, you will see a rather sparse "R Console" (Fig. 9). To confirm that R is
working, type 2 + 2 into the console and press enter:
17
-------
3 THE TOOLS
U.S. EPA and USGS
2+2 #R returns the answer!
[1] 4
• The ">" is the "command prompt" where you input commands.
• The cursor is R telling you that it is ready to do your bidding.
• The output from R is blue and often preceded by a bracketed number.
• Anything preceded by a # (green in many text editors) is a user comment that R ignores.
This is used to document your work.
Figure 9: R console with some code entered (2 + 2).
i- RGui (3J-bit)
File Edit View Misc Packages Windows Help
R version 2.15.1 (2012-06-22) — "Roasted Marshmallows"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i336-pc-rcingw32/i336 (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation(}' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'helpO1 for on-line help, or
•help.start()' for an HTML browser interface to help.
Type 'q()' to guit R.
> 2-2
[1] 4
J
3.3 Input data
The WestuRe tools require a user supplied dataset that includes latitudes and longitudes of
sample points from U.S. Pacific Coast estuaries. The data must be formatted as a CSV file (Fig.
10). A CSV file can be created from Excel using "Save As" and then selecting "CSV (Comma
delimited)(*.csv)" from "Save as type:".
18
-------
3 THE TOOLS
U.S. EPA and USGS
Latitude and longitude variables must be in decimal degrees. Methods of converting to dec-
imal degrees can be found online (example: http://en.¥ikipedia.org/¥iki/Geographic_
coordinate_conversion) and can be done in R using deg2num from the gmt package. All
longitude coordinates should be preceded by a "-" (negative sign), to indicate the data are
located in the western hemisphere.
Correct format: 40.446195, -179.948862
Incorrect formats: 40°26'47"N, 79°58'36"W; 40:26:46N, 79:56:55W; 40.446195, 179.948862
Here are a few additional tips for preparing the CSV data file:
• Column names should be concise and contain no spaces
• Variables can be in any order, and there is no limit to the number of variables; however:
• It is better to cut variables that include extensive notes because they can sometimes cause
errors when importing into R and when displaying in Google Earth
• Data with "#" symbols can cause import errors in R, and "&" symbols may cause errors in
KML display
Figure 10: An example CSV file displayed in Excel that is ready for the WestuRe tools.
f
— -S Home Insert
A Cut
J 4l Copy
Paste
•r ^/ Format Painter
[ Clipboard ^
Page Layout Formulas Data R
Calibri -|ll -||A" AT|
B i n '||EEJ 'H'S** A"
Font ii
;:
Al
Site
A
Site
2 xl-1
3 1x3-1
| 4 |x3-2
5 x3-3
6
7
S
9
10
11
17
x3-4
X4-1
X5-1
X5-5
X6-1
B
X
-124.011
-124.127
-124.08
-123.93
-124.016
-124.015
-124.012
-124.048
-124.034
C D E F
y richness
46.96969 1
47.0251 3
46.89846 5
46.9011 2
44.62429 4
44.60382 15
44.5784 2
44.43147 5
44.43147 12
19
-------
3 THE TOOLS
U.S. EPA and USGS
3.4 R tools
The SampleScript.R file provides a template that guides you through the process of using
the points2est and points2kml tools. To begin using the tools, open the SampleScript.R file in R:
open R -^File -^Open script...^ and find the SampleScript.R file located in the WestuRe folder.
This will open a 2nd window, called the text editor which will display some example code and
additional instructions for using the tools (Fig. 11). In the SampleScript.R file, information
proceeded by "#" are notes that are ignored by R, but provide useful information. To use the
points2est and points2kml tools, you will need to make a few changes to the SampleScript.R
file, which are described below. You will also need to be able to send lines of the code from the
SampleScript.R file to the R Console. To do this, place the cursor at the appropriate line in the
text editor and click the "Run line or selection" button (Fig. 11) or Ctrl+r.
Figure 11: R with SampleScript file open in the text editor window. Code is sent from the text
editor window to the R Console by selecting the circled button.
STEP 0: Install packages
If you have not already done so, you will need to install the following packages: rgdal, sp,
maptools (see Section 3.5 for a definition of "packages" and other terminology). These packages
were developed to do spatial analyses and are necessary for the points2est and points2kml tools
to work.
To download a package, either: 1) From the upper menu in R select: Packages —>• Install
20
-------
3 THE TOOLS U.S. EPA and USGS
Packages; or, 2) go to the following line in SampleScript.R, and send the following code to the
R console:
install.packages(c( , , ))
R will ask which GRAN (Comprehensive R Archive Network) mirror site you want to use.
Select one that is nearby. R will automatically download the packages and save them to its
"library" folder located in the R Program Folder. You will only need to do this once, although,
you may want to update your packages fairly frequently because many developers are making
constant improvements.
STEP 1: Establish the directory path to the WestuRe folder
You will need to tell R the directory path for the WestuRe folder on your computer. In STEP
1 of the SampleScript.R, replace the existing directory path with the location of the WestuRe
folder on your computer. Then, send the code to the R console. Unless told otherwise, the setwd
(i.e., "set working directory") command established the location where R will search for and
save files.
setwd( )
NOTE: if you are using a Windows system, you will need to add an additional backslash
("\") between the folders in the directory path. Double backslashes are necessary because R
uses a single backslash as an escape character. Make sure that the pathway is enclosed by
quotes ("").
The best approach for identifying the directory path is to copy and paste it from the address
bar that appears when you are within the WestuRe folder. This will help eliminate the mistakes
that inevitably occur when typing a long path (Fig. 12).
21
-------
3 THE TOOLS
U.S. EPA and USGS
Figure 12: How to find a directory path in Windows systems.
lie Edit View Favorites Tools Help
,1 * y- ' Search |j Folders IjjM"
C:\Doajments and 5ettings\mrrazi03\DesktQp\WestuRe_vlJ
To identify the path in R:
1) Open the relevant folder
2) Copy the path in the address window
3) Paste into R
4) Add double back slashes if working in Windows:
C:\\Documents and Settings\\mfrazi03\\Desktop\\WestuRe_vl
STEP 2: Load the WestuRe tools
Now, R must be told to read the functions.R file in the RTools folder. This file contains the
code for the points2est and points2kml tools. To make R read this file, send the following code
to the R Console:
source("\\RTools\\functions.R")
# Feedback should be returned in the R
# Console: [1] 'estuary v. 1' [1]
# 'Loading necessary packages' Loading
# required package:....
STEP 3: Importing your data into R
In STEP 3 of the SampleScript.R document, replace the existing directory path with the
location of the CSV file on your computer (Fig. 12). After the code is sent to the R Console, the
data will be imported into R and called "MyData". The <- symbol is used by R to assign a name
to an object, it can roughly be thought of as an equals sign.
22
-------
3 THE TOOLS U.S. EPA and USGS
MyData <- read.csv( )
If you don't have any available data, but still want to try the tools, send the following code
to the R console:
MyData <- read.csv( )
In this case, R will import the "ExampleData.csv" file that is located in WestuRe folder.
Once the data have been imported into R, it is critical to check that everything is correct. If
you have a short dataset, you can view all the data by typing the following into the R console:
MyData
However, if you have a longer data file, it is better to use commands such as:
head(MyData) # returns first few lines of data frame object
tail(MyData) # returns last few lines of data frame object
summary(MyData) # returns summary of each variable
dim(MyData) # returns number of rows and number of columns
STEP 4: points2est
The points2est tool (Fig. 13) aligns the user data with the spatial data and returns three
CSV data files:
Sample Original user data with appended data for each point describing the estuary name,
watershed ID, NWI habitat, and NOAA salinity class (for select estuaries) (Table 4)
Estuary Estuary and watershed data for estuaries with sample points (Table 5)
Climate Monthly climate data for estuaries with sample points (Table 10)
The data in the CSV files are linked by the unique watershed ID variable (wsID).
23
-------
User input
a
•S
1-!
n
H-»
to
D
O)
to
tfl
H
O
O
t-1
CSV data file:
Usage:
to
Site
xl-1
x3-l
x3-2
x3-3
x3-4
x4-l
x5-l
x5-5
x6-l
X
-124.011
-124.127
-124.080
-123.930
-124.016
-124.015
-124.012
-124.048
-124.034
V
46.9696
47.0251
46.8984
46.9011
44.6242
44.6038
44.5784
44.4314
44.4314
SP
1
3
5
2
4
15
2
5
12
Tips:
• Latitude and longitude coordinates
must be decimal degrees
• Variable (i.e. column) names should
be concise and have no spaces
• Variables can be in any order and
there is no limit to the number of
variables
• Variables with long notes may cause
import errors
• CSV files are imported into R using
the read.csv function
Example:
MyData <-
read. csv("C:\\My Pat n\\Exam pie Data, csv")
points2est(x, lat, Ion, climate=TRUE, FileName, saveDir)
1. Sample: Original data with newly
appended estuary habitat data
Site
NWI NWI
sp wsID code short
E2AB/
Arguments:
X
Name the CSV file was given when
imported into R. Example: MyData
data. Example: lon="x"
xl-l -124.011 46.9696 1 P280x USN
E2AB/
t/AB/
x3-l -124.127 47.0251 3 P280x USN
X3-2 -124.08 46.8984 5 P280x E1UBL
E2
E2
El
...
-hoH
lat Name of the latitude variable in the
data. Example: lat="y"
climate Logical. If TRUE, climate data is returned
FileName The name that will be given to the
output data files. Example: FileName =
"SamplesFeb2013"
saveDir Directory path that describes where to
save output data . This should refer to an
existingfile on your computer. Example:
saveDir = "C:\\MyPath\\EstuaryData"
Example:
EstData <- points2est(x=MyData, lon="x", lat="y"
climate=TRUE, FileName="SamplesFeb2013",
saveDir="C:\\MyPath\\EstuaryData")
data associated with sample data
est est
wsID estName State Longitude
Alsea
PZOOx Estuary OR -124.07
Yaquina
P210x Estuary OR -124.06
Grays
Harbor
P280x Estuary WA -124.14
3. ClimateMonthly: Monthly climate
data for watershed and estuaries
wsTair
wsID month wsPPT min
P200x Jan 332.41 1.39
P200x Feb 284.74 2.37
P200X Mar 245.67 2.89
4. Object used by points2kml
o
!3
O
o
3'
O
O
C
C/3
tfl
C
(X)
-------
3 THE TOOLS U.S. EPA and USGS
An example of the points2est tool:
EstData <- points2est(x = MyData, Ion = ,
lat = , climate = TRUE, FileName =
saveDir = )
The arguments are:
1. x = name of the data imported into R. Example: MyData (STEP 3)
2. Ion = name of the longitude variable in the data. Example: Ion = "x"
3. lat = name of the latitude variable in hte data. Example: lat = "y"
4. climate = TRUE/FALSE, if TRUE climate data are returned
5. FileName = a name for the output files. Example: FileName="SamplesFeb2013"
6. saveDir = directory path describing where to save output files. Example: "C:\\MyPath\\EstuaryData"
In the above example, points2est generates 3 CSV data files that are saved in the directory:
"C:\\MyPath\\EstuaryData". In addition to these files, points2est creates an object that includes
all the relevant geospatial data. In this case, the object is named "EstData". You do not need to
know anything about this object except that it can be used by the points2kml tool to create KML
maps of the data.
25
-------
3 THE TOOLS
U.S. EPA and USGS
Table 4: "Sample" is a CSV data file generated by points2est that includes the following
variables that are appended to the original data.
Variable
Description
Origin
wsID
ID of the watershed where the sample point is
located
Modified from Lee and
Brown (2009)
SalinityZone Salinity zone where the sample point is located
NOAA's Coastal
Assessment
Framework
estName Name of the estuary where the sample point is
located
Derived by overlaying
sample data on NWI
geospatial data layer
NWIcode The NWI habitat class at the location of the
sample point
Derived by overlaying
sample data on NWI
geospatial data layer
NWIshort The first two characters of the NWI habitat
class.
• El = estuarine subtidal habitat
• E2 = estuarine intertidal habitat
• Ml = marine subtidal habitat
• M2 = marine intertidal habitat
• Rl = tidal river habitat
• NA = does not fall within an NWI
estuarine polygon
Derived from the NWI
geospatial data
26
-------
3 THE TOOLS
U.S. EPA and USGS
Table 5: "Estuary" CSV data file generated by points2est. NOTE: Estuary data is not returned
in the few cases when there is more than one estuary in a watershed.
Variable
Description
Origin
wsID
ID of the watershed where the sample point is located EstuaryData.csv (Table 8)
estName
Name of the estuary
EstuaryData.csv (Table 8)
estState
State in which estuary is located (WA, CA, OR)
EstuaryData.csv (Table 8)
estLongitude
Longitudinal coordinate of estuary mouthpoint
(decimal degrees)
EstuaryData.csv (Table 8)
estLatitude
Latitudinal coordinate of estuary mouthpoint (decimal
degrees)
EstuaryData.csv (Table 8)
estArea
Total area (m2) of estuarine habitat within each
watershed, calculated by summing the area of NWI
polygons: El, E2, Rl, and occasionally, M2 (based on
best professional judgment).
EstuaryData.csv (Table 8)
subtidalArea
Total area (m2) of subtidal (El) NWI polygons within
an estuary
EstuaryData.csv (Table 8)
intertidalArea
Total area (m2) of intertidal (E2) NWI polygons within
an estuary
EstuaryData.csv (Table 8)
marineArea
Total area (m2) of marine (M2) NWI polygons within
an estuary. Typically, marine habitat was not included
as part of estuarine area with the exception of a few
bays. The decision to include M2 area was based on
best professional judgment.
EstuaryData.csv (Table 8)
riverArea
Total area (m2) of tidal influenced rivers (Rl) NWI
polygons within an estuary
EstuaryData.csv (Table 8)
estNotes
Comments related to data
EstuaryData.csv (Table 8)
Continued on next page..
27
-------
3 THE TOOLS
U.S. EPA and USGS
Table 5: ....Continued
Variable Description
Origin
wsType
Describes relationship between watersheds and
estuaries:
EDA Estuarine drainage area, watershed delineation is
defined by an estuary
CDA Coastal drainage area. These typically do not
contain an estuary. However, a few now include
one or more estuaries due to revision of the NWI
When estuaries are located in a CDA, watershed
data may not be meaningful.
WatershedData.csv (Table 9)
wsArea
Area of watershed (m2)
WatershedData.csv (Table 9)
wsNotes
Comments related to data
WatershedData.csv (Table 9)
wsPPT cumulative
Cumulative yearly precipitation (mm) averaged over
the watershed. Calculated by summing the average
monthly rainfall data.
Derived from
ClimateMonthly.csv (Table
10)
AreaNormFreshFlow
Area normalized freshwater flow (m3 m 2 year :).
Relative measure of freshwater flow through the estuary
based on watershed precipitation data and estuary size.
This variable is calculated by dividing the annual
cumulative volume of rainfall over the entire watershed
(m3) by the area of the estuary (m2), or
(wsPPT^cumulative x 0.001 x wsArea) / estArea. See
section on "Area-normalized freshwater flow" for more
details.
Derived from
ClimateMonthly.csv (Table
10)
wsTair_YearMean Annual mean air temperature (°C) averaged over the
watershed. Calculated by averaging the monthly
watershed air temperature data.
Derived from
ClimateMonthly.csv (Table
10)
estTair_YearMean Annual mean air temperature (°C) near the estuary
mouth. Calculated by averaging the monthly estuary air
temperature data.
Derived from
ClimateMonthly.csv (Table
10)
estSST_YearMean Annual mean sea surface temperature (°C) outside the
estuary mouth within a 10 km radius. Calculated by
averaging the monthly sea surface temperature data.
Derived from
ClimateMonthly.csv (Table
10)
28
-------
3 THE TOOLS U.S. EPA and USGS
STEP 5: points2kml
Points2kml (Fig. 14) uses the object created bypoints2est to create three KML map files
to display: 1) the sample points, 2) watershed boundaries, and 3) NWI estuarine habitat
classification. These KML files can be opened in Google Earth (as well as ArcGIS software).
To use points2kml, it is necessary to first run points2est.
An example of the points2kml tool:
points2kml(x = EstData, kmlWS = TRUE, kmlNWI = TRUE,
IDvar = , FileName = ,
saveDir = )
The arguments are:
1. x = name of the object created by points2est (In STEP4, this was: "EstData")
2. kmlWS = TRUE/FALSE, if TRUE a watershed KML file will be created
3. kmlNWI = TRUE/FALSE, if TRUE a NWI estuary KML file will be created
4. IDvar = name of variable (i.e., column) in the original data that identifies samples. These
data will be used to label the points on the KML file (this can be left blank). Example:
IDvar = "Site"
5. FileName = a name for the output data files. Example: FileName = "SamplesFeb2013"
6. saveDir = directory path describing where to save output maps. Example: saveDir =
"C:\\MyPath\\EstuaryData")
In the above example, points2kml generates 3 KML data files that are saved in the directory:
"C: \\MyPath\\EstuaryData". If Google Earth is installed, you can visualize your data by double
clicking on the newly created KML files.
There are a couple known issues with the points2kml tool. It can take a very long time if
many complex estuaries are being saved to the KML file. In some cases, symbols such as "&"
within the original data files may prevent the creation of the KML file. Holes in polygons are
not displayed correctly in the KML file (but are otherwise interpreted correctly).
29
-------
00
O
The points2est tool must first be run,
in order to create an object that is
used by the po!nts2kml tool.
Example:
EstData <- ooints2est(x=MyData,
lon="x", lat="y", climate=TRUE,
FileName="SamplesFeb2013",
saveDir="C:\\MyPath\\EstuaryData")
In this example, the object that is
passed to the po/nts2/
VI
3.
CJ.
o
o
3'
F
ISO
O
O
oo
tfl
H
O
O
t-1
C
in
tfl
C
oo
in
-------
3 THE TOOLS U.S. EPA and USGS
Leaving R
When you finish working in the R text editor, save the script (File ->• Save As). You can
then reopen the script at a later date and rerun the analysis. When closing R, you will get this
message: save workspace image? [y/n/c]. You will probably want to choose the V option. If the "y"
option is chosen, all the R objects (".RData" file) and commands (".Rhistory" file) from your
session will be saved to the working directory. I typically find this unnecessary, because the
R script preserves the steps for redoing the analyses in a more organized fashion. Option "c"
cancels the shutdown. If you continue using R, you will eventually want to transition to a text
editor, such as RStudio (http: //rstudio. org/). Text editors designed to work with .R files
offer many advantages such as color coding and indenting of code to make it easier to read.
3.5 R terminology
GRAN The Comprehensive R Network: A network of FTP and Web servers around the world
that store identical, up-to-date, version of R code and documentation.
Function An object that includes the code needed by R to perform a particular task. Examples
include: mean, source, and read.csv. The points2est and points2kml tools are functions.
Packages Text files that contain code (i.e., functions) that give R its functionality. For many
programs everything is included in the initial installation. R works differently. When you
install R on your computer, it includes the program and a set of "base" packages. Every
time you open R, these "base" packages are automatically read into R. In addition to these
"base" packages, 1000s of additional packages are available from the CRAN website for
more specialized operations. These packages are not automatically downloaded with R,
but you can download them yourself. For a list of available packages, follow the "Packages"
link at [{http: //cr}] an. r-pro j ect. org/.
Working directory The working directory is the location on your computer that R is working
from; or in other words, the location where R reads and saves files. The R command for
defining the working directory is setwd.
31
-------
3 THE TOOLS
U.S. EPA and USGS
3.6 Error messages
Table 6: Possible errors from the points2est and points2kml.
Error
Action
data points removed due to missing
latitude/longitude
Some sample points did not contain complete location
information. You can ignore this error if you are already
aware of this issue.
data points appear to be outside the geographic Some sample points are located outside the U.S. Pacific
extent, you may want to check this! Coast. You can ignore this error if you are already
aware of this issue.
cannot open file—
The directory pathways are not correct which is
preventing data from being accessed or saved. Check
the directory pathways. Check that double back
slashes are used to separate folders.
Longitude coordinates should be negative
The longitude coordinates for the U.S. Pacific coast
must be negative.
Longitude/Latitude contains non-numeric data
The longitude/latitude coordinates contain
non-numeric characters. These data must be identified
and replaced.
No data are located in NWI estuaries
The data are located along the U.S. Pacific coast but not
within an estuary. This could occur if data are located
along the coast, but not in an estuary. Or, if data fall
just outside the NWI estuary boundaries.
KML file is created, but will not display when
opened in Google Earth
Some characters in the original user input data may be
causing the KML file to break. Note the error line that is
reported and open the KML file in a text editor to
determine what might be causing the error. Replace this
character in the original CSV file. Cut long notes from
input file.
32
-------
4 METADATA
U.S. EPA and USGS
4 Metadata
The following tables describe the variables in the: EstuaryData.csv (Table 8); Watershed-
Data.csv (Table 9); and ClimateMontly.csv data (Table 10).
Table 7: Description of geospatial data files. All spatial data is in projection WGS 84.
Data file
Format
Description
NWI_WA
NWI_OR
NWI CA
polygon SHP files
Estuary habitat delineations based on NWI (U.S. Fish
and Wildlife Service 2011). These data including only
estuary-related polygons (estuarine, marine, and tidal
river). A unique watershed identifier was added to each
NWI habitat polygon to identify polygons in the same
estuary.
Watershed
polygon SHP and
KML file
Watershed delineations for US Pacific Coast estuaries
and coastal drainage areas (Lee and Brown 2009). Each
watershed polygon has a unique identifier. There is a 1-1
correspondence between most watersheds and estuaries.
However, a few watersheds include more than one
estuary due to recent updates of the NWI. These
watersheds/estuaries are identified in the database.
Note: Humboldt is composed of two watersheds
(P130x02 N, "Arcata Bay"; PISOxOl S, "Humboldt Bay")-
San Francisco is composed of two watersheds (P090a N;
P090wS).
EstMouths point SHP and Point locations near the estuary mouth midpoints.
KML file NOTE: Estuaries are dynamic and unjettied estuary
mouths can and do move.
NOAAsalinity polygon SHP and
KML file
NOAA salinity zones for 36 U.S. Pacific coast estuaries
(plus some bays):
tidal fresh zone = 0.0-0.5 ppt
mixing zone = 0.5 to 25 ppt
seawater zone = > 25 ppt
If data are unavailable, NA values are returned.
33
-------
4 METADATA
U.S. EPA and USGS
Table 8: Description of estuary data variables (EstuaryData.csv). For more information see
section 2.1.
Variable name Description
Origin
wsID
Unique ID of watershed where estuary is located
Modified from Lee and Brown (2009)
estName
Name of estuary
Modified from Lee and Brown (2009)
estState
State of estuary location (WA, CA, OR)
Based on comparisons of NWI estuary
polygons with state boundaries in
Google Earth
estLongitude Longitudinal coordinate of estuary mouthpoint (decimal
degrees)
Midpoint of estuarine mouthpoint
estimated from Google Earth
estLatitude Latitudinal coordinate of estuary mouthpoint (decimal
degrees)
Midpoint of estuarine mouthpoint
estimated from Google Earth
estArea
Total area (m2) of estuarine habitat within each
watershed. Typically, a watershed includes only one
estuary. However for a few smaller estuaries, more than
one estuary is located within a watershed due to recent
revisions of the NWI. In these cases, estArea is not
provided. Watersheds with more than one estuary can
be identified by the estCount variable in the
WatershedData.csv data; estuaries with a wsID that
corresponds to watersheds with estCount > 1 share the
watershed with other estuaries.
Calculated from NWI geospatial data
subtidalArea Total area (m2) of subtidal (El) NWI polygons within an Calculated from NWI geospatial data
estuary
intertidalArea
Total area (m2) of intertidal (E2) NWI polygons within Calculated from NWI geospatial data
an estuary
marineArea Total area (m2) of marine (M2) NWI polygons within an Calculated from NWI geospatial data
estuary. Typically, marine habitats were not included in
estuarine area. However, they were included in a few
cases, such as in some bays. Decision to include M2
areas were based on best professional judgment and
data availability.
riverArea
Total area (m2) of tidal influenced river (Rl) NWI
polygons within an estuary
Calculated from NWI geospatial data
estNotes
Comments related to data
34
-------
4 METADATA
U.S. EPA and USGS
Table 9: Description of watershed data (WatershedData.csv). For more information, see
section 2.2.
Variable Name Description
Origin
wsID
Unique watershed identifier
Modified from Lee and
Brown (2009)
wsType
Describes relationship between watersheds Based on comparisons of
and estuaries (Fig. 6): watershed and estuary
SHP files
EDA Estuarine Drainage Area. Watershed
delineation was defined by an estuary.
CDA Coastal Drainage Area. These typically
contain no estuary; however, in some
cases, they may include one or more
estuaries due to revision of NWI after
watersheds were delineated. This is
not an issue for larger estuaries.
estCount
Number of estuaries in the watershed.
Typically CDAs will have no estuaries and
EDAs will have 1 estuary. However, this is
not always the case due to the recent
update of the NWI.
Modified from Lee and
Brown (2009)
wsArea
Area of watershed (m2)
Calculated from
geospatial data
wsNotes
Comments related to data
35
-------
4 METADATA
U.S. EPA and USGS
Table 10: Description of climate data. For more information, see Section 2.4.
Variable name Description
Origin
wsID
Unique watershed identifier
Modified from Lee and Brown
(2009)
month
Month of temperature data (Jan-Dec)
Watershed climate data
wsPPT
wsTair min
wsTair max
wsTair avg
Monthly average of cumulative precipitation
(mm) averaged over the watershed area.
Monthly average of minimum daily temperature
(°C) averaged over the watershed area
Monthly average of maximum daily temperature
(°C) averaged over the watershed area
Average of monthly minimum and maximum
temperature (°C) and then averaged over the
watershed area (as described in FAQ of PRISM
website)
PRISM (Daly et al. 2007)
monthly climate data averaged
from 1971-2000 at resolution
30-arcsec, or 800m
Estuary climate data
estTair_ min Monthly average of daily minimum air
temperatures (°C) near estuary mouth
estTairjnax Monthly average of daily maximum air
temperatures (°C) near estuary mouth
estTair_avg Monthly average of daily average air temperature
(°C) near estuary mouth
Daymet (Thornton 2009) daily
climate data from 1980 to
2003 at resolution of 1 km
Daymet
estSST
Monthly sea surface temperatures (SST, °C)
averaged over a 28 year period (1982-2009; 1981
was excluded because it did not include the entire
year) within 10 km of the estuary mouth
Monthly sea surface
temperature data averaged
over a 28+ year period
(1981-2009) from AVHRR
satellite based remote sensing
observations at 4 km resolution
and within 20 km of shoreline
(Payne et al. 2011)
36
-------
4 METADATA U.S. EPA and USGS
References
Cowardin, L. M., V. Carter, F. C. Golet, E. T. LaRoe. 1979. Classification of wetlands and deep-
water habitats of the United States. U. S. Department of the Interior, Fish and Wildlife
Service, Washington, D.C. FWS/OBS-79/31.
Daly, C., J. I. Smith and R. McKane. 2007. High-resolution spatial modeling of daily weather
elements for a catchment in the Oregon Cascade Mountains, United States. Journal of
Applied Meteorology and Climatology 46:1565-1586.
Dillon, M. E. 2011. University of Wyoming: Dillon: Teaching, http://www.uwyo.edu/mdillon/
teaching.html (Apr 6, 2011) .
Kahle, D. and H. Wickham. 2013. ggmap: A package for spatial visualization with Google
Maps and OpenStreetMap. R package v. 2.3.
Lackey, R. T. 2004. A salmon-centric view of the twenty-first century in the western United
States, pp. 131-137, In P. Gallaugher and L. Wood (eds.) Proceedings of the World
Summit on Salmon, Simon Fraser University Burnaby British Columbia, Canada.
Lackey, R. T., D. H Lach and S. L. Duncan. 2006a. Policy options to reverse the decline of
wild pacific salmon. Fisheries 31:344-351.
Lackey, R. T., D. H. Lach and S. L. Duncan. 2006b. Salmon 2100: the future of wild salmon.
American Fisheries Society Bethesda, MD.
Lamberson, J. O., M. R. Frazier, W. G. Nelson and P. J. Clinton. 2011. Utilization patterns
of intertidal habitats by birds in Yaquina Estuary, Oregon. U.S. EPA, Office of Research and
Development, National Health and Environmental Effects Research Laboratory, Western
Ecology Division, Newport OR. EPA/600/R-11/118.
Lawson, P. W., et al. 2004. Identification of historical populations of Coho Salmon (On-
corhynchus kisutch) in the Oregon coast evolutionarily significant unit. U.S. Dept. Commer,
NOAA Tech. Memo. NMFS-NWFSC-79.
Lee II, H. and C. A. Brown (editors). 2009. Classification of regional patterns of environmen-
tal drivers and benthic habitats in Pacific Northwest estuaries. U. S. EPA, Office of Research
and Development, National Health and Environmental Effects Research Laboratory, West-
ern Ecology Division, Newport OR. EPA/600/R-09/140. http://nepis.epa.gov/Adobe/
PDF/P1006Q2H.PDF
37
-------
4 METADATA U.S. EPA and USGS
National Estuarine Inventory. 1985. Data atlas: volume 1: physical and hydrologic charac-
teristics. U.S. Dept of Commerce, National Oceanic and Atmospheric Administration,
National Ocean Service.
NOAA. 1999. Coastal assessment framework. NOAA/NOS/SPO, Silver Spring, MD. http:
//coastalgeospatial.noaa.gov
Payne, M. C., D. A. Reusser, H. Lee II and C. A. Brown. 2011. Moderate-resolution sea sur-
face temperature data for the nearshore North Pacific. U.S. Geological Survey Open-File
Report 2010-1251. http://pubs.usgs.gov/of/2010/1251/ (downloaded Apr 4 2011)
PRISM Climate Group. 2011. PRISM climate data. Oregon State University, Corvallis, OR.
http: //¥¥¥. prism. oregonstate . edu (downloaded Dec 2 2011)
R Development Core Team. 2011. R: A language and environment for statistical computing.
R Foundation for Statistical Computing, Vienna, Austria, http: //www. R-proj ect. org/
Thornton, P. 2009. Daymet single-point data extraction users guide. University of Montana,
Numerical Terradynamic Simulation Group, http://daymet.org (downloaded Oct 18
2011)
U.S. Fish and Wildlife Service. 2011. National wetlands inventory website. U.S. Depart-
ment of the Interior, Fish and Wildlife Service, Washington, D.C. http: //www. fvs. gov/
wetlands/ (state data downloaded Apr 29, 2011)
38
-------
4 METADATA
U.S. EPA and USGS
Appendix A
National Wetlands Inventory wetlands and deepwater habitats hierarchical classification
system.
u.
«
to
<
o
CO
<
:::
<
I
a:
yj
<
a.
yj
:.,.,
Q
O
Z
<
CO
a
z
UJ
lit
li
ID sr
39
-------
4 METADATA
U.S. EPA and USGS
Appendix B
R Installation and Setup1
Installing R is very easy, regardless of your operating system.
• Go to ¥¥¥.r-project.org, where you'll see the R homepage (Fig B.I).
• Click on download R, which will take you to the "CRAN mirrors" page.
CRAN refers to the "Comprehensive R Archive Network". This is simply a bunch of computers
scattered around the world that keep exact duplicates of all the files associated with R. Select a
mirror that is geographically close to you - this is good for both the servers (reduced load) and
the users (reduced download times). You will do this again when you install packages.
Figure B.I: R project homepage
I File Ml ten History Bookmarks Tool? Help
I (g TSe R Project for Statistical Computing | + ,
^- _i
i re a I. I
What's new?
~Pl It
The R Project for Statistical Computing
Getting Started:
• Ris a free software enviMHHHIMTm .-tic;d i: 0111]. utuig and giaphics It :cmi.ili?s anlrut.s "ti a-wle variety of TIMX platfoiK
"Windows andMacOS fo download Rjlt-ase ch"o.-e your preferred <~B AN mirror
• R version 2.15.1 (Roasted Marshmsl.->v^) h:^ V=n rp.p^M on 2012-06-22.
• The R Journal Vol.4/1 is available
• inpH1 :()i;» t o:-:ila:e at Vrn.letbi'UJnvers-y. ^af.ivUe'Ifn-iersee. UJA..utie 12-15. 2nli
• useR! 2013. will take place at the University of Castfla-La Mancha, Albacete, Spain, July 10-12 2013. .
This server is hosted by the Institute For Statistics and Mathematics of the WU "Wien.
After selecting a mirror, a download window will appear (Fig. B.2), which will look the
same regardless of the mirror you choose.
1From Michael Dillon's website: http://wwH.uwyo.edu/mdlllon/HoR.html
40
-------
4 METADATA
U.S. EPA and USGS
• Choose the link for your operating system from the Download and Install R box at the top.
(Source code needs to be compiled, which is probably not something you are interested in
doing).
For Windows, choose the base link, then Download R X.XX.X for Windows and double-click the
file to install.
Figure B.2: Download R for your operating system from a CRAN mirror.
1 Qj The Comnrerierisive R Archive Network +
^ ^ ti -tfir.r-protettorg
aiintviMCaamiM
^S\
^
Mirrors
What's new?
Task Views
Search
The R Journal
Software
R Sources
R Binaries
Other
Manuals
FAQs
eim-R«n
^^Jju.^.—^^^.-^..^
| • Download R For Linux
1 • Download P. for MacOS X 1
Ris part of many Linux distributions, you should check with your Linux package management system in
Source Code for all Platforms
Windows and Mac -H*K mat hke.y v.'jnt to dov.:d iie piec'-mi-dei bji:in:3 ktec. in tne upper box, not the
• The latest release (2012-06-22, Roasted Marshmallows) R-2 15 1 tar gz. read what's new in the latest
• Sources of R alpha and beta relraff-- Mailv marft.ots rjpatpd f.iily ir. nnii= t'i=:if > '|i'-«ilr cvl ai'l nst;ill tne .'••'fhvjj:, >•! -A'liX th: kcnse terniL!
are. olease read our answers to freauentlv asked questions before you send an email
,-
41
------- |