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Adding to the toolbox for tidal-inundation mapping in estuarine areas
Rebecca Flitcroft
1
&Patrick Clinton
2
&Kelly Christiansen
1
Received: 9 May 2017 / R evised: 12 December 2017 / Accepted: 20 February 2018 / Published online: 12 March 2018
#This is a U.S. government work and its textis not subject to copyright protection in the United States; however, its text may be subjectto foreign copyright
protection 2018
Abstract
In estuaries, land-surface and tidal elevation conspire to influence the amount of salt-water inundation in a specific location,
ultimately affecting the distribution of estuary vegetation.Plants vary in their tolerances to salinity and inundation. Understanding
even small changes in land-surface elevation at a site scale provides relevant information to managers seeking to design effective
long-term restoration projects. Restoration of estuary habitats has been identified as a tool to mediate some anticipated effects of
climate change, including flooding from sea-level rise, precipitation regimes, and storminess. Further, habitat restoration that is
effective in the face of climate uncertainty is critical to the sustainable production of seafood and maintenance of ecosystem
functions. We offer a simple methodthat links tidal elevations to upslope topography, allowing managers to determine where tidal
inundation of upslope areas may occur. This method does not require complex modeling, rather we combine existing high-
accuracy tide-gage information with LiDAR imagery. However, we found that if LiDAR is not flown at low tide, or at consistent
tidal heights, it poses significant challenges in the interpretation of tidal elevations. Where LiDAR is consistently collected at low
tide, this method of linking the tidal datum to upslope topography is not data-intensive, and does not require long-term data
collection. Along with locally specific information, the types of map products that can be developed using this method should
identify places that may be potentially vulnerable to salt-water inundation, along with places that may be effective migration
corridors for marshes and other habitats.
Keywords Climate change .Sea-level rise .Tida l inundation .Restoration .LiDAR .GIS
Introduction
In estuaries, land-surface and tidal elevation (or sea-level) com-
bine to influence the amount of salt-water inundation in a spe-
cific location, ultimately affecting the distribution of estuary
vegetation. Because plants vary in their tolerance to salinity
and inundation (Janousek et al. 2016), even very small changes
in land-surface elevation can influence the extent and function of
salt-marsh habitats (Janousek and Folger 2014; Valiela et al.
1978). Clearly, other processes, including cyclical salinity pat-
terns, storm surges, sedimentation rates, salinity levels, interspe-
cies competition, and seasonal freshwater inputs, are also strong
determinants of the type of estuary habitat that may develop in a
particular place (Mann 1982; Pennings and Callaway 1992). In
Oregon, land-surface elevation, in relation to tidal elevation (or
sea-level), has been identified as a strong predictor of the type of
vegetation community that may be found in a specific location
(Janousek and Folger 2014). Owing to the utility of land-surface
and tidal elevations in predicting the distribution of different
types of estuary habitats, tools that enable the consistent and
accurate collection of these data may assist in restoration, con-
servation, and protection efforts.
Most estuaries have been significantly altered by humans
during the past century, reducing the quantity and diversity of
available habitats (Dahl and Stedman 2013; Good 2000)and
the capacity of estuarine systems to absorb change (Folke
et al. 2004; Lotze et al. 2006). Land-management agencies
have responded with projects to restore and enhance rare es-
tuary and ecotonal habitats, often through partnerships with
private and non-profit organizations (i.e., Neskowin Coastal
Erosion Adaptation Plan 2013). However, predicted changes
in inundation patterns that are anticipated with sea-level rise
(Craft et al. 2009; Flitcroft et al. 2013; Glick et al. 2007)could
render many current restoration projects ineffective over time
*Rebecca Flitcroft
rflitcroft@fs.fed.us
1
U.S. Department of Agriculture, Forest Service, Pacific Northwest
Research Station, 3200 SW Jefferson Way,
Corvallis, OR 97331-8550, USA
2
U.S. Environmental Protection Agency, 2111 S.E. Marine Science
Center Drive, Newport, OR 97365-5260, USA
Journal of Coastal Conservation (2018) 22:745–753
https://doi.org/10.1007/s11852-018-0605-1
(Callaway et al. 2007). Vertical accuracy of digital elevation
models utilized for mapping of tidal inundation from sea-level
rise is another component of accurate modeling to correctly
plan for future tidal elevation scenarios (Fraile-Jurado and
Ojeda-Zújar 2013). Planning for habitat restoration that con-
siders the need to keep pace with climate change will be crit-
ical to the sustainable production of seafood and ongoing
maintenance of ecosystem function (Flitcroft and Giannico
2013; Montefalcone et al. 2011).
There are several tools available for estimating the spa-
tial extent of sea-level rise (SLR) in estuaries and along
coasts. One key element in all of these methods is the
determination of tidal elevation, which is critical for the
interpretation of how changes in tidal elevation (from
SLR, or storm surges) will affect lowland areas. For exam-
ple, the Digital Coast Sea-Level Rise viewer (https://coast.
noaa.gov/digitalcoast/tools/slr) provides a visual means to
explore scenarios of SLR and tidal inundation across broad
spatial extents. This sophisticated tool incorporates
VDatum as a key method for determining tidal elevation.
VDatum is a comprehensive and seamless tidal datum
developed by the National Ocean Service at NOAA
(NOAA NOS), available for most of the west and east
coasts of the US. This datum is based on interpolation of
tidal heights between stations (VDatum: http://vdatum.
noaa.gov/). One challenge in the use of VDatum is that
this datum may only extend a short distance above mean
high water and may not include all estuarine habitats
(NOAA OCM 2017). Another tool for exploring SLR is
the Sea Level Affecting Marshes Model (SLAMM; https://
coast.noaa.gov/digitalcoast/tools/slamm). Unlike the
Digital Coast Sea-Level Rise viewer, which is intended to
assess large portions of the U.S. coastline, SLAMM often
focuses on specific locations. In applications in Spain,
SLAMM developed with LiDAR imagery was shown to
provide a useful framework for predicting sea-level rise
on salt marshes, but additional information such as
saltmarsh accretion rates and historical trends in sea-level
were critical in concert with elevation information (Núñez
2016). Further, understanding the importance of vegetation
elevations when using remotely-sensed imagery such as
LiDAR may be critical in developing accurate bare-earth
models that will directly affect predictions about future
inundation patterns due to sea-level rise (Ewald 2013;
Fernandez-Nunez et al. 2017).
The Digital Coast Sea-Level Rise viewer and SLAMM
both rely on data that describe land-surface and tidal eleva-
tions. Digital data sets that describe topography are common
in geographic enquiry, but vary in grain size and extent.
Digital elevation models (DEMs) are a commonly used topo-
graphic tool, derived from a variety of different types of ele-
vation data including aerial photography and light detection
and ranging (LiDAR) data. LiDAR is an increasingly
common form of elevation data collected on aerial surveys
with equipment that sends and receives laser signals. The in-
terval between the emission of laser signals by the equipment
and the return time when signals bounce back from the earth’s
surface to the receiver allows for the calculation of surface
elevation. Because LiDAR lasers bounce off the top-most
surface, interpretation of the Bcloud^of return points is needed
to identify the bare-earth model (the lowest laser returns) or
the land surface with no vegetation. Additional analysis can be
done to identifyvegetation heights or physical structures. Two
forms of LiDAR data are available: topographic LiDAR (of-
ten called infrared or red LiDAR) that does not penetrate wa-
ter; and bathymetric LiDAR (often called blue-green LiDAR)
that can penetrate as deep as 70 m into water, depending on
clarity and other factors (Gao 2009).
Here we offer a simple geographic information system
(GIS) method that links LiDAR bare-earth data models
with the local tidal datum for spatially explicit investiga-
tion of inundation potential at the site level. This simple
method can be used in places where SLAMM or VDatum
are unavailable, or the Digital Coast Sea-Level Rise
Viewer output is too coarse for site-specific planning. By
interpreting LiDAR elevation using the local tidal datum,
we can show the intersection between tidal elevation and
upslope topography. Using this method, we mapped poten-
tial changes in tidal inundation for three estuaries on the
Oregon coast: Salmon River, Alsea Bay, and Coos Bay.
Along with locally specific information, map products like
these offer a window into potential planning alternatives
for restoration, land use, and land-development efforts by
identifying areas that are potentially vulnerable to salt-
water inundation, or places that may serve as effective
migration corridors for marshes and other habitats.
Methods
Study areas
We chose three Oregon estuaries for this study, including,
from north to south, Salmon River (438-acre [177-ha or
1.77-km
2
] estuary, 190-km
2
drainage basin), Alsea Bay
(2516-acre [1018-ha or 10.18-km
2
]estuary,1209-km
2
drain-
age basin), and Coos Bay (13,348-acre [5402-ha or 54.02-
km
2
] estuary, 1521-km
2
-drainage basin) (estuary area from
Oregon Coastal Atlas http://www.coastalatlas.net/) (Fig. 1).
The Salmon River estuary is underlain by a complex mix of
recent sedimentary and volcanic rocks and drains a relatively
small area. However, this system has the most extensively
restored salt marsh and lower estuary on the Oregon coast
(Flitcroft et al. 2016). Alsea Bay and Coos Bay, are located
on the central Oregon coast and are characterized by older
marine sedimentary geology. These estuaries are classified
746 R. Flitcroft et al.
as drowned river-mouth systems. Additionally, salt water
dominates estuaries in the summer when freshwater inputs
are low, but salinity is reduced in winter owing to precipitation
falling over large watersheds upstream (Cortright et al. 1987).
Similar estuary habitat types in all estuaries include high and
low salt marsh, mudflat, and eel grass beds (Fig. 2). All three
Oregon estuaries are part of watersheds with headwaters in the
Oregon Coast Range. Land use for all watersheds includes
timber harvest in the headwaters, with varying amounts of land
held in public and commercial timber ownership. Agriculture
and rural residential land use are found in the lowlands of all
three estuaries, with urban areas occurring in the Alsea and
Coos Bay systems. Recreational fisheries are present in all
estuaries, with commercial fishing fleets based in the Alsea
and Coos Bay systems. The Oregon coastal climate is gener-
ally mild, with average temperatures varying between 6.1 °C
in winter and 18.3 °C in summer; precipitation ranges from 1.5
to 4.06 m per year, falling predominantly as rain, with some
winter snow but no permanent snowpack (PRISM Climate
Group; http://www.prism.oregonstate.edu/).
Fig. 1 The three estuaries chosen for this study were Salmon River, Alsea Bay, and Coos Bay. Multiple benchmark locations were mapped for the
NOAA tidal station identified for each system (shown in top row of maps)
Adding to the toolbox for tidal-inundation mapping in estuarine areas 747
Elevation data sets
We used two LiDAR data sets in our analysis. For our analysis
of Alsea Bay and Coos Bay, we downloaded publically avail-
able LiDAR funded by a consortium of agencies and flown by
the Oregon Department of Geology and Mineral Industries for
the entire Oregon coast (flown October 2008; 0.9 × 0.9 m res-
olution) (obtained at http://www.oregongeology.org/sub/
lidardataviewer/). We used the bare-earth model that was pro-
vided by the vendor but could be generated at the discretion of
the user (for model specification, see http://www.
oregongeology.org/sub/LiDARdataviewer/resources.
htm—OLC North Coast, for the Alsea; and OLC South Coast,
Oregon, for Coos Bay). Because the LiDAR data were
collected to support road infrastructure maintenance, not
tidal conditions, surveys were flown using the infrared
spectrum, which that cannot penetrate water, without regard
to tidal height.
LiDAR data that are not part of the Oregon coastal LiDAR
data set are available for the Salmon River estuary. These
LiDAR data were privately collected for the USDA Forest
Service, flown at low tide (flown August 2007; 1 × 1 m reso-
lution). The Salmon River Estuary LiDAR surveys were also
collected using the infrared spectrum, but because they were
flown at low tide, much of the bathymetry of the estuary is
represented in the bare-earth LiDAR data model. We again
chose to use the vendor-created bare-earth model (for addi-
tional model and data collection information, see http://www.
oregongeology.org/sub/LiDARdataviewer/resources.htm -
Salmon River Study Area, 2008).
We acquired tidal elevation benchmarks from the U.S.
Department of Commerce, National Oceanic and
Atmospheric Administration (NOAA) at the Center for
Operational Oceanographic Products and Services (obtained
at http://tidesandcurrents.noaa.gov/tide_predictions.html?
gid=252#listing) for one station at Salmon River, OR (4
benchmarks at Cascade Head station, #9436381); one station
at Alsea Bay (5 benchmarks at Waldport station, #9434939)
and; at one station in Coos Bay (6 benchmarks at Charleston
station, #9432780) (Table 1). Each of the selected stations
collect Harmonic predictions for the local estuary. Station
benchmarks are used by NOAA to connect recorded tidal
heights from the present tidal epoch (1983–2001) to the
North American Vertical Datum of 1988 (NAVD88)
(Zilkoski et al. 1992). The precision of the benchmark coor-
dinates that we used was to the level of degrees-minutes-
seconds-tenths of seconds.
Linking LiDAR to NOAA tidal datums and benchmarks
Each NOAA tidal-station records mean lower low water
(MLLW), defined for each benchmark location. Tidal eleva-
tions are then placed into local context at the tidal gage, with
reference to local elevations (Gill and Schultz 2001). To link
LiDAR elevations to tidal height, we sampled LiDAR eleva-
tions at tidal benchmarks located in each of our estuaries of
interest. To accomplish this, we mapped benchmark locations
in a GIS (with software ESRI ArcMap V.10.3.1), using lati-
tude and longitude coordinates, physical descriptions of the
benchmark as listed by NOAA in the benchmark location
Fig. 2 Common estuary
vegetation types occurring across
Oregon estuaries: amud flat and
eel grass bed in Tillamook Bay,
OR; bhigh salt marsh habitat,
Salmon River, OR Photo by
Megan Chellew; cforested
brackish tidal channel, Coos Bay,
OR; dlow salt marsh habitat,
Coos Bay, OR
748 R. Flitcroft et al.
description (obtained at: http://tidesandcurrents.noaa.gov/
benchmarks.html?id=9436381;http://tidesandcurrents.noaa.
gov/benchmarks.html?id=9434939;http://tidesandcurrents.
noaa.gov/benchmarks.html?id=9432780), and aerial
photography (using Google Earth). We sampled the
elevation on the LiDAR data at the site of the mapped
benchmark. We compared the MLLW elevation documented
by NOAA at the benchmark with our sampled elevation from
the LiDAR data set (Fig. 3).
For each estuary, we calculated the difference between the
NOAA recorded MLLW elevation at the benchmarks with the
sampled LiDAR elevation. We then calculated the average of
these differences for all of the benchmarks at each location. The
average difference became an Badjustment factor^that we could
then use to interpret existing LiDAR data values as tidal eleva-
tions (Fig. 4). This allowed the mapping of tidal inundation
scenarios above Mean High High Water(MHHW) on the upland
topography captured by the LiDAR imagery. We mapped two
scenarios: 50 cm above MHHW, and 100 cm above MHHW.
Results
Linking LiDAR to NOAA tidal datums and benchmarks
The difference between tidal benchmarks at MLLW and
LiDAR surface elevations varied by estuary (Table 1).
We found the largest average difference at the Salmon
River estuary, a value of 0.846 m (range 5.003 m to
−1.806 m). Alsea Bay had the next largest average dif-
ference (−0.440 m; range −1.178 m to −0.024 m), and
Coos Bay the smallest average difference (−0.140 m;
range −0.344mand0.335m).
Table 1 Differences between NOAA tidal benchmark elevations and LiDAR elevations at Salmon River, Alsea River, and Coos River Estuaries,
Oregon, USA
Site ID NOAA benchmark
elevations above
MLLW (m)
LiDAR
estimate (m)
Difference (m) Average difference
(m) by estuary
Salmon River
Estuary (1.77-km
2
;190km
2
watershed)
Cascade Head Station 6381 A 2013 4.763 4.631 −0.132 0.846
#9436381 6381 B 2013 4.078 9.681 5.003
6381 C 2013 4.500 2.694 −1.806
6381 D 2013 3.642 3.959 0.317
Alsea River
Estuary (10.18-km
2
;1209km
2
watershed)
Waldport Station ×79 1933 EL 12 4.906 4.747 −0.159 −0.440
#9434939 4939 A 2013 4.060 3.918 −0.142
4939 B 2013 3.954 3.258 −0.696
A2 1936 13.4 5.241 4.063 −1.178
2 1933 4.682 4.658 −0.024
Coos River
Estuary (52.02 km
2
;1521km
2
watershed)
Charleston Station Tidal 8 1970 4.838 4.536 −0.302 −0.140
#9432780 Tidal 9 1970 5.000 4.807 −0.193
Tidal 10 1970 5.212 5.029 −0.183
Tidal 11 1974 5.208 4.864 −0.344
b Tidal 1981 4.636 4.483 −0.153
c Tidal 1981 4.798 5.133 0.335
In each study estuary, NOAA tidal benchmarks were mapped. MLLW elevation was determined from tide-gage records and also identified on LiDAR
imagery. The average difference between these measurements was calculated for each estuary and became the adjustment factor used to translate LiDAR
elevations into tidal elevations
Fig. 3 To interpret LiDAR data through the lens of tidal elevations, we
selected tidal stations with multiple benchmark locations. We identified
the terrestrial elevation of each benchmark on the LiDAR. We then
referenced the tidal elevation for that location. We calculated the
average difference between benchmarks and sampled LiDAR elevations
for each station. This became the Badjustment factor^used to interpret
LiDAR with the tidal datum
Adding to the toolbox for tidal-inundation mapping in estuarine areas 749
Local tidal inundation mapping using tidal datums
linked to LiDAR
We interpreted and mapped (Fig. 5a–c) LiDAR data using
the adjustment based on elevation comparisons with the
NOAA tidal datum (Table 2). Tidal heights can be consis-
tently identified using the LiDAR data set for the Salmon
River and Alsea Bay (Fig. 5a, b). The small Salmon River
estuary shows the inundation pattern of subsided salt
marshes in the restored portions of the estuary (Fig. 5a).
The drowned river-mouth configuration of the Alsea system
appears to have few potential locations for salt-marsh migra-
tion under projected future conditions (Fig. 5b). The LiDAR
data for Coos Bay are difficult to interpret. The striping pat-
tern is due to the different flight lines that were stitched
together to generate the composite LiDAR data for the bay
(Fig. 5c). Flights flown at different times (and sometimes on
different days) captured different tidal heights. Because the
LiDAR data did not penetrate water, the variation in tidal
heights produced the striped elevation configuration, partic-
ularly noticeable in open water. However, the gradient of
elevations in the smaller tributaries and upstream in the
Coos Bay system are still detectable (Fig. 5c).
Discussion
Climate change is predicted to affect a wide variety of envi-
ronmental conditions in low-lying coastal systems, including
increases in sea-level, frequency and intensity of storm surges,
and alterations in precipitation and thermal regimes (NRC
Committee on SLR 2012;Poffetal.2002). However, because
climate predictions are uncertain, enhancement and restoration
of natural resilience mechanisms in native ecosystems can be a
unifying goal for planning (Bottom et al. 2009;Milleretal.
2010). This approach of restoring capacity to ecosystems runs
counter to engineered solutions to coastal flooding such as sea
walls and armoring (Dugan et al. 2011). Tools and applications
that facilitate development of site-specific restoration planning
using existing and available data sets can provide support to
local restoration-planning efforts. Linking the NOAA tidal da-
tum (NAVD88) to local LiDAR elevations provides a simple
method of mapping actual tidal elevations onto landward to-
pography, producing maps that display upslope inundation at
varying tidal heights. LiDAR flown at low tide (as at Salmon
River), or at the same tidal height (as at Alsea Bay) makes
interpretation of upslope elevations simple. LiDAR that has
been mosaicked together from multiple flights (as at Coos
Bay) is more challenging, but upslope elevations above mean
higher high water, which are most relevant for mapping tidal
inundation, can still be interpreted. These types of mapping
products facilitate proactive planning by identifying areas with
the greatest likelihood of future inundation, to explore both the
potential for the drowning of existing habitats or proposed
restoration sites and for habitat migration.
Limitations of this method center on the timing of LiDAR
data collection. Water-penetrating bathymetric LiDAR data
flown at a consistent low-tide level during winter (when leaves
have fallen) is the most ideal for coastal applications. However,
even with less-ideal conditions, much can still be learned from
available LiDAR data sets. By linking LiDAR data to an ac-
curate tidal datum, our ability to understand tidal-inundation
patterns relative to upslope topography is enhanced.
Conservation planning for restoration of ecosystem and
social-ecological resilience has been identified as a critical
issue for coastal systems (Adger et al. 2005) and fisheries
managers (Bottom et al. 2009), particularly in light of climate
change. Resilience of native ecosystems is thought to be
linked to the diversity of phenology expressed by native spe-
cies, and to the relatively wide range of environmental condi-
tions that they are capable of tolerating (Bisson et al. 2009;
Bottom et al. 2009;NaimanandDécamps1997).
Anthropogenic actions, including the filling and diking of
estuary and floodplain areas and the intensive land manage-
ment associated with timber harvest, have altered disturbance
processes throughout the region (Waples et al. 2009).
Restoring the capacity of systems to absorb environmental
change has been identified as a key mechanism to enhance
Fig. 4 Process flow chart of methods to display and interpret LiDAR
elevations as tidal elevations
750 R. Flitcroft et al.
resilience (Ebersole et al. 1997) and mediate the anticipated
effects of climate change (Crooks et al. 2011). Research that
models future conditions demonstrates the potential for man-
agement to mediate some aspects of climate change (Battin
et al. 2007; Falke et al. 2015). Restoration that works toward
this goal requires comprehensive, site-specific, and detailed
information. This method of linking LiDAR to tidal datums
offers a simple, cost-effective tool for local managers to in-
form the siting and planning of restoration activities in estuar-
ies and the estuary-freshwater ecotone.
Fig. 5 Tidal elevations were
referenced to benchmarks to
create an adjustment to LiDAR
data so that tidal heights could be
mapped on upslope topography.
To explore tidal inundation
patterns, we mapped 0.5–1.0 m
above mean high water at: a
Salmon River, Oregon, USA; b
Alsea Bay, Oregon, USA; and (c)
Coos Bay, Oregon, USA
Adding to the toolbox for tidal-inundation mapping in estuarine areas 751
Acknowledgements The authors extend their appreciation to partner or-
ganizations and others working to develop restoration strategies for
Oregon Estuaries. We would also like to thank Justin Saarinen for his
thorough and thoughtful review of the manuscript prior to submission.
Salary support for this research was provided by the United States
Department of Agriculture, Forest Service Pacific, Northwest Research
Station and the United States Environmental Protection Agency.
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Table 2 Each study estuary had a different adjustment factor
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elevations and LiDAR elevations
Estuary MLW MHW MHHW
Salmon River Tidal Datum 0.296 1.964 2.174
LiDAR adjustment (0.846) 1.142 2.810 3.020
Alsea Bay Tidal Datum 0.381 2.096 2.313
LiDAR adjustment (−0.440) −0.059 1.656 1.873
Coos Bay Tidal Datum 0.387 2.120 2.323
LiDAR adjustment (−0.140) 0.247 1.980 2.183
For each estuary, the adjustment factor was used to translate LiDAR
elevations into tidal elevations. These measurements were then used to
map tidal elevations for each estuary
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